Automation for deep space vehicle monitoring

NASA Technical Reports Server (NTRS)

Schwuttke, Ursula M.

1991-01-01

Information on automation for deep space vehicle monitoring is given in viewgraph form. Information is given on automation goals and strategy; the Monitor Analyzer of Real-time Voyager Engineering Link (MARVEL); intelligent input data management; decision theory for making tradeoffs; dynamic tradeoff evaluation; evaluation of anomaly detection results; evaluation of data management methods; system level analysis with cooperating expert systems; the distributed architecture of multiple expert systems; and event driven response.

Crystal nucleation of colloidal hard dumbbells

NASA Astrophysics Data System (ADS)

Ni, Ran; Dijkstra, Marjolein

2011-01-01

Using computer simulations, we investigate the homogeneous crystal nucleation in suspensions of colloidal hard dumbbells. The free energy barriers are determined by Monte Carlo simulations using the umbrella sampling technique. We calculate the nucleation rates for the plastic crystal and the aperiodic crystal phase using the kinetic prefactor as determined from event driven molecular dynamics simulations. We find good agreement with the nucleation rates determined from spontaneous nucleation events observed in event driven molecular dynamics simulations within error bars of one order of magnitude. We study the effect of aspect ratio of the dumbbells on the nucleation of plastic and aperiodic crystal phases, and we also determine the structure of the critical nuclei. Moreover, we find that the nucleation of the aligned close-packed crystal structure is strongly suppressed by a high free energy barrier at low supersaturations and slow dynamics at high supersaturations.

Introduction to State Estimation of High-Rate System Dynamics.

PubMed

Hong, Jonathan; Laflamme, Simon; Dodson, Jacob; Joyce, Bryan

2018-01-13

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer's convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model.

Science Goal Monitor: Science Goal Driven Automation for NASA Missions

NASA Technical Reports Server (NTRS)

Koratkar, Anuradha; Grosvenor, Sandy; Jung, John; Pell, Melissa; Matusow, David; Bailyn, Charles

2004-01-01

Infusion of automation technologies into NASA s future missions will be essential because of the need to: (1) effectively handle an exponentially increasing volume of scientific data, (2) successfully meet dynamic, opportunistic scientific goals and objectives, and (3) substantially reduce mission operations staff and costs. While much effort has gone into automating routine spacecraft operations to reduce human workload and hence costs, applying intelligent automation to the science side, i.e., science data acquisition, data analysis and reactions to that data analysis in a timely and still scientifically valid manner, has been relatively under-emphasized. In order to introduce science driven automation in missions, we must be able to: capture and interpret the science goals of observing programs, represent those goals in machine interpretable language; and allow spacecrafts onboard systems to autonomously react to the scientist's goals. In short, we must teach our platforms to dynamically understand, recognize, and react to the scientists goals. The Science Goal Monitor (SGM) project at NASA Goddard Space Flight Center is a prototype software tool being developed to determine the best strategies for implementing science goal driven automation in missions. The tools being developed in SGM improve the ability to monitor and react to the changing status of scientific events. The SGM system enables scientists to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of science data to identify occurrences of key events previously specified by the scientist. When an event occurs, the system autonomously coordinates the execution of the scientist s desired reactions. Through SGM, we will improve om understanding about the capabilities needed onboard for success, develop metrics to understand the potential increase in science returns, and develop an operational prototype so that the perceived risks associated with increased use of automation can be reduced.

Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution

NASA Astrophysics Data System (ADS)

Ren, Silin; Jin, Xiao; Chan, Chung; Jian, Yiqiang; Mulnix, Tim; Liu, Chi; E Carson, Richard

2017-06-01

Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer 18F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.

Data-driven event-by-event respiratory motion correction using TOF PET list-mode centroid of distribution.

PubMed

Ren, Silin; Jin, Xiao; Chan, Chung; Jian, Yiqiang; Mulnix, Tim; Liu, Chi; Carson, Richard E

2017-06-21

Data-driven respiratory gating techniques were developed to correct for respiratory motion in PET studies, without the help of external motion tracking systems. Due to the greatly increased image noise in gated reconstructions, it is desirable to develop a data-driven event-by-event respiratory motion correction method. In this study, using the Centroid-of-distribution (COD) algorithm, we established a data-driven event-by-event respiratory motion correction technique using TOF PET list-mode data, and investigated its performance by comparing with an external system-based correction method. Ten human scans with the pancreatic β-cell tracer 18 F-FP-(+)-DTBZ were employed. Data-driven respiratory motions in superior-inferior (SI) and anterior-posterior (AP) directions were first determined by computing the centroid of all radioactive events during each short time frame with further processing. The Anzai belt system was employed to record respiratory motion in all studies. COD traces in both SI and AP directions were first compared with Anzai traces by computing the Pearson correlation coefficients. Then, respiratory gated reconstructions based on either COD or Anzai traces were performed to evaluate their relative performance in capturing respiratory motion. Finally, based on correlations of displacements of organ locations in all directions and COD information, continuous 3D internal organ motion in SI and AP directions was calculated based on COD traces to guide event-by-event respiratory motion correction in the MOLAR reconstruction framework. Continuous respiratory correction results based on COD were compared with that based on Anzai, and without motion correction. Data-driven COD traces showed a good correlation with Anzai in both SI and AP directions for the majority of studies, with correlation coefficients ranging from 63% to 89%. Based on the determined respiratory displacements of pancreas between end-expiration and end-inspiration from gated reconstructions, there was no significant difference between COD-based and Anzai-based methods. Finally, data-driven COD-based event-by-event respiratory motion correction yielded comparable results to that based on Anzai respiratory traces, in terms of contrast recovery and reduced motion-induced blur. Data-driven event-by-event respiratory motion correction using COD showed significant image quality improvement compared with reconstructions with no motion correction, and gave comparable results to the Anzai-based method.

Study of an External Neutron Source for an Accelerator-Driven System using the PHITS Code

DOE Office of Scientific and Technical Information (OSTI.GOV)

Sugawara, Takanori; Iwasaki, Tomohiko; Chiba, Takashi

A code system for the Accelerator Driven System (ADS) has been under development for analyzing dynamic behaviors of a subcritical core coupled with an accelerator. This code system named DSE (Dynamics calculation code system for a Subcritical system with an External neutron source) consists of an accelerator part and a reactor part. The accelerator part employs a database, which is calculated by using PHITS, for investigating the effect related to the accelerator such as the changes of beam energy, beam diameter, void generation, and target level. This analysis method using the database may introduce some errors into dynamics calculations sincemore » the neutron source data derived from the database has some errors in fitting or interpolating procedures. In this study, the effects of various events are investigated to confirm that the method based on the database is appropriate.« less

An Event-Driven Classifier for Spiking Neural Networks Fed with Synthetic or Dynamic Vision Sensor Data.

PubMed

Stromatias, Evangelos; Soto, Miguel; Serrano-Gotarredona, Teresa; Linares-Barranco, Bernabé

2017-01-01

This paper introduces a novel methodology for training an event-driven classifier within a Spiking Neural Network (SNN) System capable of yielding good classification results when using both synthetic input data and real data captured from Dynamic Vision Sensor (DVS) chips. The proposed supervised method uses the spiking activity provided by an arbitrary topology of prior SNN layers to build histograms and train the classifier in the frame domain using the stochastic gradient descent algorithm. In addition, this approach can cope with leaky integrate-and-fire neuron models within the SNN, a desirable feature for real-world SNN applications, where neural activation must fade away after some time in the absence of inputs. Consequently, this way of building histograms captures the dynamics of spikes immediately before the classifier. We tested our method on the MNIST data set using different synthetic encodings and real DVS sensory data sets such as N-MNIST, MNIST-DVS, and Poker-DVS using the same network topology and feature maps. We demonstrate the effectiveness of our approach by achieving the highest classification accuracy reported on the N-MNIST (97.77%) and Poker-DVS (100%) real DVS data sets to date with a spiking convolutional network. Moreover, by using the proposed method we were able to retrain the output layer of a previously reported spiking neural network and increase its performance by 2%, suggesting that the proposed classifier can be used as the output layer in works where features are extracted using unsupervised spike-based learning methods. In addition, we also analyze SNN performance figures such as total event activity and network latencies, which are relevant for eventual hardware implementations. In summary, the paper aggregates unsupervised-trained SNNs with a supervised-trained SNN classifier, combining and applying them to heterogeneous sets of benchmarks, both synthetic and from real DVS chips.

On the Role of Ionospheric Ions in Sawtooth Events

NASA Astrophysics Data System (ADS)

Lund, E. J.; Nowrouzi, N.; Kistler, L. M.; Cai, X.; Frey, H. U.

2018-01-01

Simulations have suggested that feedback of heavy ions originating in the ionosphere is an important mechanism for driving sawtooth injections. However, this feedback may only be necessary for events driven by coronal mass ejections (CMEs), whereas in events driven by streaming interaction regions (SIRs), solar wind variability may suffice to drive these injections. Here we present case studies of two sawtooth events for which in situ data are available in both the magnetotail (Cluster) and the nightside auroral region (FAST), as well as global auroral images (IMAGE). One event, on 1 October 2001, was driven by a CME; the other, on 24 October 2002, was driven by an SIR. The available data do not support the hypothesis that heavy ion feedback is necessary to drive either event. This result is consistent with simulations of the SIR-driven event but disagrees with simulation results for a different CME-driven event. We also find that in an overwhelming majority of the sawtooth injections for which Cluster tail data are available, the O+ observed in the tail comes from the cusp rather than the nightside auroral region, which further casts doubt on the hypothesis that ionospheric heavy ion feedback is the cause of sawtooth injections.

Introduction to State Estimation of High-Rate System Dynamics

PubMed Central

Dodson, Jacob; Joyce, Bryan

2018-01-01

Engineering systems experiencing high-rate dynamic events, including airbags, debris detection, and active blast protection systems, could benefit from real-time observability for enhanced performance. However, the task of high-rate state estimation is challenging, in particular for real-time applications where the rate of the observer’s convergence needs to be in the microsecond range. This paper identifies the challenges of state estimation of high-rate systems and discusses the fundamental characteristics of high-rate systems. A survey of applications and methods for estimators that have the potential to produce accurate estimations for a complex system experiencing highly dynamic events is presented. It is argued that adaptive observers are important to this research. In particular, adaptive data-driven observers are advantageous due to their adaptability and lack of dependence on the system model. PMID:29342855

Data-driven Simulations of Magnetic Connectivity in Behind-the-Limb Gamma-ray Flares and Associated Coronal Mass Ejections

NASA Astrophysics Data System (ADS)

Jin, M.; Petrosian, V.; Liu, W.; Nitta, N.; Omodei, N.; Rubio da Costa, F.; Effenberger, F.; Li, G.; Pesce-Rollins, M.

2017-12-01

Recent Fermi detection of high-energy gamma-ray emission from the behind-the-limb (BTL) solar flares pose a puzzle on the particle acceleration and transport mechanisms in such events. Due to the large separation between the flare site and the location of gamma-ray emission, it is believed that the associated coronal mass ejections (CMEs) play an important role in accelerating and subsequently transporting particles back to the Sun to produce obseved gamma-rays. We explore this scenario by simulating the CME associated with a well-observed flare on 2014 September 1 about 40 degrees behind the east solar limb and by comparing the simulation and observational results. We utilize a data-driven global magnetohydrodynamics model (AWSoM: Alfven-wave Solar Model) to track the dynamical evolution of the global magnetic field during the event and investigate the magnetic connectivity between the CME/CME-driven shock and the Fermi emission region. Moreover, we derive the time-varying shock parameters (e.g., compression ratio, Alfven Mach number, and ThetaBN) over the area that is magnetically connected to the visible solar disk where Fermi gamma-ray emission originates. Our simulation shows that the visible solar disk develops connections both to the flare site and to the CME-driven shock during the eruption, which indicate that the CME's interaction with the global solar corona is critical for understanding such Fermi BTL events and gamma-ray flares in general. We discuss the causes and implications of Fermi BTL events, in the framework of a potential shift of paradigm on particle acceleration in solar flares/CMEs.

A fuzzy Petri-net-based mode identification algorithm for fault diagnosis of complex systems

NASA Astrophysics Data System (ADS)

Propes, Nicholas C.; Vachtsevanos, George

2003-08-01

Complex dynamical systems such as aircraft, manufacturing systems, chillers, motor vehicles, submarines, etc. exhibit continuous and event-driven dynamics. These systems undergo several discrete operating modes from startup to shutdown. For example, a certain shipboard system may be operating at half load or full load or may be at start-up or shutdown. Of particular interest are extreme or "shock" operating conditions, which tend to severely impact fault diagnosis or the progression of a fault leading to a failure. Fault conditions are strongly dependent on the operating mode. Therefore, it is essential that in any diagnostic/prognostic architecture, the operating mode be identified as accurately as possible so that such functions as feature extraction, diagnostics, prognostics, etc. can be correlated with the predominant operating conditions. This paper introduces a mode identification methodology that incorporates both time- and event-driven information about the process. A fuzzy Petri net is used to represent the possible successive mode transitions and to detect events from processed sensor signals signifying a mode change. The operating mode is initialized and verified by analysis of the time-driven dynamics through a fuzzy logic classifier. An evidence combiner module is used to combine the results from both the fuzzy Petri net and the fuzzy logic classifier to determine the mode. Unlike most event-driven mode identifiers, this architecture will provide automatic mode initialization through the fuzzy logic classifier and robustness through the combining of evidence of the two algorithms. The mode identification methodology is applied to an AC Plant typically found as a component of a shipboard system.

Hard Sphere Simulation by Event-Driven Molecular Dynamics: Breakthrough, Numerical Difficulty, and Overcoming the issues

NASA Astrophysics Data System (ADS)

Isobe, Masaharu

Hard sphere/disk systems are among the simplest models and have been used to address numerous fundamental problems in the field of statistical physics. The pioneering numerical works on the solid-fluid phase transition based on Monte Carlo (MC) and molecular dynamics (MD) methods published in 1957 represent historical milestones, which have had a significant influence on the development of computer algorithms and novel tools to obtain physical insights. This chapter addresses the works of Alder's breakthrough regarding hard sphere/disk simulation: (i) event-driven molecular dynamics, (ii) long-time tail, (iii) molasses tail, and (iv) two-dimensional melting/crystallization. From a numerical viewpoint, there are serious issues that must be overcome for further breakthrough. Here, we present a brief review of recent progress in this area.

A High-Speed, Event-Driven, Active Pixel Sensor Readout for Photon-Counting Microchannel Plate Detectors

NASA Technical Reports Server (NTRS)

Kimble, Randy A.; Pain, Bedabrata; Norton, Timothy J.; Haas, J. Patrick; Oegerle, William R. (Technical Monitor)

2002-01-01

Silicon array readouts for microchannel plate intensifiers offer several attractive features. In this class of detector, the electron cloud output of the MCP intensifier is converted to visible light by a phosphor; that light is then fiber-optically coupled to the silicon array. In photon-counting mode, the resulting light splashes on the silicon array are recognized and centroided to fractional pixel accuracy by off-chip electronics. This process can result in very high (MCP-limited) spatial resolution while operating at a modest MCP gain (desirable for dynamic range and long term stability). The principal limitation of intensified CCD systems of this type is their severely limited local dynamic range, as accurate photon counting is achieved only if there are not overlapping event splashes within the frame time of the device. This problem can be ameliorated somewhat by processing events only in pre-selected windows of interest of by using an addressable charge injection device (CID) for the readout array. We are currently pursuing the development of an intriguing alternative readout concept based on using an event-driven CMOS Active Pixel Sensor. APS technology permits the incorporation of discriminator circuitry within each pixel. When coupled with suitable CMOS logic outside the array area, the discriminator circuitry can be used to trigger the readout of small sub-array windows only when and where an event splash has been detected, completely eliminating the local dynamic range problem, while achieving a high global count rate capability and maintaining high spatial resolution. We elaborate on this concept and present our progress toward implementing an event-driven APS readout.

Event-driven simulation in SELMON: An overview of EDSE

NASA Technical Reports Server (NTRS)

Rouquette, Nicolas F.; Chien, Steve A.; Charest, Leonard, Jr.

1992-01-01

EDSE (event-driven simulation engine), a model-based event-driven simulator implemented for SELMON, a tool for sensor selection and anomaly detection in real-time monitoring is described. The simulator is used in conjunction with a causal model to predict future behavior of the model from observed data. The behavior of the causal model is interpreted as equivalent to the behavior of the physical system being modeled. An overview of the functionality of the simulator and the model-based event-driven simulation paradigm on which it is based is provided. Included are high-level descriptions of the following key properties: event consumption and event creation, iterative simulation, synchronization and filtering of monitoring data from the physical system. Finally, how EDSE stands with respect to the relevant open issues of discrete-event and model-based simulation is discussed.

Numerical investigations with WRF about atmospheric features leading to heavy precipitation and flood events over the Central Andes' complex topography

NASA Astrophysics Data System (ADS)

Zamuriano, Marcelo; Brönnimann, Stefan

2017-04-01

It's known that some extremes such as heavy rainfalls, flood events, heatwaves and droughts depend largely on the atmospheric circulation and local features. Bolivia is no exception and while the large scale dynamics over the Amazon has been largely investigated, the local features driven by the Andes Cordillera and the Altiplano is still poorly documented. New insights on the regional atmospheric dynamics preceding heavy precipitation and flood events over the complex topography of the Andes-Amazon interface are added through numerical investigations of several case events: flash flood episodes over La Paz city and the extreme 2014 flood in south-western Amazon basin. Large scale atmospheric water transport is dynamically downscaled in order to take into account the complex topography forcing and local features as modulators of these events. For this purpose, a series of high resolution numerical experiments with the WRF-ARW model is conducted using various global datasets and parameterizations. While several mechanisms have been suggested to explain the dynamics of these episodes, they have not been tested yet through numerical modelling experiments. The simulations captures realistically the local water transport and the terrain influence over atmospheric circulation, even though the precipitation intensity is in general unrealistic. Nevertheless, the results show that Dynamical Downscaling over the tropical Andes' complex terrain provides useful meteorological data for a variety of studies and contributes to a better understanding of physical processes involved in the configuration of these events.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems.

PubMed

Suh, Myung-Kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2012-09-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %.

Dynamic Task Optimization in Remote Diabetes Monitoring Systems

PubMed Central

Suh, Myung-kyung; Woodbridge, Jonathan; Moin, Tannaz; Lan, Mars; Alshurafa, Nabil; Samy, Lauren; Mortazavi, Bobak; Ghasemzadeh, Hassan; Bui, Alex; Ahmadi, Sheila; Sarrafzadeh, Majid

2016-01-01

Diabetes is the seventh leading cause of death in the United States, but careful symptom monitoring can prevent adverse events. A real-time patient monitoring and feedback system is one of the solutions to help patients with diabetes and their healthcare professionals monitor health-related measurements and provide dynamic feedback. However, data-driven methods to dynamically prioritize and generate tasks are not well investigated in the domain of remote health monitoring. This paper presents a wireless health project (WANDA) that leverages sensor technology and wireless communication to monitor the health status of patients with diabetes. The WANDA dynamic task management function applies data analytics in real-time to discretize continuous features, applying data clustering and association rule mining techniques to manage a sliding window size dynamically and to prioritize required user tasks. The developed algorithm minimizes the number of daily action items required by patients with diabetes using association rules that satisfy a minimum support, confidence and conditional probability thresholds. Each of these tasks maximizes information gain, thereby improving the overall level of patient adherence and satisfaction. Experimental results from applying EM-based clustering and Apriori algorithms show that the developed algorithm can predict further events with higher confidence levels and reduce the number of user tasks by up to 76.19 %. PMID:27617297

Interannual Variations in Tropical Upper-Tropospheric Humidity: Understanding Tropical Convective and Dynamical Processes

NASA Technical Reports Server (NTRS)

Robertson, Franklin R.; Fitzjarrald, Dan E.; Miller, Timothy L.

2005-01-01

Uncertainty remains as to what extent variability in mid to upper tropospheric moisture, especially over the tropics, behaves as constant relative humidity during interannual climate variations associated with ENSO. Systematic variations in HIRS 6.7 micron and MLS 205 GHz suggest that dry subtropical regions evolving during warm SST events depress relative humidity, but the interpretation of these events is still uncertain. Additional specific concerns have to do with regional signatures of convective processes: How does the origin of dry air in the eastern subtropical N. Pacific differ in ENSO warm versus cold years? The dynamics of Rossby wave forcing by convective heating, subtropical jet stream dynamics, and dynamics driven subsidence all come into play here. How variations in precipitating ice hydrometeors from tropical anvils relate to variations in UTH is also a subject of debate? Do variations in precipitating ice, cloud cover and water vapor behavior show any support for the Iris-hypothesis mechanism? Here we examine historical records of SSM/T-2 data to gain a better physical understanding of the effects of deep convective moisture sources and dynamically-induced vertical circulations on UTH. These high frequency microwave measurements (183.3 GHz) take advantage of far less sensitivity to cloud hydrometeors than the 6.7 micron data to yield a record of upper tropospheric relative humidity. Furthermore, signatures of precipitating ice from these channels facilitate comparisons to TRMM hydrometeors detected by radar. In analyzing these observations, we isolate water vapor and temperature change components that affect brightness temperatures and the inferred relative humidity. Trajectory modeling is also used to understand interannual humidity anomalies in terms of outflow fbm convective regions and history of diabatically-driven sinking which modifies relative humidity.

DSCOVR Science Data and Retrospective Access

NASA Astrophysics Data System (ADS)

Rowland, W. F.; Codrescu, S.; Tilton, M.; Cartwright, J.; Redmon, R. J.; Loto'aniu, P. T. M.; Mccullough, H.; Denig, W. F.

2016-12-01

On July 27, 2016 the Deep Space Climate Observatory (DSCOVR) became the first operational satellite at the first Lagrange point (L1). This vantage, approximately one percent of the distance from the Earth to the Sun along the Earth-Sun line, means that DSCOVR data provide critical advanced warning of impending space weather events. As such, DSCOVR data are essential for forecasters, modelers, and the scientific community. The National Oceanic and Atmospheric Administration's National Centers for Environmental Information (NOAA/NCEI) archives the retrospective data and shares them with the public. We examine the data available, with a focus on some of the more interesting events that have occurred. We also discuss mechanisms created to facilitate search and access for those data, including a user-driven interface that allows one to dynamically generate plots and order relevant data of interest.

Joint independent component analysis for simultaneous EEG-fMRI: principle and simulation.

PubMed

Moosmann, Matthias; Eichele, Tom; Nordby, Helge; Hugdahl, Kenneth; Calhoun, Vince D

2008-03-01

An optimized scheme for the fusion of electroencephalography and event related potentials with functional magnetic resonance imaging (BOLD-fMRI) data should simultaneously assess all available electrophysiologic and hemodynamic information in a common data space. In doing so, it should be possible to identify features of latent neural sources whose trial-to-trial dynamics are jointly reflected in both modalities. We present a joint independent component analysis (jICA) model for analysis of simultaneous single trial EEG-fMRI measurements from multiple subjects. We outline the general idea underlying the jICA approach and present results from simulated data under realistic noise conditions. Our results indicate that this approach is a feasible and physiologically plausible data-driven way to achieve spatiotemporal mapping of event related responses in the human brain.

Non-equilibrium forces drive the anomalous diffusion of telomeres in the nucleus of mammalian cells

NASA Astrophysics Data System (ADS)

Stadler, Lorenz; Weiss, Matthias

2017-11-01

Telomeres are vital nucleotide sequences at both ends of each chromosome, and their motion reports on the local dynamics of decondensed chromatin in the nucleus of interphase cells. Here, we show that the previously reported subdiffusive motion of telomeres is driven by non-equilibrium cytoskeletal forces. In particular, breaking down microtubules leads to a significantly reduced generalized diffusion coefficient of telomeres. This translates into a markedly reduced effective temperature in the stochastic forces that govern the telomeres’ random walk. Moreover, telomere motion in cells that lack microtubules is well described by the monomer dynamics of a Rouse polymer that is embeddded in a viscoelastic medium. In contrast, active cytoskeletal forces in untreated cells override the environment’s elastic contributions, resulting in the well-known scaling for conventional Rouse dynamics in viscous media. Our data highlight that even subdiffusive motion in cells in most cases may not be a simple thermal transport process but rather is driven by non-equilibrium events.

The Coronal Analysis of SHocks and Waves (CASHeW) framework

NASA Astrophysics Data System (ADS)

Kozarev, Kamen A.; Davey, Alisdair; Kendrick, Alexander; Hammer, Michael; Keith, Celeste

2017-11-01

Coronal bright fronts (CBF) are large-scale wavelike disturbances in the solar corona, related to solar eruptions. They are observed (mostly in extreme ultraviolet (EUV) light) as transient bright fronts of finite width, propagating away from the eruption source location. Recent studies of individual solar eruptive events have used EUV observations of CBFs and metric radio type II burst observations to show the intimate connection between waves in the low corona and coronal mass ejection (CME)-driven shocks. EUV imaging with the atmospheric imaging assembly instrument on the solar dynamics observatory has proven particularly useful for detecting large-scale short-lived CBFs, which, combined with radio and in situ observations, holds great promise for early CME-driven shock characterization capability. This characterization can further be automated, and related to models of particle acceleration to produce estimates of particle fluxes in the corona and in the near Earth environment early in events. We present a framework for the coronal analysis of shocks and waves (CASHeW). It combines analysis of NASA Heliophysics System Observatory data products and relevant data-driven models, into an automated system for the characterization of off-limb coronal waves and shocks and the evaluation of their capability to accelerate solar energetic particles (SEPs). The system utilizes EUV observations and models written in the interactive data language. In addition, it leverages analysis tools from the SolarSoft package of libraries, as well as third party libraries. We have tested the CASHeW framework on a representative list of coronal bright front events. Here we present its features, as well as initial results. With this framework, we hope to contribute to the overall understanding of coronal shock waves, their importance for energetic particle acceleration, as well as to the better ability to forecast SEP events fluxes.

Application of Petri Nets in Bone Remodeling

PubMed Central

Li, Lingxi; Yokota, Hiroki

2009-01-01

Understanding a mechanism of bone remodeling is a challenging task for both life scientists and model builders, since this highly interactive and nonlinear process can seldom be grasped by simple intuition. A set of ordinary differential equations (ODEs) have been built for simulating bone formation as well as bone resorption. Although solving ODEs numerically can provide useful predictions for dynamical behaviors in a continuous time frame, an actual bone remodeling process in living tissues is driven by discrete events of molecular and cellular interactions. Thus, an event-driven tool such as Petri nets (PNs), which may dynamically and graphically mimic individual molecular collisions or cellular interactions, seems to augment the existing ODE-based systems analysis. Here, we applied PNs to expand the ODE-based approach and examined discrete, dynamical behaviors of key regulatory molecules and bone cells. PNs have been used in many engineering areas, but their application to biological systems needs to be explored. Our PN model was based on 8 ODEs that described an osteoprotegerin linked molecular pathway consisting of 4 types of bone cells. The models allowed us to conduct both qualitative and quantitative evaluations and evaluate homeostatic equilibrium states. The results support that application of PN models assists understanding of an event-driven bone remodeling mechanism using PN-specific procedures such as places, transitions, and firings. PMID:19838338

A Multi-mission Event-Driven Component-Based System for Support of Flight Software Development, ATLO, and Operations first used by the Mars Science Laboratory (MSL) Project

NASA Technical Reports Server (NTRS)

Dehghani, Navid; Tankenson, Michael

2006-01-01

This paper details an architectural description of the Mission Data Processing and Control System (MPCS), an event-driven, multi-mission ground data processing components providing uplink, downlink, and data management capabilities which will support the Mars Science Laboratory (MSL) project as its first target mission. MPCS is developed based on a set of small reusable components, implemented in Java, each designed with a specific function and well-defined interfaces. An industry standard messaging bus is used to transfer information among system components. Components generate standard messages which are used to capture system information, as well as triggers to support the event-driven architecture of the system. Event-driven systems are highly desirable for processing high-rate telemetry (science and engineering) data, and for supporting automation for many mission operations processes.

A variational approach to probing extreme events in turbulent dynamical systems

PubMed Central

Farazmand, Mohammad; Sapsis, Themistoklis P.

2017-01-01

Extreme events are ubiquitous in a wide range of dynamical systems, including turbulent fluid flows, nonlinear waves, large-scale networks, and biological systems. We propose a variational framework for probing conditions that trigger intermittent extreme events in high-dimensional nonlinear dynamical systems. We seek the triggers as the probabilistically feasible solutions of an appropriately constrained optimization problem, where the function to be maximized is a system observable exhibiting intermittent extreme bursts. The constraints are imposed to ensure the physical admissibility of the optimal solutions, that is, significant probability for their occurrence under the natural flow of the dynamical system. We apply the method to a body-forced incompressible Navier-Stokes equation, known as the Kolmogorov flow. We find that the intermittent bursts of the energy dissipation are independent of the external forcing and are instead caused by the spontaneous transfer of energy from large scales to the mean flow via nonlinear triad interactions. The global maximizer of the corresponding variational problem identifies the responsible triad, hence providing a precursor for the occurrence of extreme dissipation events. Specifically, monitoring the energy transfers within this triad allows us to develop a data-driven short-term predictor for the intermittent bursts of energy dissipation. We assess the performance of this predictor through direct numerical simulations. PMID:28948226

Water regime history drives responses of soil Namib Desert microbial communities to wetting events

NASA Astrophysics Data System (ADS)

Frossard, Aline; Ramond, Jean-Baptiste; Seely, Mary; Cowan, Don A.

2015-07-01

Despite the dominance of microorganisms in arid soils, the structures and functional dynamics of microbial communities in hot deserts remain largely unresolved. The effects of wetting event frequency and intensity on Namib Desert microbial communities from two soils with different water-regime histories were tested over 36 days. A total of 168 soil microcosms received wetting events mimicking fog, light rain and heavy rainfall, with a parallel “dry condition” control. T-RFLP data showed that the different wetting events affected desert microbial community structures, but these effects were attenuated by the effects related to the long-term adaptation of both fungal and bacterial communities to soil origins (i.e. soil water regime histories). The intensity of the water pulses (i.e. the amount of water added) rather than the frequency of wetting events had greatest effect in shaping bacterial and fungal community structures. In contrast to microbial diversity, microbial activities (enzyme activities) showed very little response to the wetting events and were mainly driven by soil origin. This experiment clearly demonstrates the complexity of microbial community responses to wetting events in hyperarid hot desert soil ecosystems and underlines the dynamism of their indigenous microbial communities.

Water regime history drives responses of soil Namib Desert microbial communities to wetting events.

PubMed

Frossard, Aline; Ramond, Jean-Baptiste; Seely, Mary; Cowan, Don A

2015-07-21

Despite the dominance of microorganisms in arid soils, the structures and functional dynamics of microbial communities in hot deserts remain largely unresolved. The effects of wetting event frequency and intensity on Namib Desert microbial communities from two soils with different water-regime histories were tested over 36 days. A total of 168 soil microcosms received wetting events mimicking fog, light rain and heavy rainfall, with a parallel "dry condition" control. T-RFLP data showed that the different wetting events affected desert microbial community structures, but these effects were attenuated by the effects related to the long-term adaptation of both fungal and bacterial communities to soil origins (i.e. soil water regime histories). The intensity of the water pulses (i.e. the amount of water added) rather than the frequency of wetting events had greatest effect in shaping bacterial and fungal community structures. In contrast to microbial diversity, microbial activities (enzyme activities) showed very little response to the wetting events and were mainly driven by soil origin. This experiment clearly demonstrates the complexity of microbial community responses to wetting events in hyperarid hot desert soil ecosystems and underlines the dynamism of their indigenous microbial communities.

A High-Speed, Event-Driven, Active Pixel Sensor Readout for Photon-Counting Microchannel Plate Detectors

NASA Technical Reports Server (NTRS)

Kimble, Randy A.; Pain, B.; Norton, T. J.; Haas, P.; Fisher, Richard R. (Technical Monitor)

2001-01-01

Silicon array readouts for microchannel plate intensifiers offer several attractive features. In this class of detector, the electron cloud output of the MCP intensifier is converted to visible light by a phosphor; that light is then fiber-optically coupled to the silicon array. In photon-counting mode, the resulting light splashes on the silicon array are recognized and centroided to fractional pixel accuracy by off-chip electronics. This process can result in very high (MCP-limited) spatial resolution for the readout while operating at a modest MCP gain (desirable for dynamic range and long term stability). The principal limitation of intensified CCD systems of this type is their severely limited local dynamic range, as accurate photon counting is achieved only if there are not overlapping event splashes within the frame time of the device. This problem can be ameliorated somewhat by processing events only in pre-selected windows of interest or by using an addressable charge injection device (CID) for the readout array. We are currently pursuing the development of an intriguing alternative readout concept based on using an event-driven CMOS Active Pixel Sensor. APS technology permits the incorporation of discriminator circuitry within each pixel. When coupled with suitable CMOS logic outside the array area, the discriminator circuitry can be used to trigger the readout of small sub-array windows only when and where an event splash has been detected, completely eliminating the local dynamic range problem, while achieving a high global count rate capability and maintaining high spatial resolution. We elaborate on this concept and present our progress toward implementing an event-driven APS readout.

On the Role of Ionospheric Ions in Sawtooth Events

NASA Astrophysics Data System (ADS)

Lund, E. J.; Nowrouzi, N.; Kistler, L. M.; Cai, X.; Frey, H. U.

2016-12-01

Global multifluid simulations have suggested that ions of ionospheric origin play a key role in driving sawtooth events, particularly events driven by coronal mass ejections (CMEs), through a feedback mechanism.1,2 The energy input from the first substorm causes ion outflow, which is claimed to drive the next substorm. We show that in situ data from Cluster in the tail during sawtooth events do not support this hypothesis. We show two detailed event studies, one driven by a CME and one driven by a streaming interaction region (SIR), as well as a statistical survey of all sawtooth events for which Cluster tail data are available. While examples exist of nightside outflow reaching the mid-tail ( 19 RE) region during CME-driven events, the overwhelming majority of both CME-driven and SIR-driven sawtooth injections have ionospheric ions in this region originating from the cusp, where the outflow is predominantly directly driven by the solar wind. The 19 RE region is critical because that is the region where near-Earth neutral line reconnection occurs. We conclude that while ionospheric outflow may contribute to sawtooth events, the injections are not the result of a feedback between the tail and the ionosphere. 1O. J. Brambles et al. (2011), Science 332, 1183, doi:10.1126/science.1202869.2O. J. Brambles et al. (2013), JGR 118, 6026, doi:10.1002/jgra.50522.

Tropical Cyclone-Driven Sediment Dynamics Over the Australian North West Shelf

NASA Astrophysics Data System (ADS)

Dufois, François; Lowe, Ryan J.; Branson, Paul; Fearns, Peter

2017-12-01

Owing to their strong forcing at the air-sea interface, tropical cyclones are a major driver of hydrodynamics and sediment dynamics of continental shelves, strongly impacting marine habitats and offshore industries. Despite the North West Shelf of Australia being one of the most frequently impacted tropical cyclone regions worldwide, there is limited knowledge of how tropical cyclones influence the sediment dynamics of this shelf region, including the significance of these episodic extreme events to the normal background conditions that occur. Using an extensive 2 year data set of the in situ sediment dynamics and 14 yearlong calibrated satellite ocean-color data set, we demonstrate that alongshore propagating cyclones are responsible for simultaneously generating both strong wave-induced sediment resuspension events and significant southwestward subtidal currents. Over the 2 year study period, two particular cyclones (Iggy and Narelle) dominated the sediment fluxes resulting in a residual southwestward sediment transport over the southern part of the shelf. By analyzing results from a long-term (37 year) wind and wave hindcast, our results suggest that at least 16 tropical cyclones had a strong potential to contribute to that southwestward sediment pathway in a similar way to Iggy and Narelle.

Real-time monitoring of Lévy flights in a single quantum system

NASA Astrophysics Data System (ADS)

Issler, M.; Höller, J.; Imamoǧlu, A.

2016-02-01

Lévy flights are random walks where the dynamics is dominated by rare events. Even though they have been studied in vastly different physical systems, their observation in a single quantum system has remained elusive. Here we analyze a periodically driven open central spin system and demonstrate theoretically that the dynamics of the spin environment exhibits Lévy flights. For the particular realization in a single-electron charged quantum dot driven by periodic resonant laser pulses, we use Monte Carlo simulations to confirm that the long waiting times between successive nuclear spin-flip events are governed by a power-law distribution; the corresponding exponent η =-3 /2 can be directly measured in real time by observing the waiting time distribution of successive photon emission events. Remarkably, the dominant intrinsic limitation of the scheme arising from nuclear quadrupole coupling can be minimized by adjusting the magnetic field or by implementing spin echo.

Reproducing the observed energy-dependent structure of Earth's electron radiation belts during storm recovery with an event-specific diffusion model

DOE PAGES

Ripoll, J. -F.; Reeves, Geoffrey D.; Cunningham, Gregory Scott; ...

2016-06-11

Here, we present dynamic simulations of energy-dependent losses in the radiation belt “slot region” and the formation of the two-belt structure for the quiet days after the 1 March storm. The simulations combine radial diffusion with a realistic scattering model, based data-driven spatially and temporally resolved whistler-mode hiss wave observations from the Van Allen Probes satellites. The simulations reproduce Van Allen Probes observations for all energies and L shells (2–6) including (a) the strong energy dependence to the radiation belt dynamics (b) an energy-dependent outer boundary to the inner zone that extends to higher L shells at lower energies andmore » (c) an “S-shaped” energy-dependent inner boundary to the outer zone that results from the competition between diffusive radial transport and losses. We find that the characteristic energy-dependent structure of the radiation belts and slot region is dynamic and can be formed gradually in ~15 days, although the “S shape” can also be reproduced by assuming equilibrium conditions. The highest-energy electrons (E > 300 keV) of the inner region of the outer belt (L ~ 4–5) also constantly decay, demonstrating that hiss wave scattering affects the outer belt during times of extended plasmasphere. Through these simulations, we explain the full structure in energy and L shell of the belts and the slot formation by hiss scattering during storm recovery. We show the power and complexity of looking dynamically at the effects over all energies and L shells and the need for using data-driven and event-specific conditions.« less

Adaptive Sampling using Support Vector Machines

DOE Office of Scientific and Technical Information (OSTI.GOV)

D. Mandelli; C. Smith

2012-11-01

Reliability/safety analysis of stochastic dynamic systems (e.g., nuclear power plants, airplanes, chemical plants) is currently performed through a combination of Event-Tress and Fault-Trees. However, these conventional methods suffer from certain drawbacks: • Timing of events is not explicitly modeled • Ordering of events is preset by the analyst • The modeling of complex accident scenarios is driven by expert-judgment For these reasons, there is currently an increasing interest into the development of dynamic PRA methodologies since they can be used to address the deficiencies of conventional methods listed above.

Discovering Event Structure in Continuous Narrative Perception and Memory.

PubMed

Baldassano, Christopher; Chen, Janice; Zadbood, Asieh; Pillow, Jonathan W; Hasson, Uri; Norman, Kenneth A

2017-08-02

During realistic, continuous perception, humans automatically segment experiences into discrete events. Using a novel model of cortical event dynamics, we investigate how cortical structures generate event representations during narrative perception and how these events are stored to and retrieved from memory. Our data-driven approach allows us to detect event boundaries as shifts between stable patterns of brain activity without relying on stimulus annotations and reveals a nested hierarchy from short events in sensory regions to long events in high-order areas (including angular gyrus and posterior medial cortex), which represent abstract, multimodal situation models. High-order event boundaries are coupled to increases in hippocampal activity, which predict pattern reinstatement during later free recall. These areas also show evidence of anticipatory reinstatement as subjects listen to a familiar narrative. Based on these results, we propose that brain activity is naturally structured into nested events, which form the basis of long-term memory representations. Copyright © 2017 Elsevier Inc. All rights reserved.

Stimulus-Driven Attentional Capture by a Static Discontinuity between Perceptual Groups

ERIC Educational Resources Information Center

Burnham, Bryan R.; Neely, James H.; Naginsky, Yelena; Thomas, Matthew

2010-01-01

After C. L. Folk, R. W. Remington, and J. C. Johnston (1992) proposed their contingent-orienting hypothesis, there has been an ongoing debate over whether purely stimulus-driven attentional capture can occur for visual events that are salient by virtue of a distinctive static property (as opposed to a dynamic property such as abrupt onset). The…

On the use of orientation filters for 3D reconstruction in event-driven stereo vision

PubMed Central

Camuñas-Mesa, Luis A.; Serrano-Gotarredona, Teresa; Ieng, Sio H.; Benosman, Ryad B.; Linares-Barranco, Bernabe

2014-01-01

The recently developed Dynamic Vision Sensors (DVS) sense visual information asynchronously and code it into trains of events with sub-micro second temporal resolution. This high temporal precision makes the output of these sensors especially suited for dynamic 3D visual reconstruction, by matching corresponding events generated by two different sensors in a stereo setup. This paper explores the use of Gabor filters to extract information about the orientation of the object edges that produce the events, therefore increasing the number of constraints applied to the matching algorithm. This strategy provides more reliably matched pairs of events, improving the final 3D reconstruction. PMID:24744694

Training Deep Spiking Neural Networks Using Backpropagation.

PubMed

Lee, Jun Haeng; Delbruck, Tobi; Pfeiffer, Michael

2016-01-01

Deep spiking neural networks (SNNs) hold the potential for improving the latency and energy efficiency of deep neural networks through data-driven event-based computation. However, training such networks is difficult due to the non-differentiable nature of spike events. In this paper, we introduce a novel technique, which treats the membrane potentials of spiking neurons as differentiable signals, where discontinuities at spike times are considered as noise. This enables an error backpropagation mechanism for deep SNNs that follows the same principles as in conventional deep networks, but works directly on spike signals and membrane potentials. Compared with previous methods relying on indirect training and conversion, our technique has the potential to capture the statistics of spikes more precisely. We evaluate the proposed framework on artificially generated events from the original MNIST handwritten digit benchmark, and also on the N-MNIST benchmark recorded with an event-based dynamic vision sensor, in which the proposed method reduces the error rate by a factor of more than three compared to the best previous SNN, and also achieves a higher accuracy than a conventional convolutional neural network (CNN) trained and tested on the same data. We demonstrate in the context of the MNIST task that thanks to their event-driven operation, deep SNNs (both fully connected and convolutional) trained with our method achieve accuracy equivalent with conventional neural networks. In the N-MNIST example, equivalent accuracy is achieved with about five times fewer computational operations.

Detecting causality in policy diffusion processes.

PubMed

Grabow, Carsten; Macinko, James; Silver, Diana; Porfiri, Maurizio

2016-08-01

A universal question in network science entails learning about the topology of interaction from collective dynamics. Here, we address this question by examining diffusion of laws across US states. We propose two complementary techniques to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, we establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions. Through extensive parametric studies, we demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity. Our results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving Federal mandates or incentives. This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

Detecting causality in policy diffusion processes

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Macinko, James; Silver, Diana; Porfiri, Maurizio

2016-08-01

A universal question in network science entails learning about the topology of interaction from collective dynamics. Here, we address this question by examining diffusion of laws across US states. We propose two complementary techniques to unravel determinants of this diffusion process: information-theoretic union transfer entropy and event synchronization. In order to systematically investigate their performance on law activity data, we establish a new stochastic model to generate synthetic law activity data based on plausible networks of interactions. Through extensive parametric studies, we demonstrate the ability of these methods to reconstruct networks, varying in size, link density, and degree heterogeneity. Our results suggest that union transfer entropy should be preferred for slowly varying processes, which may be associated with policies attending to specific local problems that occur only rarely or with policies facing high levels of opposition. In contrast, event synchronization is effective for faster enactment rates, which may be related to policies involving Federal mandates or incentives. This study puts forward a data-driven toolbox to explain the determinants of legal activity applicable to political science, across dynamical systems, information theory, and complex networks.

The temporal evolution of explosive events and its implication on reconnection dynamics

NASA Astrophysics Data System (ADS)

Guo, L.; Liu, W.; De Pontieu, B.; Huang, Y. M.; Peter, H.; Bhattacharjee, A.

2017-12-01

Transition-region explosive events and other bursts seen in extreme UV light are characterized by broad spectral line profiles, and the more violent ones show a strong enhancement of emission. They are thought to be driven by magnetic reconnection, because of their characteristic spectral profiles often indicating strong Alfvénic flows, and because of the fact that they typically occur where magnetic flux concentrations of opposite polarity intersect. In this presentation, we will focus on the temporal evolution of transition-region explosive events. In particular, we will investigate fast onsets of these events and the rapid oscillations of intensity during these event. The fast onset refers to the beginning of an explosive event, where the intensities and the widths of its line profiles increase dramatically (often within less than 10 seconds) and the rapid oscillations of intensity refer to blinks of emission that usually last less than 10 seconds during the event. In order to interpret and understand underlying mechanisms of these observations, we conduct numerical simulation of an explosive event and calculate its spectra. We observe a similar temporal evolution in the synthetic Si IV spectra when the explosive event is driven by time-dependent reconnection—plasmoid instability. The qualitative agreement between observations and simulations suggests that the temporal evolution of Si IV spectra of explosive events are closely related to reconnection dynamics.

The role of fluctuations and interactions in pedestrian dynamics

NASA Astrophysics Data System (ADS)

Corbetta, Alessandro; Meeusen, Jasper; Benzi, Roberto; Lee, Chung-Min; Toschi, Federico

Understanding quantitatively the statistical behaviour of pedestrians walking in crowds is a major scientific challenge of paramount societal relevance. Walking humans exhibit a rich (stochastic) dynamics whose small and large deviations are driven, among others, by own will as well as by environmental conditions. Via 24/7 automatic pedestrian tracking from multiple overhead Microsoft Kinect depth sensors, we collected large ensembles of pedestrian trajectories (in the order of tens of millions) in different real-life scenarios. These scenarios include both narrow corridors and large urban hallways, enabling us to cover and compare a wide spectrum of typical pedestrian dynamics. We investigate the pedestrian motion measuring the PDFs, e.g. those of position, velocity and acceleration, and at unprecedentedly high statistical resolution. We consider the dependence of PDFs on flow conditions, focusing on diluted dynamics and pair-wise interactions (''collisions'') for mutual avoidance. By means of Langevin-like models we provide models for the measured data, inclusive typical fluctuations and rare events. This work is part of the JSTP research programme ``Vision driven visitor behaviour analysis and crowd management'' with Project Number 341-10-001, which is financed by the Netherlands Organisation for Scientific Research (NWO).

Quantitative 4D analyses of epithelial folding during Drosophila gastrulation.

PubMed

Khan, Zia; Wang, Yu-Chiun; Wieschaus, Eric F; Kaschube, Matthias

2014-07-01

Understanding the cellular and mechanical processes that underlie the shape changes of individual cells and their collective behaviors in a tissue during dynamic and complex morphogenetic events is currently one of the major frontiers in developmental biology. The advent of high-speed time-lapse microscopy and its use in monitoring the cellular events in fluorescently labeled developing organisms demonstrate tremendous promise in establishing detailed descriptions of these events and could potentially provide a foundation for subsequent hypothesis-driven research strategies. However, obtaining quantitative measurements of dynamic shapes and behaviors of cells and tissues in a rapidly developing metazoan embryo using time-lapse 3D microscopy remains technically challenging, with the main hurdle being the shortage of robust imaging processing and analysis tools. We have developed EDGE4D, a software tool for segmenting and tracking membrane-labeled cells using multi-photon microscopy data. Our results demonstrate that EDGE4D enables quantification of the dynamics of cell shape changes, cell interfaces and neighbor relations at single-cell resolution during a complex epithelial folding event in the early Drosophila embryo. We expect this tool to be broadly useful for the analysis of epithelial cell geometries and movements in a wide variety of developmental contexts. © 2014. Published by The Company of Biologists Ltd.

Autonomous Multi-Sensor Coordination: The Science Goal Monitor

NASA Technical Reports Server (NTRS)

Koratkar, Anuradha; Grosvenor, Sandy; Jung, John; Hess, Melissa; Jones, Jeremy

2004-01-01

Many dramatic earth phenomena are dynamic and coupled. In order to fully understand them, we need to obtain timely coordinated multi-sensor observations from widely dispersed instruments. Such a dynamic observing system must include the ability to Schedule flexibly and react autonomously to sciencehser driven events; Understand higher-level goals of a sciencehser defined campaign; Coordinate various space-based and ground-based resources/sensors effectively and efficiently to achieve goals. In order to capture transient events, such a 'sensor web' system must have an automated reactive capability built into its scientific operations. To do this, we must overcome a number of challenges inherent in infusing autonomy. The Science Goal Monitor (SGM) is a prototype software tool being developed to explore the nature of automation necessary to enable dynamic observing. The tools being developed in SGM improve our ability to autonomously monitor multiple independent sensors and coordinate reactions to better observe dynamic phenomena. The SGM system enables users to specify what to look for and how to react in descriptive rather than technical terms. The system monitors streams of data to identify occurrences of the key events previously specified by the scientisther. When an event occurs, the system autonomously coordinates the execution of the users' desired reactions between different sensors. The information can be used to rapidly respond to a variety of fast temporal events. Investigators will no longer have to rely on after-the-fact data analysis to determine what happened. Our paper describes a series of prototype demonstrations that we have developed using SGM and NASA's Earth Observing-1 (EO-1) satellite and Earth Observing Systems' Aqua/Terra spacecrafts' MODIS instrument. Our demonstrations show the promise of coordinating data from different sources, analyzing the data for a relevant event, autonomously updating and rapidly obtaining a follow-on relevant image. SGM was used to investigate forest fires, floods and volcanic eruptions. We are now identifying new Earth science scenarios that will have more complex SGM reasoning. By developing and testing a prototype in an operational environment, we are also establishing and gathering metrics to gauge the success of automating science campaigns.

How much are Chevrolet Volts in The EV Project driven in EV Mode?

DOE Office of Scientific and Technical Information (OSTI.GOV)

Smart, John

2013-08-01

This report summarizes key conclusions from analysis of data collected from Chevrolet Volts participating in The EV Project. Topics include how many miles are driven in EV mode, how far vehicles are driven between charging events, and how much energy is charged from the electric grid per charging event.

WIFIRE: A Scalable Data-Driven Monitoring, Dynamic Prediction and Resilience Cyberinfrastructure for Wildfires

NASA Astrophysics Data System (ADS)

Altintas, I.; Block, J.; Braun, H.; de Callafon, R. A.; Gollner, M. J.; Smarr, L.; Trouve, A.

2013-12-01

Recent studies confirm that climate change will cause wildfires to increase in frequency and severity in the coming decades especially for California and in much of the North American West. The most critical sustainability issue in the midst of these ever-changing dynamics is how to achieve a new social-ecological equilibrium of this fire ecology. Wildfire wind speeds and directions change in an instant, and first responders can only be effective when they take action as quickly as the conditions change. To deliver information needed for sustainable policy and management in this dynamically changing fire regime, we must capture these details to understand the environmental processes. We are building an end-to-end cyberinfrastructure (CI), called WIFIRE, for real-time and data-driven simulation, prediction and visualization of wildfire behavior. The WIFIRE integrated CI system supports social-ecological resilience to the changing fire ecology regime in the face of urban dynamics and climate change. Networked observations, e.g., heterogeneous satellite data and real-time remote sensor data is integrated with computational techniques in signal processing, visualization, modeling and data assimilation to provide a scalable, technological, and educational solution to monitor weather patterns to predict a wildfire's Rate of Spread. Our collaborative WIFIRE team of scientists, engineers, technologists, government policy managers, private industry, and firefighters architects implement CI pathways that enable joint innovation for wildfire management. Scientific workflows are used as an integrative distributed programming model and simplify the implementation of engineering modules for data-driven simulation, prediction and visualization while allowing integration with large-scale computing facilities. WIFIRE will be scalable to users with different skill-levels via specialized web interfaces and user-specified alerts for environmental events broadcasted to receivers before, during and after a wildfire. Scalability of the WIFIRE approach allows many sensors to be subjected to user-specified data processing algorithms to generate threshold alerts within seconds. Integration of this sensor data into both rapidly available fire image data and models will better enable situational awareness, responses and decision support at local, state, national, and international levels. The products of WIFIRE will be initially disseminated to our collaborators (SDG&E, CAL FIRE, USFS), covering academic, private, and government laboratories while generating values to emergency officials, and consequently to the general public. WIFIRE may be used by government agencies in the future to save lives and property during wildfire events, test the effectiveness of response and evacuation scenarios before they occur and assess the effectiveness of high-density sensor networks in improving fire and weather predictions. WIFIRE's high-density network, therefore, will serve as a testbed for future applications worldwide.

Perfect Detection of Spikes in the Linear Sub-threshold Dynamics of Point Neurons

PubMed Central

Krishnan, Jeyashree; Porta Mana, PierGianLuca; Helias, Moritz; Diesmann, Markus; Di Napoli, Edoardo

2018-01-01

Spiking neuronal networks are usually simulated with one of three main schemes: the classical time-driven and event-driven schemes, and the more recent hybrid scheme. All three schemes evolve the state of a neuron through a series of checkpoints: equally spaced in the first scheme and determined neuron-wise by spike events in the latter two. The time-driven and the hybrid scheme determine whether the membrane potential of a neuron crosses a threshold at the end of the time interval between consecutive checkpoints. Threshold crossing can, however, occur within the interval even if this test is negative. Spikes can therefore be missed. The present work offers an alternative geometric point of view on neuronal dynamics, and derives, implements, and benchmarks a method for perfect retrospective spike detection. This method can be applied to neuron models with affine or linear subthreshold dynamics. The idea behind the method is to propagate the threshold with a time-inverted dynamics, testing whether the threshold crosses the neuron state to be evolved, rather than vice versa. Algebraically this translates into a set of inequalities necessary and sufficient for threshold crossing. This test is slower than the imperfect one, but can be optimized in several ways. Comparison confirms earlier results that the imperfect tests rarely miss spikes (less than a fraction 1/108 of missed spikes) in biologically relevant settings. PMID:29379430

Fast Distributed Dynamics of Semantic Networks via Social Media.

PubMed

Carrillo, Facundo; Cecchi, Guillermo A; Sigman, Mariano; Slezak, Diego Fernández

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network.

Fast Distributed Dynamics of Semantic Networks via Social Media

PubMed Central

Carrillo, Facundo; Cecchi, Guillermo A.; Sigman, Mariano; Fernández Slezak, Diego

2015-01-01

We investigate the dynamics of semantic organization using social media, a collective expression of human thought. We propose a novel, time-dependent semantic similarity measure (TSS), based on the social network Twitter. We show that TSS is consistent with static measures of similarity but provides high temporal resolution for the identification of real-world events and induced changes in the distributed structure of semantic relationships across the entire lexicon. Using TSS, we measured the evolution of a concept and its movement along the semantic neighborhood, driven by specific news/events. Finally, we showed that particular events may trigger a temporary reorganization of elements in the semantic network. PMID:26074953

A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink.

PubMed

Zhu, Chuan; Zhang, Sai; Han, Guangjie; Jiang, Jinfang; Rodrigues, Joel J P C

2016-09-06

Mobile sink is widely used for data collection in wireless sensor networks. It can avoid 'hot spot' problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios.

A Greedy Scanning Data Collection Strategy for Large-Scale Wireless Sensor Networks with a Mobile Sink

PubMed Central

Zhu, Chuan; Zhang, Sai; Han, Guangjie; Jiang, Jinfang; Rodrigues, Joel J. P. C.

2016-01-01

Mobile sink is widely used for data collection in wireless sensor networks. It can avoid ‘hot spot’ problems but energy consumption caused by multihop transmission is still inefficient in real-time application scenarios. In this paper, a greedy scanning data collection strategy (GSDCS) is proposed, and we focus on how to reduce routing energy consumption by shortening total length of routing paths. We propose that the mobile sink adjusts its trajectory dynamically according to the changes of network, instead of predetermined trajectory or random walk. Next, the mobile sink determines which area has more source nodes, then it moves toward this area. The benefit of GSDCS is that most source nodes are no longer needed to upload sensory data for long distances. Especially in event-driven application scenarios, when event area changes, the mobile sink could arrive at the new event area where most source nodes are located currently. Hence energy can be saved. Analytical and simulation results show that compared with existing work, our GSDCS has a better performance in specific application scenarios. PMID:27608022

Application of process monitoring to anomaly detection in nuclear material processing systems via system-centric event interpretation of data from multiple sensors of varying reliability

DOE Office of Scientific and Technical Information (OSTI.GOV)

Garcia, Humberto E.; Simpson, Michael F.; Lin, Wen-Chiao

In this paper, we apply an advanced safeguards approach and associated methods for process monitoring to a hypothetical nuclear material processing system. The assessment regarding the state of the processing facility is conducted at a systemcentric level formulated in a hybrid framework. This utilizes architecture for integrating both time- and event-driven data and analysis for decision making. While the time-driven layers of the proposed architecture encompass more traditional process monitoring methods based on time series data and analysis, the event-driven layers encompass operation monitoring methods based on discrete event data and analysis. By integrating process- and operation-related information and methodologiesmore » within a unified framework, the task of anomaly detection is greatly improved. This is because decision-making can benefit from not only known time-series relationships among measured signals but also from known event sequence relationships among generated events. This available knowledge at both time series and discrete event layers can then be effectively used to synthesize observation solutions that optimally balance sensor and data processing requirements. The application of the proposed approach is then implemented on an illustrative monitored system based on pyroprocessing and results are discussed.« less

Listening in on Friction: Stick-Slip Acoustical Signatures in Velcro

NASA Astrophysics Data System (ADS)

Hurtado Parra, Sebastian; Morrow, Leslie; Radziwanowski, Miles; Angiolillo, Paul

2013-03-01

The onset of kinetic friction and the possible resulting stick-slip motion remain mysterious phenomena. Moreover, stick-slip dynamics are typically accompanied by acoustic bursts that occur temporally with the slip event. The dry sliding dynamics of the hook-and-loop system, as exemplified by Velcro, manifest stick-slip behavior along with audible bursts that are easily micrphonically collected. Synchronized measurements of the friction force and acoustic emissions were collected as hooked Velcro was driven at constant velocity over a bed of looped Velcro in an anechoic chamber. Not surprising, the envelope of the acoustic bursts maps well onto the slip events of the friction force time series and the intensity of the bursts trends with the magnitude of the difference of the friction force during a stick-slip event. However, the analysis of the acoustic emission can serve as a sensitive tool for revealing some of the hidden details of the evolution of the transition from static to kinetic friction. For instance, small acoustic bursts are seen prior to the Amontons-Coulomb threshold, signaling precursor events prior to the onset of macroscopically observed motion. Preliminary spectral analysis of the acoustic emissions including intensity-frequency data will be presented.

An urban observatory for quantifying phosphorus and suspended solid loads in combined natural and stormwater conveyances.

PubMed

Melcher, Anthony A; Horsburgh, Jeffery S

2017-06-01

Water quality in urban streams and stormwater systems is highly dynamic, both spatially and temporally, and can change drastically during storm events. Infrequent grab samples commonly collected for estimating pollutant loadings are insufficient to characterize water quality in many urban water systems. In situ water quality measurements are being used as surrogates for continuous pollutant load estimates; however, relatively few studies have tested the validity of surrogate indicators in urban stormwater conveyances. In this paper, we describe an observatory aimed at demonstrating the infrastructure required for surrogate monitoring in urban water systems and for capturing the dynamic behavior of stormwater-driven pollutant loads. We describe the instrumentation of multiple, autonomous water quality and quantity monitoring sites within an urban observatory. We also describe smart and adaptive sampling procedures implemented to improve data collection for developing surrogate relationships and for capturing the temporal and spatial variability of pollutant loading events in urban watersheds. Results show that the observatory is able to capture short-duration storm events within multiple catchments and, through inter-site communication, sampling efforts can be synchronized across multiple monitoring sites.

Investigating rare events with nonequilibrium work measurements. I. Nonequilibrium transition path probabilities

NASA Astrophysics Data System (ADS)

Moradi, Mahmoud; Sagui, Celeste; Roland, Christopher

2014-01-01

We have developed a formalism for investigating transition pathways and transition probabilities for rare events in biomolecular systems. In this paper, we set the theoretical framework for employing nonequilibrium work relations to estimate the relative reaction rates associated with different classes of transition pathways. Particularly, we derive an extension of Crook's transient fluctuation theorem, which relates the relative transition rates of driven systems in the forward and reverse directions, and allows for the calculation of these relative rates using work measurements (e.g., in Steered Molecular Dynamics). The formalism presented here can be combined with Transition Path Theory to relate the equilibrium and driven transition rates. The usefulness of this framework is illustrated by means of a Gaussian model and a driven proline dimer.

Temporal dynamics of musical emotions examined through intersubject synchrony of brain activity

PubMed Central

Frühholz, Sascha; Cochrane, Tom; Cojan, Yann; Vuilleumier, Patrik

2015-01-01

To study emotional reactions to music, it is important to consider the temporal dynamics of both affective responses and underlying brain activity. Here, we investigated emotions induced by music using functional magnetic resonance imaging (fMRI) with a data-driven approach based on intersubject correlations (ISC). This method allowed us to identify moments in the music that produced similar brain activity (i.e. synchrony) among listeners under relatively natural listening conditions. Continuous ratings of subjective pleasantness and arousal elicited by the music were also obtained for the music outside of the scanner. Our results reveal synchronous activations in left amygdala, left insula and right caudate nucleus that were associated with higher arousal, whereas positive valence ratings correlated with decreases in amygdala and caudate activity. Additional analyses showed that synchronous amygdala responses were driven by energy-related features in the music such as root mean square and dissonance, while synchrony in insula was additionally sensitive to acoustic event density. Intersubject synchrony also occurred in the left nucleus accumbens, a region critically implicated in reward processing. Our study demonstrates the feasibility and usefulness of an approach based on ISC to explore the temporal dynamics of music perception and emotion in naturalistic conditions. PMID:25994970

Selective attention in multi-chip address-event systems.

PubMed

Bartolozzi, Chiara; Indiveri, Giacomo

2009-01-01

Selective attention is the strategy used by biological systems to cope with the inherent limits in their available computational resources, in order to efficiently process sensory information. The same strategy can be used in artificial systems that have to process vast amounts of sensory data with limited resources. In this paper we present a neuromorphic VLSI device, the "Selective Attention Chip" (SAC), which can be used to implement these models in multi-chip address-event systems. We also describe a real-time sensory-motor system, which integrates the SAC with a dynamic vision sensor and a robotic actuator. We present experimental results from each component in the system, and demonstrate how the complete system implements a real-time stimulus-driven selective attention model.

Predictability of rogue events.

PubMed

Birkholz, Simon; Brée, Carsten; Demircan, Ayhan; Steinmeyer, Günter

2015-05-29

Using experimental data from three different rogue wave supporting systems, determinism, and predictability of the underlying dynamics are evaluated with methods of nonlinear time series analysis. We included original records from the Draupner platform in the North Sea as well as time series from two optical systems in our analysis. One of the latter was measured in the infrared tail of optical fiber supercontinua, the other in the fluence profiles of multifilaments. All three data sets exhibit extreme-value statistics and exceed the significant wave height in the respective system by a factor larger than 2. Nonlinear time series analysis indicates a different degree of determinism in the systems. The optical fiber scenario is found to be driven by quantum noise whereas rogue waves emerge as a consequence of turbulence in the others. With the large number of rogue events observed in the multifilament system, we can systematically explore the predictability of such events in a turbulent system. We observe that rogue events do not necessarily appear without a warning, but are often preceded by a short phase of relative order. This surprising finding sheds some new light on the fascinating phenomenon of rogue waves.

Safety analysis of proposed data-driven physiologic alarm parameters for hospitalized children.

PubMed

Goel, Veena V; Poole, Sarah F; Longhurst, Christopher A; Platchek, Terry S; Pageler, Natalie M; Sharek, Paul J; Palma, Jonathan P

2016-12-01

Modification of alarm limits is one approach to mitigating alarm fatigue. We aimed to create and validate heart rate (HR) and respiratory rate (RR) percentiles for hospitalized children, and analyze the safety of replacing current vital sign reference ranges with proposed data-driven, age-stratified 5th and 95th percentile values. In this retrospective cross-sectional study, nurse-charted HR and RR data from a training set of 7202 hospitalized children were used to develop percentile tables. We compared 5th and 95th percentile values with currently accepted reference ranges in a validation set of 2287 patients. We analyzed 148 rapid response team (RRT) and cardiorespiratory arrest (CRA) events over a 12-month period, using HR and RR values in the 12 hours prior to the event, to determine the proportion of patients with out-of-range vitals based upon reference versus data-driven limits. There were 24,045 (55.6%) fewer out-of-range measurements using data-driven vital sign limits. Overall, 144/148 RRT and CRA patients had out-of-range HR or RR values preceding the event using current limits, and 138/148 were abnormal using data-driven limits. Chart review of RRT and CRA patients with abnormal HR and RR per current limits considered normal by data-driven limits revealed that clinical status change was identified by other vital sign abnormalities or clinical context. A large proportion of vital signs in hospitalized children are outside presently used norms. Safety evaluation of data-driven limits suggests they are as safe as those currently used. Implementation of these parameters in physiologic monitors may mitigate alarm fatigue. Journal of Hospital Medicine 2015;11:817-823. © 2015 Society of Hospital Medicine. © 2016 Society of Hospital Medicine.

HOTS: A Hierarchy of Event-Based Time-Surfaces for Pattern Recognition.

PubMed

Lagorce, Xavier; Orchard, Garrick; Galluppi, Francesco; Shi, Bertram E; Benosman, Ryad B

2017-07-01

This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.This paper describes novel event-based spatio-temporal features called time-surfaces and how they can be used to create a hierarchical event-based pattern recognition architecture. Unlike existing hierarchical architectures for pattern recognition, the presented model relies on a time oriented approach to extract spatio-temporal features from the asynchronously acquired dynamics of a visual scene. These dynamics are acquired using biologically inspired frameless asynchronous event-driven vision sensors. Similarly to cortical structures, subsequent layers in our hierarchy extract increasingly abstract features using increasingly large spatio-temporal windows. The central concept is to use the rich temporal information provided by events to create contexts in the form of time-surfaces which represent the recent temporal activity within a local spatial neighborhood. We demonstrate that this concept can robustly be used at all stages of an event-based hierarchical model. First layer feature units operate on groups of pixels, while subsequent layer feature units operate on the output of lower level feature units. We report results on a previously published 36 class character recognition task and a four class canonical dynamic card pip task, achieving near 100 percent accuracy on each. We introduce a new seven class moving face recognition task, achieving 79 percent accuracy.

Coherence explored between emotion components: evidence from event-related potentials and facial electromyography.

PubMed

Gentsch, Kornelia; Grandjean, Didier; Scherer, Klaus R

2014-04-01

Componential theories assume that emotion episodes consist of emergent and dynamic response changes to relevant events in different components, such as appraisal, physiology, motivation, expression, and subjective feeling. In particular, Scherer's Component Process Model hypothesizes that subjective feeling emerges when the synchronization (or coherence) of appraisal-driven changes between emotion components has reached a critical threshold. We examined the prerequisite of this synchronization hypothesis for appraisal-driven response changes in facial expression. The appraisal process was manipulated by using feedback stimuli, presented in a gambling task. Participants' responses to the feedback were investigated in concurrently recorded brain activity related to appraisal (event-related potentials, ERP) and facial muscle activity (electromyography, EMG). Using principal component analysis, the prediction of appraisal-driven response changes in facial EMG was examined. Results support this prediction: early cognitive processes (related to the feedback-related negativity) seem to primarily affect the upper face, whereas processes that modulate P300 amplitudes tend to predominantly drive cheek region responses. Copyright © 2013 Elsevier B.V. All rights reserved.

Data-Driven Model Reduction and Transfer Operator Approximation

NASA Astrophysics Data System (ADS)

Klus, Stefan; Nüske, Feliks; Koltai, Péter; Wu, Hao; Kevrekidis, Ioannis; Schütte, Christof; Noé, Frank

2018-06-01

In this review paper, we will present different data-driven dimension reduction techniques for dynamical systems that are based on transfer operator theory as well as methods to approximate transfer operators and their eigenvalues, eigenfunctions, and eigenmodes. The goal is to point out similarities and differences between methods developed independently by the dynamical systems, fluid dynamics, and molecular dynamics communities such as time-lagged independent component analysis, dynamic mode decomposition, and their respective generalizations. As a result, extensions and best practices developed for one particular method can be carried over to other related methods.

Ionospheric Irregularities at Mars Probed by MARSIS Topside Sounding

NASA Astrophysics Data System (ADS)

Harada, Y.; Gurnett, D. A.; Kopf, A. J.; Halekas, J. S.; Ruhunusiri, S.

2018-01-01

The upper ionosphere of Mars contains a variety of perturbations driven by solar wind forcing from above and upward propagating atmospheric waves from below. Here we explore the global distribution and variability of ionospheric irregularities around the exobase at Mars by analyzing topside sounding data from the Mars Advanced Radar for Subsurface and Ionosphere Sounding (MARSIS) instrument on board Mars Express. As irregular structure gives rise to off-vertical echoes with excess propagation time, the diffuseness of ionospheric echo traces can be used as a diagnostic tool for perturbed reflection surfaces. The observed properties of diffuse echoes above unmagnetized regions suggest that ionospheric irregularities with horizontal wavelengths of tens to hundreds of kilometers are particularly enhanced in the winter hemisphere and at high solar zenith angles. Given the known inverse dependence of neutral gravity wave amplitudes on the background atmospheric temperature, the ionospheric irregularities probed by MARSIS are most likely associated with plasma perturbations driven by atmospheric gravity waves. Though extreme events with unusually diffuse echoes are more frequently observed for high solar wind dynamic pressures during some time intervals, the vast majority of the diffuse echo events are unaffected by varying solar wind conditions, implying limited influence of solar wind forcing on the generation of ionospheric irregularities. Combination of remote and in situ measurements of ionospheric irregularities would offer the opportunity for a better understanding of the ionospheric dynamics at Mars.

Empirical confirmation of creative destruction from world trade data.

PubMed

Klimek, Peter; Hausmann, Ricardo; Thurner, Stefan

2012-01-01

We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite-disappearances followed by periods of appearances-is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country's product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics.

Empirical Confirmation of Creative Destruction from World Trade Data

PubMed Central

Klimek, Peter; Hausmann, Ricardo; Thurner, Stefan

2012-01-01

We show that world trade network datasets contain empirical evidence that the dynamics of innovation in the world economy indeed follows the concept of creative destruction, as proposed by J.A. Schumpeter more than half a century ago. National economies can be viewed as complex, evolving systems, driven by a stream of appearance and disappearance of goods and services. Products appear in bursts of creative cascades. We find that products systematically tend to co-appear, and that product appearances lead to massive disappearance events of existing products in the following years. The opposite–disappearances followed by periods of appearances–is not observed. This is an empirical validation of the dominance of cascading competitive replacement events on the scale of national economies, i.e., creative destruction. We find a tendency that more complex products drive out less complex ones, i.e., progress has a direction. Finally we show that the growth trajectory of a country’s product output diversity can be understood by a recently proposed evolutionary model of Schumpeterian economic dynamics. PMID:22719989

IL-1β, RAGE and FABP4: targeting the dynamic trio in metabolic inflammation and related pathologies

PubMed Central

Hardaway, Aimalie L; Podgorski, Izabela

2013-01-01

Within the past decade, inflammatory and lipid mediators, such as IL-1β, FABP4 and RAGE, have emerged as important contributors to metabolic dysfunction. As growing experimental and clinical evidence continues to tie obesity-induced chronic inflammation with dysregulated lipid, insulin signaling and related pathologies, IL-1β, FABP4 and RAGE each are being independently implicated as culprits in these events. There are also convincing data that molecular pathways driven by these molecules are interconnected in exacerbating metabolic consequences of obesity. This article highlights the roles of IL-1β, FABP4 and RAGE in normal physiology as well as focusing specifically on their contribution to inflammation, insulin resistance, atherosclerosis, Type 2 diabetes and cancer. Studies implicating the interconnection between these pathways, current and emerging therapeutics, and their use as potential biomarkers are also discussed. Evidence of impact of IL-1β, FABP4 and RAGE pathways on severity of metabolic dysfunction underlines the strong links between inflammatory events, lipid metabolism and insulin regulation, and offers new intriguing approaches for future therapies of obesity-driven pathologies. PMID:23795967

IL-1β, RAGE and FABP4: targeting the dynamic trio in metabolic inflammation and related pathologies.

PubMed

Hardaway, Aimalie L; Podgorski, Izabela

2013-06-01

Within the past decade, inflammatory and lipid mediators, such as IL-1β, FABP4 and RAGE, have emerged as important contributors to metabolic dysfunction. As growing experimental and clinical evidence continues to tie obesity-induced chronic inflammation with dysregulated lipid, insulin signaling and related pathologies, IL-1β, FABP4 and RAGE each are being independently implicated as culprits in these events. There are also convincing data that molecular pathways driven by these molecules are interconnected in exacerbating metabolic consequences of obesity. This article highlights the roles of IL-1β, FABP4 and RAGE in normal physiology as well as focusing specifically on their contribution to inflammation, insulin resistance, atherosclerosis, Type 2 diabetes and cancer. Studies implicating the interconnection between these pathways, current and emerging therapeutics, and their use as potential biomarkers are also discussed. Evidence of impact of IL-1β, FABP4 and RAGE pathways on severity of metabolic dysfunction underlines the strong links between inflammatory events, lipid metabolism and insulin regulation, and offers new intriguing approaches for future therapies of obesity-driven pathologies.

Single stock dynamics on high-frequency data: from a compressed coding perspective.

PubMed

Fushing, Hsieh; Chen, Shu-Chun; Hwang, Chii-Ruey

2014-01-01

High-frequency return, trading volume and transaction number are digitally coded via a nonparametric computing algorithm, called hierarchical factor segmentation (HFS), and then are coupled together to reveal a single stock dynamics without global state-space structural assumptions. The base-8 digital coding sequence, which is capable of revealing contrasting aggregation against sparsity of extreme events, is further compressed into a shortened sequence of state transitions. This compressed digital code sequence vividly demonstrates that the aggregation of large absolute returns is the primary driving force for stimulating both the aggregations of large trading volumes and transaction numbers. The state of system-wise synchrony is manifested with very frequent recurrence in the stock dynamics. And this data-driven dynamic mechanism is seen to correspondingly vary as the global market transiting in and out of contraction-expansion cycles. These results not only elaborate the stock dynamics of interest to a fuller extent, but also contradict some classical theories in finance. Overall this version of stock dynamics is potentially more coherent and realistic, especially when the current financial market is increasingly powered by high-frequency trading via computer algorithms, rather than by individual investors.

Single Stock Dynamics on High-Frequency Data: From a Compressed Coding Perspective

PubMed Central

Fushing, Hsieh; Chen, Shu-Chun; Hwang, Chii-Ruey

2014-01-01

High-frequency return, trading volume and transaction number are digitally coded via a nonparametric computing algorithm, called hierarchical factor segmentation (HFS), and then are coupled together to reveal a single stock dynamics without global state-space structural assumptions. The base-8 digital coding sequence, which is capable of revealing contrasting aggregation against sparsity of extreme events, is further compressed into a shortened sequence of state transitions. This compressed digital code sequence vividly demonstrates that the aggregation of large absolute returns is the primary driving force for stimulating both the aggregations of large trading volumes and transaction numbers. The state of system-wise synchrony is manifested with very frequent recurrence in the stock dynamics. And this data-driven dynamic mechanism is seen to correspondingly vary as the global market transiting in and out of contraction-expansion cycles. These results not only elaborate the stock dynamics of interest to a fuller extent, but also contradict some classical theories in finance. Overall this version of stock dynamics is potentially more coherent and realistic, especially when the current financial market is increasingly powered by high-frequency trading via computer algorithms, rather than by individual investors. PMID:24586235

Event management for large scale event-driven digital hardware spiking neural networks.

PubMed

Caron, Louis-Charles; D'Haene, Michiel; Mailhot, Frédéric; Schrauwen, Benjamin; Rouat, Jean

2013-09-01

The interest in brain-like computation has led to the design of a plethora of innovative neuromorphic systems. Individually, spiking neural networks (SNNs), event-driven simulation and digital hardware neuromorphic systems get a lot of attention. Despite the popularity of event-driven SNNs in software, very few digital hardware architectures are found. This is because existing hardware solutions for event management scale badly with the number of events. This paper introduces the structured heap queue, a pipelined digital hardware data structure, and demonstrates its suitability for event management. The structured heap queue scales gracefully with the number of events, allowing the efficient implementation of large scale digital hardware event-driven SNNs. The scaling is linear for memory, logarithmic for logic resources and constant for processing time. The use of the structured heap queue is demonstrated on a field-programmable gate array (FPGA) with an image segmentation experiment and a SNN of 65,536 neurons and 513,184 synapses. Events can be processed at the rate of 1 every 7 clock cycles and a 406×158 pixel image is segmented in 200 ms. Copyright © 2013 Elsevier Ltd. All rights reserved.

Exact subthreshold integration with continuous spike times in discrete-time neural network simulations.

PubMed

Morrison, Abigail; Straube, Sirko; Plesser, Hans Ekkehard; Diesmann, Markus

2007-01-01

Very large networks of spiking neurons can be simulated efficiently in parallel under the constraint that spike times are bound to an equidistant time grid. Within this scheme, the subthreshold dynamics of a wide class of integrate-and-fire-type neuron models can be integrated exactly from one grid point to the next. However, the loss in accuracy caused by restricting spike times to the grid can have undesirable consequences, which has led to interest in interpolating spike times between the grid points to retrieve an adequate representation of network dynamics. We demonstrate that the exact integration scheme can be combined naturally with off-grid spike events found by interpolation. We show that by exploiting the existence of a minimal synaptic propagation delay, the need for a central event queue is removed, so that the precision of event-driven simulation on the level of single neurons is combined with the efficiency of time-driven global scheduling. Further, for neuron models with linear subthreshold dynamics, even local event queuing can be avoided, resulting in much greater efficiency on the single-neuron level. These ideas are exemplified by two implementations of a widely used neuron model. We present a measure for the efficiency of network simulations in terms of their integration error and show that for a wide range of input spike rates, the novel techniques we present are both more accurate and faster than standard techniques.

Data-driven Modeling of Metal-oxide Sensors with Dynamic Bayesian Networks

NASA Astrophysics Data System (ADS)

Gosangi, Rakesh; Gutierrez-Osuna, Ricardo

2011-09-01

We present a data-driven probabilistic framework to model the transient response of MOX sensors modulated with a sequence of voltage steps. Analytical models of MOX sensors are usually built based on the physico-chemical properties of the sensing materials. Although building these models provides an insight into the sensor behavior, they also require a thorough understanding of the underlying operating principles. Here we propose a data-driven approach to characterize the dynamical relationship between sensor inputs and outputs. Namely, we use dynamic Bayesian networks (DBNs), probabilistic models that represent temporal relations between a set of random variables. We identify a set of control variables that influence the sensor responses, create a graphical representation that captures the causal relations between these variables, and finally train the model with experimental data. We validated the approach on experimental data in terms of predictive accuracy and classification performance. Our results show that DBNs can accurately predict the dynamic response of MOX sensors, as well as capture the discriminatory information present in the sensor transients.

Computational Model of Secondary Palate Fusion and Disruption

EPA Science Inventory

Morphogenetic events are driven by cell-generated physical forces and complex cellular dynamics. To improve our capacity to predict developmental effects from cellular alterations, we built a multi-cellular agent-based model in CompuCell3D that recapitulates the cellular networks...

Sawtooth events and O+ in the plasma sheet and boundary layer: CME- and SIR-driven events

NASA Astrophysics Data System (ADS)

Lund, E. J.; Nowrouzi, N.; Kistler, L. M.; Cai, X.; Liao, J.

2017-12-01

The role of ionospheric ions in sawtooth events is an open question. Simulations[1,2,3] suggest that O+ from the ionosphere produces a feedback mechanism for driving sawtooth events. However, observational evidence[4,5] suggest that the presence of O+ in the plasma sheet is neither necessary nor sufficient. In this study we investigate whether the solar wind driver of the geomagnetic storm has an effect on the result. Building on an earlier study[4] that used events for which Cluster data is available in the plasma sheet and boundary layer, we perform a superposed epoch analysis for coronal mass ejection (CME) driven storms and streaming interaction region (SIR) driven storms separately, to investigate the hypothesis that ionospheric O+ is an important contributor for CME-driven storms but not SIR-driven storms[2]. [1]O. J. Brambles et al. (2011), Science 332, 1183.[2]O. J. Brambles et al. (2013), JGR 118, 6026.[3]R. H. Varney et al. (2016), JGR 121, 9688.[4]J. Liao et al. (2014), JGR 119, 1572.[5]E. J. Lund et al. (2017), JGR, submitted.

Patterns of precipitation and soil moisture extremes in Texas, US: A complex network analysis

NASA Astrophysics Data System (ADS)

Sun, Alexander Y.; Xia, Youlong; Caldwell, Todd G.; Hao, Zengchao

2018-02-01

Understanding of the spatial and temporal dynamics of extreme precipitation not only improves prediction skills, but also helps to prioritize hazard mitigation efforts. This study seeks to enhance the understanding of spatiotemporal covariation patterns embedded in precipitation (P) and soil moisture (SM) by using an event-based, complex-network-theoretic approach. Events concurrences are quantified using a nonparametric event synchronization measure, and spatial patterns of hydroclimate variables are analyzed by using several network measures and a community detection algorithm. SM-P coupling is examined using a directional event coincidence analysis measure that takes the order of event occurrences into account. The complex network approach is demonstrated for Texas, US, a region possessing a rich set of hydroclimate features and is frequented by catastrophic flooding. Gridded daily observed P data and simulated SM data are used to create complex networks of P and SM extremes. The uncovered high degree centrality regions and community structures are qualitatively in agreement with the overall existing knowledge of hydroclimate extremes in the study region. Our analyses provide new visual insights on the propagation, connectivity, and synchronicity of P extremes, as well as the SM-P coupling, in this flood-prone region, and can be readily used as a basis for event-driven predictive analytics for other regions.

Data-driven robust approximate optimal tracking control for unknown general nonlinear systems using adaptive dynamic programming method.

PubMed

Zhang, Huaguang; Cui, Lili; Zhang, Xin; Luo, Yanhong

2011-12-01

In this paper, a novel data-driven robust approximate optimal tracking control scheme is proposed for unknown general nonlinear systems by using the adaptive dynamic programming (ADP) method. In the design of the controller, only available input-output data is required instead of known system dynamics. A data-driven model is established by a recurrent neural network (NN) to reconstruct the unknown system dynamics using available input-output data. By adding a novel adjustable term related to the modeling error, the resultant modeling error is first guaranteed to converge to zero. Then, based on the obtained data-driven model, the ADP method is utilized to design the approximate optimal tracking controller, which consists of the steady-state controller and the optimal feedback controller. Further, a robustifying term is developed to compensate for the NN approximation errors introduced by implementing the ADP method. Based on Lyapunov approach, stability analysis of the closed-loop system is performed to show that the proposed controller guarantees the system state asymptotically tracking the desired trajectory. Additionally, the obtained control input is proven to be close to the optimal control input within a small bound. Finally, two numerical examples are used to demonstrate the effectiveness of the proposed control scheme.

A data-driven dynamics simulation framework for railway vehicles

NASA Astrophysics Data System (ADS)

Nie, Yinyu; Tang, Zhao; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2018-03-01

The finite element (FE) method is essential for simulating vehicle dynamics with fine details, especially for train crash simulations. However, factors such as the complexity of meshes and the distortion involved in a large deformation would undermine its calculation efficiency. An alternative method, the multi-body (MB) dynamics simulation provides satisfying time efficiency but limited accuracy when highly nonlinear dynamic process is involved. To maintain the advantages of both methods, this paper proposes a data-driven simulation framework for dynamics simulation of railway vehicles. This framework uses machine learning techniques to extract nonlinear features from training data generated by FE simulations so that specific mesh structures can be formulated by a surrogate element (or surrogate elements) to replace the original mechanical elements, and the dynamics simulation can be implemented by co-simulation with the surrogate element(s) embedded into a MB model. This framework consists of a series of techniques including data collection, feature extraction, training data sampling, surrogate element building, and model evaluation and selection. To verify the feasibility of this framework, we present two case studies, a vertical dynamics simulation and a longitudinal dynamics simulation, based on co-simulation with MATLAB/Simulink and Simpack, and a further comparison with a popular data-driven model (the Kriging model) is provided. The simulation result shows that using the legendre polynomial regression model in building surrogate elements can largely cut down the simulation time without sacrifice in accuracy.

Near Optimal Event-Triggered Control of Nonlinear Discrete-Time Systems Using Neurodynamic Programming.

PubMed

Sahoo, Avimanyu; Xu, Hao; Jagannathan, Sarangapani

2016-09-01

This paper presents an event-triggered near optimal control of uncertain nonlinear discrete-time systems. Event-driven neurodynamic programming (NDP) is utilized to design the control policy. A neural network (NN)-based identifier, with event-based state and input vectors, is utilized to learn the system dynamics. An actor-critic framework is used to learn the cost function and the optimal control input. The NN weights of the identifier, the critic, and the actor NNs are tuned aperiodically once every triggered instant. An adaptive event-trigger condition to decide the trigger instants is derived. Thus, a suitable number of events are generated to ensure a desired accuracy of approximation. A near optimal performance is achieved without using value and/or policy iterations. A detailed analysis of nontrivial inter-event times with an explicit formula to show the reduction in computation is also derived. The Lyapunov technique is used in conjunction with the event-trigger condition to guarantee the ultimate boundedness of the closed-loop system. The simulation results are included to verify the performance of the controller. The net result is the development of event-driven NDP.

Sharing adverse drug event data using business intelligence technology.

PubMed

Horvath, Monica M; Cozart, Heidi; Ahmad, Asif; Langman, Matthew K; Ferranti, Jeffrey

2009-03-01

Duke University Health System uses computerized adverse drug event surveillance as an integral part of medication safety at 2 community hospitals and an academic medical center. This information must be swiftly communicated to organizational patient safety stakeholders to find opportunities to improve patient care; however, this process is encumbered by highly manual methods of preparing the data. Following the examples of other industries, we deployed a business intelligence tool to provide dynamic safety reports on adverse drug events. Once data were migrated into the health system data warehouse, we developed census-adjusted reports with user-driven prompts. Drill down functionality enables navigation from aggregate trends to event details by clicking report graphics. Reports can be accessed by patient safety leadership either through an existing safety reporting portal or the health system performance improvement Web site. Elaborate prompt screens allow many varieties of reports to be created quickly by patient safety personnel without consultation with the research analyst. The reduction in research analyst workload because of business intelligence implementation made this individual available to additional patient safety projects thereby leveraging their talents more effectively. Dedicated liaisons are essential to ensure clear communication between clinical and technical staff throughout the development life cycle. Design and development of the business intelligence model for adverse drug event data must reflect the eccentricities of the operational system, especially as new areas of emphasis evolve. Future usability studies examining the data presentation and access model are needed.

Context recognition and situation assessment in autonomous mobile robots

NASA Astrophysics Data System (ADS)

Yavnai, Arie

1993-05-01

The capability to recognize the operating context and to assess the situation in real-time is needed, if a high functionality autonomous mobile robot has to react properly and effectively to continuously changing situations and events, either external or internal, while the robot is performing its assigned tasks. A new approach and architecture for context recognition and situation assessment module (CORSA) is presented in this paper. CORSA is a multi-level information processing module which consists of adaptive decision and classification algorithms. It performs dynamic mapping from the data space to the context space, and dynamically decides on the context class. Learning mechanism is employed to update the decision variables so as to minimize the probability of misclassification. CORSA is embedded within the Mission Manager module of the intelligent autonomous hyper-controller (IAHC) of the mobile robot. The information regarding operating context, events and situation is then communicated to other modules of the IAHC where it is used to: (a) select the appropriate action strategy; (b) support the processes to arbitration and conflict resolution between reflexive behaviors and reasoning-driven behaviors; (c) predict future events and situations; and (d) determine criteria and priorities for planning, replanning, and decision making.

Model of mobile agents for sexual interactions networks

NASA Astrophysics Data System (ADS)

González, M. C.; Lind, P. G.; Herrmann, H. J.

2006-02-01

We present a novel model to simulate real social networks of complex interactions, based in a system of colliding particles (agents). The network is build by keeping track of the collisions and evolves in time with correlations which emerge due to the mobility of the agents. Therefore, statistical features are a consequence only of local collisions among its individual agents. Agent dynamics is realized by an event-driven algorithm of collisions where energy is gained as opposed to physical systems which have dissipation. The model reproduces empirical data from networks of sexual interactions, not previously obtained with other approaches.

Large-scale and Long-duration Simulation of a Multi-stage Eruptive Solar Event

NASA Astrophysics Data System (ADS)

Jiang, chaowei; Hu, Qiang; Wu, S. T.

2015-04-01

We employ a data-driven 3D MHD active region evolution model by using the Conservation Element and Solution Element (CESE) numerical method. This newly developed model retains the full MHD effects, allowing time-dependent boundary conditions and time evolution studies. The time-dependent simulation is driven by measured vector magnetograms and the method of MHD characteristics on the bottom boundary. We have applied the model to investigate the coronal magnetic field evolution of AR11283 which was characterized by a pre-existing sigmoid structure in the core region and multiple eruptions, both in relatively small and large scales. We have succeeded in producing the core magnetic field structure and the subsequent eruptions of flux-rope structures (see https://dl.dropboxusercontent.com/u/96898685/large.mp4 for an animation) as the measured vector magnetograms on the bottom boundary evolve in time with constant flux emergence. The whole process, lasting for about an hour in real time, compares well with the corresponding SDO/AIA and coronagraph imaging observations. From these results, we show the capability of the model, largely data-driven, that is able to simulate complex, topological, and highly dynamic active region evolutions. (We acknowledge partial support of NSF grants AGS 1153323 and AGS 1062050, and data support from SDO/HMI and AIA teams).

Climate events synchronize the dynamics of a resident vertebrate community in the high Arctic.

PubMed

Hansen, Brage B; Grøtan, Vidar; Aanes, Ronny; Sæther, Bernt-Erik; Stien, Audun; Fuglei, Eva; Ims, Rolf A; Yoccoz, Nigel G; Pedersen, Ashild Ø

2013-01-18

Recently accumulated evidence has documented a climate impact on the demography and dynamics of single species, yet the impact at the community level is poorly understood. Here, we show that in Svalbard in the high Arctic, extreme weather events synchronize population fluctuations across an entire community of resident vertebrate herbivores and cause lagged correlations with the secondary consumer, the arctic fox. This synchronization is mainly driven by heavy rain on snow that encapsulates the vegetation in ice and blocks winter forage availability for herbivores. Thus, indirect and bottom-up climate forcing drives the population dynamics across all overwintering vertebrates. Icing is predicted to become more frequent in the circumpolar Arctic and may therefore strongly affect terrestrial ecosystem characteristics.

Moving Beyond ERP Components: A Selective Review of Approaches to Integrate EEG and Behavior

PubMed Central

Bridwell, David A.; Cavanagh, James F.; Collins, Anne G. E.; Nunez, Michael D.; Srinivasan, Ramesh; Stober, Sebastian; Calhoun, Vince D.

2018-01-01

Relationships between neuroimaging measures and behavior provide important clues about brain function and cognition in healthy and clinical populations. While electroencephalography (EEG) provides a portable, low cost measure of brain dynamics, it has been somewhat underrepresented in the emerging field of model-based inference. We seek to address this gap in this article by highlighting the utility of linking EEG and behavior, with an emphasis on approaches for EEG analysis that move beyond focusing on peaks or “components” derived from averaging EEG responses across trials and subjects (generating the event-related potential, ERP). First, we review methods for deriving features from EEG in order to enhance the signal within single-trials. These methods include filtering based on user-defined features (i.e., frequency decomposition, time-frequency decomposition), filtering based on data-driven properties (i.e., blind source separation, BSS), and generating more abstract representations of data (e.g., using deep learning). We then review cognitive models which extract latent variables from experimental tasks, including the drift diffusion model (DDM) and reinforcement learning (RL) approaches. Next, we discuss ways to access associations among these measures, including statistical models, data-driven joint models and cognitive joint modeling using hierarchical Bayesian models (HBMs). We think that these methodological tools are likely to contribute to theoretical advancements, and will help inform our understandings of brain dynamics that contribute to moment-to-moment cognitive function. PMID:29632480

Multichannel emission spectrometer for high dynamic range optical pyrometry of shock-driven materials

NASA Astrophysics Data System (ADS)

Bassett, Will P.; Dlott, Dana D.

2016-10-01

An emission spectrometer (450-850 nm) using a high-throughput, high numerical aperture (N.A. = 0.3) prism spectrograph with stepped fiberoptic coupling, 32 fast photomultipliers and thirty-two 1.25 GHz digitizers is described. The spectrometer can capture single-shot events with a high dynamic range in amplitude and time (nanoseconds to milliseconds or longer). Methods to calibrate the spectrometer and verify its performance and accuracy are described. When a reference thermal source is used for calibration, the spectrometer can function as a fast optical pyrometer. Applications of the spectrometer are illustrated by using it to capture single-shot emission transients from energetic materials or reactive materials initiated by kmṡs-1 impacts with laser-driven flyer plates. A log (time) data analysis method is used to visualize multiple kinetic processes resulting from impact initiation of HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) or a Zr/CuO nanolaminate thermite. Using a gray body algorithm to interpret the spectral radiance from shocked HMX, a time history of temperature and emissivity was obtained, which could be used to investigate HMX hot spot dynamics. Finally, two examples are presented showing how the spectrometer can avoid temperature determination errors in systems where thermal emission is accompanied by atomic or molecular emission lines.

Multichannel emission spectrometer for high dynamic range optical pyrometry of shock-driven materials.

PubMed

Bassett, Will P; Dlott, Dana D

2016-10-01

An emission spectrometer (450-850 nm) using a high-throughput, high numerical aperture (N.A. = 0.3) prism spectrograph with stepped fiberoptic coupling, 32 fast photomultipliers and thirty-two 1.25 GHz digitizers is described. The spectrometer can capture single-shot events with a high dynamic range in amplitude and time (nanoseconds to milliseconds or longer). Methods to calibrate the spectrometer and verify its performance and accuracy are described. When a reference thermal source is used for calibration, the spectrometer can function as a fast optical pyrometer. Applications of the spectrometer are illustrated by using it to capture single-shot emission transients from energetic materials or reactive materials initiated by km⋅s -1 impacts with laser-driven flyer plates. A log (time) data analysis method is used to visualize multiple kinetic processes resulting from impact initiation of HMX (octahydro-1,3,5,7-tetranitro-1,3,5,7-tetrazocine) or a Zr/CuO nanolaminate thermite. Using a gray body algorithm to interpret the spectral radiance from shocked HMX, a time history of temperature and emissivity was obtained, which could be used to investigate HMX hot spot dynamics. Finally, two examples are presented showing how the spectrometer can avoid temperature determination errors in systems where thermal emission is accompanied by atomic or molecular emission lines.

A reduction for spiking integrate-and-fire network dynamics ranging from homogeneity to synchrony.

PubMed

Zhang, J W; Rangan, A V

2015-04-01

In this paper we provide a general methodology for systematically reducing the dynamics of a class of integrate-and-fire networks down to an augmented 4-dimensional system of ordinary-differential-equations. The class of integrate-and-fire networks we focus on are homogeneously-structured, strongly coupled, and fluctuation-driven. Our reduction succeeds where most current firing-rate and population-dynamics models fail because we account for the emergence of 'multiple-firing-events' involving the semi-synchronous firing of many neurons. These multiple-firing-events are largely responsible for the fluctuations generated by the network and, as a result, our reduction faithfully describes many dynamic regimes ranging from homogeneous to synchronous. Our reduction is based on first principles, and provides an analyzable link between the integrate-and-fire network parameters and the relatively low-dimensional dynamics underlying the 4-dimensional augmented ODE.

Event-Based Stereo Depth Estimation Using Belief Propagation.

PubMed

Xie, Zhen; Chen, Shengyong; Orchard, Garrick

2017-01-01

Compared to standard frame-based cameras, biologically-inspired event-based sensors capture visual information with low latency and minimal redundancy. These event-based sensors are also far less prone to motion blur than traditional cameras, and still operate effectively in high dynamic range scenes. However, classical framed-based algorithms are not typically suitable for these event-based data and new processing algorithms are required. This paper focuses on the problem of depth estimation from a stereo pair of event-based sensors. A fully event-based stereo depth estimation algorithm which relies on message passing is proposed. The algorithm not only considers the properties of a single event but also uses a Markov Random Field (MRF) to consider the constraints between the nearby events, such as disparity uniqueness and depth continuity. The method is tested on five different scenes and compared to other state-of-art event-based stereo matching methods. The results show that the method detects more stereo matches than other methods, with each match having a higher accuracy. The method can operate in an event-driven manner where depths are reported for individual events as they are received, or the network can be queried at any time to generate a sparse depth frame which represents the current state of the network.

Preliminary Results from a Model-Driven Architecture Methodology for Development of an Event-Driven Space Communications Service Concept

NASA Technical Reports Server (NTRS)

Roberts, Christopher J.; Morgenstern, Robert M.; Israel, David J.; Borky, John M.; Bradley, Thomas H.

2017-01-01

NASA's next generation space communications network will involve dynamic and autonomous services analogous to services provided by current terrestrial wireless networks. This architecture concept, known as the Space Mobile Network (SMN), is enabled by several technologies now in development. A pillar of the SMN architecture is the establishment and utilization of a continuous bidirectional control plane space link channel and a new User Initiated Service (UIS) protocol to enable more dynamic and autonomous mission operations concepts, reduced user space communications planning burden, and more efficient and effective provider network resource utilization. This paper provides preliminary results from the application of model driven architecture methodology to develop UIS. Such an approach is necessary to ensure systematic investigation of several open questions concerning the efficiency, robustness, interoperability, scalability and security of the control plane space link and UIS protocol.

Qualitative assessment of climate-driven ecological shifts in the Caspian Sea

PubMed Central

Beyraghdar Kashkooli, Omid; Gröger, Joachim; Núñez-Riboni, Ismael

2017-01-01

The worldwide occurrence of complex climate-induced ecological shifts in marine systems is one of the major challenges in sustainable bio-resources management. The occurrence of ecological environment-driven shifts was studied in the Southern Caspian Sea using the “shiftogram” method on available fisheries-related (i.e. commercially important bentho-pelagic fish stocks) ecological and climatic variables. As indicators of potential environmentally driven shift patterns we used indices for the North Atlantic Oscillation, the Southern Oscillation, the Siberian High, the East Atlantic-West Russia pattern, as well as Sea Surface Temperature and surface chlorophyll-a concentration. Given the explorative findings from the serial shift analyses, the cascading and serial order of multiple shift events in climatic-ecologic conditions of the southern Caspian Sea suggested a linkage between external forces and dynamics of ecosystem components and structures in the following order: global-scale climate forces lead to local environmental processes, which in turn lead to biological components dynamics. For the first time, this study indicates that ecological shifts are an integral component of bentho-pelagic subsystem regulatory processes and dynamics. Qualitative correspondence of biological responses of bentho-pelagic stocks to climatic events is one of the supporting evidences that overall Caspian ecosystem structures and functioning might have–at least partially–been impacted by global-scale climatic or local environmental shifts. These findings may help to foster a regional Ecosystem-based Approach to Management (EAM) as an integral part of bentho-pelagic fisheries management plans. PMID:28475609

CIFAR10-DVS: An Event-Stream Dataset for Object Classification

PubMed Central

Li, Hongmin; Liu, Hanchao; Ji, Xiangyang; Li, Guoqi; Shi, Luping

2017-01-01

Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as “CIFAR10-DVS.” The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification. PMID:28611582

CIFAR10-DVS: An Event-Stream Dataset for Object Classification.

PubMed

Li, Hongmin; Liu, Hanchao; Ji, Xiangyang; Li, Guoqi; Shi, Luping

2017-01-01

Neuromorphic vision research requires high-quality and appropriately challenging event-stream datasets to support continuous improvement of algorithms and methods. However, creating event-stream datasets is a time-consuming task, which needs to be recorded using the neuromorphic cameras. Currently, there are limited event-stream datasets available. In this work, by utilizing the popular computer vision dataset CIFAR-10, we converted 10,000 frame-based images into 10,000 event streams using a dynamic vision sensor (DVS), providing an event-stream dataset of intermediate difficulty in 10 different classes, named as "CIFAR10-DVS." The conversion of event-stream dataset was implemented by a repeated closed-loop smooth (RCLS) movement of frame-based images. Unlike the conversion of frame-based images by moving the camera, the image movement is more realistic in respect of its practical applications. The repeated closed-loop image movement generates rich local intensity changes in continuous time which are quantized by each pixel of the DVS camera to generate events. Furthermore, a performance benchmark in event-driven object classification is provided based on state-of-the-art classification algorithms. This work provides a large event-stream dataset and an initial benchmark for comparison, which may boost algorithm developments in even-driven pattern recognition and object classification.

Data-driven Model of the ICME Propagation through the Solar Corona and Inner Heliosphere

NASA Astrophysics Data System (ADS)

Yalim, M. S.; Pogorelov, N.; Singh, T.; Liu, Y.

2017-12-01

The solar wind (SW) emerging from the Sun is the main driving mechanism of solar events which may lead to geomagnetic storms that are the primary causes of space weather disturbances that affect the magnetic environment of Earth and may have hazardous effects on the space-borne and ground-based technological systems as well as human health. Therefore, accurate modeling of the SW is very important to understand the underlying mechanisms of such storms.Getting ready for the Parker Solar Probe mission, we have developed a data-driven magnetohydrodynamic (MHD) model of the global solar corona which utilizes characteristic boundary conditions implemented within the Multi-Scale Fluid-Kinetic Simulation Suite (MS-FLUKSS) - a collection of problem oriented routines incorporated into the Chombo adaptive mesh refinement framework developed at Lawrence Berkeley National Laboratory. Our global solar corona model can be driven by both synoptic and synchronic vector magnetogram data obtained by the Solar Dynamics Observatory/Helioseismic and Magnetic Imager (SDO/HMI) and the horizontal velocity data on the photosphere obtained by applying the Differential Affine Velocity Estimatorfor Vector Magnetograms (DAVE4VM) method on the HMI-observed vector magnetic fields.Our CME generation model is based on Gibson-Low-type flux ropes the parameters of which are determined from analysis of observational data from STEREO/SECCHI, SDO/AIA and SOHO/LASCO, and by applying the Graduate Cylindrical Shell model for the flux rope reconstruction.In this study, we will present the results of three-dimensional global simulations of ICME propagation through our characteristically-consistent MHD model of the background SW from the Sun to Earth driven by HMI-observed vector magnetic fields and validate our results using multiple spacecraft data at 1 AU.

A predictive model of nuclear power plant crew decision-making and performance in a dynamic simulation environment

NASA Astrophysics Data System (ADS)

Coyne, Kevin Anthony

The safe operation of complex systems such as nuclear power plants requires close coordination between the human operators and plant systems. In order to maintain an adequate level of safety following an accident or other off-normal event, the operators often are called upon to perform complex tasks during dynamic situations with incomplete information. The safety of such complex systems can be greatly improved if the conditions that could lead operators to make poor decisions and commit erroneous actions during these situations can be predicted and mitigated. The primary goal of this research project was the development and validation of a cognitive model capable of simulating nuclear plant operator decision-making during accident conditions. Dynamic probabilistic risk assessment methods can improve the prediction of human error events by providing rich contextual information and an explicit consideration of feedback arising from man-machine interactions. The Accident Dynamics Simulator paired with the Information, Decision, and Action in a Crew context cognitive model (ADS-IDAC) shows promise for predicting situational contexts that might lead to human error events, particularly knowledge driven errors of commission. ADS-IDAC generates a discrete dynamic event tree (DDET) by applying simple branching rules that reflect variations in crew responses to plant events and system status changes. Branches can be generated to simulate slow or fast procedure execution speed, skipping of procedure steps, reliance on memorized information, activation of mental beliefs, variations in control inputs, and equipment failures. Complex operator mental models of plant behavior that guide crew actions can be represented within the ADS-IDAC mental belief framework and used to identify situational contexts that may lead to human error events. This research increased the capabilities of ADS-IDAC in several key areas. The ADS-IDAC computer code was improved to support additional branching events and provide a better representation of the IDAC cognitive model. An operator decision-making engine capable of responding to dynamic changes in situational context was implemented. The IDAC human performance model was fully integrated with a detailed nuclear plant model in order to realistically simulate plant accident scenarios. Finally, the improved ADS-IDAC model was calibrated, validated, and updated using actual nuclear plant crew performance data. This research led to the following general conclusions: (1) A relatively small number of branching rules are capable of efficiently capturing a wide spectrum of crew-to-crew variabilities. (2) Compared to traditional static risk assessment methods, ADS-IDAC can provide a more realistic and integrated assessment of human error events by directly determining the effect of operator behaviors on plant thermal hydraulic parameters. (3) The ADS-IDAC approach provides an efficient framework for capturing actual operator performance data such as timing of operator actions, mental models, and decision-making activities.

Dynamic Structure Factor and Transport Coefficients of a Homogeneously Driven Granular Fluid in Steady State

NASA Astrophysics Data System (ADS)

Vollmayr-Lee, Katharina; Zippelius, Annette; Aspelmeier, Timo

2011-03-01

We study the dynamic structure factor of a granular fluid of hard spheres, driven into a stationary nonequilibrium state by balancing the energy loss due to inelastic collisions with the energy input due to driving. The driving is chosen to conserve momentum, so that fluctuating hydrodynamics predicts the existence of sound modes. We present results of computer simulations which are based on an event driven algorithm. The dynamic structure factor F (q , ω) is determined for volume fractions 0.05, 0.1 and 0.2 and coefficients of normal restitution 0.8 and 0.9. We observe sound waves, and compare our results for F (q , ω) with the predictions of generalized fluctuating hydrodynamics which takes into account that temperature fluctuations decay either diffusively or with a finite relaxation rate, depending on wave number and inelasticity. We determine the speed of sound and the transport coefficients and compare them to the results of kinetic theory. K.V.L. thanks the Institute of Theoretical Physics, University of Goettingen, for financial support and hospitality.

A Study of Primary Collision Dynamics in Inverse-Kinematics Reaction of 78Kr on 40Ca at a Bombarding Energy of 10 MeV per Nucleon

NASA Astrophysics Data System (ADS)

Henry, Eric M.

The CHIMERA multi-detector array at LNS Catania has been used to study the inverse-kinematics reaction of 78Kr + 40Ca at a bombarding energy of 10 A MeV. The multi-detector is capable of detecting individual products of the collision essential for the reconstruction of the collision dynamics. This is the first time CHIMERA has been used at low-energy, which offered a unique challenge for the calibration and interpretation of experimental data. Initial interrogation of the calibrated data revealed a class of selected events characterized by two coincident heavy fragments (atomic number Z>3) that together account for the majority of the total mass of the colliding system. These events are consistent with the complete fusion and subsequent binary split (fission) of a composite nucleus. The observed fission fragments are characterized by a broad A, Z distribution and are centered about symmetric fission while exhibiting relative velocities significantly higher than given by Viola systematics. Additional analysis of the kinematic relationship between the fission fragments was performed. Of note, is that the center-of-mass angular distribution (dsigma/dtheta) of the fission fragments exhibits an unexpected anisotropy inconsistent with a compound-nucleus reaction. This anisotropy is indicative of a dynamic fusion/fission-like process. The observed angular distribution features a forward-backward anisotropy most prevalent for mass-asymmetric events. Furthermore, the more massive fragment of mass-asymmetric events appears to emerge preferentially in the forward direction, along the beam axis. Analysis of the angular distribution of alpha particles emitted from these fission fragments suggests the events are associated mostly with central collisions. The observations associated with this subset of events are similar to those reported for dynamic fragmentation of projectile-like fragments, but have not before been observed for a fusion/fission-like process. Comparisons to dynamic and statistical reaction model predictions are inconsistent with known phenomena, but suggest a peculiar dynamics-driven scenario. A plausible explanation of the experimental results is the existence of a phenomenon similar to a "fusion window", or a range of impact parameters in which complete fusion cannot be achieved. In this scenario, the system must absorb all the relative motion and convert it to vibrational energy or heat. As the energy increases the system may not be able to accommodate this conversion of energy without breaking apart.

High-fidelity numerical modeling of the Upper Mississippi River under extreme flood condition

NASA Astrophysics Data System (ADS)

Khosronejad, Ali; Le, Trung; DeWall, Petra; Bartelt, Nicole; Woldeamlak, Solomon; Yang, Xiaolei; Sotiropoulos, Fotis

2016-12-01

We present data-driven numerical simulations of extreme flooding in a large-scale river coupling coherent-structure resolving hydrodynamics with bed morphodynamics under live-bed conditions. The study area is a ∼ 3.2 km long and ∼ 300 m wide reach of the Upper Mississippi River, near Minneapolis MN, which contains several natural islands and man-made hydraulic structures. We employ the large-eddy simulation (LES) and bed-morphodynamic modules of the Virtual Flow Simulator (VFS-Rivers) model, a recently developed in-house code, to investigate the flow and bed evolution of the river during a 100-year flood event. The coupling of the two modules is carried out via a fluid-structure interaction approach using a nested domain approach to enhance the resolution of bridge scour predictions. We integrate data from airborne Light Detection and Ranging (LiDAR), sub-aqueous sonar apparatus on-board a boat and in-situ laser scanners to construct a digital elevation model of the river bathymetry and surrounding flood plain, including islands and bridge piers. A field campaign under base-flow condition is also carried out to collect mean flow measurements via Acoustic Doppler Current Profiler (ADCP) to validate the hydrodynamic module of the VFS-Rivers model. Our simulation results for the bed evolution of the river under the 100-year flood reveal complex sediment transport dynamics near the bridge piers consisting of both scour and refilling events due to the continuous passage of sand dunes. We find that the scour depth near the bridge piers can reach to a maximum of ∼ 9 m. The data-driven simulation strategy we present in this work exemplifies a practical simulation-based-engineering-approach to investigate the resilience of infrastructures to extreme flood events in intricate field-scale riverine systems.

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization

PubMed Central

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications. PMID:26771830

Data-Driven Engineering of Social Dynamics: Pattern Matching and Profit Maximization.

PubMed

Peng, Huan-Kai; Lee, Hao-Chih; Pan, Jia-Yu; Marculescu, Radu

2016-01-01

In this paper, we define a new problem related to social media, namely, the data-driven engineering of social dynamics. More precisely, given a set of observations from the past, we aim at finding the best short-term intervention that can lead to predefined long-term outcomes. Toward this end, we propose a general formulation that covers two useful engineering tasks as special cases, namely, pattern matching and profit maximization. By incorporating a deep learning model, we derive a solution using convex relaxation and quadratic-programming transformation. Moreover, we propose a data-driven evaluation method in place of the expensive field experiments. Using a Twitter dataset, we demonstrate the effectiveness of our dynamics engineering approach for both pattern matching and profit maximization, and study the multifaceted interplay among several important factors of dynamics engineering, such as solution validity, pattern-matching accuracy, and intervention cost. Finally, the method we propose is general enough to work with multi-dimensional time series, so it can potentially be used in many other applications.

Investigation of GICs Associated with Large dB/dt Variations in Space

NASA Astrophysics Data System (ADS)

Dimitrakoudis, S.; Mann, I. R.; Murphy, K. R.; Rae, J.; Denton, M.; Milling, D. K.

2016-12-01

Geomagnetically induced currents (GICs) can be driven in terrestrial electrical power grids as a result of the induced electric fields arising from magnetic field changes driven in the coupled magnetosphere-ionosphere-ground system. Substorms are often hypothesised to be associated with the largest GIC effects on the ground, especially at higher latitudes. However, recent studies have suggested that other dayside phenomena such as sudden impulses and even ULF wave trains might also drive significant GICs. Using data from the CARISMA ground-based magnetometer network we examine the GIC response driven from a variety of magnetospheric processes. In particular we focus on events where large dB/dt is observed in-situ on GOES East and West satellites. Auroras, resulting from magnetospheric substorms, give us a dynamical view of sudden destabilizations in the nightside magnetosphere, of large spatial and temporal extent, that can drive large and potentially damaging geomagnetically induced currents (GICs) in terrestrial power grids. Since ground dB/dt can be used as a GIC proxy, we have surveyed GOES data since 2011 for the largest dB/dT events, and found some to be of the order of hundreds of nT in the span of a few seconds. These are observed in both the nightside and dayside, and, as such, we seek to establish connections to drivers affecting both sides of the terminator; tail activations and substorms on the nightside, large amplitude ULF waves, solar wind sudden impulses, and rapid changes in MIC current systems on the dayside. The short duration of these events, coupled with the use of conjugate satellite measurements and ground magnetometer arrays when possible, allows us to investigate their localization and the latitudinal extent of their effects and to further examine the potential role of non-substorm phenomena in generating GICs which may have adverse impacts in electrical power grids.

On Connection Between Topology and Memory Loss in Sheared Granular Materials

NASA Astrophysics Data System (ADS)

Kovalcinova, Lenka; Kramar, Miro; Mischaikow, Konstantin; Kondic, Lou

We present combined results of discrete element simulations and topological data analysis that allows us to characterize the geometrical properties of force networks. Our numerical setup consists of the system of cylindrical particles placed inside rectangular box with periodic boundary conditions along the horizontal direction. System dynamics is driven by constant shearing speed of the top and bottom walls (in the opposite directions) and pressure applied on the top wall in a dense flow regime. Our study reveals the origin of memory loss in granular systems through local rapid changes in force networks. To understand these rapid events we analyze the evolution of the largest Lyapunov exponent in a simpler case of granular system without inter-particle friction and explore a correlation with topological measures. Surprisingly, our results suggest that the memory loss is driven mainly by pressure even in the case of fixed inertial number. We conclude that the interplay between physical properties of the granular system and force network geometry is a key to understand the dynamics of the sheared systems. This research was supported by NSF Grant No. DMS-1521717 and DARPA No. HR0011-16-2-0033.

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Kulkarni, Ajinkya; Maskey, Manil; Bakare, Rohan; Basyal, Sabin; Li, Xiang; Flynn, Shannon

2014-01-01

Approaches used in Earth science research such as case study analysis and climatology studies involve discovering and gathering diverse data sets and information to support the research goals. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. In cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. This paper presents a specialized search, aggregation and curation tool for Earth science to address these challenges. The search rool automatically creates curated 'Data Albums', aggregated collections of information related to a specific event, containing links to relevant data files [granules] from different instruments, tools and services for visualization and analysis, and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non relevant information and data.

Non-daily pre-exposure prophylaxis for HIV prevention

PubMed Central

Anderson, Peter L.; García-Lerma, J. Gerardo; Heneine, Walid

2015-01-01

Purpose of review To discuss non-daily pre-exposure prophylaxis (PrEP) modalities that may provide advantages compared with daily PrEP in cost and cumulative toxicity, but may have lower adherence forgiveness. Recent Findings Animal models have informed our understanding of early viral transmission events, which help guide event-driven PrEP dosing strategies. These models indicate early establishment of viral replication in rectal or cervicovaginal tissues, so event-driven PrEP should rapidly deliver high mucosal drug concentrations within hours of the potential exposure event. Macaque models have demonstrated the high biological efficacy for event-driven dosing of oral tenofovir disoproxil fumarate (TDF) and emtricitabine (FTC) against both vaginal and rectal virus transmission. In humans, the IPERGAY study demonstrated 86% efficacy for event-driven oral TDF/FTC dosing among men who have sex with men (MSM), while no similar efficacy data are available on women or heterosexual men. The HPTN 067 study showed that certain MSM populations adhere well to non-daily PrEP while other populations of women adhere more poorly to non-daily versus daily regimens. Pharmacokinetic studies following oral TDF/FTC dosing in humans, indicate that TFV-diphosphate (the active form of TFV) accumulates to higher concentrations in rectal versus cervicovaginal tissue but non-adherence in trials complicates the interpretation of differential mucosal drug concentrations. Summary Event-driven dosing for TFV-based PrEP has promise for HIV prevention in MSM. Future research of event-driven PrEP in women and heterosexual men should be guided by a better understanding of the importance of mucosal drug concentrations for PrEP efficacy and its sensitivity to adherence. PMID:26633641

Wave Driven Disturbances of the Thermal Structure in the Polar Winter Upper Stratosphere and Lower Mesosphere

NASA Astrophysics Data System (ADS)

Greer, Katelynn R.

The polar winter middle atmosphere is a dynamically active region that is driven primarily by wave activity. Planetary waves intermittently disturbed the region at different levels and the most spectacular type of disturbance is a major Sudden Stratospheric Warming (SSW). However, other types of extreme disturbances occur on a more frequent, intraseasonal basis. One such disturbance is a synoptic-scale "weather event" observed in lidar and rocket soundings, soundings from the TIMED/SABER instrument and UK Meteorological Office (MetO) assimilated data. These disturbances are most easily identified near 42 km where temperatures are elevated over baseline conditions by a remarkable 50 K and an associated cooling is observed near 75 km. As these disturbances have a coupled vertical structure extending into the lower mesosphere, they are termed Upper Stratospheric/Lower Mesospheric (USLM) disturbances. This research begins with description of the phenomenology of USLM events in observations and the assimilated data set MetO, develops a description of the dynamics responsible for their development and places them in the context of the family of polar winter middle atmospheric disturbances. Climatologies indicates that USLM disturbances are commonly occurring polar wintertime disturbances of the middle atmosphere, have a remarkably repeating thermal structure, are located on the East side of the polar low and are related planetary wave activity. Using the same methodology for identifying USLM events and building climatologies of these events, the Whole Atmosphere Community Climate Model WACCM version 4 is established to spontaneously and internally generate USLM disturbances. Planetary waves are seen to break at a level just above the stratopause and convergence of the EP-flux vector is occurring in this region, decelerating the eastward zonal-mean wind and inducing ageostrophic vertical motion to maintain mass continuity. The descending air increases the horizontal temperature gradient at 2 hPa and is responsible for the stratopause warming. Embedded in this planetary wave breaking process is baroclinic instability, as indicated by the Charney-Stern criteria and an EP-flux analysis decomposed by planetary and synoptic-scale waves. It is recognized that USLM events are part of a family of disturbances that occur in the polar winter middle atmosphere which have the potential to impact the entire atmospheric column. Relationships between USLM events, minor SSWs and major SSWs are examined and displayed through a Venn diagram which looked for events that were linked to each other (or not) by temporal evolution of the polar vortex within 14 days. Critically, every identified major SSW (in both MetO and WACCM) is preceded by a USLM disturbance, and the baroclinic instability that is embedded in the planetary wave breaking of USLM disturbances mark significant disruption to the middle atmosphere, which may aid in the forecast of major SSWs. This leads to the proposal of new dynamics based definitions of minor and major SSWs.

Multi-day activity scheduling reactions to planned activities and future events in a dynamic model of activity-travel behavior

NASA Astrophysics Data System (ADS)

Nijland, Linda; Arentze, Theo; Timmermans, Harry

2014-01-01

Modeling multi-day planning has received scarce attention in activity-based transport demand modeling so far. However, new dynamic activity-based approaches are being developed at the current moment. The frequency and inflexibility of planned activities and events in activity schedules of individuals indicate the importance of incorporating those pre-planned activities in the new generation of dynamic travel demand models. Elaborating and combining previous work on event-driven activity generation, the aim of this paper is to develop and illustrate an extension of a need-based model of activity generation that takes into account possible influences of pre-planned activities and events. This paper describes the theory and shows the results of simulations of the extension. The simulation was conducted for six different activities, and the parameter values used were consistent with an earlier estimation study. The results show that the model works well and that the influences of the parameters are consistent, logical, and have clear interpretations. These findings offer further evidence of face and construct validity to the suggested modeling approach.

Event-Driven Technology to Generate Relevant Collections of Near-Realtime Data

NASA Astrophysics Data System (ADS)

Graves, S. J.; Keiser, K.; Nair, U. S.; Beck, J. M.; Ebersole, S.

2017-12-01

Getting the right data when it is needed continues to be a challenge for researchers and decision makers. Event-Driven Data Delivery (ED3), funded by the NASA Applied Science program, is a technology that allows researchers and decision makers to pre-plan what data, information and processes they need to have collected or executed in response to future events. The Information Technology and Systems Center at the University of Alabama in Huntsville (UAH) has developed the ED3 framework in collaboration with atmospheric scientists at UAH, scientists at the Geological Survey of Alabama, and other federal, state and local stakeholders to meet the data preparedness needs for research, decisions and situational awareness. The ED3 framework supports an API that supports the addition of loosely-coupled, distributed event handlers and data processes. This approach allows the easy addition of new events and data processes so the system can scale to support virtually any type of event or data process. Using ED3's underlying services, applications have been developed that monitor for alerts of registered event types and automatically triggers subscriptions that match new events, providing users with a living "album" of results that can continued to be curated as more information for an event becomes available. This capability can allow users to improve capacity for the collection, creation and use of data and real-time processes (data access, model execution, product generation, sensor tasking, social media filtering, etc), in response to disaster (and other) events by preparing in advance for data and information needs for future events. This presentation will provide an update on the ED3 developments and deployments, and further explain the applicability for utilizing near-realtime data in hazards research, response and situational awareness.

A data driven nonlinear stochastic model for blood glucose dynamics.

PubMed

Zhang, Yan; Holt, Tim A; Khovanova, Natalia

2016-03-01

The development of adequate mathematical models for blood glucose dynamics may improve early diagnosis and control of diabetes mellitus (DM). We have developed a stochastic nonlinear second order differential equation to describe the response of blood glucose concentration to food intake using continuous glucose monitoring (CGM) data. A variational Bayesian learning scheme was applied to define the number and values of the system's parameters by iterative optimisation of free energy. The model has the minimal order and number of parameters to successfully describe blood glucose dynamics in people with and without DM. The model accounts for the nonlinearity and stochasticity of the underlying glucose-insulin dynamic process. Being data-driven, it takes full advantage of available CGM data and, at the same time, reflects the intrinsic characteristics of the glucose-insulin system without detailed knowledge of the physiological mechanisms. We have shown that the dynamics of some postprandial blood glucose excursions can be described by a reduced (linear) model, previously seen in the literature. A comprehensive analysis demonstrates that deterministic system parameters belong to different ranges for diabetes and controls. Implications for clinical practice are discussed. This is the first study introducing a continuous data-driven nonlinear stochastic model capable of describing both DM and non-DM profiles. Copyright © 2015 The Authors. Published by Elsevier Ireland Ltd.. All rights reserved.

Understanding human dynamics in microblog posting activities

NASA Astrophysics Data System (ADS)

Jiang, Zhihong; Zhang, Yubao; Wang, Hui; Li, Pei

2013-02-01

Human activity patterns are an important issue in behavior dynamics research. Empirical evidence indicates that human activity patterns can be characterized by a heavy-tailed inter-event time distribution. However, most researchers give an understanding by only modeling the power-law feature of the inter-event time distribution, and those overlooked non-power-law features are likely to be nontrivial. In this work, we propose a behavior dynamics model, called the finite memory model, in which humans adaptively change their activity rates based on a finite memory of recent activities, which is driven by inherent individual interest. Theoretical analysis shows a finite memory model can properly explain various heavy-tailed inter-event time distributions, including a regular power law and some non-power-law deviations. To validate the model, we carry out an empirical study based on microblogging activity from thousands of microbloggers in the Celebrity Hall of the Sina microblog. The results show further that the model is reasonably effective. We conclude that finite memory is an effective dynamics element to describe the heavy-tailed human activity pattern.

Hydrodynamic Capture of Particles by Micro-swimmers under Hele-Shaw Flows

NASA Astrophysics Data System (ADS)

Mishler, Grant; Tsang, Alan Cheng Hou; Pak, On Shun

2017-11-01

We explore a hydrodynamic capture mechanism of a driven particle by a micro-swimmer in confined microfluidic environments with an idealized model. The capture is mediated by the hydrodynamic interactions between the micro-swimmer, the driven particle, and the background flow. This capture mechanism relies on the existence of attractive stable equilibrium configurations between the driven particle and the micro-swimmer, which occurs when the background flow is larger than a certain critical threshold. Dynamics and stability of capture and non-capture events will be discussed. This study may have potential applications in the study of capture and delivery of therapeutic payloads by micro-swimmers as well as particle self-assembly under confinements.

Dynamic reusable workflows for ocean science

USGS Publications Warehouse

Signell, Richard; Fernandez, Filipe; Wilcox, Kyle

2016-01-01

Digital catalogs of ocean data have been available for decades, but advances in standardized services and software for catalog search and data access make it now possible to create catalog-driven workflows that automate — end-to-end — data search, analysis and visualization of data from multiple distributed sources. Further, these workflows may be shared, reused and adapted with ease. Here we describe a workflow developed within the US Integrated Ocean Observing System (IOOS) which automates the skill-assessment of water temperature forecasts from multiple ocean forecast models, allowing improved forecast products to be delivered for an open water swim event. A series of Jupyter Notebooks are used to capture and document the end-to-end workflow using a collection of Python tools that facilitate working with standardized catalog and data services. The workflow first searches a catalog of metadata using the Open Geospatial Consortium (OGC) Catalog Service for the Web (CSW), then accesses data service endpoints found in the metadata records using the OGC Sensor Observation Service (SOS) for in situ sensor data and OPeNDAP services for remotely-sensed and model data. Skill metrics are computed and time series comparisons of forecast model and observed data are displayed interactively, leveraging the capabilities of modern web browsers. The resulting workflow not only solves a challenging specific problem, but highlights the benefits of dynamic, reusable workflows in general. These workflows adapt as new data enters the data system, facilitate reproducible science, provide templates from which new scientific workflows can be developed, and encourage data providers to use standardized services. As applied to the ocean swim event, the workflow exposed problems with two of the ocean forecast products which led to improved regional forecasts once errors were corrected. While the example is specific, the approach is general, and we hope to see increased use of dynamic notebooks across the geoscience domains.

Data-driven Inference and Investigation of Thermosphere Dynamics and Variations

NASA Astrophysics Data System (ADS)

Mehta, P. M.; Linares, R.

2017-12-01

This paper presents a methodology for data-driven inference and investigation of thermosphere dynamics and variations. The approach uses data-driven modal analysis to extract the most energetic modes of variations for neutral thermospheric species using proper orthogonal decomposition, where the time-independent modes or basis represent the dynamics and the time-depedent coefficients or amplitudes represent the model parameters. The data-driven modal analysis approach combined with sparse, discrete observations is used to infer amplitues for the dynamic modes and to calibrate the energy content of the system. In this work, two different data-types, namely the number density measurements from TIMED/GUVI and the mass density measurements from CHAMP/GRACE are simultaneously ingested for an accurate and self-consistent specification of the thermosphere. The assimilation process is achieved with a non-linear least squares solver and allows estimation/tuning of the model parameters or amplitudes rather than the driver. In this work, we use the Naval Research Lab's MSIS model to derive the most energetic modes for six different species, He, O, N2, O2, H, and N. We examine the dominant drivers of variations for helium in MSIS and observe that seasonal latitudinal variation accounts for about 80% of the dynamic energy with a strong preference of helium for the winter hemisphere. We also observe enhanced helium presence near the poles at GRACE altitudes during periods of low solar activity (Feb 2007) as previously deduced. We will also examine the storm-time response of helium derived from observations. The results are expected to be useful in tuning/calibration of the physics-based models.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application

PubMed Central

Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-01-01

Background The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. Objective The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Methods Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. Results The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. Conclusions This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. PMID:28903894

Data-based virtual unmodeled dynamics driven multivariable nonlinear adaptive switching control.

PubMed

Chai, Tianyou; Zhang, Yajun; Wang, Hong; Su, Chun-Yi; Sun, Jing

2011-12-01

For a complex industrial system, its multivariable and nonlinear nature generally make it very difficult, if not impossible, to obtain an accurate model, especially when the model structure is unknown. The control of this class of complex systems is difficult to handle by the traditional controller designs around their operating points. This paper, however, explores the concepts of controller-driven model and virtual unmodeled dynamics to propose a new design framework. The design consists of two controllers with distinct functions. First, using input and output data, a self-tuning controller is constructed based on a linear controller-driven model. Then the output signals of the controller-driven model are compared with the true outputs of the system to produce so-called virtual unmodeled dynamics. Based on the compensator of the virtual unmodeled dynamics, the second controller based on a nonlinear controller-driven model is proposed. Those two controllers are integrated by an adaptive switching control algorithm to take advantage of their complementary features: one offers stabilization function and another provides improved performance. The conditions on the stability and convergence of the closed-loop system are analyzed. Both simulation and experimental tests on a heavily coupled nonlinear twin-tank system are carried out to confirm the effectiveness of the proposed method.

Asynchronous Data-Driven Classification of Weapon Systems

DTIC Science & Technology

2009-10-01

Classification of Weapon SystemsF Xin Jin† Kushal Mukherjee† Shalabh Gupta† Asok Ray † Shashi Phoha† Thyagaraju Damarla‡ xuj103@psu.edu kum162@psu.edu szg107...Orlando, FL. [8] A. Ray , “Symbolic dynamic analysis of complex systems for anomaly detection,” Signal Processing, vol. 84, no. 7, pp. 1115–1130, July...2004. [9] S. Gupta and A. Ray , “Symbolic dynamic filtering for data-driven pat- tern recognition,” PATTERN RECOGNITION: Theory and Application

FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery

PubMed Central

Piazza, Rocco; Pirola, Alessandra; Spinelli, Roberta; Valletta, Simona; Redaelli, Sara; Magistroni, Vera; Gambacorti-Passerini, Carlo

2012-01-01

Gene fusions are common driver events in leukaemias and solid tumours; here we present FusionAnalyser, a tool dedicated to the identification of driver fusion rearrangements in human cancer through the analysis of paired-end high-throughput transcriptome sequencing data. We initially tested FusionAnalyser by using a set of in silico randomly generated sequencing data from 20 known human translocations occurring in cancer and subsequently using transcriptome data from three chronic and three acute myeloid leukaemia samples. in all the cases our tool was invariably able to detect the presence of the correct driver fusion event(s) with high specificity. In one of the acute myeloid leukaemia samples, FusionAnalyser identified a novel, cryptic, in-frame ETS2–ERG fusion. A fully event-driven graphical interface and a flexible filtering system allow complex analyses to be run in the absence of any a priori programming or scripting knowledge. Therefore, we propose FusionAnalyser as an efficient and robust graphical tool for the identification of functional rearrangements in the context of high-throughput transcriptome sequencing data. PMID:22570408

FusionAnalyser: a new graphical, event-driven tool for fusion rearrangements discovery.

PubMed

Piazza, Rocco; Pirola, Alessandra; Spinelli, Roberta; Valletta, Simona; Redaelli, Sara; Magistroni, Vera; Gambacorti-Passerini, Carlo

2012-09-01

Gene fusions are common driver events in leukaemias and solid tumours; here we present FusionAnalyser, a tool dedicated to the identification of driver fusion rearrangements in human cancer through the analysis of paired-end high-throughput transcriptome sequencing data. We initially tested FusionAnalyser by using a set of in silico randomly generated sequencing data from 20 known human translocations occurring in cancer and subsequently using transcriptome data from three chronic and three acute myeloid leukaemia samples. in all the cases our tool was invariably able to detect the presence of the correct driver fusion event(s) with high specificity. In one of the acute myeloid leukaemia samples, FusionAnalyser identified a novel, cryptic, in-frame ETS2-ERG fusion. A fully event-driven graphical interface and a flexible filtering system allow complex analyses to be run in the absence of any a priori programming or scripting knowledge. Therefore, we propose FusionAnalyser as an efficient and robust graphical tool for the identification of functional rearrangements in the context of high-throughput transcriptome sequencing data.

Direct observation of oxygen vacancy-driven structural and resistive phase transitions in La2/3Sr1/3MnO3

NASA Astrophysics Data System (ADS)

Yao, Lide; Inkinen, Sampo; van Dijken, Sebastiaan

2017-02-01

Resistive switching in transition metal oxides involves intricate physical and chemical behaviours with potential for non-volatile memory and memristive devices. Although oxygen vacancy migration is known to play a crucial role in resistive switching of oxides, an in-depth understanding of oxygen vacancy-driven effects requires direct imaging of atomic-scale dynamic processes and their real-time impact on resistance changes. Here we use in situ transmission electron microscopy to demonstrate reversible switching between three resistance states in epitaxial La2/3Sr1/3MnO3 films. Simultaneous high-resolution imaging and resistance probing indicate that the switching events are caused by the formation of uniform structural phases. Reversible horizontal migration of oxygen vacancies within the manganite film, driven by combined effects of Joule heating and bias voltage, predominantly triggers the structural and resistive transitions. Our findings open prospects for ionotronic devices based on dynamic control of physical properties in complex oxide nanostructures.

Inter-subject phase synchronization for exploratory analysis of task-fMRI.

PubMed

Bolt, Taylor; Nomi, Jason S; Vij, Shruti G; Chang, Catie; Uddin, Lucina Q

2018-08-01

Analysis of task-based fMRI data is conventionally carried out using a hypothesis-driven approach, where blood-oxygen-level dependent (BOLD) time courses are correlated with a hypothesized temporal structure. In some experimental designs, this temporal structure can be difficult to define. In other cases, experimenters may wish to take a more exploratory, data-driven approach to detecting task-driven BOLD activity. In this study, we demonstrate the efficiency and power of an inter-subject synchronization approach for exploratory analysis of task-based fMRI data. Combining the tools of instantaneous phase synchronization and independent component analysis, we characterize whole-brain task-driven responses in terms of group-wise similarity in temporal signal dynamics of brain networks. We applied this framework to fMRI data collected during performance of a simple motor task and a social cognitive task. Analyses using an inter-subject phase synchronization approach revealed a large number of brain networks that dynamically synchronized to various features of the task, often not predicted by the hypothesized temporal structure of the task. We suggest that this methodological framework, along with readily available tools in the fMRI community, provides a powerful exploratory, data-driven approach for analysis of task-driven BOLD activity. Copyright © 2018 Elsevier Inc. All rights reserved.

Data Driven Model Development for the Supersonic Semispan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

We investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models and a data-driven system identification procedure. It is shown via analysis of experimental Super- Sonic SemiSpan Transport (S4T) wind-tunnel data using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

Ergodicity in natural earthquake fault networks

DOE Office of Scientific and Technical Information (OSTI.GOV)

Tiampo, K. F.; Rundle, J. B.; Holliday, J.

2007-06-15

Numerical simulations have shown that certain driven nonlinear systems can be characterized by mean-field statistical properties often associated with ergodic dynamics [C. D. Ferguson, W. Klein, and J. B. Rundle, Phys. Rev. E 60, 1359 (1999); D. Egolf, Science 287, 101 (2000)]. These driven mean-field threshold systems feature long-range interactions and can be treated as equilibriumlike systems with statistically stationary dynamics over long time intervals. Recently the equilibrium property of ergodicity was identified in an earthquake fault system, a natural driven threshold system, by means of the Thirumalai-Mountain (TM) fluctuation metric developed in the study of diffusive systems [K. F.more » Tiampo, J. B. Rundle, W. Klein, J. S. Sa Martins, and C. D. Ferguson, Phys. Rev. Lett. 91, 238501 (2003)]. We analyze the seismicity of three naturally occurring earthquake fault networks from a variety of tectonic settings in an attempt to investigate the range of applicability of effective ergodicity, using the TM metric and other related statistics. Results suggest that, once variations in the catalog data resulting from technical and network issues are accounted for, all of these natural earthquake systems display stationary periods of metastable equilibrium and effective ergodicity that are disrupted by large events. We conclude that a constant rate of events is an important prerequisite for these periods of punctuated ergodicity and that, while the level of temporal variability in the spatial statistics is the controlling factor in the ergodic behavior of seismic networks, no single statistic is sufficient to ensure quantification of ergodicity. Ergodicity in this application not only requires that the system be stationary for these networks at the applicable spatial and temporal scales, but also implies that they are in a state of metastable equilibrium, one in which the ensemble averages can be substituted for temporal averages in studying their spatiotemporal evolution.« less

Holistic view to integrated climate change assessment and extreme weather adaptation in the Lake Victoria Basin East Africa

NASA Astrophysics Data System (ADS)

Mutua, F.; Koike, T.

2013-12-01

Extreme weather events have been the leading cause of disasters and damage all over the world.The primary ingredient to these disasters especially floods is rainfall which over the years, despite advances in modeling, computing power and use of new data and technologies, has proven to be difficult to predict. Also, recent climate projections showed a pattern consistent with increase in the intensity and frequency of extreme events in the East African region.We propose a holistic integrated approach to climate change assessment and extreme event adaptation through coupling of analysis techniques, tools and data. The Lake Victoria Basin (LVB) in East Africa supports over three million livelihoods and is a valuable resource to five East African countries as a source of water and means of transport. However, with a Mesoscale weather regime driven by land and lake dynamics,extreme Mesoscale events have been prevalent and the region has been on the receiving end during anomalously wet years in the region. This has resulted in loss of lives, displacements, and food insecurity. In the LVB, the effects of climate change are increasingly being recognized as a significant contributor to poverty, by its linkage to agriculture, food security and water resources. Of particular importance are the likely impacts of climate change in frequency and intensity of extreme events. To tackle this aspect, this study adopted an integrated regional, mesoscale and basin scale approach to climate change assessment. We investigated the projected changes in mean climate over East Africa, diagnosed the signals of climate change in the atmosphere, and transferred this understanding to mesoscale and basin scale. Changes in rainfall were analyzed and similar to the IPCC AR4 report; the selected three General Circulation Models (GCMs) project a wetter East Africa with intermittent dry periods in June-August. Extreme events in the region are projected to increase; with the number of wet days exceeding the 90% percentile of 1981-2000 likely to increase by 20-40% in the whole region. We also focused on short-term weather forecasting as a step towards adapting to a changing climate. This involved dynamic downscaling of global weather forecasts to high resolution with a special focus on extreme events. By utilizing complex model dynamics, the system was able to reproduce the Mesoscale dynamics well, simulated the land/lake breeze and diurnal pattern but was inadequate in some aspects. The quantitative prediction of rainfall was inaccurate with overestimation and misplacement but with reasonable occurrence. To address these shortcomings we investigated the value added by assimilating Advanced Microwave Scanning Radiometer (AMSR-E) brightness temperature during the event. By assimilating 23GHz (sensitive to water) and 89GHz (sensitive to cloud) frequency brightness temperature; the predictability of an extreme rain weather event was investigated. The assimilation through a Cloud Microphysics Data Assimilation (CMDAS) into the weather prediction model considerably improved the spatial distribution of this event.

Smart Meter Driven Segmentation: What Your Consumption Says About You

DOE Office of Scientific and Technical Information (OSTI.GOV)

Albert, A; Rajagopal, R

With the rollout of smart metering infrastructure at scale, demand-response (DR) programs may now be tailored based on users' consumption patterns as mined from sensed data. For issuing DR events it is key to understand the inter-temporal consumption dynamics as to appropriately segment the user population. We propose to infer occupancy states from consumption time series data using a hidden Markov model framework. Occupancy is characterized in this model by 1) magnitude, 2) duration, and 3) variability. We show that users may be grouped according to their consumption patterns into groups that exhibit qualitatively different dynamics that may be exploitedmore » for program enrollment purposes. We investigate empirically the information that residential energy consumers' temporal energy demand patterns characterized by these three dimensions may convey about their demographic, household, and appliance stock characteristics. Our analysis shows that temporal patterns in the user's consumption data can predict with good accuracy certain user characteristics. We use this framework to argue that there is a large degree of individual predictability in user consumption at a population level.« less

Diagnosis of dynamic process over rainband of landfall typhoon

NASA Astrophysics Data System (ADS)

Ran, Ling-Kun; Yang, Wen-Xia; Chu, Yan-Li

2010-07-01

This paper introduces a new physical parameter — thermodynamic shear advection parameter combining the perturbation vertical component of convective vorticity vector with the coupling of horizontal divergence perturbation and vertical gradient of general potential temperature perturbation. For a heavy-rainfall event resulting from the landfall typhoon 'Wipha', the parameter is calculated by using National Centres for Enviromental Prediction/National Centre for Atmospheric Research global final analysis data. The results showed that the parameter corresponds to the observed 6 h accumulative rainband since it is capable of catching hold of the dynamic and thermodynamic disturbance in the lower troposphere over the observed rainband. Before the typhoon landed, the advection of the parameter by basic-state flow and the coupling of general potential temperature perturbation with curl of Coriolis force perturbation are the primary dynamic processes which are responsible for the local change of the parameter. After the typhoon landed, the disturbance is mainly driven by the combination of five primary dynamic processes. The advection of the parameter by basic-state flow was weakened after the typhoon landed.

Disentangling the stochastic behavior of complex time series

NASA Astrophysics Data System (ADS)

Anvari, Mehrnaz; Tabar, M. Reza Rahimi; Peinke, Joachim; Lehnertz, Klaus

2016-10-01

Complex systems involving a large number of degrees of freedom, generally exhibit non-stationary dynamics, which can result in either continuous or discontinuous sample paths of the corresponding time series. The latter sample paths may be caused by discontinuous events - or jumps - with some distributed amplitudes, and disentangling effects caused by such jumps from effects caused by normal diffusion processes is a main problem for a detailed understanding of stochastic dynamics of complex systems. Here we introduce a non-parametric method to address this general problem. By means of a stochastic dynamical jump-diffusion modelling, we separate deterministic drift terms from different stochastic behaviors, namely diffusive and jumpy ones, and show that all of the unknown functions and coefficients of this modelling can be derived directly from measured time series. We demonstrate appli- cability of our method to empirical observations by a data-driven inference of the deterministic drift term and of the diffusive and jumpy behavior in brain dynamics from ten epilepsy patients. Particularly these different stochastic behaviors provide extra information that can be regarded valuable for diagnostic purposes.

Projecting Future Changes in Extreme Weather During the North American Monsoon in the Southwest with High Resolution, Convective-Permitting Regional Atmospheric Modeling

NASA Astrophysics Data System (ADS)

Chang, H. I.; Castro, C. L.; Luong, T. M.; Lahmers, T.; Jares, M.; Carrillo, C. M.

2014-12-01

Most severe weather during the North American monsoon in the Southwest U.S. occurs in association with organized convection, including microbursts, dust storms, flash flooding and lightning. Our objective is to project how monsoon severe weather is changing due to anthropogenic global warming. We first consider a dynamically downscaled reanalysis (35 km grid spacing), generated with the Weather Research and Forecasting (WRF) model during the period 1948-2010. Individual severe weather events, identified by favorable thermodynamic conditions of instability and precipitable water, are then simulated for short-term, numerical weather prediction-type simulations of 24h at a convective-permitting scale (2 km grid spacing). Changes in the character of severe weather events within this period likely reflect long-term climate change driven by anthropogenic forcing. Next, we apply the identical model simulation and analysis procedures to several dynamically downscaled CMIP3 and CMIP5 models for the period 1950-2100, to assess how monsoon severe weather may change in the future and if these changes correspond with what is already occurring per the downscaled renalaysis and available observational data. The CMIP5 models we are downscaling (HadGEM and MPI-ECHAM6) will be included as part of North American CORDEX. The regional model experimental design for severe weather event projection reasonably accounts for the known operational forecast prerequisites for severe monsoon weather. The convective-permitting simulations show that monsoon convection appears to be reasonably well captured with the use of the dynamically downscaled reanalysis, in comparison to Stage IV precipitation data. The regional model tends to initiate convection too early, though correctly simulates the diurnal maximum in convection in the afternoon and subsequent westward propagation of thunderstorms. Projected changes in extreme event precipitation will be described in relation to the long-term changes in thermodynamic and dynamic forcing mechanisms for severe weather. Results from this project will be used for climate change impacts assessment for U.S. military installations in the region.

75 FR 35329 - Notification and Reporting of Aircraft Accidents or Incidents and Overdue Aircraft, and...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-06-22

...-driven fixed-wing aircraft heavier than air, that is supported in flight by the dynamic reaction of the... reporting of runway incursions: ``Any event in which an aircraft operated by an air carrier: (i) Lands or... during normal operations, such as those involving seaplanes, hot-air balloons, unmanned aircraft systems...

Dynamic Scheduling for Web Monitoring Crawler

DTIC Science & Technology

2009-02-27

researches on static scheduling methods , but they are not included in this project, because this project mainly focuses on the event-driven...pages from public search engines. This research aims to propose various query generation methods using MCRDR knowledge base and evaluates them to...South Wales Professor Hiroshi Motoda/Osaka University Dr. John Salerno, Air Force Research Laboratory/Information Directorate Report

Energy Dissipation and Dynamics in Large Guide Field Turbulence Driven Reconnection at the Magnetopause

NASA Astrophysics Data System (ADS)

TenBarge, J. M.; Shay, M. A.; Sharma, P.; Juno, J.; Haggerty, C. C.; Drake, J. F.; Bhattacharjee, A.; Hakim, A.

2017-12-01

Turbulence and magnetic reconnection are the primary mechanisms responsible for the conversion of stored magnetic energy into particle energy in many space and astrophysical plasmas. The magnetospheric multiscale mission (MMS) has given us unprecedented access to high cadence particle and field data of turbulence and magnetic reconnection at earth's magnetopause. The observations include large guide field reconnection events generated within the turbulent magnetopause. Motivated by these observations, we present a study of large guide reconnection using the fully kinetic Eulerian Vlasov-Maxwell component of the Gkeyll simulation framework, and we also employ and compare with gyrokinetics to explore the asymptotically large guide field limit. In addition to studying the configuration space dynamics, we leverage the recently developed field-particle correlations to diagnose the dominant sources of dissipation and compare the results of the field-particle correlation to other energy dissipation measures.

Composite Analysis of Cold Season Atmospheric River Events: Extreme Precipitation and Flooding over the Western United States

NASA Astrophysics Data System (ADS)

Eldardiry, H.; Hossain, F.

2017-12-01

Atmospheric Rivers (ARs) are narrow elongated corridors with horizontal water vapor transport located within the warm sector of extratropical cyclones. While it is widely known that most of heavy rainfall events across the western United States (US) are driven by ARs, the connection between atmospheric conditions and precipitation during an AR event has not been fully documented. In this study, we present a statistical analysis of the connection between precipitation, temperature, wind, and snowpack during the cold season AR events hitting the coastal regions of the western US. For each AR event, the precipitation and other atmospheric variables are retrieved through the dynamic downscaling of NCEP/NCAR Reanalysis product using the Advanced Research Weather Research and Forecasting Model (ARW-WRF). The results show a low frequency of precipitation (below 0.3) during AR events that reflects the connection of AR with extreme precipitation. Examining the horizontal wind speed during AR events indicates a high correlation (above 0.7) with precipitation. In addition, high levels of snow water equivalence (SWE) are also noticed along the mountainous regions, e.g., Cascade Range and Sierra-Nevada mountain range, during most of AR events. Addressing the impact of duration on the frequency of precipitation, we develop Intensity-Duration-Frequency (IDF) curves during AR events that can potentially describe the future predictability of precipitation along the north and south coast. To complement our analysis, we further investigate the flooding events recorded in the National Centers for Environmental Information (NCEI) storm events database. While some flooding events are attributed to heavy rainfall associated with an AR event, other flooding events are significantly connected to the increase in the snowmelt before the flooding date. Thus, we introduce an index that describes the contribution of rainfall vs snowmelt and categorizes the flooding events during an AR event into rain-driven and snow-driven events. Such categorization can provide insight into whether or not an AR will produce extreme precipitation or flooding. The results from such investigations are important to understand historical AR events and assess how precipitation and flooding might evolve in future climate.

A low-noise wide-dynamic-range event-driven detector using SOI pixel technology for high-energy particle imaging

NASA Astrophysics Data System (ADS)

Shrestha, Sumeet; Kamehama, Hiroki; Kawahito, Shoji; Yasutomi, Keita; Kagawa, Keiichiro; Takeda, Ayaki; Tsuru, Takeshi Go; Arai, Yasuo

2015-08-01

This paper presents a low-noise wide-dynamic-range pixel design for a high-energy particle detector in astronomical applications. A silicon on insulator (SOI) based detector is used for the detection of wide energy range of high energy particles (mainly for X-ray). The sensor has a thin layer of SOI CMOS readout circuitry and a thick layer of high-resistivity detector vertically stacked in a single chip. Pixel circuits are divided into two parts; signal sensing circuit and event detection circuit. The event detection circuit consisting of a comparator and logic circuits which detect the incidence of high energy particle categorizes the incident photon it into two energy groups using an appropriate energy threshold and generate a two-bit code for an event and energy level. The code for energy level is then used for selection of the gain of the in-pixel amplifier for the detected signal, providing a function of high-dynamic-range signal measurement. The two-bit code for the event and energy level is scanned in the event scanning block and the signals from the hit pixels only are read out. The variable-gain in-pixel amplifier uses a continuous integrator and integration-time control for the variable gain. The proposed design allows the small signal detection and wide dynamic range due to the adaptive gain technique and capability of correlated double sampling (CDS) technique of kTC noise canceling of the charge detector.

Accretion dynamics in pre-main sequence binaries

NASA Astrophysics Data System (ADS)

Tofflemire, B.; Mathieu, R.; Herczeg, G.; Ardila, D.; Akeson, R.; Ciardi, D.; Johns-Krull, C.

Binary stars are a common outcome of star formation. Orbital resonances, especially in short-period systems, are capable of reshaping the distribution and flows of circumstellar material. Simulations of the binary-disk interaction predict a dynamically cleared gap around the central binary, accompanied by periodic ``pulsed'' accretion events that are driven by orbital motion. To place observational constraints on the binary-disk interaction, we have conducted a long-term monitoring program tracing the time-variable accretion behavior of 9 short-period binaries. In this proceeding we present two results from our campaign: 1) the detection of periodic pulsed accretion events in DQ Tau and TWA 3A, and 2) evidence that the TWA 3A primary is the dominant accretor in the system.

The Application of SNiPER to the JUNO Simulation

NASA Astrophysics Data System (ADS)

Lin, Tao; Zou, Jiaheng; Li, Weidong; Deng, Ziyan; Fang, Xiao; Cao, Guofu; Huang, Xingtao; You, Zhengyun; JUNO Collaboration

2017-10-01

The JUNO (Jiangmen Underground Neutrino Observatory) is a multipurpose neutrino experiment which is designed to determine neutrino mass hierarchy and precisely measure oscillation parameters. As one of the important systems, the JUNO offline software is being developed using the SNiPER software. In this proceeding, we focus on the requirements of JUNO simulation and present the working solution based on the SNiPER. The JUNO simulation framework is in charge of managing event data, detector geometries and materials, physics processes, simulation truth information etc. It glues physics generator, detector simulation and electronics simulation modules together to achieve a full simulation chain. In the implementation of the framework, many attractive characteristics of the SNiPER have been used, such as dynamic loading, flexible flow control, multiple event management and Python binding. Furthermore, additional efforts have been made to make both detector and electronics simulation flexible enough to accommodate and optimize different detector designs. For the Geant4-based detector simulation, each sub-detector component is implemented as a SNiPER tool which is a dynamically loadable and configurable plugin. So it is possible to select the detector configuration at runtime. The framework provides the event loop to drive the detector simulation and interacts with the Geant4 which is implemented as a passive service. All levels of user actions are wrapped into different customizable tools, so that user functions can be easily extended by just adding new tools. The electronics simulation has been implemented by following an event driven scheme. The SNiPER task component is used to simulate data processing steps in the electronics modules. The electronics and trigger are synchronized by triggered events containing possible physics signals. The JUNO simulation software has been released and is being used by the JUNO collaboration to do detector design optimization, event reconstruction algorithm development and physics sensitivity studies.

Large Deformation Dynamic Bending of Composite Beams

NASA Technical Reports Server (NTRS)

Derian, E. J.; Hyer, M. W.

1986-01-01

Studies were conducted on the large deformation response of composite beams subjected to a dynamic axial load. The beams were loaded with a moderate eccentricity to promote bending. The study was primarily experimental but some finite element results were obtained. Both the deformation and the failure of the beams were of interest. The static response of the beams was also studied to determine potential differences between the static and dynamic failure. Twelve different laminate types were tested. The beams were loaded dynamically with a gravity driven impactor traveling at 19.6 ft/sec and quasi-static tests were conducted on identical beams in a displacement controlled manner. For laminates of practical interest, the failure modes under static and dynamic loadings were identical. Failure in most of the laminate types occurred in a single event involving 40% to 50% of the plies. However, failure in laminates with 30 deg or 15 deg off-axis plies occured in several events. All laminates exhibited bimodular elastic properties. Using empirically determined flexural properties, a finite element analysis was reasonably accurate in predicting the static and dynamic deformation response.

Driven to distraction: A lack of change gives rise to mind wandering.

PubMed

Faber, Myrthe; Radvansky, Gabriel A; D'Mello, Sidney K

2018-04-01

How does the dynamic structure of the external world direct attention? We examined the relationship between event structure and attention to test the hypothesis that narrative shifts (both theoretical and perceived) negatively predict attentional lapses. Self-caught instances of mind wandering were collected while 108 participants watched a 32.5 min film called The Red Balloon. We used theoretical codings of situational change and human perceptions of event boundaries to predict mind wandering in 5-s intervals. Our findings suggest a temporal alignment between the structural dynamics of the film and mind wandering reports. Specifically, the number of situational changes and likelihood of perceiving event boundaries in the prior 0-15 s interval negatively predicted mind wandering net of low-level audiovisual features. Thus, mind wandering is less likely to occur when there is more event change, suggesting that narrative shifts keep attention from drifting inwards. Copyright © 2018 Elsevier B.V. All rights reserved.

Risk and dynamics of unprecedented hot months in South East China

NASA Astrophysics Data System (ADS)

Thompson, Vikki; Dunstone, Nick J.; Scaife, Adam A.; Smith, Doug M.; Hardiman, Steven C.; Ren, Hong-Li; Lu, Bo; Belcher, Stephen E.

2018-06-01

The Yangtze region of South East China has experienced several extreme hot summer months in recent years. Such events can have devastating socio-economic impacts. We use a large ensemble of initialised climate simulations to assess the current chance of unprecedented hot summer months in the Yangtze River region. We find a 10% chance of an unprecedented hot summer month each year. Our simulations suggest that monthly mean temperatures up to 3 °C hotter than the current record are possible. The dynamics of these unprecedented extremes highlights the occurrence of a stationary atmospheric wave, the Silk Road Pattern, in a significant number of extreme hot events. We present evidence that this atmospheric wave is driven by variability in the Indian summer monsoon. Other extreme events are associated with a westward shift in the western North Pacific subtropical high. The most extreme simulated events exhibit combined characteristics of both the Silk Road Pattern and the shifted western North Pacific subtropical high.

Are the spring and fall blooms on the Scotian Shelf related to short-term physical events?

NASA Astrophysics Data System (ADS)

Greenan, B. J. W.; Petrie, B. D.; Harrison, W. G.; Oakey, N. S.

2004-03-01

Physical, chemical and biological data from the Scotian Shelf indicate that short-term physical events affect the dynamics of spring and fall blooms. This is based on results from a three-week mooring deployment measuring currents, temperature, salinity and fluorescence in October 2000, combined with biweekly sampling of temperature, salinity, nutrients and chlorophyll throughout the year at this mooring site. A wind-driven upwelling event in mid-October shows temperature, salinity and density iso-surfaces rising by approximately 20 m. During this event, a bloom with peak chlorophyll concentrations of about 2.5 mg m -3 began as nutrients are brought into the upper part of the water column. Gradient Richardson Numbers ( Ri), a proxy for vertical mixing, are estimated for the mooring period in 2 m vertical bins using SeaHorse CTD data and nearby ADCP current measurements. These data indicate that vertical mixing may have played a complementary role to the upwelling in bringing nutrients into the euphotic zone. A trend of decreasing Ri in the ocean mixed layer with increasing surface wind stress is suggested. It appears that this short-term physical event is a primary factor in initiating the fall bloom on the inner Scotian Shelf in 2000. In April of that year, the termination of the spring bloom coincided with a downwelling event suggesting that it played a role in determining the duration of the bloom. SeaWiFS ocean color satellite provided a spatial context for chlorophyll observations, however, the lack of temporal resolution due to poor atmospheric conditions means that these data provide limited information on short-term chlorophyll variability.

Quasi two day wave-related variability in the background dynamics and composition of the mesosphere/thermosphere and the ionosphere

PubMed Central

Chang, Loren C; Yue, Jia; Wang, Wenbin; Wu, Qian; Meier, R R

2014-01-01

Dissipating planetary waves in the mesosphere/lower thermosphere (MLT) region may cause changes in the background dynamics of that region, subsequently driving variability throughout the broader thermosphere/ionosphere system via mixing due to the induced circulation changes. We report the results of case studies examining the possibility of such coupling during the northern winter in the context of the quasi two day wave (QTDW)—a planetary wave that recurrently grows to large amplitudes from the summer MLT during the postsolstice period. Six distinct QTDW events between 2003 and 2011 are identified in the MLT using Sounding of the Atmosphere using Broadband Emission Radiometry temperature observations. Concurrent changes to the background zonal winds, zonal mean column O/N2 density ratio, and ionospheric total electron content (TEC) are examined using data sets from Thermosphere Ionosphere Mesosphere Energetics and Dynamics Doppler Interferometer, Global Ultraviolet Imager, and Global Ionospheric Maps, respectively. We find that in the 5–10 days following a QTDW event, the background zonal winds in the MLT show patterns of eastward and westward anomalies in the low and middle latitudes consistent with past modeling studies on QTDW-induced mean wind forcing, both below and at turbopause altitudes. This is accompanied by potentially related decreases in zonal mean thermospheric column O/N2, as well as to low-latitude TECs. The recurrent nature of the above changes during the six QTDW events examined point to an avenue for vertical coupling via background dynamics and chemistry of the thermosphere/ionosphere not previously observed. Key Points Dissipating planetary waves (PWs) in the MLT can drive background wind changes Mixing from dissipating PWs drive thermosphere/ionosphere composition changes First observations of QTDW-driven variability from this mechanism PMID:26312201

Quasi two day wave-related variability in the background dynamics and composition of the mesosphere/thermosphere and the ionosphere.

PubMed

Chang, Loren C; Yue, Jia; Wang, Wenbin; Wu, Qian; Meier, R R

2014-06-01

Dissipating planetary waves in the mesosphere/lower thermosphere (MLT) region may cause changes in the background dynamics of that region, subsequently driving variability throughout the broader thermosphere/ionosphere system via mixing due to the induced circulation changes. We report the results of case studies examining the possibility of such coupling during the northern winter in the context of the quasi two day wave (QTDW)-a planetary wave that recurrently grows to large amplitudes from the summer MLT during the postsolstice period. Six distinct QTDW events between 2003 and 2011 are identified in the MLT using Sounding of the Atmosphere using Broadband Emission Radiometry temperature observations. Concurrent changes to the background zonal winds, zonal mean column O/N 2 density ratio, and ionospheric total electron content (TEC) are examined using data sets from Thermosphere Ionosphere Mesosphere Energetics and Dynamics Doppler Interferometer, Global Ultraviolet Imager, and Global Ionospheric Maps, respectively. We find that in the 5-10 days following a QTDW event, the background zonal winds in the MLT show patterns of eastward and westward anomalies in the low and middle latitudes consistent with past modeling studies on QTDW-induced mean wind forcing, both below and at turbopause altitudes. This is accompanied by potentially related decreases in zonal mean thermospheric column O/N 2 , as well as to low-latitude TECs. The recurrent nature of the above changes during the six QTDW events examined point to an avenue for vertical coupling via background dynamics and chemistry of the thermosphere/ionosphere not previously observed. Dissipating planetary waves (PWs) in the MLT can drive background wind changesMixing from dissipating PWs drive thermosphere/ionosphere composition changesFirst observations of QTDW-driven variability from this mechanism.

Static structure of active Brownian hard disks

NASA Astrophysics Data System (ADS)

de Macedo Biniossek, N.; Löwen, H.; Voigtmann, Th; Smallenburg, F.

2018-02-01

We explore the changes in static structure of a two-dimensional system of active Brownian particles (ABP) with hard-disk interactions, using event-driven Brownian dynamics simulations. In particular, the effect of the self-propulsion velocity and the rotational diffusivity on the orientationally-averaged fluid structure factor is discussed. Typically activity increases structural ordering and generates a structure factor peak at zero wave vector which is a precursor of motility-induced phase separation. Our results provide reference data to test future statistical theories for the fluid structure of active Brownian systems. This manuscript was submitted for the special issue of the Journal of Physics: Condensed Matter associated with the Liquid Matter Conference 2017.

A Distributed Laboratory for Event-Driven Coastal Prediction and Hazard Planning

NASA Astrophysics Data System (ADS)

Bogden, P.; Allen, G.; MacLaren, J.; Creager, G. J.; Flournoy, L.; Sheng, Y. P.; Graber, H.; Graves, S.; Conover, H.; Luettich, R.; Perrie, W.; Ramakrishnan, L.; Reed, D. A.; Wang, H. V.

2006-12-01

The 2005 Atlantic hurricane season was the most active in recorded history. Collectively, 2005 hurricanes caused more than 2,280 deaths and record damages of over 100 billion dollars. Of the storms that made landfall, Dennis, Emily, Katrina, Rita, and Wilma caused most of the destruction. Accurate predictions of storm-driven surge, wave height, and inundation can save lives and help keep recovery costs down, provided the information gets to emergency response managers in time. The information must be available well in advance of landfall so that responders can weigh the costs of unnecessary evacuation against the costs of inadequate preparation. The SURA Coastal Ocean Observing and Prediction (SCOOP) Program is a multi-institution collaboration implementing a modular, distributed service-oriented architecture for real time prediction and visualization of the impacts of extreme atmospheric events. The modular infrastructure enables real-time prediction of multi- scale, multi-model, dynamic, data-driven applications. SURA institutions are working together to create a virtual and distributed laboratory integrating coastal models, simulation data, and observations with computational resources and high speed networks. The loosely coupled architecture allows teams of computer and coastal scientists at multiple institutions to innovate complex system components that are interconnected with relatively stable interfaces. The operational system standardizes at the interface level to enable substantial innovation by complementary communities of coastal and computer scientists. This architectural philosophy solves a long-standing problem associated with the transition from research to operations. The SCOOP Program thereby implements a prototype laboratory consistent with the vision of a national, multi-agency initiative called the Integrated Ocean Observing System (IOOS). Several service- oriented components of the SCOOP enterprise architecture have already been designed and implemented, including data archive and transport services, metadata registry and retrieval (catalog), resource management, and portal interfaces. SCOOP partners are integrating these at the service level and implementing reconfigurable workflows for several kinds of user scenarios, and are working with resource providers to prototype new policies and technologies for on-demand computing.

Auditory salience using natural soundscapes.

PubMed

Huang, Nicholas; Elhilali, Mounya

2017-03-01

Salience describes the phenomenon by which an object stands out from a scene. While its underlying processes are extensively studied in vision, mechanisms of auditory salience remain largely unknown. Previous studies have used well-controlled auditory scenes to shed light on some of the acoustic attributes that drive the salience of sound events. Unfortunately, the use of constrained stimuli in addition to a lack of well-established benchmarks of salience judgments hampers the development of comprehensive theories of sensory-driven auditory attention. The present study explores auditory salience in a set of dynamic natural scenes. A behavioral measure of salience is collected by having human volunteers listen to two concurrent scenes and indicate continuously which one attracts their attention. By using natural scenes, the study takes a data-driven rather than experimenter-driven approach to exploring the parameters of auditory salience. The findings indicate that the space of auditory salience is multidimensional (spanning loudness, pitch, spectral shape, as well as other acoustic attributes), nonlinear and highly context-dependent. Importantly, the results indicate that contextual information about the entire scene over both short and long scales needs to be considered in order to properly account for perceptual judgments of salience.

Very large release of mostly volcanic carbon during the Palaeocene-Eocene Thermal Maximum

NASA Astrophysics Data System (ADS)

Gutjahr, Marcus; Ridgwell, Andy; Sexton, Philip F.; Anagnostou, Eleni; Pearson, Paul N.; Pälike, Heiko; Norris, Richard D.; Thomas, Ellen; Foster, Gavin L.

2017-08-01

The Palaeocene-Eocene Thermal Maximum (PETM) was a global warming event that occurred about 56 million years ago, and is commonly thought to have been driven primarily by the destabilization of carbon from surface sedimentary reservoirs such as methane hydrates. However, it remains controversial whether such reservoirs were indeed the source of the carbon that drove the warming. Resolving this issue is key to understanding the proximal cause of the warming, and to quantifying the roles of triggers versus feedbacks. Here we present boron isotope data—a proxy for seawater pH—that show that the ocean surface pH was persistently low during the PETM. We combine our pH data with a paired carbon isotope record in an Earth system model in order to reconstruct the unfolding carbon-cycle dynamics during the event. We find strong evidence for a much larger (more than 10,000 petagrams)—and, on average, isotopically heavier—carbon source than considered previously. This leads us to identify volcanism associated with the North Atlantic Igneous Province, rather than carbon from a surface reservoir, as the main driver of the PETM. This finding implies that climate-driven amplification of organic carbon feedbacks probably played only a minor part in driving the event. However, we find that enhanced burial of organic matter seems to have been important in eventually sequestering the released carbon and accelerating the recovery of the Earth system.

Time-resolved scanning electron microscopy with polarization analysis

DOE Office of Scientific and Technical Information (OSTI.GOV)

Frömter, Robert, E-mail: rfroemte@physik.uni-hamburg.de; Oepen, Hans Peter; The Hamburg Centre for Ultrafast Imaging, Luruper Chaussee 149, 22761 Hamburg

2016-04-04

We demonstrate the feasibility of investigating periodically driven magnetization dynamics in a scanning electron microscope with polarization analysis based on spin-polarized low-energy electron diffraction. With the present setup, analyzing the time structure of the scattering events, we obtain a temporal resolution of 700 ps, which is demonstrated by means of imaging the field-driven 100 MHz gyration of the vortex in a soft-magnetic FeCoSiB square. Owing to the efficient intrinsic timing scheme, high-quality movies, giving two components of the magnetization simultaneously, can be recorded on the time scale of hours.

Late Miocene Pacific plate kinematic change explained with coupled global models of mantle and lithosphere dynamics

NASA Astrophysics Data System (ADS)

Stotz, I. L.; Iaffaldano, G.; Davies, D. R.

2017-07-01

The timing and magnitude of a Pacific plate motion change within the past 10 Ma remains enigmatic, due to the noise associated with finite-rotation data. Nonetheless, it has been hypothesized that this change was driven by the arrival of the Ontong Java Plateau (OJP) at the Melanesian arc and the consequent subduction polarity reversal. The uncertainties associated with the timing of this event, however, make it difficult to quantitatively demonstrate a dynamical association. Here, we first reconstruct the Pacific plate's absolute motion since the mid-Miocene (15 Ma), at high-temporal resolution, building on previous efforts to mitigate the impact of finite-rotation data noise. We find that the largest change in Pacific plate-motion direction occurred between 10 and 5 Ma, with the plate rotating clockwise. We subsequently develop and use coupled global numerical models of the mantle/lithosphere system to test hypotheses on the dynamics driving this change. These indicate that the arrival of the OJP at the Melanesian arc, between 10 and 5 Ma, followed by a subduction polarity reversal that marked the initiation of subduction of the Australian plate underneath the Pacific realm, were the key drivers of this kinematic change.

A multiscale, hierarchical model of pulse dynamics in arid-land ecosystems

USGS Publications Warehouse

Collins, Scott L.; Belnap, Jayne; Grimm, N. B.; Rudgers, J. A.; Dahm, Clifford N.; D'Odorico, P.; Litvak, M.; Natvig, D. O.; Peters, Douglas C.; Pockman, W. T.; Sinsabaugh, R. L.; Wolf, B. O.

2014-01-01

Ecological processes in arid lands are often described by the pulse-reserve paradigm, in which rain events drive biological activity until moisture is depleted, leaving a reserve. This paradigm is frequently applied to processes stimulated by one or a few precipitation events within a growing season. Here we expand the original framework in time and space and include other pulses that interact with rainfall. This new hierarchical pulse-dynamics framework integrates space and time through pulse-driven exchanges, interactions, transitions, and transfers that occur across individual to multiple pulses extending from micro to watershed scales. Climate change will likely alter the size, frequency, and intensity of precipitation pulses in the future, and arid-land ecosystems are known to be highly sensitive to climate variability. Thus, a more comprehensive understanding of arid-land pulse dynamics is needed to determine how these ecosystems will respond to, and be shaped by, increased climate variability.

A Multi-mission Event-Driven Component-Based System for Support of Flight Software Development, ATLO, and Operations first used by the Mars Science Laboratory (MSL) Project

NASA Technical Reports Server (NTRS)

Dehghani, Navid; Tankenson, Michael

2006-01-01

This viewgraph presentation reviews the architectural description of the Mission Data Processing and Control System (MPCS). MPCS is an event-driven, multi-mission ground data processing components providing uplink, downlink, and data management capabilities which will support the Mars Science Laboratory (MSL) project as its first target mission. MPCS is designed with these factors (1) Enabling plug and play architecture (2) MPCS has strong inheritance from GDS components that have been developed for other Flight Projects (MER, MRO, DAWN, MSAP), and are currently being used in operations and ATLO, and (3) MPCS components are Java-based, platform independent, and are designed to consume and produce XML-formatted data

Data Driven Model Development for the SuperSonic SemiSpan Transport (S(sup 4)T)

NASA Technical Reports Server (NTRS)

Kukreja, Sunil L.

2011-01-01

In this report, we will investigate two common approaches to model development for robust control synthesis in the aerospace community; namely, reduced order aeroservoelastic modelling based on structural finite-element and computational fluid dynamics based aerodynamic models, and a data-driven system identification procedure. It is shown via analysis of experimental SuperSonic SemiSpan Transport (S4T) wind-tunnel data that by using a system identification approach it is possible to estimate a model at a fixed Mach, which is parsimonious and robust across varying dynamic pressures.

System Dynamics to Climate-Driven Water Budget Analysis in the Eastern Snake Plains Aquifer

NASA Astrophysics Data System (ADS)

Ryu, J.; Contor, B.; Wylie, A.; Johnson, G.; Allen, R. G.

2010-12-01

Climate variability, weather extremes and climate change continue to threaten the sustainability of water resources in the western United States. Given current climate change projections, increasing temperature is likely to modify the timing, form, and intensity of precipitation events, which consequently affect regional and local hydrologic cycles. As a result, drought, water shortage, and subsequent water conflicts may become an increasing threat in monotone hydrologic systems in arid lands, such as the Eastern Snake Plain Aquifer (ESPA). The ESPA, in particular, is a critical asset in the state of Idaho. It is known as the economic lifeblood for more than half of Idaho’s population so that water resources availability and aquifer management due to climate change is of great interest, especially over the next few decades. In this study, we apply system dynamics as a methodology with which to address dynamically complex problems in ESPA’s water resources management. Aquifer recharge and discharge dynamics are coded in STELLA modeling system as input and output, respectively to identify long-term behavior of aquifer responses to climate-driven hydrological changes.

Data-driven train set crash dynamics simulation

NASA Astrophysics Data System (ADS)

Tang, Zhao; Zhu, Yunrui; Nie, Yinyu; Guo, Shihui; Liu, Fengjia; Chang, Jian; Zhang, Jianjun

2017-02-01

Traditional finite element (FE) methods are arguably expensive in computation/simulation of the train crash. High computational cost limits their direct applications in investigating dynamic behaviours of an entire train set for crashworthiness design and structural optimisation. On the contrary, multi-body modelling is widely used because of its low computational cost with the trade-off in accuracy. In this study, a data-driven train crash modelling method is proposed to improve the performance of a multi-body dynamics simulation of train set crash without increasing the computational burden. This is achieved by the parallel random forest algorithm, which is a machine learning approach that extracts useful patterns of force-displacement curves and predicts a force-displacement relation in a given collision condition from a collection of offline FE simulation data on various collision conditions, namely different crash velocities in our analysis. Using the FE simulation results as a benchmark, we compared our method with traditional multi-body modelling methods and the result shows that our data-driven method improves the accuracy over traditional multi-body models in train crash simulation and runs at the same level of efficiency.

Coupled Leidenfrost states as a monodisperse granular clock

NASA Astrophysics Data System (ADS)

Liu, Rui; Yang, Mingcheng; Chen, Ke; Hou, Meiying; To, Kiwing

2016-08-01

Using an event-driven molecular dynamics simulation, we show that simple monodisperse granular beads confined in coupled columns may oscillate as a different type of granular clock. To trigger this oscillation, the system needs to be driven against gravity into a density-inverted state, with a high-density clustering phase supported from below by a gaslike low-density phase (Leidenfrost effect) in each column. Our analysis reveals that the density-inverted structure and the relaxation dynamics between the phases can amplify any small asymmetry between the columns, and lead to a giant oscillation. The oscillation occurs only for an intermediate range of the coupling strength, and the corresponding phase diagram can be universally described with a characteristic height of the density-inverted structure. A minimal two-phase model is proposed and a linear stability analysis shows that the triggering mechanism of the oscillation can be explained as a switchable two-parameter Andronov-Hopf bifurcation. Numerical solutions of the model also reproduce similar oscillatory dynamics to the simulation results.

Observational and Dynamical Wave Climatologies. VOS vs Satellite Data

NASA Astrophysics Data System (ADS)

Grigorieva, Victoria; Badulin, Sergei; Chernyshova, Anna

2013-04-01

The understanding physics of wind-driven waves is crucially important for fundamental science and practical applications. This is why experimental efforts are targeted at both getting reliable information on sea state and elaborating effective tools of the sea wave forecasting. The global Visual Wave Observations and satellite data from the GLOBWAVE project of the European Space Agency are analyzed in the context of these two viewpoints. Within the first "observational" aspect we re-analyze conventional climatologies of all basic wave parameters for the last decades [5]. An alternative "dynamical" climatology is introduced as a tool of prediction of dynamical features of sea waves on global scales. The features of wave dynamics are studied in terms of one-parametric dependencies of wave heights on wave periods following the theoretical concept of self-similar wind-driven seas [3, 1, 4] and recently proposed approach to analysis of Voluntary Observing Ship (VOS) data [2]. Traditional "observational" climatologies based on VOS and satellite data collections demonstrate extremely consistent pictures for significant wave heights and dominant periods. On the other hand, collocated satellite and VOS data show significant differences in wave heights, wind speeds and, especially, in wave periods. Uncertainties of visual wave observations can explain these differences only partially. We see the key reason of this inconsistency in the methods of satellite data processing which are based on formal application of data interpolation methods rather than on up-to-date physics of wind-driven waves. The problem is considered within the alternative climatology approach where dynamical criteria of wave height-to-period linkage are used for retrieving wave periods and constructing physically consistent dynamical climatology. The key dynamical parameter - exponent R of one-parametric dependence Hs ~ TR shows dramatically less pronounced latitudinal dependence as compared to observed Hs and T of conventional climatology in both satellite and VOS data collections. It can be treated as an effect of interaction of wind-driven seas and swell on global scales as it was stated in [2]. Further study combining the alternative and conventional climatologies can help to detail this important dynamical effect of global wave dynamics. The progress in satellite data processing and their physical interpretation is of great value for such study. The work was supported by Russian Foundation for Basic Research grant 11-05-01114-a and the Russian government contracts No.11.G34.31.0035, No.11.G34.31.0078. References [1] S. I. Badulin, A. V. Babanin, D. Resio, and V. Zakharov. Weakly turbulent laws of wind-wave growth. J. Fluid Mech., 591:339-378, 2007. [2] S. I. Badulin and Grigorieva V. G. On discriminating swell and wind-driven seas in voluntary observing ship data. J. Geophys. Res., 117(C00J29), 2012. [3] S. I. Badulin, A. N. Pushkarev, D. Resio, and V. E. Zakharov. Self-similarity of wind-driven seas. Nonl. Proc. Geophys., 12:891-946, 2005. [4] E. Gagnaire-Renou, M. Benoit, and S. I. Badulin. On weakly turbulent scaling of wind sea in simulations of fetch-limited growth. J. Fluid Mech., 669:178-213, 2011. [5] S. K. Gulev, V. Grigorieva, A. Sterl, and D. Woolf. Assessment for the reliability of wave observations from voluntary observing ships: insights from the validation of a global wind wave climatology based on voluntary observing ship data. J. Geophys. Res. - Oceans, 108(C7):3236, doi:10,1029/2002JC001437, 2003.

Exact event-driven implementation for recurrent networks of stochastic perfect integrate-and-fire neurons.

PubMed

Taillefumier, Thibaud; Touboul, Jonathan; Magnasco, Marcelo

2012-12-01

In vivo cortical recording reveals that indirectly driven neural assemblies can produce reliable and temporally precise spiking patterns in response to stereotyped stimulation. This suggests that despite being fundamentally noisy, the collective activity of neurons conveys information through temporal coding. Stochastic integrate-and-fire models delineate a natural theoretical framework to study the interplay of intrinsic neural noise and spike timing precision. However, there are inherent difficulties in simulating their networks' dynamics in silico with standard numerical discretization schemes. Indeed, the well-posedness of the evolution of such networks requires temporally ordering every neuronal interaction, whereas the order of interactions is highly sensitive to the random variability of spiking times. Here, we answer these issues for perfect stochastic integrate-and-fire neurons by designing an exact event-driven algorithm for the simulation of recurrent networks, with delayed Dirac-like interactions. In addition to being exact from the mathematical standpoint, our proposed method is highly efficient numerically. We envision that our algorithm is especially indicated for studying the emergence of polychronized motifs in networks evolving under spike-timing-dependent plasticity with intrinsic noise.

Data-driven discovery of Koopman eigenfunctions using deep learning

NASA Astrophysics Data System (ADS)

Lusch, Bethany; Brunton, Steven L.; Kutz, J. Nathan

2017-11-01

Koopman operator theory transforms any autonomous non-linear dynamical system into an infinite-dimensional linear system. Since linear systems are well-understood, a mapping of non-linear dynamics to linear dynamics provides a powerful approach to understanding and controlling fluid flows. However, finding the correct change of variables remains an open challenge. We present a strategy to discover an approximate mapping using deep learning. Our neural networks find this change of variables, its inverse, and a finite-dimensional linear dynamical system defined on the new variables. Our method is completely data-driven and only requires measurements of the system, i.e. it does not require derivatives or knowledge of the governing equations. We find a minimal set of approximate Koopman eigenfunctions that are sufficient to reconstruct and advance the system to future states. We demonstrate the method on several dynamical systems.

Computational challenges in modeling gene regulatory events.

PubMed

Pataskar, Abhijeet; Tiwari, Vijay K

2016-10-19

Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating "omics" data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology.

Modeling 2D and 3D diffusion.

PubMed

Saxton, Michael J

2007-01-01

Modeling obstructed diffusion is essential to the understanding of diffusion-mediated processes in the crowded cellular environment. Simple Monte Carlo techniques for modeling obstructed random walks are explained and related to Brownian dynamics and more complicated Monte Carlo methods. Random number generation is reviewed in the context of random walk simulations. Programming techniques and event-driven algorithms are discussed as ways to speed simulations.

Data-Driven Information Extraction from Chinese Electronic Medical Records

PubMed Central

Zhao, Tianwan; Ge, Chen; Gao, Weiguo; Wei, Jia; Zhu, Kenny Q.

2015-01-01

Objective This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event. Materials and Methods Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions. Results The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846. Discussion In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838). Conclusions The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica. PMID:26295801

Data-Driven Information Extraction from Chinese Electronic Medical Records.

PubMed

Xu, Dong; Zhang, Meizhuo; Zhao, Tianwan; Ge, Chen; Gao, Weiguo; Wei, Jia; Zhu, Kenny Q

2015-01-01

This study aims to propose a data-driven framework that takes unstructured free text narratives in Chinese Electronic Medical Records (EMRs) as input and converts them into structured time-event-description triples, where the description is either an elaboration or an outcome of the medical event. Our framework uses a hybrid approach. It consists of constructing cross-domain core medical lexica, an unsupervised, iterative algorithm to accrue more accurate terms into the lexica, rules to address Chinese writing conventions and temporal descriptors, and a Support Vector Machine (SVM) algorithm that innovatively utilizes Normalized Google Distance (NGD) to estimate the correlation between medical events and their descriptions. The effectiveness of the framework was demonstrated with a dataset of 24,817 de-identified Chinese EMRs. The cross-domain medical lexica were capable of recognizing terms with an F1-score of 0.896. 98.5% of recorded medical events were linked to temporal descriptors. The NGD SVM description-event matching achieved an F1-score of 0.874. The end-to-end time-event-description extraction of our framework achieved an F1-score of 0.846. In terms of named entity recognition, the proposed framework outperforms state-of-the-art supervised learning algorithms (F1-score: 0.896 vs. 0.886). In event-description association, the NGD SVM is superior to SVM using only local context and semantic features (F1-score: 0.874 vs. 0.838). The framework is data-driven, weakly supervised, and robust against the variations and noises that tend to occur in a large corpus. It addresses Chinese medical writing conventions and variations in writing styles through patterns used for discovering new terms and rules for updating the lexica.

Swell-generated Set-up and Infragravity Wave Propagation Over a Fringing Coral Reef: Implications for Wave-driven Inundation of Atoll Islands

NASA Astrophysics Data System (ADS)

Cheriton, O. M.; Storlazzi, C. D.; Rosenberger, K. J.; Quataert, E.; van Dongeren, A.

2014-12-01

The Republic of the Marshall Islands is comprised of 1156 islands on 29 low-lying atolls with a mean elevation of 2 m that are susceptible to sea-level rise and often subjected to overwash during large wave events. A 6-month deployment of wave and tide gauges across two shore-normal sections of north-facing coral reef on the Roi-Namur Island on Kwajalein Atoll was conducted during 2013-2014 to quantify wave dynamics and wave-driven water levels on the fringing coral reef. Wave heights and periods on the reef flat were strongly correlated to the water levels. On the fore reef, the majority of wave energy was concentrated in the incident band (5-25 s); due to breaking at the reef crest, however, the wave energy over the reef flat was dominated by infragravity-band (25-250 s) motions. Two large wave events with heights of 6-8 m at 15 s over the fore reef were observed. During these events, infragravity-band wave heights exceeded the incident band wave heights and approximately 1.0 m of set-up was established over the innermost reef flat. This set-up enabled the propagation of large waves across the reef flat, reaching maximum heights of nearly 2 m on the innermost reef flat adjacent to the toe of the beach. XBEACH models of the instrument transects were able to replicate the incident waves, infragravity waves, and wave-driven set-up across the reef when the hydrodynamic roughness of the reef was correctly parameterized. These events led to more than 3 m of wave-driven run-up and inundation of the island that drove substantial morphological change to the beach face.

Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies

PubMed Central

Uhlemann, Elisabeth

2018-01-01

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications. PMID:29570676

Supporting Beacon and Event-Driven Messages in Vehicular Platoons through Token-Based Strategies.

PubMed

Balador, Ali; Uhlemann, Elisabeth; Calafate, Carlos T; Cano, Juan-Carlos

2018-03-23

Timely and reliable inter-vehicle communications is a critical requirement to support traffic safety applications, such as vehicle platooning. Furthermore, low-delay communications allow the platoon to react quickly to unexpected events. In this scope, having a predictable and highly effective medium access control (MAC) method is of utmost importance. However, the currently available IEEE 802.11p technology is unable to adequately address these challenges. In this paper, we propose a MAC method especially adapted to platoons, able to transmit beacons within the required time constraints, but with a higher reliability level than IEEE 802.11p, while concurrently enabling efficient dissemination of event-driven messages. The protocol circulates the token within the platoon not in a round-robin fashion, but based on beacon data age, i.e., the time that has passed since the previous collection of status information, thereby automatically offering repeated beacon transmission opportunities for increased reliability. In addition, we propose three different methods for supporting event-driven messages co-existing with beacons. Analysis and simulation results in single and multi-hop scenarios showed that, by providing non-competitive channel access and frequent retransmission opportunities, our protocol can offer beacon delivery within one beacon generation interval while fulfilling the requirements on low-delay dissemination of event-driven messages for traffic safety applications.

Prototype Development: Context-Driven Dynamic XML Ophthalmologic Data Capture Application.

PubMed

Peissig, Peggy; Schwei, Kelsey M; Kadolph, Christopher; Finamore, Joseph; Cancel, Efrain; McCarty, Catherine A; Okorie, Asha; Thomas, Kate L; Allen Pacheco, Jennifer; Pathak, Jyotishman; Ellis, Stephen B; Denny, Joshua C; Rasmussen, Luke V; Tromp, Gerard; Williams, Marc S; Vrabec, Tamara R; Brilliant, Murray H

2017-09-13

The capture and integration of structured ophthalmologic data into electronic health records (EHRs) has historically been a challenge. However, the importance of this activity for patient care and research is critical. The purpose of this study was to develop a prototype of a context-driven dynamic extensible markup language (XML) ophthalmologic data capture application for research and clinical care that could be easily integrated into an EHR system. Stakeholders in the medical, research, and informatics fields were interviewed and surveyed to determine data and system requirements for ophthalmologic data capture. On the basis of these requirements, an ophthalmology data capture application was developed to collect and store discrete data elements with important graphical information. The context-driven data entry application supports several features, including ink-over drawing capability for documenting eye abnormalities, context-based Web controls that guide data entry based on preestablished dependencies, and an adaptable database or XML schema that stores Web form specifications and allows for immediate changes in form layout or content. The application utilizes Web services to enable data integration with a variety of EHRs for retrieval and storage of patient data. This paper describes the development process used to create a context-driven dynamic XML data capture application for optometry and ophthalmology. The list of ophthalmologic data elements identified as important for care and research can be used as a baseline list for future ophthalmologic data collection activities. ©Peggy Peissig, Kelsey M Schwei, Christopher Kadolph, Joseph Finamore, Efrain Cancel, Catherine A McCarty, Asha Okorie, Kate L Thomas, Jennifer Allen Pacheco, Jyotishman Pathak, Stephen B Ellis, Joshua C Denny, Luke V Rasmussen, Gerard Tromp, Marc S Williams, Tamara R Vrabec, Murray H Brilliant. Originally published in JMIR Medical Informatics (http://medinform.jmir.org), 13.09.2017.

NASA GES DISC support of CO2 Data from OCO-2, ACOS, and AIRS

NASA Technical Reports Server (NTRS)

Wei, Jennifer C; Vollmer, Bruce E.; Savtchenko, Andrey K.; Hearty, Thomas J; Albayrak, Rustem Arif; Deshong, Barbara E.

2013-01-01

NASA Goddard Earth Sciences Data and Information Services Centers (GES DISC) is the data center assigned to archive and distribute current AIRS, ACOS data and data from the upcoming OCO-2 mission. The GES DISC archives and supports data containing information on CO2 as well as other atmospheric composition, atmospheric dynamics, modeling and precipitation. Along with the data stewardship, an important mission of GES DISC is to facilitate access to and enhance the usability of data as well as to broaden the user base. GES DISC strives to promote the awareness of science content and novelty of the data by working with Science Team members and releasing news articles as appropriate. Analysis of events that are of interest to the general public, and that help in understanding the goals of NASA Earth Observing missions, have been among most popular practices.Users have unrestricted access to a user-friendly search interface, Mirador, that allows temporal, spatial, keyword and event searches, as well as an ontology-driven drill down. Variable subsetting, format conversion, quality screening, and quick browse, are among the services available in Mirador. The majority of the GES DISC data are also accessible through OPeNDAP (Open-source Project for a Network Data Access Protocol) and WMS (Web Map Service). These services add more options for specialized subsetting, format conversion, image viewing and contributing to data interoperability.

Network-based simulation of aircraft at gates in airport terminals

DOE Office of Scientific and Technical Information (OSTI.GOV)

Cheng, Y.

1998-03-01

Simulation is becoming an essential tool for planning, design, and management of airport facilities. A simulation of aircraft at gates at an airport can be applied for various periodically performed applications, relating to the dynamic behavior of aircraft at gates in airport terminals for analyses, evaluations, and decision supports. Conventionally, such simulations are implemented using an event-driven method. For a more efficient simulation, this paper proposes a network-based method. The basic idea is to transform all the sequence constraint relations of aircraft at gates into a network. The simulation is done by calculating the longest path to all the nodesmore » in the network. The effect of the algorithm of the proposed method has been examined by experiments, and the superiority of the proposed method over the event-driven method is revealed through comprehensive comparisons of their overall simulation performance.« less

Disturbance and the dynamics of coral cover on the Great Barrier Reef (1995-2009).

PubMed

Osborne, Kate; Dolman, Andrew M; Burgess, Scott C; Johns, Kerryn A

2011-03-10

Coral reef ecosystems worldwide are under pressure from chronic and acute stressors that threaten their continued existence. Most obvious among changes to reefs is loss of hard coral cover, but a precise multi-scale estimate of coral cover dynamics for the Great Barrier Reef (GBR) is currently lacking. Monitoring data collected annually from fixed sites at 47 reefs across 1300 km of the GBR indicate that overall regional coral cover was stable (averaging 29% and ranging from 23% to 33% cover across years) with no net decline between 1995 and 2009. Subregional trends (10-100 km) in hard coral were diverse with some being very dynamic and others changing little. Coral cover increased in six subregions and decreased in seven subregions. Persistent decline of corals occurred in one subregion for hard coral and Acroporidae and in four subregions in non-Acroporidae families. Change in Acroporidae accounted for 68% of change in hard coral. Crown-of-thorns starfish (Acanthaster planci) outbreaks and storm damage were responsible for more coral loss during this period than either bleaching or disease despite two mass bleaching events and an increase in the incidence of coral disease. While the limited data for the GBR prior to the 1980's suggests that coral cover was higher than in our survey, we found no evidence of consistent, system-wide decline in coral cover since 1995. Instead, fluctuations in coral cover at subregional scales (10-100 km), driven mostly by changes in fast-growing Acroporidae, occurred as a result of localized disturbance events and subsequent recovery.

Data driven CAN node reliability assessment for manufacturing system

NASA Astrophysics Data System (ADS)

Zhang, Leiming; Yuan, Yong; Lei, Yong

2017-01-01

The reliability of the Controller Area Network(CAN) is critical to the performance and safety of the system. However, direct bus-off time assessment tools are lacking in practice due to inaccessibility of the node information and the complexity of the node interactions upon errors. In order to measure the mean time to bus-off(MTTB) of all the nodes, a novel data driven node bus-off time assessment method for CAN network is proposed by directly using network error information. First, the corresponding network error event sequence for each node is constructed using multiple-layer network error information. Then, the generalized zero inflated Poisson process(GZIP) model is established for each node based on the error event sequence. Finally, the stochastic model is constructed to predict the MTTB of the node. The accelerated case studies with different error injection rates are conducted on a laboratory network to demonstrate the proposed method, where the network errors are generated by a computer controlled error injection system. Experiment results show that the MTTB of nodes predicted by the proposed method agree well with observations in the case studies. The proposed data driven node time to bus-off assessment method for CAN networks can successfully predict the MTTB of nodes by directly using network error event data.

Non-Extensive Statistical Analysis of Solar Wind Electric, Magnetic Fields and Solar Energetic Particle time series.

NASA Astrophysics Data System (ADS)

Pavlos, G. P.; Malandraki, O.; Khabarova, O.; Livadiotis, G.; Pavlos, E.; Karakatsanis, L. P.; Iliopoulos, A. C.; Parisis, K.

2017-12-01

In this work we study the non-extensivity of Solar Wind space plasma by using electric-magnetic field data obtained by in situ spacecraft observations at different dynamical states of solar wind system especially in interplanetary coronal mass ejections (ICMEs), Interplanetary shocks, magnetic islands, or near the Earth Bow shock. Especially, we study the energetic particle non extensive fractional acceleration mechanism producing kappa distributions as well as the intermittent turbulence mechanism producing multifractal structures related with the Tsallis q-entropy principle. We present some new and significant results concerning the dynamics of ICMEs observed in the near Earth at L1 solar wind environment, as well as its effect in Earth's magnetosphere as well as magnetic islands. In-situ measurements of energetic particles at L1 are analyzed, in response to major solar eruptive events at the Sun (intense flares, fast CMEs). The statistical characteristics are obtained and compared for the Solar Energetic Particles (SEPs) originating at the Sun, the energetic particle enhancements associated with local acceleration during the CME-driven shock passage over the spacecraft (Energetic Particle Enhancements, ESPs) as well as the energetic particle signatures observed during the passage of the ICME. The results are referred to Tsallis non-extensive statistics and in particular to the estimation of Tsallis q-triplet, (qstat, qsen, qrel) of electric-magnetic field and the kappa distributions of solar energetic particles time series of the ICME, magnetic islands, resulting from the solar eruptive activity or the internal Solar Wind dynamics. Our results reveal significant differences in statistical and dynamical features, indicating important variations of the magnetic field dynamics both in time and space domains during the shock event, in terms of rate of entropy production, relaxation dynamics and non-equilibrium meta-stable stationary states.

Arctic Tundra Greening and Browning at Circumpolar and Regional Scales

NASA Astrophysics Data System (ADS)

Epstein, H. E.; Bhatt, U. S.; Walker, D. A.; Raynolds, M. K.; Yang, X.

2017-12-01

Remote sensing data have historically been used to assess the dynamics of arctic tundra vegetation. Until recently the scientific literature has largely described the "greening" of the Arctic; from a remote sensing perspective, an increase in the Normalized Difference Vegetation Index (NDVI), or a similar satellite-based vegetation index. Vegetation increases have been heterogeneous throughout the Arctic, and were reported to be up to 25% in certain areas over a 30-year timespan. However, more recently, arctic tundra vegetation dynamics have gotten more complex, with observations of more widespread tundra "browning" being reported. We used a combination of remote sensing data, including the Global Inventory Monitoring and Modeling System (GIMMS), as well as higher spatial resolution Landsat data, to evaluate the spatio-temporal patterns of arctic tundra vegetation dynamics (greening and browning) at circumpolar and regional scales over the past 3-4 decades. At the circumpolar scale, we focus on the spatial heterogeneity (by tundra subzone and continent) of tundra browning over the past 5-15 years, followed by a more recent recovery (greening since 2015). Landsat time series allow us to evaluate the landscape-scale heterogeneity of tundra greening and browning for northern Alaska and the Yamal Peninsula in northwestern Siberia, Russia. Multi-dataset analyses reveal that tundra greening and browning (i.e. increases or decreases in the NDVI respectively) are generated by different sets of processes. Tundra greening is largely a result of either climate warming, lengthening of the growing season, or responses to disturbances, such as fires, landslides, and freeze-thaw processes. Browning on the other hand tends to be more event-driven, such as the shorter-term decline in vegetation due to fire, insect defoliation, consumption by larger herbivores, or extreme weather events (e.g. winter warming or early summer frost damage). Browning can also be caused by local or regional cooling, or changes in the snow regime (e.g. depth, timing of melt). The spatio-temporal dynamics of tundra vegetation are only now beginning to get serious attention from the scientific community and the continual use of remote sensing data across spatial scales allows us to monitor these dynamics and elucidate their controls.

Koopman operator theory: Past, present, and future

NASA Astrophysics Data System (ADS)

Brunton, Steven; Kaiser, Eurika; Kutz, Nathan

2017-11-01

Koopman operator theory has emerged as a dominant method to represent nonlinear dynamics in terms of an infinite-dimensional linear operator. The Koopman operator acts on the space of all possible measurement functions of the system state, advancing these measurements with the flow of the dynamics. A linear representation of nonlinear dynamics has tremendous potential to enable the prediction, estimation, and control of nonlinear systems with standard textbook methods developed for linear systems. Dynamic mode decomposition has become the leading data-driven method to approximate the Koopman operator, although there are still open questions and challenges around how to obtain accurate approximations for strongly nonlinear systems. This talk will provide an introductory overview of modern Koopman operator theory, reviewing the basics and describing recent theoretical and algorithmic developments. Particular emphasis will be placed on the use of data-driven Koopman theory to characterize and control high-dimensional fluid dynamic systems. This talk will also address key advances in the rapidly growing fields of machine learning and data science that are likely to drive future developments.

FireMap: A Web Tool for Dynamic Data-Driven Predictive Wildfire Modeling Powered by the WIFIRE Cyberinfrastructure

NASA Astrophysics Data System (ADS)

Block, J.; Crawl, D.; Artes, T.; Cowart, C.; de Callafon, R.; DeFanti, T.; Graham, J.; Smarr, L.; Srivas, T.; Altintas, I.

2016-12-01

The NSF-funded WIFIRE project has designed a web-based wildfire modeling simulation and visualization tool called FireMap. The tool executes FARSITE to model fire propagation using dynamic weather and fire data, configuration settings provided by the user, and static topography and fuel datasets already built-in. Using GIS capabilities combined with scalable big data integration and processing, FireMap enables simple execution of the model with options for running ensembles by taking the information uncertainty into account. The results are easily viewable, sharable, repeatable, and can be animated as a time series. From these capabilities, users can model real-time fire behavior, analyze what-if scenarios, and keep a history of model runs over time for sharing with collaborators. Firemap runs FARSITE with national and local sensor networks for real-time weather data ingestion and High-Resolution Rapid Refresh (HRRR) weather for forecasted weather. The HRRR is a NOAA/NCEP operational weather prediction system comprised of a numerical forecast model and an analysis/assimilation system to initialize the model. It is run with a horizontal resolution of 3 km, has 50 vertical levels, and has a temporal resolution of 15 minutes. The HRRR requires an Environmental Data Exchange (EDEX) server to receive the feed and generate secondary products out of it for the modeling. UCSD's EDEX server, funded by NSF, makes high-resolution weather data available to researchers worldwide and enables visualization of weather systems and weather events lasting months or even years. The high-speed server aggregates weather data from the University Consortium for Atmospheric Research by way of a subscription service from the Consortium called the Internet Data Distribution system. These features are part of WIFIRE's long term goals to build an end-to-end cyberinfrastructure for real-time and data-driven simulation, prediction and visualization of wildfire behavior. Although Firemap is a research product of WIFIRE, developed in collaboration with a number of fire departments, the tool is operational in pilot form for providing big data-driven predictive fire spread modeling. Most recently, FireMap was used for situational awareness in the July 2016 Sand Fire by LA City and LA County Fire Departments.

Assessing temporal variations in connectivity through suspended sediment hysteresis analysis

NASA Astrophysics Data System (ADS)

Sherriff, Sophie; Rowan, John; Fenton, Owen; Jordan, Phil; Melland, Alice; Mellander, Per-Erik; hUallacháin, Daire Ó.

2016-04-01

Connectivity provides a valuable concept for understanding catchment-scale sediment dynamics. In intensive agricultural catchments, land management through tillage, high livestock densities and extensive land drainage practices significantly change hydromorphological behaviour and alter sediment supply and downstream delivery. Analysis of suspended sediment-discharge hysteresis has offered insights into sediment dynamics but typically on a limited selection of events. Greater availability of continuous high-resolution discharge and turbidity data and qualitative hysteresis metrics enables assessment of sediment dynamics during more events and over time. This paper assesses the utility of this approach to explore seasonal variations in connectivity. Data were collected from three small (c. 10 km2) intensive agricultural catchments in Ireland with contrasting morphologies, soil types, land use patterns and management practices, and are broadly defined as low-permeability supporting grassland, moderate-permeability supporting arable and high-permeability supporting arable. Suspended sediment concentration (using calibrated turbidity measurements) and discharge data were collected at 10-min resolution from each catchment outlet and precipitation data were collected from a weather station within each catchment. Event databases (67-90 events per catchment) collated information on sediment export metrics, hysteresis category (e.g., clockwise, anti-clockwise, no hysteresis), numeric hysteresis index, and potential hydro-meteorological controls on sediment transport including precipitation amount, duration, intensity, stream flow and antecedent soil moisture and rainfall. Statistical analysis of potential controls on sediment export was undertaken using Pearson's correlation coefficient on separate hysteresis categories in each catchment. Sediment hysteresis fluctuations through time were subsequently assessed using the hysteresis index. Results showed the numeric hysteresis index varied over time in all three catchments. The exact response was catchment specific reflecting changing sediment availability and connectivity through time as indicated by dominant controls. In the low-permeability grassland catchment, proximal sources dominated which was consistent with observations of active channel bank erosion. Seasonal increases in rainfall increased the erosion potential but continuous grassland cover mitigated against hillslope sediment contributions despite high hydrological connectivity and surface pathways. The moderate-permeability arable catchment was dominated by events with a distal source component but those with both proximal and distal sediment sources yielded the highest sediment quantities. These events were driven by rainfall parameters suggesting sediment were surface derived and the hillslope was hydrologically connected during most events. Through time, a sustained period of rainfall increased the magnitude of negative hysteresis, likely demonstrating increasing surface hydrological connectivity due to increased groundwater saturation. Where increased hydrological connectivity coincided with low groundcover, the largest sediment exports were recorded. Events in the high permeability catchment indicated predominantly proximal sediments despite abundant distal sources from tilled fields. The infiltration dominated high permeability soils hydrologically disconnected these field sources and limited sediment supply. However, the greatest sediment export occurred in this catchment suggesting thresholds existed, which when exceeded during higher magnitude events, resulted in efficient conveyance of sediments. Hysteresis analysis offers wider utility as a tool to understand sediment pathways and connectivity issues with applications to catchment management strategies.

Computational challenges in modeling gene regulatory events

PubMed Central

Pataskar, Abhijeet; Tiwari, Vijay K.

2016-01-01

ABSTRACT Cellular transcriptional programs driven by genetic and epigenetic mechanisms could be better understood by integrating “omics” data and subsequently modeling the gene-regulatory events. Toward this end, computational biology should keep pace with evolving experimental procedures and data availability. This article gives an exemplified account of the current computational challenges in molecular biology. PMID:27390891

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics.

PubMed

Itter, Malcolm S; Finley, Andrew O; D'Amato, Anthony W; Foster, Jane R; Bradford, John B

2017-06-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics-changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands. © 2017 by the Ecological Society of America.

A geospatial framework for dynamic route planning using congestion prediction in transportation systems.

DOT National Transportation Integrated Search

2011-01-01

The goal this research is to develop an end-to-end data-driven system, dubbed TransDec : (short for Transportation Decision-Making), to enable decision-making queries in : transportation systems with dynamic, real-time and historical data. With Trans...

Seasonal and flow-driven dynamics of particulate and dissolved mercury and methylmercury in a stream impacted by an industrial mercury source

DOE PAGES

Riscassi, Ami; Miller, Carrie; Brooks, Scott

2015-11-17

Sediments and floodplain soils in the East Fork Poplar Creek watershed (Oak Ridge, TN, USA) are contaminated with high levels of mercury (Hg) from an industrial source at the headwaters. Although baseflow conditions have been monitored, concentrations of Hg and methylmercury (MeHg) during high-flow storm events, when the stream is more hydrologically connected to the floodplain, have yet to be assessed. This paper evaluated baseflow and event-driven Hg and MeHg dynamics in East Fork Poplar Creek, 5 km upstream of the confluence with Poplar Creek, to determine the importance of hydrology to in-stream concentrations and downstream loads and to ascertainmore » whether the dynamics are comparable to those of systems without an industrial Hg source. Particulate Hg and MeHg were positively correlated with discharge (r 2 = 0.64 and 0.58, respectively) and total suspended sediment (r 2 = 0.97 and 0.89, respectively), and dissolved Hg also increased with increasing flow (r 2 = 0.18) and was associated with increases in dissolved organic carbon (r 2 = 0.65), similar to the dynamics observed in uncontaminated systems. Dissolved MeHg decreased with increases in discharge (r 2 = 0.23) and was not related to dissolved organic carbon concentrations (p = 0.56), dynamics comparable to relatively uncontaminated watersheds with a small percentage of wetlands (<10%). Finally, although stormflows exert a dominant control on particulate Hg, particulate MeHg, and dissolved Hg concentrations and loads, baseflows were associated with the highest dissolved MeHg concentration (0.38 ng/L) and represented the majority of the annual dissolved MeHg load.« less

Analyzing Electric Field Morphology Through Data-Model Comparisons of the GEM IM/S Assessment Challenge Events

NASA Technical Reports Server (NTRS)

Liemohn, Michael W.; Ridley, Aaron J.; Kozyra, Janet U.; Gallagher, Dennis L.; Thomsen, Michelle F.; Henderson, Michael G.; Denton, Michael H.; Brandt, Pontus C.; Goldstein, Jerry

2006-01-01

The storm-time inner magnetospheric electric field morphology and dynamics are assessed by comparing numerical modeling results of the plasmasphere and ring current with many in situ and remote sensing data sets. Two magnetic storms are analyzed, April 22,2001 and October 21-23,2001, which are the events selected for the Geospace Environment Modeling (GEM) Inner Magnetosphere/Storms (IM/S) Assessment Challenge (IMSAC). The IMSAC seeks to quantify the accuracy of inner magnetospheric models as well as synthesize our understanding of this region. For each storm, the ring current-atmosphere interaction model (RAM) and the dynamic global core plasma model (DGCPM) were run together with various settings for the large-scale convection electric field and the nightside ionospheric conductance. DGCPM plasmaspheric parameters were compared with IMAGE-EUV plasmapause extractions and LANL-MPA plume locations and velocities. RAM parameters were compared with Dst*, LANL-MPA fluxes and moments, IMAGE-MENA images, and IMAGE-HENA images. Both qualitative and quantitative comparisons were made to determine the electric field morphology that allows the model results to best fit the plasma data at various times during these events. The simulations with self-consistent electric fields were, in general, better than those with prescribed field choices. This indicates that the time-dependent modulation of the inner magnetospheric electric fields by the nightside ionosphere is quite significant for accurate determination of these fields (and their effects). It was determined that a shielded Volland-Stern field description driven by the 3-hour Kp index yields accurate results much of the time, but can be quite inconsistent. The modified Mcllwain field description clearly lagged in overall accuracy compared to the other fields, but matched some data sets (like Dst*) quite well. The rankings between the simulations varied depending on the storm and the individual data sets, indicating that each field description did well for some place, time, and energy range during the events, as well as doing less well in other places, times, and energies. Several unresolved issues regarding the storm-time inner magnetospheric electric field are discussed.

Data-driven diagnostics of terrestrial carbon dynamics over North America

Treesearch

Jingfeng Xiao; Scott V. Ollinger; Steve Frolking; George C. Hurtt; David Y. Hollinger; Kenneth J. Davis; Yude Pan; Xiaoyang Zhang; Feng Deng; Jiquan Chen; Dennis D. Baldocchi; Bevery E. Law; M. Altaf Arain; Ankur R. Desai; Andrew D. Richardson; Ge Sun; Brian Amiro; Hank Margolis; Lianhong Gu; Russell L. Scott; Peter D. Blanken; Andrew E. Suyker

2014-01-01

The exchange of carbon dioxide is a key measure of ecosystem metabolism and a critical intersection between the terrestrial biosphere and the Earth's climate. Despite the general agreement that the terrestrial ecosystems in North America provide a sizeable carbon sink, the size and distribution of the sink remain uncertain. We use a data-driven approach to upscale...

Flow field topology of transient mixing driven by buoyancy

NASA Technical Reports Server (NTRS)

Duval, Walter M B.

2004-01-01

Transient mixing driven by buoyancy occurs through the birth of a symmetric Rayleigh-Taylor morphology (RTM) structure for large length scales. Beyond its critical bifurcation the RTM structure exhibits self-similarity and occurs on smaller and smaller length scales. The dynamics of the RTM structure, its nonlinear growth and internal collision, show that its genesis occurs from an explosive bifurcation which leads to the overlap of resonance regions in phase space. This event shows the coexistence of regular and chaotic regions in phase space which is corroborated with the existence of horseshoe maps. A measure of local chaos given by the topological entropy indicates that as the system evolves there is growth of uncertainty. Breakdown of the dissipative RTM structure occurs during the transition from explosive to catastrophic bifurcation; this event gives rise to annihilation of the separatrices which drives overlap of resonance regions. The global bifurcation of explosive and catastrophic events in phase space for the large length scale of the RTM structure serves as a template for which mixing occurs on smaller and smaller length scales. Copyright 2004 American Institute of Physics.

Linear dynamical modes as new variables for data-driven ENSO forecast

NASA Astrophysics Data System (ADS)

Gavrilov, Andrey; Seleznev, Aleksei; Mukhin, Dmitry; Loskutov, Evgeny; Feigin, Alexander; Kurths, Juergen

2018-05-01

A new data-driven model for analysis and prediction of spatially distributed time series is proposed. The model is based on a linear dynamical mode (LDM) decomposition of the observed data which is derived from a recently developed nonlinear dimensionality reduction approach. The key point of this approach is its ability to take into account simple dynamical properties of the observed system by means of revealing the system's dominant time scales. The LDMs are used as new variables for empirical construction of a nonlinear stochastic evolution operator. The method is applied to the sea surface temperature anomaly field in the tropical belt where the El Nino Southern Oscillation (ENSO) is the main mode of variability. The advantage of LDMs versus traditionally used empirical orthogonal function decomposition is demonstrated for this data. Specifically, it is shown that the new model has a competitive ENSO forecast skill in comparison with the other existing ENSO models.

The Role of Inflammation Resolution Speed in Airway Smooth Muscle Mass Accumulation in Asthma: Insight from a Theoretical Model

PubMed Central

Chernyavsky, Igor L.; Croisier, Huguette; Chapman, Lloyd A. C.; Kimpton, Laura S.; Hiorns, Jonathan E.; Brook, Bindi S.; Jensen, Oliver E.; Billington, Charlotte K.; Hall, Ian P.; Johnson, Simon R.

2014-01-01

Despite a large amount of in vitro data, the dynamics of airway smooth muscle (ASM) mass increase in the airways of patients with asthma is not well understood. Here, we present a novel mathematical model that describes qualitatively the growth dynamics of ASM cells over short and long terms in the normal and inflammatory environments typically observed in asthma. The degree of ASM accumulation can be explained by an increase in the rate at which ASM cells switch between non-proliferative and proliferative states, driven by episodic inflammatory events. Our model explores the idea that remodelling due to ASM hyperplasia increases with the frequency and magnitude of these inflammatory events, relative to certain sensitivity thresholds. It highlights the importance of inflammation resolution speed by showing that when resolution is slow, even a series of small exacerbation events can result in significant remodelling, which persists after the inflammatory episodes. In addition, we demonstrate how the uncertainty in long-term outcome may be quantified and used to design an optimal low-risk individual anti-proliferative treatment strategy. The model shows that the rate of clearance of ASM proliferation and recruitment factors after an acute inflammatory event is a potentially important, and hitherto unrecognised, target for anti-remodelling therapy in asthma. It also suggests new ways of quantifying inflammation severity that could improve prediction of the extent of ASM accumulation. This ASM growth model should prove useful for designing new experiments or as a building block of more detailed multi-cellular tissue-level models. PMID:24632688

Comparison of the performance of tracer kinetic model-driven registration for dynamic contrast enhanced MRI using different models of contrast enhancement.

PubMed

Buonaccorsi, Giovanni A; Roberts, Caleb; Cheung, Sue; Watson, Yvonne; O'Connor, James P B; Davies, Karen; Jackson, Alan; Jayson, Gordon C; Parker, Geoff J M

2006-09-01

The quantitative analysis of dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) data is subject to model fitting errors caused by motion during the time-series data acquisition. However, the time-varying features that occur as a result of contrast enhancement can confound motion correction techniques based on conventional registration similarity measures. We have therefore developed a heuristic, locally controlled tracer kinetic model-driven registration procedure, in which the model accounts for contrast enhancement, and applied it to the registration of abdominal DCE-MRI data at high temporal resolution. Using severely motion-corrupted data sets that had been excluded from analysis in a clinical trial of an antiangiogenic agent, we compared the results obtained when using different models to drive the tracer kinetic model-driven registration with those obtained when using a conventional registration against the time series mean image volume. Using tracer kinetic model-driven registration, it was possible to improve model fitting by reducing the sum of squared errors but the improvement was only realized when using a model that adequately described the features of the time series data. The registration against the time series mean significantly distorted the time series data, as did tracer kinetic model-driven registration using a simpler model of contrast enhancement. When an appropriate model is used, tracer kinetic model-driven registration influences motion-corrupted model fit parameter estimates and provides significant improvements in localization in three-dimensional parameter maps. This has positive implications for the use of quantitative DCE-MRI for example in clinical trials of antiangiogenic or antivascular agents.

Impact of an extreme climatic event on community assembly.

PubMed

Thibault, Katherine M; Brown, James H

2008-03-04

Extreme climatic events are predicted to increase in frequency and magnitude, but their ecological impacts are poorly understood. Such events are large, infrequent, stochastic perturbations that can change the outcome of entrained ecological processes. Here we show how an extreme flood event affected a desert rodent community that has been monitored for 30 years. The flood (i) caused catastrophic, species-specific mortality; (ii) eliminated the incumbency advantage of previously dominant species; (iii) reset long-term population and community trends; (iv) interacted with competitive and metapopulation dynamics; and (v) resulted in rapid, wholesale reorganization of the community. This and a previous extreme rainfall event were punctuational perturbations-they caused large, rapid population- and community-level changes that were superimposed on a background of more gradual trends driven by climate and vegetation change. Captured by chance through long-term monitoring, the impacts of such large, infrequent events provide unique insights into the processes that structure ecological communities.

Crossing the front: contrasting storm-forced dispersal dynamics revealed by biological, geological and genetic analysis of beach-cast kelp.

PubMed

Waters, Jonathan M; King, Tania M; Fraser, Ceridwen I; Craw, Dave

2018-03-01

The subtropical front (STF) generally represents a substantial oceanographic barrier to dispersal between cold-sub-Antarctic and warm-temperate water masses. Recent studies have suggested that storm events can drastically influence marine dispersal and patterns. Here we analyse biological and geological dispersal driven by two major, contrasting storm events in southern New Zealand, 2017. We integrate biological and physical data to show that a severe southerly system in July 2017 disrupted this barrier by promoting movement of substantial numbers of southern sub-Antarctic Durvillaea kelp rafts across the STF, to make landfall in mainland NZ. By contrast, a less intense easterly storm (Cyclone Cook, April 2017) resulted in more moderate dispersal distances, with minimal dispersal between the sub-Antarctic and mainland New Zealand. These quantitative analyses of approximately 200 freshly beach-cast kelp specimens indicate that storm intensity and wind direction can strongly influence marine dispersal and landfall outcomes. © 2018 The Author(s).

Dynamic Triggering of Microseismicity in the North Island of New Zealand following the Mw7.8 Kaikoura Earthquake

NASA Astrophysics Data System (ADS)

Peng, Z.; Yao, D.; Fry, B.; Wallace, L. M.; Kaneko, Y.; Meng, X.

2017-12-01

We conduct a systematic search for dynamically triggered earthquakes in the North Island of New Zealand following the 11/13/2016 Mw7.8 Kaikoura earthquake. This event ruptured multiple faults in the northeastern South Island of New Zealand, and propagated for more than 170 km mostly in the NE direction. By examining earthquakes listed in the GeoNet catalog, we can observe a clear increase of microseismicity in the North Island following the Kaikoura mainshock. However, visual inspection of high-frequency seismograms recorded by 130 dense broadband and short-period sensors revealed that many local earthquakes were not captured by the GeoNet catalog, likely due to being obscured by the mainshock coda and intense aftershock sequence in the South Island. To further quantify the triggering phenomenon in the North Island following the mainshock, we apply a waveform matched filter technique to obtain a more complete catalog around the mainshock occurrence time. Assuming many of the triggered North Island events occur on faults that were active prior to the Mw7.8 earthquake, we select 17,500 events listed in the GeoNet catalog from 04/2016 to 03/2017 as templates to scan through continuous data from 11/01/2017 to 11/30/2017. Currently, 19,000 additional events are detected within the one month study period, comparing to 1,950 events listed in the catalog. The initial locations from the GeoNet catalog reveal that most triggered earthquakes occurred in the uppermost crust (<20km), likely linked to inland crustal faults and/or volcanic systems. In addition, another burst of seismicity (including a magnitude 6 event) occurred near the Wairarapa coastline about 400km north of the main rupture, which was likely driven by a M7 slow slip events triggered by the mainshock. Our next step is to calibrate the magnitudes of both catalog and newly detected events by measuring their principle component slopes. In addition, we plan to measure cross-correlation differential times between newly detected and template events to obtain better relative locations, and compare seismicity rate changes with both static and dynamic stress changes to better understand the triggering mechanisms. Updated results will be presented at the meeting.

WE-G-BRA-02: SafetyNet: Automating Radiotherapy QA with An Event Driven Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Hadley, S; Kessler, M; Litzenberg, D

2015-06-15

Purpose: Quality assurance is an essential task in radiotherapy that often requires many manual tasks. We investigate the use of an event driven framework in conjunction with software agents to automate QA and eliminate wait times. Methods: An in house developed subscription-publication service, EventNet, was added to the Aria OIS to be a message broker for critical events occurring in the OIS and software agents. Software agents operate without user intervention and perform critical QA steps. The results of the QA are documented and the resulting event is generated and passed back to EventNet. Users can subscribe to those eventsmore » and receive messages based on custom filters designed to send passing or failing results to physicists or dosimetrists. Agents were developed to expedite the following QA tasks: Plan Revision, Plan 2nd Check, SRS Winston-Lutz isocenter, Treatment History Audit, Treatment Machine Configuration. Results: Plan approval in the Aria OIS was used as the event trigger for plan revision QA and Plan 2nd check agents. The agents pulled the plan data, executed the prescribed QA, stored the results and updated EventNet for publication. The Winston Lutz agent reduced QA time from 20 minutes to 4 minutes and provided a more accurate quantitative estimate of radiation isocenter. The Treatment Machine Configuration agent automatically reports any changes to the Treatment machine or HDR unit configuration. The agents are reliable, act immediately, and execute each task identically every time. Conclusion: An event driven framework has inverted the data chase in our radiotherapy QA process. Rather than have dosimetrists and physicists push data to QA software and pull results back into the OIS, the software agents perform these steps immediately upon receiving the sentinel events from EventNet. Mr Keranen is an employee of Varian Medical Systems. Dr. Moran’s institution receives research support for her effort for a linear accelerator QA project from Varian Medical Systems. Other quality projects involving her effort are funded by Blue Cross Blue Shield of Michigan, Breast Cancer Research Foundation, and the NIH.« less

Influence of Barrier Wind Forcing on Heat Delivery Toward the Greenland Ice Sheet

NASA Astrophysics Data System (ADS)

Fraser, Neil J.; Inall, Mark E.

2018-04-01

A high-resolution numerical hydrodynamic model of Kangerdlugssuaq Fjord and the adjacent southeast Greenland shelf region was constructed in order to investigate the dynamics of fjord-shelf exchange. Recent studies have suggested that rapid exchange flows, driven by along-shelf barrier wind events, are the dominant agent of exchange between fjord and shelf. These events are prone to occur during the winter, when freshwater forcing is minimal and observations of the fjord interior are scarce. Subglacial freshwater discharge was held at zero, so that any buoyancy-driven overturning circulation was driven by melting alone. The model described a geostrophically balanced background flow transporting water masses between the fjord mouth and the glacier terminus, indicating that rotational effects are of order-one importance. Barrier wind events were found to trigger coastally trapped internal wave activity within fjord, temporarily enhancing exchange and vertical mixing, and causing warm water to oscillate in the along-fjord direction. These internal waves were also found to enhance the background flow via Stokes' drift. Heat delivery through the fjord mouth was smaller than that recorded in summer observations, however the system is more effective at delivering this heat to the head of the fjord. There exists the potential for wintertime melting at the ice-ocean interface to be significant to the same order as summertime melting.

Intensification of convective extremes driven by cloud-cloud interaction

NASA Astrophysics Data System (ADS)

Moseley, Christopher; Hohenegger, Cathy; Berg, Peter; Haerter, Jan O.

2016-10-01

In a changing climate, a key role may be played by the response of convective-type cloud and precipitation to temperature changes. Yet, it is unclear if convective precipitation intensities will increase mainly due to thermodynamic or dynamical processes. Here we perform large eddy simulations of convection by imposing a realistic diurnal cycle of surface temperature. We find convective events to gradually self-organize into larger cloud clusters and those events occurring late in the day to produce the highest precipitation intensities. Tracking rain cells throughout their life cycles, we show that events which result from collisions respond strongly to changes in boundary conditions, such as temperature changes. Conversely, events not resulting from collisions remain largely unaffected by the boundary conditions. Increased surface temperature indeed leads to more interaction between events and stronger precipitation extremes. However, comparable intensification occurs when leaving temperature unchanged but simply granting more time for self-organization. These findings imply that the convective field as a whole acquires a memory of past precipitation and inter-cloud dynamics, driving extremes. For global climate model projections, our results suggest that the interaction between convective clouds must be incorporated to simulate convective extremes and the diurnal cycle more realistically.

Photospheric Current Spikes And Their Possible Association With Flares - Results from an HMI Data Driven Model

NASA Astrophysics Data System (ADS)

Goodman, M. L.; Kwan, C.; Ayhan, B.; Eric, S. L.

2016-12-01

A data driven, near photospheric magnetohydrodynamic model predicts spikes in the horizontal current density, and associated resistive heating rate. The spikes appear as increases by orders of magnitude above background values in neutral line regions (NLRs) of active regions (ARs). The largest spikes typically occur a few hours to a few days prior to M or X flares. The spikes correspond to large vertical derivatives of the horizontal magnetic field. The model takes as input the photospheric magnetic field observed by the Helioseismic & Magnetic Imager (HMI) on the Solar Dynamics Observatory (SDO) satellite. This 2.5 D field is used to determine an analytic expression for a 3 D magnetic field, from which the current density, vector potential, and electric field are computed in every AR pixel for 14 ARs. The field is not assumed to be force-free. The spurious 6, 12, and 24 hour Doppler periods due to SDO orbital motion are filtered out of the time series of the HMI magnetic field for each pixel. The subset of spikes analyzed at the pixel level are found to occur on HMI and granulation scales of 1 arcsec and 12 minutes. Spikes are found in ARs with and without M or X flares, and outside as well as inside NLRs, but the largest spikes are localized in the NLRs of ARs with M or X flares. The energy to drive the heating associated with the largest current spikes comes from bulk flow kinetic energy, not the electromagnetic field, and the current density is highly non-force free. The results suggest that, in combination with the model, HMI is revealing strong, convection driven, non-force free heating events on granulation scales, and it is plausible these events are correlated with subsequent M or X flares. More and longer time series need to be analyzed to determine if such a correlation exists.

LIGHT BRIDGE IN A DEVELOPING ACTIVE REGION. I. OBSERVATION OF LIGHT BRIDGE AND ITS DYNAMIC ACTIVITY PHENOMENA

DOE Office of Scientific and Technical Information (OSTI.GOV)

Toriumi, Shin; Katsukawa, Yukio; Cheung, Mark C. M., E-mail: shin.toriumi@nao.ac.jp

Light bridges, the bright structures that divide the umbra of sunspots and pores into smaller pieces, are known to produce a wide variety of activity events in solar active regions (ARs). It is also known that the light bridges appear in the assembling process of nascent sunspots. The ultimate goal of this series of papers is to reveal the nature of light bridges in developing ARs and the occurrence of activity events associated with the light bridge structures from both observational and numerical approaches. In this first paper, exploiting the observational data obtained by Hinode, the Interface Region Imaging Spectrograph, andmore » the Solar Dynamics Observatory, we investigate the detailed structure of the light bridge in NOAA AR 11974 and its dynamic activity phenomena. As a result, we find that the light bridge has a weak, horizontal magnetic field, which is transported from the interior by a large-scale convective upflow and is surrounded by strong, vertical fields of adjacent pores. In the chromosphere above the bridge, a transient brightening occurs repeatedly and intermittently, followed by a recurrent dark surge ejection into higher altitudes. Our analysis indicates that the brightening is the plasma heating due to magnetic reconnection at lower altitudes, while the dark surge is the cool, dense plasma ejected from the reconnection region. From the observational results, we conclude that the dynamic activity observed in a light bridge structure such as chromospheric brightenings and dark surge ejections are driven by magnetoconvective evolution within the light bridge and its interaction with the surrounding magnetic fields.« less

Modelling the impacts of reoccurring fires in tropical savannahs using Biome-BGC.

NASA Astrophysics Data System (ADS)

Fletcher, Charlotte; Petritsch, Richard; Pietsch, Stephan

2010-05-01

Fires are a dominant feature of tropical savannahs and have occurred throughout history by natural as well as human-induced means. These fires have a profound influence on the landscape in terms of flux dynamics and vegetative species composition. This study attempts to understand the impacts of fire regimes on flux dynamics and vegetation composition in savannahs using the Biome-BGC model. The Batéké Plateau, Gabon - an area of savannah grasslands in the Congo basin, serves as a case-study. To achieve model validation for savannahs, data sets from stands with differing levels of past burning are used. It is hypothesised that the field measurements from those stands with lower-levels of past burning will correlate with the Biome-BGC model output, meaning that the model is validated for the savannah excluding fire regimes. However, in reality, fire is frequent in the savannah. Data on past fire events are available from the Moderate Resolution Imaging Spectroradiometer (MODIS) to provide the fire regimes of the model. As the field data-driven measurements of the burnt stands are influenced by fire in the savannah, this will therefore result in a Biome-BGC model validated for the impacts of fire on savannah ecology. The validated model can then be used to predict the savannah's flux dynamics under the fire scenarios expected with climate and/or human impact change.

Sampled-data consensus in switching networks of integrators based on edge events

NASA Astrophysics Data System (ADS)

Xiao, Feng; Meng, Xiangyu; Chen, Tongwen

2015-02-01

This paper investigates the event-driven sampled-data consensus in switching networks of multiple integrators and studies both the bidirectional interaction and leader-following passive reaction topologies in a unified framework. In these topologies, each information link is modelled by an edge of the information graph and assigned a sequence of edge events, which activate the mutual data sampling and controller updates of the two linked agents. Two kinds of edge-event-detecting rules are proposed for the general asynchronous data-sampling case and the synchronous periodic event-detecting case. They are implemented in a distributed fashion, and their effectiveness in reducing communication costs and solving consensus problems under a jointly connected topology condition is shown by both theoretical analysis and simulation examples.

The ambiguity of drought events, a bottleneck for Amazon forest drought response modelling

NASA Astrophysics Data System (ADS)

De Deurwaerder, Hannes; Verbeeck, Hans; Baker, Timothy; Christoffersen, Bradley; Ciais, Philippe; Galbraith, David; Guimberteau, Matthieu; Kruijt, Bart; Langerwisch, Fanny; Meir, Patrick; Rammig, Anja; Thonicke, Kirsten; Von Randow, Celso; Zhang, Ke

2016-04-01

Considering the important role of the Amazon forest in the global water and carbon cycle, the prognosis of altered hydrological patterns resulting from climate change provides strong incentive for apprehending the direct implications of drought on the vegetation of this ecosystem. Dynamic global vegetation models have the potential of providing a useful tool to study drought impacts on various spatial and temporal scales. This however assumes the models being able to properly represent drought impact mechanisms. But how well do the models succeed in meeting this assumption? Within this study meteorological driver data and model output data of 4 different DGVMs, i.e. ORCHIDEE, JULES, INLAND and LPGmL, are studied. Using the palmer drought severity index (PDSI) and the mean cumulative water deficit (MWD), temporal and spatial representation of drought events are studied in the driver data and are referenced to historical extreme drought events in the Amazon. Subsequently, within the resulting temporal and spatial frame, we studied the drought impact on the above ground biomass (AGB) and gross primary production (GPP) fluxes. Flux tower data, field inventory data and the JUNG data-driven GPP product for the Amazon region are used for validation. Our findings not only suggest that the current state of the studied DGVMs is inadequate in representing Amazon droughts in general, but also highlights strong inter-model differences in drought responses. Using scatterplot-studies and input-output correlations, we provide insight in the origin of these encountered inter-model differences. In addition, we present directives of model development and improvement in scope of Amazon forest drought response modelling.

Configuring and Characterizing X-Rays for Laser-Driven Compression Experiments at the Dynamic Compression Sector

NASA Astrophysics Data System (ADS)

Li, Y.; Capatina, D.; D'Amico, K.; Eng, P.; Hawreliak, J.; Graber, T.; Rickerson, D.; Klug, J.; Rigg, P. A.; Gupta, Y. M.

2017-06-01

Coupling laser-driven compression experiments to the x-ray beam at the Dynamic Compression Sector (DCS) at the Advanced Photon Source (APS) of Argonne National Laboratory requires state-of-the-art x-ray focusing, pulse isolation, and diagnostics capabilities. The 100J UV pulsed laser system can be fired once every 20 minutes so precise alignment and focusing of the x-rays on each new sample must be fast and reproducible. Multiple Kirkpatrick-Baez (KB) mirrors are used to achieve a focal spot size as small as 50 μm at the target, while the strategic placement of scintillating screens, cameras, and detectors allows for fast diagnosis of the beam shape, intensity, and alignment of the sample to the x-ray beam. In addition, a series of x-ray choppers and shutters are used to ensure that the sample is exposed to only a single x-ray pulse ( 80ps) during the dynamic compression event and require highly precise synchronization. Details of the technical requirements, layout, and performance of these instruments will be presented. Work supported by DOE/NNSA.

Diffuse Interplanetary Radio Emission (DIRE) Accompanying Type II Radio Bursts

NASA Astrophysics Data System (ADS)

Teklu, T. B.; Gopalswamy, N.; Makela, P. A.; Yashiro, S.; Akiyama, S.; Xie, H.

2015-12-01

We report on an unusual drifting feature in the radio dynamic spectra at frequencies below 14 MHz observed by the Radio and Plasma Wave (WAVES) experiment on board the Wind spacecraft. We call this feature as "Diffuse Interplanetary Radio Emission (DIRE)". The DIRE events are generally associated with intense interplanetary type II radio bursts produced by shocks driven by coronal mass ejections (CMEs). DIREs drift like type II bursts in the dynamic spectra, but the drifting feature consist of a series of short-duration spikes (similar to a type I chain). DIREs occur at higher frequencies than the associated type II bursts, with no harmonic relationship with the type II burst. The onset of DIREs is delayed by several hours from the onset of the eruption. Comparing the radio dynamic spectra with white-light observations from the Solar and Heliospheric Observatory (SOHO) mission, we find that the CMEs are generally very energetic (fast and mostly halos). We suggest that the DIRE source is typically located at the flanks of the CME-driven shock that is still at lower heliocentric distances.

A General and Efficient Method for Incorporating Precise Spike Times in Globally Time-Driven Simulations

PubMed Central

Hanuschkin, Alexander; Kunkel, Susanne; Helias, Moritz; Morrison, Abigail; Diesmann, Markus

2010-01-01

Traditionally, event-driven simulations have been limited to the very restricted class of neuronal models for which the timing of future spikes can be expressed in closed form. Recently, the class of models that is amenable to event-driven simulation has been extended by the development of techniques to accurately calculate firing times for some integrate-and-fire neuron models that do not enable the prediction of future spikes in closed form. The motivation of this development is the general perception that time-driven simulations are imprecise. Here, we demonstrate that a globally time-driven scheme can calculate firing times that cannot be discriminated from those calculated by an event-driven implementation of the same model; moreover, the time-driven scheme incurs lower computational costs. The key insight is that time-driven methods are based on identifying a threshold crossing in the recent past, which can be implemented by a much simpler algorithm than the techniques for predicting future threshold crossings that are necessary for event-driven approaches. As run time is dominated by the cost of the operations performed at each incoming spike, which includes spike prediction in the case of event-driven simulation and retrospective detection in the case of time-driven simulation, the simple time-driven algorithm outperforms the event-driven approaches. Additionally, our method is generally applicable to all commonly used integrate-and-fire neuronal models; we show that a non-linear model employing a standard adaptive solver can reproduce a reference spike train with a high degree of precision. PMID:21031031

Sources of shaking and flooding during the Tohoku-Oki earthquake: a mixture of rupture styles

USGS Publications Warehouse

Wei, Shengji; Graves, Robert; Helmberger, Don; Avouac, Jean-Philippe; Jiang, Junle

2012-01-01

Modeling strong ground motions from great subduction zone earthquakes is one of the great challenges of computational seismology. To separate the rupture characteristics from complexities caused by 3D sub-surface geology requires an extraordinary data set such as provided by the recent Mw9.0 Tohoku-Oki earthquake. Here we combine deterministic inversion and dynamically guided forward simulation methods to model over one thousand high-rate GPS and strong motion observations from 0 to 0.25 Hz across the entire Honshu Island. Our results display distinct styles of rupture with a deeper generic interplate event (~Mw8.5) transitioning to a shallow tsunamigenic earthquake (~Mw9.0) at about 25 km depth in a process driven by a strong dynamic weakening mechanism, possibly thermal pressurization. This source model predicts many important features of the broad set of seismic, geodetic and seafloor observations providing a major advance in our understanding of such great natural hazards.

Modeling urban coastal flood severity from crowd-sourced flood reports using Poisson regression and Random Forest

NASA Astrophysics Data System (ADS)

Sadler, J. M.; Goodall, J. L.; Morsy, M. M.; Spencer, K.

2018-04-01

Sea level rise has already caused more frequent and severe coastal flooding and this trend will likely continue. Flood prediction is an essential part of a coastal city's capacity to adapt to and mitigate this growing problem. Complex coastal urban hydrological systems however, do not always lend themselves easily to physically-based flood prediction approaches. This paper presents a method for using a data-driven approach to estimate flood severity in an urban coastal setting using crowd-sourced data, a non-traditional but growing data source, along with environmental observation data. Two data-driven models, Poisson regression and Random Forest regression, are trained to predict the number of flood reports per storm event as a proxy for flood severity, given extensive environmental data (i.e., rainfall, tide, groundwater table level, and wind conditions) as input. The method is demonstrated using data from Norfolk, Virginia USA from September 2010 to October 2016. Quality-controlled, crowd-sourced street flooding reports ranging from 1 to 159 per storm event for 45 storm events are used to train and evaluate the models. Random Forest performed better than Poisson regression at predicting the number of flood reports and had a lower false negative rate. From the Random Forest model, total cumulative rainfall was by far the most dominant input variable in predicting flood severity, followed by low tide and lower low tide. These methods serve as a first step toward using data-driven methods for spatially and temporally detailed coastal urban flood prediction.

Final Project Report, DynAX Innovations in Programming Models, Compilers and Runtime Systems for Dynamic Adaptive Event Driven Execution Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gao, Guang; Meister, Benoit; Padua, David

2015-12-31

This report contains a summary of the work done for the Dynax Project from 9/1/2012 to 8/31/2015. Much of the information presented is discussed in further detail in other STI annual and quarterly reports. Additionally, a NCE report was submitted covering work done from the period of 9/1/2015 to 12/31/2015.

Dynamic simulation of storm-driven barrier island morphology under future sea level rise

NASA Astrophysics Data System (ADS)

Passeri, D. L.; Long, J.; Plant, N. G.; Bilskie, M. V.; Hagen, S. C.

2016-12-01

The impacts of short-term processes such as tropical and extratropical storms have the potential to alter barrier island morphology. On the event scale, the effects of storm-driven morphology may result in damage or loss of property, infrastructure and habitat. On the decadal scale, the combination of storms and sea level rise (SLR) will evolve barrier islands. The effects of SLR on hydrodynamics and coastal morphology are dynamic and inter-related; nonlinearities in SLR can cause larger peak surges, lengthier inundation times and additional inundated land, which may result in increased erosion, overwash or breaching along barrier islands. This study uses a two-dimensional morphodynamic model (XBeach) to examine the response of Dauphin Island, AL to storm surge under future SLR. The model is forced with water levels and waves provided by a large-domain hydrodynamic model. A historic validation of hurricanes Ivan and Katrina indicates the model is capable of predicting morphologic response with high skill (0.5). The validated model is used to simulate storm surge driven by Ivan and Katrina under four future SLR scenarios, ranging from 20 cm to 2 m. Each SLR scenario is implemented using a static or "bathtub" approach (in which water levels are increased linearly by the amount of SLR) versus a dynamic approach (in which SLR is applied at the open ocean boundary of the hydrodynamic model and allowed to propagate through the domain as guided by the governing equations). Results illustrate that higher amounts of SLR result in additional shoreline change, dune erosion, overwash and breaching. Compared to the dynamic approach, the static approach over-predicts inundation, dune erosion, overwash and breaching of the island. Overall, results provide a better understanding of the effects of SLR on storm-driven barrier island morphology and support a paradigm shift away from the "bathtub" approach, towards considering the integrated, dynamic effects of SLR.

Key design elements of a data utility for national biosurveillance: event-driven architecture, caching, and Web service model.

PubMed

Tsui, Fu-Chiang; Espino, Jeremy U; Weng, Yan; Choudary, Arvinder; Su, Hoah-Der; Wagner, Michael M

2005-01-01

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance-systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions.

On the Boussinesq-Burgers equations driven by dynamic boundary conditions

NASA Astrophysics Data System (ADS)

Zhu, Neng; Liu, Zhengrong; Zhao, Kun

2018-02-01

We study the qualitative behavior of the Boussinesq-Burgers equations on a finite interval subject to the Dirichlet type dynamic boundary conditions. Assuming H1 ×H2 initial data which are compatible with boundary conditions and utilizing energy methods, we show that under appropriate conditions on the dynamic boundary data, there exist unique global-in-time solutions to the initial-boundary value problem, and the solutions converge to the boundary data as time goes to infinity, regardless of the magnitude of the initial data.

Real-time Social Internet Data to Guide Forecasting Models

DOE Office of Scientific and Technical Information (OSTI.GOV)

Del Valle, Sara Y.

Our goal is to improve decision support by monitoring and forecasting events using social media, mathematical models, and quantifying model uncertainty. Our approach is real-time, data-driven forecasts with quantified uncertainty: Not just for weather anymore. Information flow from human observations of events through an Internet system and classification algorithms is used to produce quantitatively uncertain forecast. In summary, we want to develop new tools to extract useful information from Internet data streams, develop new approaches to assimilate real-time information into predictive models, validate approaches by forecasting events, and our ultimate goal is to develop an event forecasting system using mathematicalmore » approaches and heterogeneous data streams.« less

Event-driven processing for hardware-efficient neural spike sorting

NASA Astrophysics Data System (ADS)

Liu, Yan; Pereira, João L.; Constandinou, Timothy G.

2018-02-01

Objective. The prospect of real-time and on-node spike sorting provides a genuine opportunity to push the envelope of large-scale integrated neural recording systems. In such systems the hardware resources, power requirements and data bandwidth increase linearly with channel count. Event-based (or data-driven) processing can provide here a new efficient means for hardware implementation that is completely activity dependant. In this work, we investigate using continuous-time level-crossing sampling for efficient data representation and subsequent spike processing. Approach. (1) We first compare signals (synthetic neural datasets) encoded with this technique against conventional sampling. (2) We then show how such a representation can be directly exploited by extracting simple time domain features from the bitstream to perform neural spike sorting. (3) The proposed method is implemented in a low power FPGA platform to demonstrate its hardware viability. Main results. It is observed that considerably lower data rates are achievable when using 7 bits or less to represent the signals, whilst maintaining the signal fidelity. Results obtained using both MATLAB and reconfigurable logic hardware (FPGA) indicate that feature extraction and spike sorting accuracies can be achieved with comparable or better accuracy than reference methods whilst also requiring relatively low hardware resources. Significance. By effectively exploiting continuous-time data representation, neural signal processing can be achieved in a completely event-driven manner, reducing both the required resources (memory, complexity) and computations (operations). This will see future large-scale neural systems integrating on-node processing in real-time hardware.

Extended MHD Modeling of Tearing-Driven Magnetic Relaxation

NASA Astrophysics Data System (ADS)

Sauppe, Joshua

2016-10-01

Driven plasma pinch configurations are characterized by the gradual accumulation and episodic release of free energy in discrete relaxation events. The hallmark of this relaxation in a reversed-field pinch (RFP) plasma is flattening of the parallel current density profile effected by a fluctuation-induced dynamo emf in Ohm's law. Nonlinear two-fluid modeling of macroscopic RFP dynamics has shown appreciable coupling of magnetic relaxation and the evolution of plasma flow. Accurate modeling of RFP dynamics requires the Hall effect in Ohm's law as well as first order ion finite Larmor radius (FLR) effects, represented by the Braginskii ion gyroviscous stress tensor. New results find that the Hall dynamo effect from < J × B > / ne can counter the MHD effect from - < V × B > in some of the relaxation events. The MHD effect dominates these events and relaxes the current profile toward the Taylor state, but the opposition of the two dynamos generates plasma flow in the direction of equilibrium current density, consistent with experimental measurements. Detailed experimental measurements of the MHD and Hall emf terms are compared to these extended MHD predictions. Tracking the evolution of magnetic energy, helicity, and hybrid helicity during relaxation identifies the most important contributions in single-fluid and two-fluid models. Magnetic helicity is well conserved relative to the magnetic energy during relaxation. The hybrid helicity is dominated by magnetic helicity in realistic low-beta pinch conditions and is also well conserved. Differences of less than 1 % between magnetic helicity and hybrid helicity are observed with two-fluid modeling and result from cross helicity evolution through ion FLR effects, which have not been included in contemporary relaxation theories. The kinetic energy driven by relaxation in the computations is dominated by velocity components perpendicular to the magnetic field, an effect that had not been predicted. Work performed at University of Wisconsin-Madison. LA-UR-16-24727.

Assessing and quantifying changes in precipitation patterns using event-driven analysis

USDA-ARS?s Scientific Manuscript database

Studies have claimed that climate change may adversely affect precipitation patterns by increasing the occurrence of extreme events. The effects of climate change on precipitation is expected to take place over a long period of time and will require long-term data to demonstrate. Frequency analysis ...

Data-based adjoint and H2 optimal control of the Ginzburg-Landau equation

NASA Astrophysics Data System (ADS)

Banks, Michael; Bodony, Daniel

2017-11-01

Equation-free, reduced-order methods of control are desirable when the governing system of interest is of very high dimension or the control is to be applied to a physical experiment. Two-phase flow optimal control problems, our target application, fit these criteria. Dynamic Mode Decomposition (DMD) is a data-driven method for model reduction that can be used to resolve the dynamics of very high dimensional systems and project the dynamics onto a smaller, more manageable basis. We evaluate the effectiveness of DMD-based forward and adjoint operator estimation when applied to H2 optimal control approaches applied to the linear and nonlinear Ginzburg-Landau equation. Perspectives on applying the data-driven adjoint to two phase flow control will be given. Office of Naval Research (ONR) as part of the Multidisciplinary University Research Initiatives (MURI) Program, under Grant Number N00014-16-1-2617.

Digital Suicide Prevention: Can Technology Become a Game-changer?

PubMed

Vahabzadeh, Arshya; Sahin, Ned; Kalali, Amir

2016-01-01

Suicide continues to be a leading cause of death and has been recognized as a significant public health issue. Rapid advances in data science can provide us with useful tools for suicide prevention, and help to dynamically assess suicide risk in quantitative data-driven ways. In this article, the authors highlight the most current international research in digital suicide prevention, including the use of machine learning, smartphone applications, and wearable sensor driven systems. The authors also discuss future opportunities for digital suicide prevention, and propose a novel Sensor-driven Mental State Assessment System.

Chaos as an intermittently forced linear system.

PubMed

Brunton, Steven L; Brunton, Bingni W; Proctor, Joshua L; Kaiser, Eurika; Kutz, J Nathan

2017-05-30

Understanding the interplay of order and disorder in chaos is a central challenge in modern quantitative science. Approximate linear representations of nonlinear dynamics have long been sought, driving considerable interest in Koopman theory. We present a universal, data-driven decomposition of chaos as an intermittently forced linear system. This work combines delay embedding and Koopman theory to decompose chaotic dynamics into a linear model in the leading delay coordinates with forcing by low-energy delay coordinates; this is called the Hankel alternative view of Koopman (HAVOK) analysis. This analysis is applied to the Lorenz system and real-world examples including Earth's magnetic field reversal and measles outbreaks. In each case, forcing statistics are non-Gaussian, with long tails corresponding to rare intermittent forcing that precedes switching and bursting phenomena. The forcing activity demarcates coherent phase space regions where the dynamics are approximately linear from those that are strongly nonlinear.The huge amount of data generated in fields like neuroscience or finance calls for effective strategies that mine data to reveal underlying dynamics. Here Brunton et al.develop a data-driven technique to analyze chaotic systems and predict their dynamics in terms of a forced linear model.

Discovering governing equations from data by sparse identification of nonlinear dynamics

NASA Astrophysics Data System (ADS)

Brunton, Steven

The ability to discover physical laws and governing equations from data is one of humankind's greatest intellectual achievements. A quantitative understanding of dynamic constraints and balances in nature has facilitated rapid development of knowledge and enabled advanced technology, including aircraft, combustion engines, satellites, and electrical power. There are many more critical data-driven problems, such as understanding cognition from neural recordings, inferring patterns in climate, determining stability of financial markets, predicting and suppressing the spread of disease, and controlling turbulence for greener transportation and energy. With abundant data and elusive laws, data-driven discovery of dynamics will continue to play an increasingly important role in these efforts. This work develops a general framework to discover the governing equations underlying a dynamical system simply from data measurements, leveraging advances in sparsity-promoting techniques and machine learning. The resulting models are parsimonious, balancing model complexity with descriptive ability while avoiding overfitting. The only assumption about the structure of the model is that there are only a few important terms that govern the dynamics, so that the equations are sparse in the space of possible functions. This perspective, combining dynamical systems with machine learning and sparse sensing, is explored with the overarching goal of real-time closed-loop feedback control of complex systems. This is joint work with Joshua L. Proctor and J. Nathan Kutz. Video Abstract: https://www.youtube.com/watch?v=gSCa78TIldg

Data-driven inference of network connectivity for modeling the dynamics of neural codes in the insect antennal lobe

PubMed Central

Shlizerman, Eli; Riffell, Jeffrey A.; Kutz, J. Nathan

2014-01-01

The antennal lobe (AL), olfactory processing center in insects, is able to process stimuli into distinct neural activity patterns, called olfactory neural codes. To model their dynamics we perform multichannel recordings from the projection neurons in the AL driven by different odorants. We then derive a dynamic neuronal network from the electrophysiological data. The network consists of lateral-inhibitory neurons and excitatory neurons (modeled as firing-rate units), and is capable of producing unique olfactory neural codes for the tested odorants. To construct the network, we (1) design a projection, an odor space, for the neural recording from the AL, which discriminates between distinct odorants trajectories (2) characterize scent recognition, i.e., decision-making based on olfactory signals and (3) infer the wiring of the neural circuit, the connectome of the AL. We show that the constructed model is consistent with biological observations, such as contrast enhancement and robustness to noise. The study suggests a data-driven approach to answer a key biological question in identifying how lateral inhibitory neurons can be wired to excitatory neurons to permit robust activity patterns. PMID:25165442

Elephant movement closely tracks precipitation-driven vegetation dynamics in a Kenyan forest-savanna landscape.

PubMed

Bohrer, Gil; Beck, Pieter Sa; Ngene, Shadrack M; Skidmore, Andrew K; Douglas-Hamilton, Ian

2014-01-01

This study investigates the ranging behavior of elephants in relation to precipitation-driven dynamics of vegetation. Movement data were acquired for five bachelors and five female family herds during three years in the Marsabit protected area in Kenya and changes in vegetation were mapped using MODIS normalized difference vegetation index time series (NDVI). In the study area, elevations of 650 to 1100 m.a.s.l experience two growth periods per year, while above 1100 m.a.s.l. growth periods last a year or longer. We find that elephants respond quickly to changes in forage and water availability, making migrations in response to both large and small rainfall events. The elevational migration of individual elephants closely matched the patterns of greening and senescing of vegetation in their home range. Elephants occupied lower elevations when vegetation activity was high, whereas they retreated to the evergreen forest at higher elevations while vegetation senesced. Elephant home ranges decreased in size, and overlapped less with increasing elevation. A recent hypothesis that ungulate migrations in savannas result from countervailing seasonally driven rainfall and fertility gradients is demonstrated, and extended to shorter-distance migrations. In other words, the trade-off between the poor forage quality and accessibility in the forest with its year-round water sources on the one hand and the higher quality forage in the low-elevation scrubland with its seasonal availability of water on the other hand, drives the relatively short migrations (the two main corridors are 20 and 90 km) of the elephants. In addition, increased intra-specific competition appears to influence the animals' habitat use during the dry season indicating that the human encroachment on the forest is affecting the elephant population.

Dynamic x-ray imaging of laser-driven nanoplasmas

NASA Astrophysics Data System (ADS)

Fennel, Thomas

2016-05-01

A major promise of current x-ray science at free electron lasers is the realization of unprecedented imaging capabilities for resolving the structure and ultrafast dynamics of matter with nanometer spatial and femtosecond temporal resolution or even below via single-shot x-ray diffraction. Laser-driven atomic clusters and nanoparticles provide an ideal platform for developing and demonstrating the required technology to extract the ultrafast transient spatiotemporal dynamics from the diffraction images. In this talk, the perspectives and challenges of dynamic x-ray imaging will be discussed using complete self-consistent microscopic electromagnetic simulations of IR pump x-ray probe imaging for the example of clusters. The results of the microscopic particle-in-cell simulations (MicPIC) enable the simulation-assisted reconstruction of corresponding experimental data. This capability is demonstrated by converting recently measured LCLS data into a ultrahigh resolution movie of laser-induced plasma expansion. Finally, routes towards reaching attosecond time resolution in the visualization of complex dynamical processes in matter by x-ray diffraction will be discussed.

Quantitative theory of driven nonlinear brain dynamics.

PubMed

Roberts, J A; Robinson, P A

2012-09-01

Strong periodic stimuli such as bright flashing lights evoke nonlinear responses in the brain and interact nonlinearly with ongoing cortical activity, but the underlying mechanisms for these phenomena are poorly understood at present. The dominant features of these experimentally observed dynamics are reproduced by the dynamics of a quantitative neural field model subject to periodic drive. Model power spectra over a range of drive frequencies show agreement with multiple features of experimental measurements, exhibiting nonlinear effects including entrainment over a range of frequencies around the natural alpha frequency f(α), subharmonic entrainment near 2f(α), and harmonic generation. Further analysis of the driven dynamics as a function of the drive parameters reveals rich nonlinear dynamics that is predicted to be observable in future experiments at high drive amplitude, including period doubling, bistable phase-locking, hysteresis, wave mixing, and chaos indicated by positive Lyapunov exponents. Moreover, photosensitive seizures are predicted for physiologically realistic model parameters yielding bistability between healthy and seizure dynamics. These results demonstrate the applicability of neural field models to the new regime of periodically driven nonlinear dynamics, enabling interpretation of experimental data in terms of specific generating mechanisms and providing new tests of the theory. Copyright © 2012 Elsevier Inc. All rights reserved.

Solar Type II Radio Bursts and IP Type II Events

NASA Technical Reports Server (NTRS)

Cane, H. V.; Erickson, W. C.

2005-01-01

We have examined radio data from the WAVES experiment on the Wind spacecraft in conjunction with ground-based data in order to investigate the relationship between the shocks responsible for metric type II radio bursts and the shocks in front of coronal mass ejections (CMEs). The bow shocks of fast, large CMEs are strong interplanetary (IP) shocks, and the associated radio emissions often consist of single broad bands starting below approx. 4 MHz; such emissions were previously called IP type II events. In contrast, metric type II bursts are usually narrowbanded and display two harmonically related bands. In addition to displaying complete dynamic spectra for a number of events, we also analyze the 135 WAVES 1 - 14 MHz slow-drift time periods in 2001-2003. We find that most of the periods contain multiple phenomena, which we divide into three groups: metric type II extensions, IP type II events, and blobs and bands. About half of the WAVES listings include probable extensions of metric type II radio bursts, but in more than half of these events, there were also other slow-drift features. In the 3 yr study period, there were 31 IP type II events; these were associated with the very fastest CMEs. The most common form of activity in the WAVES events, blobs and bands in the frequency range between 1 and 8 MHz, fall below an envelope consistent with the early signatures of an IP type II event. However, most of this activity lasts only a few tens of minutes, whereas IP type II events last for many hours. In this study we find many examples in the radio data of two shock-like phenomena with different characteristics that occur simultaneously in the metric and decametric/hectometric bands, and no clear example of a metric type II burst that extends continuously down in frequency to become an IP type II event. The simplest interpretation is that metric type II bursts, unlike IP type II events, are not caused by shocks driven in front of CMEs.

Tree Circumference Dynamics in Four Forests Characterized Using Automated Dendrometer Bands

PubMed Central

McMahon, Sean M.; Detto, Matteo; Lutz, James A.; Davies, Stuart J.; Chang-Yang, Chia-Hao; Anderson-Teixeira, Kristina J.

2016-01-01

Stem diameter is one of the most commonly measured attributes of trees, forming the foundation of forest censuses and monitoring. Changes in tree stem circumference include both irreversible woody stem growth and reversible circumference changes related to water status, yet these fine-scale dynamics are rarely leveraged to understand forest ecophysiology and typically ignored in plot- or stand-scale estimates of tree growth and forest productivity. Here, we deployed automated dendrometer bands on 12–40 trees at four different forested sites—two temperate broadleaf deciduous, one temperate conifer, and one tropical broadleaf semi-deciduous—to understand how tree circumference varies on time scales of hours to months, how these dynamics relate to environmental conditions, and whether the structure of these variations might introduce substantive error into estimates of woody growth. Diurnal stem circumference dynamics measured over the bark commonly—but not consistently—exhibited daytime shrinkage attributable to transpiration-driven changes in stem water storage. The amplitude of this shrinkage was significantly correlated with climatic variables (daily temperature range, vapor pressure deficit, and radiation), sap flow and evapotranspiration. Diurnal variations were typically <0.5 mm circumference in amplitude and unlikely to be of concern to most studies of tree growth. Over time scales of multiple days, the bands captured circumference increases in response to rain events, likely driven by combinations of increased stem water storage and bark hydration. Particularly at the tropical site, these rain responses could be quite substantial, ranging up to 1.5 mm circumference expansion within 48 hours following a rain event. We conclude that over-bark measurements of stem circumference change sometimes correlate with but have limited potential for directly estimating daily transpiration, but that they can be valuable on time scales of days to weeks for characterizing changes in stem growth and hydration. PMID:28030646

Inferring Muscle-Tendon Unit Power from Ankle Joint Power during the Push-Off Phase of Human Walking: Insights from a Multiarticular EMG-Driven Model

PubMed Central

2016-01-01

Introduction Inverse dynamics joint kinetics are often used to infer contributions from underlying groups of muscle-tendon units (MTUs). However, such interpretations are confounded by multiarticular (multi-joint) musculature, which can cause inverse dynamics to over- or under-estimate net MTU power. Misestimation of MTU power could lead to incorrect scientific conclusions, or to empirical estimates that misguide musculoskeletal simulations, assistive device designs, or clinical interventions. The objective of this study was to investigate the degree to which ankle joint power overestimates net plantarflexor MTU power during the Push-off phase of walking, due to the behavior of the flexor digitorum and hallucis longus (FDHL)–multiarticular MTUs crossing the ankle and metatarsophalangeal (toe) joints. Methods We performed a gait analysis study on six healthy participants, recording ground reaction forces, kinematics, and electromyography (EMG). Empirical data were input into an EMG-driven musculoskeletal model to estimate ankle power. This model enabled us to parse contributions from mono- and multi-articular MTUs, and required only one scaling and one time delay factor for each subject and speed, which were solved for based on empirical data. Net plantarflexing MTU power was computed by the model and quantitatively compared to inverse dynamics ankle power. Results The EMG-driven model was able to reproduce inverse dynamics ankle power across a range of gait speeds (R2 ≥ 0.97), while also providing MTU-specific power estimates. We found that FDHL dynamics caused ankle power to slightly overestimate net plantarflexor MTU power, but only by ~2–7%. Conclusions During Push-off, FDHL MTU dynamics do not substantially confound the inference of net plantarflexor MTU power from inverse dynamics ankle power. However, other methodological limitations may cause inverse dynamics to overestimate net MTU power; for instance, due to rigid-body foot assumptions. Moving forward, the EMG-driven modeling approach presented could be applied to understand other tasks or larger multiarticular MTUs. PMID:27764110

Inferring Muscle-Tendon Unit Power from Ankle Joint Power during the Push-Off Phase of Human Walking: Insights from a Multiarticular EMG-Driven Model.

PubMed

Honert, Eric C; Zelik, Karl E

2016-01-01

Inverse dynamics joint kinetics are often used to infer contributions from underlying groups of muscle-tendon units (MTUs). However, such interpretations are confounded by multiarticular (multi-joint) musculature, which can cause inverse dynamics to over- or under-estimate net MTU power. Misestimation of MTU power could lead to incorrect scientific conclusions, or to empirical estimates that misguide musculoskeletal simulations, assistive device designs, or clinical interventions. The objective of this study was to investigate the degree to which ankle joint power overestimates net plantarflexor MTU power during the Push-off phase of walking, due to the behavior of the flexor digitorum and hallucis longus (FDHL)-multiarticular MTUs crossing the ankle and metatarsophalangeal (toe) joints. We performed a gait analysis study on six healthy participants, recording ground reaction forces, kinematics, and electromyography (EMG). Empirical data were input into an EMG-driven musculoskeletal model to estimate ankle power. This model enabled us to parse contributions from mono- and multi-articular MTUs, and required only one scaling and one time delay factor for each subject and speed, which were solved for based on empirical data. Net plantarflexing MTU power was computed by the model and quantitatively compared to inverse dynamics ankle power. The EMG-driven model was able to reproduce inverse dynamics ankle power across a range of gait speeds (R2 ≥ 0.97), while also providing MTU-specific power estimates. We found that FDHL dynamics caused ankle power to slightly overestimate net plantarflexor MTU power, but only by ~2-7%. During Push-off, FDHL MTU dynamics do not substantially confound the inference of net plantarflexor MTU power from inverse dynamics ankle power. However, other methodological limitations may cause inverse dynamics to overestimate net MTU power; for instance, due to rigid-body foot assumptions. Moving forward, the EMG-driven modeling approach presented could be applied to understand other tasks or larger multiarticular MTUs.

Drivers and implications of recent large fire years in boreal North America

NASA Astrophysics Data System (ADS)

Veraverbeke, S.; Rogers, B. M.; Goulden, M.; Jandt, R.; Miller, C. E.; Wiggins, E. B.; Randerson, J. T.

2016-12-01

High latitude ecosystems are rapidly transforming because of climate change. Boreal North America recently experienced two exceptionally large fire years: 2014 in the Northwest Territories, Canada, and 2015 in Alaska, USA. We used geospatial climate, lightning, fire, and vegetation datasets to assess the mechanisms contributing to these recent extreme years and to the causes of recent decadal-scale changes in fire dynamics. We found that the two events had a record number of lightning ignitions and unusually high levels of burning near the boreal treeline, contributing to emissions of 164 ± 32 Tg C in the Northwest Territories and 65 ± 13 Tg C in Interior Alaska. The annual number ignitions in both regions displayed a significant increasing trend since 1975, driven by an increase in lightning ignitions. We found that vapor pressure deficit (VPD) in June, lightning, and ignition events were significantly correlated on interannual timescales. Future climate-driven increases in VPD and lightning near the treeline ecotone may enable northward forest expansion within tundra ecosystems.

One algorithm to rule them all? An evaluation and discussion of ten eye movement event-detection algorithms.

PubMed

Andersson, Richard; Larsson, Linnea; Holmqvist, Kenneth; Stridh, Martin; Nyström, Marcus

2017-04-01

Almost all eye-movement researchers use algorithms to parse raw data and detect distinct types of eye movement events, such as fixations, saccades, and pursuit, and then base their results on these. Surprisingly, these algorithms are rarely evaluated. We evaluated the classifications of ten eye-movement event detection algorithms, on data from an SMI HiSpeed 1250 system, and compared them to manual ratings of two human experts. The evaluation focused on fixations, saccades, and post-saccadic oscillations. The evaluation used both event duration parameters, and sample-by-sample comparisons to rank the algorithms. The resulting event durations varied substantially as a function of what algorithm was used. This evaluation differed from previous evaluations by considering a relatively large set of algorithms, multiple events, and data from both static and dynamic stimuli. The main conclusion is that current detectors of only fixations and saccades work reasonably well for static stimuli, but barely better than chance for dynamic stimuli. Differing results across evaluation methods make it difficult to select one winner for fixation detection. For saccade detection, however, the algorithm by Larsson, Nyström and Stridh (IEEE Transaction on Biomedical Engineering, 60(9):2484-2493,2013) outperforms all algorithms in data from both static and dynamic stimuli. The data also show how improperly selected algorithms applied to dynamic data misestimate fixation and saccade properties.

Variable effects of climate on forest growth in relation to climate extremes, disturbance, and forest dynamics

USGS Publications Warehouse

Itter, Malcolm S.; Finley, Andrew O.; D'Amato, Anthony W.; Foster, Jane R.; Bradford, John B.

2017-01-01

Changes in the frequency, duration, and severity of climate extremes are forecast to occur under global climate change. The impacts of climate extremes on forest productivity and health remain difficult to predict due to potential interactions with disturbance events and forest dynamics—changes in forest stand composition, density, size and age structure over time. Such interactions may lead to non-linear forest growth responses to climate involving thresholds and lag effects. Understanding how forest dynamics influence growth responses to climate is particularly important given stand structure and composition can be modified through management to increase forest resistance and resilience to climate change. To inform such adaptive management, we develop a hierarchical Bayesian state space model in which climate effects on tree growth are allowed to vary over time and in relation to past climate extremes, disturbance events, and forest dynamics. The model is an important step toward integrating disturbance and forest dynamics into predictions of forest growth responses to climate extremes. We apply the model to a dendrochronology data set from forest stands of varying composition, structure, and development stage in northeastern Minnesota that have experienced extreme climate years and forest tent caterpillar defoliation events. Mean forest growth was most sensitive to water balance variables representing climatic water deficit. Forest growth responses to water deficit were partitioned into responses driven by climatic threshold exceedances and interactions with insect defoliation. Forest growth was both resistant and resilient to climate extremes with the majority of forest growth responses occurring after multiple climatic threshold exceedances across seasons and years. Interactions between climate and disturbance were observed in a subset of years with insect defoliation increasing forest growth sensitivity to water availability. Forest growth was particularly sensitive to climate extremes during periods of high stem density following major regeneration events when average inter-tree competition was high. Results suggest the resistance and resilience of forest growth to climate extremes can be increased through management steps such as thinning to reduce competition during early stages of stand development and small-group selection harvests to maintain forest structures characteristic of older, mature stands.

Is Slow Slip a Cause or a Result of Tremor?

NASA Astrophysics Data System (ADS)

Luo, Y.; Ampuero, J. P.

2017-12-01

While various modeling efforts have been conducted to reproduce subsets of observations of tremor and slow-slip events (SSE), a fundamental but yet unanswered question is whether slow slip is a cause or a result of tremor. Tremor is commonly regarded as driven by SSE. This view is mainly based on observations of SSE without detected tremors and on (frequency-limited) estimates of total tremor seismic moment being lower than 1% of their concomitant SSE moment. In previous studies we showed that models of heterogeneous faults, composed of seismic asperities embedded in an aseismic fault zone matrix, reproduce quantitatively the hierarchical patterns of tremor migration observed in Cascadia and Shikoku. To address the title question, we design two end-member models of a heterogeneous fault. In the SSE-driven-tremor model, slow slip events are spontaneously generated by the matrix (even in the absence of seismic asperities) and drive tremor. In the Tremor-driven-SSE model the matrix is stable (it slips steadily in the absence of asperities) and slow slip events result from the collective behavior of tremor asperities interacting via transient creep (local afterslip fronts). We study these two end-member models through 2D quasi-dynamic multi-cycle simulations of faults governed by rate-and-state friction with heterogeneous frictional properties and effective normal stress, using the earthquake simulation software QDYN (https://zenodo.org/record/322459). We find that both models reproduce first-order observations of SSE and tremor and have very low seismic to aseismic moment ratio. However, the Tremor-driven-SSE model assumes a simpler rheology than the SSE-driven-tremor model and matches key observations better and without fine tuning, including the ratio of propagation speeds of forward SSE and rapid tremor reversals and the decay of inter-event times of Low Frequency Earthquakes. These modeling results indicate that, in contrast to a common view, SSE could be a result of tremor activity. We also find that, despite important interactions between asperities, tremor activity rates are proportional to the underlying aseismic slip rate, supporting an approach to estimate SSE properties with high spatial-temporal resolutions via tremor activity.

Picosecond time scale dynamics of short pulse laser-driven shocks in tin

NASA Astrophysics Data System (ADS)

Grigsby, W.; Bowes, B. T.; Dalton, D. A.; Bernstein, A. C.; Bless, S.; Downer, M. C.; Taleff, E.; Colvin, J.; Ditmire, T.

2009-05-01

The dynamics of high strain rate shock waves driven by a subnanosecond laser pulse in thin tin slabs have been investigated. These shocks, with pressure up to 1 Mbar, have been diagnosed with an 800 nm wavelength ultrafast laser pulse in a pump-probe configuration, which measured reflectivity and two-dimensional interferometry of the expanding rear surface. Time-resolved rear surface expansion data suggest that we reached pressures necessary to shock melt tin upon compression. Reflectivity measurements, however, show an anomalously high drop in the tin reflectivity for free standing foils, which can be attributed to microparticle formation at the back surface when the laser-driven shock releases.

Fault Detection for Nonlinear Process With Deterministic Disturbances: A Just-In-Time Learning Based Data Driven Method.

PubMed

Yin, Shen; Gao, Huijun; Qiu, Jianbin; Kaynak, Okyay

2017-11-01

Data-driven fault detection plays an important role in industrial systems due to its applicability in case of unknown physical models. In fault detection, disturbances must be taken into account as an inherent characteristic of processes. Nevertheless, fault detection for nonlinear processes with deterministic disturbances still receive little attention, especially in data-driven field. To solve this problem, a just-in-time learning-based data-driven (JITL-DD) fault detection method for nonlinear processes with deterministic disturbances is proposed in this paper. JITL-DD employs JITL scheme for process description with local model structures to cope with processes dynamics and nonlinearity. The proposed method provides a data-driven fault detection solution for nonlinear processes with deterministic disturbances, and owns inherent online adaptation and high accuracy of fault detection. Two nonlinear systems, i.e., a numerical example and a sewage treatment process benchmark, are employed to show the effectiveness of the proposed method.

Data-driven modelling of social forces and collective behaviour in zebrafish.

PubMed

Zienkiewicz, Adam K; Ladu, Fabrizio; Barton, David A W; Porfiri, Maurizio; Bernardo, Mario Di

2018-04-14

Zebrafish are rapidly emerging as a powerful model organism in hypothesis-driven studies targeting a number of functional and dysfunctional processes. Mathematical models of zebrafish behaviour can inform the design of experiments, through the unprecedented ability to perform pilot trials on a computer. At the same time, in-silico experiments could help refining the analysis of real data, by enabling the systematic investigation of key neurobehavioural factors. Here, we establish a data-driven model of zebrafish social interaction. Specifically, we derive a set of interaction rules to capture the primary response mechanisms which have been observed experimentally. Contrary to previous studies, we include dynamic speed regulation in addition to turning responses, which together provide attractive, repulsive and alignment interactions between individuals. The resulting multi-agent model provides a novel, bottom-up framework to describe both the spontaneous motion and individual-level interaction dynamics of zebrafish, inferred directly from experimental observations. Copyright © 2018 Elsevier Ltd. All rights reserved.

Ensuring critical event sequences in high consequence computer based systems as inspired by path expressions

DOE Office of Scientific and Technical Information (OSTI.GOV)

Kidd, M.E.C.

1997-02-01

The goal of our work is to provide a high level of confidence that critical software driven event sequences are maintained in the face of hardware failures, malevolent attacks and harsh or unstable operating environments. This will be accomplished by providing dynamic fault management measures directly to the software developer and to their varied development environments. The methodology employed here is inspired by previous work in path expressions. This paper discusses the perceived problems, a brief overview of path expressions, the proposed methods, and a discussion of the differences between the proposed methods and traditional path expression usage and implementation.

Integrating geo web services for a user driven exploratory analysis

NASA Astrophysics Data System (ADS)

Moncrieff, Simon; Turdukulov, Ulanbek; Gulland, Elizabeth-Kate

2016-04-01

In data exploration, several online data sources may need to be dynamically aggregated or summarised over spatial region, time interval, or set of attributes. With respect to thematic data, web services are mainly used to present results leading to a supplier driven service model limiting the exploration of the data. In this paper we propose a user need driven service model based on geo web processing services. The aim of the framework is to provide a method for the scalable and interactive access to various geographic data sources on the web. The architecture combines a data query, processing technique and visualisation methodology to rapidly integrate and visually summarise properties of a dataset. We illustrate the environment on a health related use case that derives Age Standardised Rate - a dynamic index that needs integration of the existing interoperable web services of demographic data in conjunction with standalone non-spatial secure database servers used in health research. Although the example is specific to the health field, the architecture and the proposed approach are relevant and applicable to other fields that require integration and visualisation of geo datasets from various web services and thus, we believe is generic in its approach.

Shock propagation in locally driven granular systems

NASA Astrophysics Data System (ADS)

Joy, Jilmy P.; Pathak, Sudhir N.; Das, Dibyendu; Rajesh, R.

2017-09-01

We study shock propagation in a system of initially stationary hard spheres that is driven by a continuous injection of particles at the origin. The disturbance created by the injection of energy spreads radially outward through collisions between particles. Using scaling arguments, we determine the exponent characterizing the power-law growth of this disturbance in all dimensions. The scaling functions describing the various physical quantities are determined using large-scale event-driven simulations in two and three dimensions for both elastic and inelastic systems. The results are shown to describe well the data from two different experiments on granular systems that are similarly driven.

Shock propagation in locally driven granular systems.

PubMed

Joy, Jilmy P; Pathak, Sudhir N; Das, Dibyendu; Rajesh, R

2017-09-01

We study shock propagation in a system of initially stationary hard spheres that is driven by a continuous injection of particles at the origin. The disturbance created by the injection of energy spreads radially outward through collisions between particles. Using scaling arguments, we determine the exponent characterizing the power-law growth of this disturbance in all dimensions. The scaling functions describing the various physical quantities are determined using large-scale event-driven simulations in two and three dimensions for both elastic and inelastic systems. The results are shown to describe well the data from two different experiments on granular systems that are similarly driven.

US Army TARDEC Ground Vehicle Mobility: Dynamics Modeling, Simluation, and Research

DTIC Science & Technology

2011-10-24

DRIVEN. WARFIGHTER FOCUSED. For official use only Stair Climbing of a Small Robot Robotic Vehicle Step Climbing UNCLASSIFIED For official use only...NOTES NASA Jet Propulsion Laboratory, mobility, and robotics section. Briefing to the jet propulsion lab. 14. ABSTRACT N/A 15. SUBJECT TERMS 16...JLTV GCV M2 M915 ASV FTTS HMMWV Platforms Supported APDSmall Robot UNCLASSIFIED For official use only Mobility Events • Vehicle stability • Ride

A Stimulus-Locked Vector Autoregressive Model for Slow Event-Related fMRI Designs

PubMed Central

Siegle, Greg

2009-01-01

Summary Neuroscientists have become increasingly interested in exploring dynamic relationships among brain regions. Such a relationship, when directed from one region toward another, is denoted by “effective connectivity.” An fMRI experimental paradigm which is well-suited for examination of effective connectivity is the slow event-related design. This design presents stimuli at sufficient temporal spacing for determining within-trial trajectories of BOLD activation, allowing for the analysis of stimulus-locked temporal covariation of brain responses in multiple regions. This may be especially important for emotional stimuli processing, which can evolve over the course of several seconds, if not longer. However, while several methods have been devised for determining fMRI effective connectivity, few are adapted to event-related designs, which include non-stationary BOLD responses and multiple levels of nesting. We propose a model tailored for exploring effective connectivity of multiple brain regions in event-related fMRI designs - a semi-parametric adaptation of vector autoregressive (VAR) models, termed “stimulus-locked VAR” (SloVAR). Connectivity coefficients vary as a function of time relative to stimulus onset, are regularized via basis expansions, and vary randomly across subjects. SloVAR obtains flexible, data-driven estimates of effective connectivity and hence is useful for building connectivity models when prior information on dynamic regional relationships is sparse. Indices derived from the coefficient estimates can also be used to relate effective connectivity estimates to behavioral or clinical measures. We demonstrate the SloVAR model on a sample of clinically depressed and normal controls, showing that early but not late cortico-amygdala connectivity appears crucial to emotional control and early but not late cortico-cortico connectivity predicts depression severity in the depressed group, relationships that would have been missed in a more traditional VAR analysis. PMID:19236927

Men who have sex with men more often chose daily than event-driven use of pre-exposure prophylaxis: baseline analysis of a demonstration study in Amsterdam.

PubMed

Hoornenborg, Elske; Achterbergh, Roel Ca; van der Loeff, Maarten F Schim; Davidovich, Udi; van der Helm, Jannie J; Hogewoning, Arjan; van Duijnhoven, Yvonne Thp; Sonder, Gerard Jb; de Vries, Henry Jc; Prins, Maria

2018-03-01

The Amsterdam PrEP project is a prospective, open-label demonstration study at a large sexually transmitted infection (STI) clinic. We examined the uptake of PrEP; the baseline characteristics of men who have sex with men (MSM) and transgender persons initiating PrEP; their choices of daily versus event-driven PrEP and the determinants of these choices. From August 2015 through May 2016, enrolment took place at the STI clinic of the Public Health Service of Amsterdam, the Netherlands. MSM or transgender persons were eligible if they had at least one risk factor for HIV infection within the preceding six months. Participants were offered a choice between daily or event-driven use of tenofovir/emtricitabine. Baseline data were analysed using descriptive statistics and multivariable analysis was employed to determine variables associated with daily versus event-driven PrEP. Online applications were submitted by 870 persons, of whom 587 were invited for a screening visit. Of them, 415 were screened for eligibility and 376 initiated PrEP. One quarter (103/376, 27%) chose event-driven PrEP. Prevalence of bacterial STI was 19.0% and mean condomless anal sex (CAS) episodes in the preceding three months were 11. In multivariable analysis, older age (≥45 vs. ≤34, aOR 2.1, 95% CI 1.2 to 3.9), being involved in a steady relationship (aOR 1.7, 95% CI 1.0 to 2.7), no other daily medication use (aOR 0.6, 95% CI 0.3 to 0.9), and fewer episodes of CAS (per log increase aOR 0.7, 95% CI 0.6 to 0.9) were determinants for choosing event-driven PrEP. PrEP programmes are becoming one of the more important intervention strategies with the goal of reducing incident HIV-infection and we were unable to accommodate many of the persons applying for this study. Offering a choice of dosing regimen to PrEP users may enable further personalization of HIV prevention strategies and enhance up-take, adherence and cost-effectiveness. The majority of participants preferred daily versus event-driven use. Within this majority, a high number of CAS episodes before PrEP initiation was reported and we observed a high prevalence of STI. Determinants of choosing event-driven PrEP were older age, fewer CAS episodes, no other daily medication use, and involved in a steady relationship. © 2018 The Authors. Journal of the International AIDS Society published by John Wiley & sons Ltd on behalf of the International AIDS Society.

Very large release of mostly volcanic carbon during the Paleocene-Eocene Thermal Maximum

PubMed Central

Gutjahr, Marcus; Ridgwell, Andy; Sexton, Philip F.; Anagnostou, Eleni; Pearson, Paul N.; Pälike, Heiko; Norris, Richard D.; Thomas, Ellen; Foster, Gavin L.

2017-01-01

Global warming during the Palaeocene-Eocene Thermal Maximum1,2 (PETM, ~56 Ma) is commonly interpreted as being primarily driven by the destabilization of carbon from surficial sedimentary reservoirs such as methane hydrates3. However, the source(s) of carbon remain controversial1,3–5. Resolving this is key to understanding the proximal cause, as well as quantifying the roles of triggers versus feedbacks in driving the event. Here we present new boron isotope data – a proxy for seawater pH – that demonstrate the occurrence of persistently suppressed surface ocean pH across the PETM. Our pH data, alongside a paired carbon isotope record, are assimilated in an Earth system model to reconstruct the unfolding carbon cycle dynamics across the event6,7. We find strong evidence for a much larger (>10,000 PgC) and on average isotopically heavier carbon source than considered previously8,9. This leads us to identify volcanism associated with the North Atlantic Igneous Province, rather than carbon from a surficial reservoir, as the main driver of the PETM10,11. We also find that, although amplifying organic carbon feedbacks with climate likely played only a subordinate role in driving the event, enhanced organic matter burial was important in ultimately sequestering the released carbon and accelerating the recovery of the Earth system12. PMID:28858305

Component Neural Systems for the Creation of Emotional Memories during Free Viewing of a Complex, Real-World Event

PubMed Central

Botzung, Anne; LaBar, Kevin S.; Kragel, Philip; Miles, Amanda; Rubin, David C.

2010-01-01

To investigate the neural systems that contribute to the formation of complex, self-relevant emotional memories, dedicated fans of rival college basketball teams watched a competitive game while undergoing functional magnetic resonance imaging (fMRI). During a subsequent recognition memory task, participants were shown video clips depicting plays of the game, stemming either from previously-viewed game segments (targets) or from non-viewed portions of the same game (foils). After an old–new judgment, participants provided emotional valence and intensity ratings of the clips. A data driven approach was first used to decompose the fMRI signal acquired during free viewing of the game into spatially independent components. Correlations were then calculated between the identified components and post-scanning emotion ratings for successfully encoded targets. Two components were correlated with intensity ratings, including temporal lobe regions implicated in memory and emotional functions, such as the hippocampus and amygdala, as well as a midline fronto-cingulo-parietal network implicated in social cognition and self-relevant processing. These data were supported by a general linear model analysis, which revealed additional valence effects in fronto-striatal-insular regions when plays were divided into positive and negative events according to the fan's perspective. Overall, these findings contribute to our understanding of how emotional factors impact distributed neural systems to successfully encode dynamic, personally-relevant event sequences. PMID:20508750

Dynamic Data Driven Methods for Self-aware Aerospace Vehicles

DTIC Science & Technology

2015-04-08

structural response model that incorporates multiple degradation or failure modes including damaged panel strength (BVID, thru- hole ), damaged panel...stiffness (BVID, thru- hole ), loose fastener, fretted fastener hole , and disbonded surface. • A new data-driven approach for the online updating of the flight...between the first and second plies. The panels were reinforced around the boarders of the panel with through holes to simulate mounting the wing skins to

Wind-driven coastal upwelling and westward circulation in the Yucatan shelf

NASA Astrophysics Data System (ADS)

Ruiz-Castillo, Eugenio; Gomez-Valdes, Jose; Sheinbaum, Julio; Rioja-Nieto, Rodolfo

2016-04-01

The wind-driven circulation and wind-induced coastal upwelling in a large shelf sea with a zonally oriented coast are examined. The Yucatan shelf is located to the north of the Yucatan peninsula in the eastern Gulf of Mexico. This area is a tropical shallow body of water with a smooth sloping bottom and is one of the largest shelves in the world. This study describes the wind-driven circulation and wind-induced coastal upwelling in the Yucatan shelf, which is forced by easterly winds throughout the year. Data obtained from hydrographic surveys, acoustic current profilers and environmental satellites are used in the analysis. Hydrographic data was analyzed and geostrophic currents were calculated in each survey. In addition an analytical model was applied to reproduce the currents. The results of a general circulation model were used with an empirical orthogonal function analysis to study the variability of the currents. The study area is divided in two regions: from the 40 m to the 200 m isobaths (outer shelf) and from the coast to the 40 m isobath (inner shelf). At the outer shelf, observations revealed upwelling events throughout the year, and a westward current with velocities of approximately 0.2 m s-1 was calculated from the numerical model output and hydrographic data. In addition, the theory developed by Pedlosky (2007) for a stratified fluid along a sloping bottom adequately explains the current's primary characteristics. The momentum of the current comes from the wind, and the stratification is an important factor in its dynamics. At the inner shelf, observations and numerical model output show a wind-driven westward current with maximum velocities of 0.20 m s-1. The momentum balance in this region is between local acceleration and friction. A cold-water band is developed during the period of maximum upwelling.

Integrating legacy data to understand agroecosystem regional dynamics to catastrophic events

USDA-ARS?s Scientific Manuscript database

Multi-year extreme drought events are part of the history of the Earth system. Legacy data on the climate drivers, geomorphic features, and agroecosystem responses across a dynamically changing landscape throughout a region can provide important insights to a future where large-scale catastrophic ev...

Adaptive Impact-Driven Detection of Silent Data Corruption for HPC Applications

DOE Office of Scientific and Technical Information (OSTI.GOV)

Di, Sheng; Cappello, Franck

For exascale HPC applications, silent data corruption (SDC) is one of the most dangerous problems because there is no indication that there are errors during the execution. We propose an adaptive impact-driven method that can detect SDCs dynamically. The key contributions are threefold. (1) We carefully characterize 18 real-world HPC applications and discuss the runtime data features, as well as the impact of the SDCs on their execution results. (2) We propose an impact-driven detection model that does not blindly improve the prediction accuracy, but instead detects only influential SDCs to guarantee user-acceptable execution results. (3) Our solution can adaptmore » to dynamic prediction errors based on local runtime data and can automatically tune detection ranges for guaranteeing low false alarms. Experiments show that our detector can detect 80-99.99% of SDCs with a false alarm rate less that 1% of iterations for most cases. The memory cost and detection overhead are reduced to 15% and 6.3%, respectively, for a large majority of applications.« less

Data-Driven Zero-Sum Neuro-Optimal Control for a Class of Continuous-Time Unknown Nonlinear Systems With Disturbance Using ADP.

PubMed

Wei, Qinglai; Song, Ruizhuo; Yan, Pengfei

2016-02-01

This paper is concerned with a new data-driven zero-sum neuro-optimal control problem for continuous-time unknown nonlinear systems with disturbance. According to the input-output data of the nonlinear system, an effective recurrent neural network is introduced to reconstruct the dynamics of the nonlinear system. Considering the system disturbance as a control input, a two-player zero-sum optimal control problem is established. Adaptive dynamic programming (ADP) is developed to obtain the optimal control under the worst case of the disturbance. Three single-layer neural networks, including one critic and two action networks, are employed to approximate the performance index function, the optimal control law, and the disturbance, respectively, for facilitating the implementation of the ADP method. Convergence properties of the ADP method are developed to show that the system state will converge to a finite neighborhood of the equilibrium. The weight matrices of the critic and the two action networks are also convergent to finite neighborhoods of their optimal ones. Finally, the simulation results will show the effectiveness of the developed data-driven ADP methods.

Probabilistic In Situ Stress Estimation and Forecasting using Sequential Data Assimilation

NASA Astrophysics Data System (ADS)

Fichtner, A.; van Dinther, Y.; Kuensch, H. R.

2017-12-01

Our physical understanding and forecasting ability of earthquakes, and other solid Earth dynamic processes, is significantly hampered by limited indications on the evolving state of stress and strength on faults. Integrating observations and physics-based numerical modeling to quantitatively estimate this evolution of a fault's state is crucial. However, systematic attempts are limited and tenuous, especially in light of the scarcity and uncertainty of natural data and the difficulty of modelling the physics governing earthquakes. We adopt the statistical framework of sequential data assimilation - extensively developed for weather forecasting - to efficiently integrate observations and prior knowledge in a forward model, while acknowledging errors in both. To prove this concept we perform a perfect model test in a simplified subduction zone setup, where we assimilate synthetic noised data on velocities and stresses from a single location. Using an Ensemble Kalman Filter, these data and their errors are assimilated to update 150 ensemble members from a Partial Differential Equation-driven seismic cycle model. Probabilistic estimates of fault stress and dynamic strength evolution capture the truth exceptionally well. This is possible, because the sampled error covariance matrix contains prior information from the physics that relates velocities, stresses and pressure at the surface to those at the fault. During the analysis step, stress and strength distributions are thus reconstructed such that fault coupling can be updated to either inhibit or trigger events. In the subsequent forecast step the physical equations are solved to propagate the updated states forward in time and thus provide probabilistic information on the occurrence of the next event. At subsequent assimilation steps, the system's forecasting ability turns out to be significantly better than that of a periodic recurrence model (requiring an alarm 17% vs. 68% of the time). This thus provides distinct added value with respect to using observations or numerical models separately. Although several challenges for applications to a natural setting remain, these first results indicate the large potential of data assimilation techniques for probabilistic seismic hazard assessment and other challenges in dynamic solid earth systems.

Synergy of Earth Observation and In-Situ Monitoring Data for Flood Hazard Early Warning System

NASA Astrophysics Data System (ADS)

Brodsky, Lukas; Kodesova, Radka; Spazierova, Katerina

2010-12-01

In this study, we demonstrate synergy of EO and in-situ monitoring data for early warning flood hazard system in the Czech Republic developed within ESA PECS project FLOREO. The development of the demonstration system is oriented to support existing monitoring activities, especially snow melt and surface water runoff contributing to flooding events. The system consists of two main parts accordingly, the first is snow cover and snow melt monitoring driven mainly by EO data and the other is surface water runoff modeling and monitoring driven by synergy of in-situ and EO data.

Exactly Solvable Models in Many-Body Theory

NASA Astrophysics Data System (ADS)

March, N. H.; Angilella, G. G. N.

2016-06-01

This book is an introduction to wave dynamics as they apply to earthquakes, among the scariest, most unpredictable, and deadliest natural phenomena on Earth. Since studying seismic activity is essentially a study of wave dynamics, this text starts with a discussion of types and representations, including wave-generation mechanics, superposition, and spectral analysis. Simple harmonic motion is used to analyze the mechanisms of wave propagation, and driven and damped systems are used to model the decay rates of various modal frequencies in different media. Direct correlation to earthquakes in California, Mexico, and Japan is used to illustrate key issues, and actual data from an event in California is presented and analyzed. Our Earth is a dynamic and changing planet, and seismic activity is the result. Hundreds of waves at different frequencies, modes, and amplitudes travel through a variety of different media, from solid rock to molten metals. Each media responds differently to each mode; consequently the result is an enormously complicated dynamic behavior. Earthquakes should serve well as a complimentary text for an upper-school course covering waves and wave mechanics, including sound and acoustics and basic geology. The mathematical requirement includes trigonometry and series summations, which should be accessible to most upper-school and college students. Animation, sound files, and videos help illustrate major topics.

Cognitive algorithms: dynamic logic, working of the mind, evolution of consciousness and cultures

NASA Astrophysics Data System (ADS)

Perlovsky, Leonid I.

2007-04-01

The paper discusses evolution of consciousness driven by the knowledge instinct, a fundamental mechanism of the mind which determines its higher cognitive functions. Dynamic logic mathematically describes the knowledge instinct. It overcomes past mathematical difficulties encountered in modeling intelligence and relates it to mechanisms of concepts, emotions, instincts, consciousness and unconscious. The two main aspects of the knowledge instinct are differentiation and synthesis. Differentiation is driven by dynamic logic and proceeds from vague and unconscious states to more crisp and conscious states, from less knowledge to more knowledge at each hierarchical level of the mind. Synthesis is driven by dynamic logic operating in a hierarchical organization of the mind; it strives to achieve unity and meaning of knowledge: every concept finds its deeper and more general meaning at a higher level. These mechanisms are in complex relationship of symbiosis and opposition, which leads to complex dynamics of evolution of consciousness and cultures. Modeling this dynamics in a population leads to predictions for the evolution of consciousness, and cultures. Cultural predictive models can be compared to experimental data and used for improvement of human conditions. We discuss existing evidence and future research directions.

A Data-Driven, Integrated Flare Model Based on Self-Organized Criticality

NASA Astrophysics Data System (ADS)

Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M.

2013-09-01

We interpret solar flares as events originating in solar active regions having reached the self-organized critical state, by alternatively using two versions of an "integrated flare model" - one static and one dynamic. In both versions the initial conditions are derived from observations aiming to investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. In the static model, we first apply a nonlinear force-free extrapolation that reconstructs the three-dimensional magnetic fields from two-dimensional vector magnetograms. We then locate magnetic discontinuities exceeding a threshold in the Laplacian of the magnetic field. These discontinuities are relaxed in local diffusion events, implemented in the form of cellular-automaton evolution rules. Subsequent loading and relaxation steps lead the system to self-organized criticality, after which the statistical properties of the simulated events are examined. In the dynamic version we deploy an enhanced driving mechanism, which utilizes the observed evolution of active regions, making use of sequential vector magnetograms. We first apply the static cellular automaton model to consecutive solar vector magnetograms until the self-organized critical state is reached. We then evolve the magnetic field inbetween these processed snapshots through spline interpolation, acting as a natural driver in the dynamic model. The identification of magnetically unstable sites as well as their relaxation follow the same rules as in the static model after each interpolation step. Subsequent interpolation/driving and relaxation steps cover all transitions until the end of the sequence. Physical requirements, such as the divergence-free condition for the magnetic field vector, are approximately satisfied in both versions of the model. We obtain robust power laws in the distribution functions of the modelled flaring events with scaling indices in good agreement with observations. We therefore conclude that well-known statistical properties of flares are reproduced after active regions reach self-organized criticality. The significant enhancement in both the static and the dynamic integrated flare models is that they initiate the simulation from observations, thus facilitating energy calculation in physical units. Especially in the dynamic version of the model, the driving of the system is based on observed, evolving vector magnetograms, allowing for the separation between MHD and kinetic timescales through the assignment of distinct MHD timestamps to each interpolation step.

Longitudinal aerodynamic characteristics of light, twin-engine, propeller-driven airplanes

NASA Technical Reports Server (NTRS)

Wolowicz, C. H.; Yancey, R. B.

1972-01-01

Representative state-of-the-art analytical procedures and design data for predicting the longitudinal static and dynamic stability and control characteristics of light, propeller-driven airplanes are presented. Procedures for predicting drag characteristics are also included. The procedures are applied to a twin-engine, propeller-driven airplane in the clean configuration from zero lift to stall conditions. The calculated characteristics are compared with wind-tunnel and flight data. Included in the comparisons are level-flight trim characteristics, period and damping of the short-period oscillatory mode, and windup-turn characteristics. All calculations are documented.

Digital Suicide Prevention: Can Technology Become a Game-changer?

PubMed Central

Sahin, Ned; Kalali, Amir

2016-01-01

Suicide continues to be a leading cause of death and has been recognized as a significant public health issue. Rapid advances in data science can provide us with useful tools for suicide prevention, and help to dynamically assess suicide risk in quantitative data-driven ways. In this article, the authors highlight the most current international research in digital suicide prevention, including the use of machine learning, smartphone applications, and wearable sensor driven systems. The authors also discuss future opportunities for digital suicide prevention, and propose a novel Sensor-driven Mental State Assessment System. PMID:27800282

¹⁸F-choline positron emission tomography/computed tomography-driven high-dose salvage radiation therapy in patients with biochemical progression after radical prostatectomy: feasibility study in 60 patients.

PubMed

D'Angelillo, Rolando M; Sciuto, Rosa; Ramella, Sara; Papalia, Rocco; Jereczek-Fossa, Barbara A; Trodella, Luca E; Fiore, Michele; Gallucci, Michele; Maini, Carlo L; Trodella, Lucio

2014-10-01

To retrospectively review data of a cohort of patients with biochemical progression after radical prostatectomy, treated according to a uniform institutional treatment policy, to evaluate toxicity and feasibility of high-dose salvage radiation therapy (80 Gy). Data on 60 patients with biochemical progression after radical prostatectomy between January 2009 and September 2011 were reviewed. The median value of prostate-specific antigen before radiation therapy was 0.9 ng/mL. All patients at time of diagnosis of biochemical recurrence underwent dynamic (18)F-choline positron emission tomography/computed tomography (PET/CT), which revealed in all cases a local recurrence. High-dose salvage radiation therapy was delivered up to total dose of 80 Gy to 18F-choline PET/CT-positive area. Toxicity was recorded according to the Common Terminology Criteria for Adverse Events, version 3.0, scale. Treatment was generally well tolerated: 54 patients (90%) completed salvage radiation therapy without any interruption. Gastrointestinal grade ≥2 acute toxicity was recorded in 6 patients (10%), whereas no patient experienced a grade ≥2 genitourinary toxicity. No grade 4 acute toxicity events were recorded. Only 1 patient (1.7%) experienced a grade 2 gastrointestinal late toxicity. With a mean follow-up of 31.2 months, 46 of 60 patients (76.6%) were free of recurrence. The 3-year biochemical progression-free survival rate was 72.5%. At early follow-up, (18)F-choline PET/CT-driven high-dose salvage radiation therapy seems to be feasible and well tolerated, with a low rate of toxicity. Copyright © 2014 Elsevier Inc. All rights reserved.

{sup 18}F-Choline Positron Emission Tomography/Computed Tomography–Driven High-Dose Salvage Radiation Therapy in Patients With Biochemical Progression After Radical Prostatectomy: Feasibility Study in 60 Patients

DOE Office of Scientific and Technical Information (OSTI.GOV)

D'Angelillo, Rolando M., E-mail: r.dangelillo@unicampus.it; Sciuto, Rosa; Ramella, Sara

Purpose: To retrospectively review data of a cohort of patients with biochemical progression after radical prostatectomy, treated according to a uniform institutional treatment policy, to evaluate toxicity and feasibility of high-dose salvage radiation therapy (80 Gy). Methods and Materials: Data on 60 patients with biochemical progression after radical prostatectomy between January 2009 and September 2011 were reviewed. The median value of prostate-specific antigen before radiation therapy was 0.9 ng/mL. All patients at time of diagnosis of biochemical recurrence underwent dynamic {sup 18}F-choline positron emission tomography/computed tomography (PET/CT), which revealed in all cases a local recurrence. High-dose salvage radiation therapy was delivered up tomore » total dose of 80 Gy to 18F-choline PET/CT-positive area. Toxicity was recorded according to the Common Terminology Criteria for Adverse Events, version 3.0, scale. Results: Treatment was generally well tolerated: 54 patients (90%) completed salvage radiation therapy without any interruption. Gastrointestinal grade ≥2 acute toxicity was recorded in 6 patients (10%), whereas no patient experienced a grade ≥2 genitourinary toxicity. No grade 4 acute toxicity events were recorded. Only 1 patient (1.7%) experienced a grade 2 gastrointestinal late toxicity. With a mean follow-up of 31.2 months, 46 of 60 patients (76.6%) were free of recurrence. The 3-year biochemical progression-free survival rate was 72.5%. Conclusions: At early follow-up, {sup 18}F-choline PET/CT-driven high-dose salvage radiation therapy seems to be feasible and well tolerated, with a low rate of toxicity.« less

Ion Acceleration by Flux Transfer Events in the Terrestrial Magnetosheath

NASA Astrophysics Data System (ADS)

Jarvinen, R.; Vainio, R.; Palmroth, M.; Juusola, L.; Hoilijoki, S.; Pfau-Kempf, Y.; Ganse, U.; Turc, L.; von Alfthan, S.

2018-02-01

We report ion acceleration by flux transfer events in the terrestrial magnetosheath in a global two-dimensional hybrid-Vlasov polar plane simulation of Earth's solar wind interaction. In the model we find that propagating flux transfer events created in magnetic reconnection at the dayside magnetopause drive fast-mode bow waves in the magnetosheath, which accelerate ions in the shocked solar wind flow. The acceleration at the bow waves is caused by a shock drift-like acceleration process under stationary solar wind and interplanetary magnetic field upstream conditions. Thus, the energization is not externally driven but results from plasma dynamics within the magnetosheath. Energetic proton populations reach the energy of 30 keV, and their velocity distributions resemble time-energy dispersive ion injections observed by the Cluster spacecraft in the magnetosheath.

Insights into vehicle trajectories at the handling limits: analysing open data from race car drivers

NASA Astrophysics Data System (ADS)

Kegelman, John C.; Harbott, Lene K.; Gerdes, J. Christian

2017-02-01

Race car drivers can offer insights into vehicle control during extreme manoeuvres; however, little data from race teams is publicly available for analysis. The Revs Program at Stanford has built a collection of vehicle dynamics data acquired from vintage race cars during live racing events with the intent of making this database publicly available for future analysis. This paper discusses the data acquisition, post-processing, and storage methods used to generate the database. An analysis of available data quantifies the repeatability of professional race car driver performance by examining the statistical dispersion of their driven paths. Certain map features, such as sections with high path curvature, consistently corresponded to local minima in path dispersion, quantifying the qualitative concept that drivers anchor their racing lines at specific locations around the track. A case study explores how two professional drivers employ distinct driving styles to achieve similar lap times, supporting the idea that driving at the limits allows a family of solutions in terms of paths and speed that can be adapted based on specific spatial, temporal, or other constraints and objectives.

A defect-driven diagnostic method for machine tool spindles

PubMed Central

Vogl, Gregory W.; Donmez, M. Alkan

2016-01-01

Simple vibration-based metrics are, in many cases, insufficient to diagnose machine tool spindle condition. These metrics couple defect-based motion with spindle dynamics; diagnostics should be defect-driven. A new method and spindle condition estimation device (SCED) were developed to acquire data and to separate system dynamics from defect geometry. Based on this method, a spindle condition metric relying only on defect geometry is proposed. Application of the SCED on various milling and turning spindles shows that the new approach is robust for diagnosing the machine tool spindle condition. PMID:28065985

Synchronization of autonomous objects in discrete event simulation

NASA Technical Reports Server (NTRS)

Rogers, Ralph V.

1990-01-01

Autonomous objects in event-driven discrete event simulation offer the potential to combine the freedom of unrestricted movement and positional accuracy through Euclidean space of time-driven models with the computational efficiency of event-driven simulation. The principal challenge to autonomous object implementation is object synchronization. The concept of a spatial blackboard is offered as a potential methodology for synchronization. The issues facing implementation of a spatial blackboard are outlined and discussed.

OBSERVATION OF MAGNETIC RECONNECTION DRIVEN BY GRANULAR SCALE ADVECTION

DOE Office of Scientific and Technical Information (OSTI.GOV)

Zeng Zhicheng; Cao Wenda; Ji Haisheng

2013-06-01

We report the first evidence of magnetic reconnection driven by advection in a rapidly developing large granule using high spatial resolution observations of a small surge event (base size {approx} 4'' Multiplication-Sign 4'') with the 1.6 m aperture New Solar Telescope at the Big Bear Solar Observatory. The observations were carried out in narrowband (0.5 A) He I 10830 A and broadband (10 A) TiO 7057 A. Since He I 10830 A triplet has a very high excitation level and is optically thin, its filtergrams enable us to investigate the surge from the photosphere through the chromosphere into the lowermore » corona. Simultaneous space data from the Atmospheric Imaging Assembly and Helioseismic and Magnetic Imager on board the Solar Dynamics Observatory were used in the analysis. It is shown that the surge is spatio-temporally associated with magnetic flux emergence in the rapidly developing large granule. During the development of the granule, its advecting flow ({approx}2 km s{sup -1}) squeezed the magnetic flux into an intergranular lane area, where a magnetic flux concentration was formed and the neighboring flux with opposite magnetic polarity was canceled. During the cancellation, the surge was produced as absorption in He I 10830 A filtergrams while simultaneous EUV brightening occurred at its base. The observations clearly indicate evidence of a finest-scale reconnection process driven by the granule's motion.« less

Key Design Elements of a Data Utility for National Biosurveillance: Event-driven Architecture, Caching, and Web Service Model

PubMed Central

Tsui, Fu-Chiang; Espino, Jeremy U.; Weng, Yan; Choudary, Arvinder; Su, Hoah-Der; Wagner, Michael M.

2005-01-01

The National Retail Data Monitor (NRDM) has monitored over-the-counter (OTC) medication sales in the United States since December 2002. The NRDM collects data from over 18,600 retail stores and processes over 0.6 million sales records per day. This paper describes key architectural features that we have found necessary for a data utility component in a national biosurveillance system. These elements include event-driven architecture to provide analyses of data in near real time, multiple levels of caching to improve query response time, high availability through the use of clustered servers, scalable data storage through the use of storage area networks and a web-service function for interoperation with affiliated systems. The methods and architectural principles are relevant to the design of any production data utility for public health surveillance—systems that collect data from multiple sources in near real time for use by analytic programs and user interfaces that have substantial requirements for time-series data aggregated in multiple dimensions. PMID:16779138

Statistical analysis of life history calendar data.

PubMed

Eerola, Mervi; Helske, Satu

2016-04-01

The life history calendar is a data-collection tool for obtaining reliable retrospective data about life events. To illustrate the analysis of such data, we compare the model-based probabilistic event history analysis and the model-free data mining method, sequence analysis. In event history analysis, we estimate instead of transition hazards the cumulative prediction probabilities of life events in the entire trajectory. In sequence analysis, we compare several dissimilarity metrics and contrast data-driven and user-defined substitution costs. As an example, we study young adults' transition to adulthood as a sequence of events in three life domains. The events define the multistate event history model and the parallel life domains in multidimensional sequence analysis. The relationship between life trajectories and excess depressive symptoms in middle age is further studied by their joint prediction in the multistate model and by regressing the symptom scores on individual-specific cluster indices. The two approaches complement each other in life course analysis; sequence analysis can effectively find typical and atypical life patterns while event history analysis is needed for causal inquiries. © The Author(s) 2012.

Extreme-volatility dynamics in crude oil markets

NASA Astrophysics Data System (ADS)

Jiang, Xiong-Fei; Zheng, Bo; Qiu, Tian; Ren, Fei

2017-02-01

Based on concepts and methods from statistical physics, we investigate extreme-volatility dynamics in the crude oil markets, using the high-frequency data from 2006 to 2010 and the daily data from 1986 to 2016. The dynamic relaxation of extreme volatilities is described by a power law, whose exponents usually depend on the magnitude of extreme volatilities. In particular, the relaxation before and after extreme volatilities is time-reversal symmetric at the high-frequency time scale, but time-reversal asymmetric at the daily time scale. This time-reversal asymmetry is mainly induced by exogenous events. However, the dynamic relaxation after exogenous events exhibits the same characteristics as that after endogenous events. An interacting herding model both with and without exogenous driving forces could qualitatively describe the extreme-volatility dynamics.

Observations of infragravity motions for reef fringed islands and atolls

NASA Astrophysics Data System (ADS)

Becker, J. M.; Merrifield, M. A.; Ford, M.

2012-12-01

The frequency of flooding events that affect low lying islands and atolls in the Pacific is expected to increase under current sea level rise projections. Infragravity (IG) motions, with periods ranging from approximately 25 to 400 seconds, are an important component of wave driven flooding events for reef fringed islands and atolls. The IG variability during wave events is analyzed and interpreted dynamically from pressure and current observations at four cross-reef transects in the North Pacific Ocean that include sites in the Republic of the Marshall Islands and Guam. The IG motions are shown to depend upon the spectral properties of the incident wave forcing and reef flat characteristics that include reef flat length (ranging from 100m to 450m at the four sites) and total water level due to setup and tides. A small inundation event at one of the sites is shown to occur due to large shoreline infragravity energy.

Mangrove microclimates alter seedling dynamics at the range edge.

PubMed

Devaney, John L; Lehmann, Michael; Feller, Ilka C; Parker, John D

2017-10-01

Recent climate warming has led to asynchronous species migrations, with major consequences for ecosystems worldwide. In woody communities, localized microclimates have the potential to create feedback mechanisms that can alter the rate of species range shifts attributed to macroclimate drivers alone. Mangrove encroachment into saltmarsh in many areas is driven by a reduction in freeze events, and this encroachment can further modify local climate, but the subsequent impacts on mangrove seedling dynamics are unknown. We monitored microclimate conditions beneath mangrove canopies and adjacent open saltmarsh at a freeze-sensitive mangrove-saltmarsh ecotone and assessed survival of experimentally transplanted mangrove seedlings. Mangrove canopies buffered night time cooling during the winter, leading to interspecific differences in freeze damage on mangrove seedlings. However, mangrove canopies also altered biotic interactions. Herbivore damage was higher under canopies, leading to greater mangrove seedling mortality beneath canopies relative to saltmarsh. While warming-induced expansion of mangroves can lead to positive microclimate feedbacks, simultaneous fluctuations in biotic drivers can also alter seedling dynamics. Thus, climate change can drive divergent feedback mechanisms through both abiotic and biotic channels, highlighting the importance of vegetation-microclimate interactions as important moderators of climate driven range shifts. © 2017 by the Ecological Society of America.

Recent Results on "Approximations to Optimal Alarm Systems for Anomaly Detection"

NASA Technical Reports Server (NTRS)

Martin, Rodney Alexander

2009-01-01

An optimal alarm system and its approximations may use Kalman filtering for univariate linear dynamic systems driven by Gaussian noise to provide a layer of predictive capability. Predicted Kalman filter future process values and a fixed critical threshold can be used to construct a candidate level-crossing event over a predetermined prediction window. An optimal alarm system can be designed to elicit the fewest false alarms for a fixed detection probability in this particular scenario.

Septo-hippocampal GABAergic signaling across multiple modalities in awake mice.

PubMed

Kaifosh, Patrick; Lovett-Barron, Matthew; Turi, Gergely F; Reardon, Thomas R; Losonczy, Attila

2013-09-01

Hippocampal interneurons receive GABAergic input from the medial septum. Using two-photon Ca(2+) imaging of axonal boutons in hippocampal CA1 of behaving mice, we found that populations of septo-hippocampal GABAergic boutons were activated during locomotion and salient sensory events; sensory responses scaled with stimulus intensity and were abolished by anesthesia. We found similar activity patterns among boutons with common putative postsynaptic targets, with low-dimensional bouton population dynamics being driven primarily by presynaptic spiking.

The origin of bursts and heavy tails in human dynamics.

PubMed

Barabási, Albert-László

2005-05-12

The dynamics of many social, technological and economic phenomena are driven by individual human actions, turning the quantitative understanding of human behaviour into a central question of modern science. Current models of human dynamics, used from risk assessment to communications, assume that human actions are randomly distributed in time and thus well approximated by Poisson processes. In contrast, there is increasing evidence that the timing of many human activities, ranging from communication to entertainment and work patterns, follow non-Poisson statistics, characterized by bursts of rapidly occurring events separated by long periods of inactivity. Here I show that the bursty nature of human behaviour is a consequence of a decision-based queuing process: when individuals execute tasks based on some perceived priority, the timing of the tasks will be heavy tailed, with most tasks being rapidly executed, whereas a few experience very long waiting times. In contrast, random or priority blind execution is well approximated by uniform inter-event statistics. These finding have important implications, ranging from resource management to service allocation, in both communications and retail.

Nanocluster building blocks of artificial square spin ice: Stray-field studies of thermal dynamics

NASA Astrophysics Data System (ADS)

Pohlit, Merlin; Porrati, Fabrizio; Huth, Michael; Ohno, Yuzo; Ohno, Hideo; Müller, Jens

2015-05-01

We present measurements of the thermal dynamics of a Co-based single building block of an artificial square spin ice fabricated by focused electron-beam-induced deposition. We employ micro-Hall magnetometry, an ultra-sensitive tool to study the stray field emanating from magnetic nanostructures, as a new technique to access the dynamical properties during the magnetization reversal of the spin-ice nanocluster. The obtained hysteresis loop exhibits distinct steps, displaying a reduction of their "coercive field" with increasing temperature. Therefore, thermally unstable states could be repetitively prepared by relatively simple temperature and field protocols allowing one to investigate the statistics of their switching behavior within experimentally accessible timescales. For a selected switching event, we find a strong reduction of the so-prepared states' "survival time" with increasing temperature and magnetic field. Besides the possibility to control the lifetime of selected switching events at will, we find evidence for a more complex behavior caused by the special spin ice arrangement of the macrospins, i.e., that the magnetic reversal statistically follows distinct "paths" most likely driven by thermal perturbation.

Dynamic Pulse-Driven Flowering Phenology in a Semiarid Shrubland

NASA Astrophysics Data System (ADS)

Krell, N.; Papuga, S. A.; Kipnis, E. L.; Nelson, K.

2014-12-01

Elevated springtime temperature has been convincingly linked to an increasingly earlier onset of phenological activity. Studies highlighting this phenomenon have generally been conducted in ecosystems where energy is the primary limiting factor. Importantly, phenological studies in semiarid ecosystems where water is the major limiting factor are rare. In semiarid ecosystems, the timing of phenological activity is also highly sensitive to discrete moisture pulses from infrequent precipitation events. The objective of this study is to identify the triggers of flowering phenology in a semiarid creosotebush-dominated ecosystem. Creosotebush (Larrea tridentata) is a repeat-flowering evergreen shrub that is the dominant species in three of the North American deserts. We present results from six years of daily meteorological and phenological data collected within the Santa Rita Experimental Range in southern Arizona. Our site is equipped with an eddy covariance tower providing estimates of water and carbon fluxes and associated meteorological variables including precipitation and soil moisture at multiple depths. Additionally, three digital cameras distributed within the footprint of the eddy provide daily images of phenological activity. Our results highlight substantial interannual variability in flowering phenology, both in spring and summer flowering. We show that spring flowering activity tends to be associated with energy triggers (e.g. temperature, growing degree days), whereas summer flowering activity tends to be associated with moisture triggers (e.g. large precipitation events, deep soil moisture). Our study suggests that changes in frequency and duration of precipitation events will impact timing of phenological activity resulting in important consequences for vegetation dynamics and pollinator behavior.

Event-by-Event Study of Space-Time Dynamics in Flux-Tube Fragmentation

DOE PAGES

Wong, Cheuk-Yin

2017-05-25

In the semi-classical description of the flux-tube fragmentation process for hadron production and hadronization in high-energymore » $e^+e^-$ annihilations and $pp$ collisions, the rapidity-space-time ordering and the local conservation laws of charge, flavor, and momentum provide a set of powerful tools that may allow the reconstruction of the space-time dynamics of quarks and mesons in exclusive measurements of produced hadrons, on an event-by-event basis. We propose procedures to reconstruct the space-time dynamics from event-by-event exclusive hadron data to exhibit explicitly the ordered chain of hadrons produced in a flux tube fragmentation. As a supplementary tool, we infer the average space-time coordinates of the $q$-$$\\bar q$$ pair production vertices from the $$\\pi^-$$ rapidity distribution data obtained by the NA61/SHINE Collaboration in $pp$ collisions at $$\\sqrt{s}$$ = 6.3 to 17.3 GeV.« less

Event-by-Event Study of Space-Time Dynamics in Flux-Tube Fragmentation

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wong, Cheuk-Yin

In the semi-classical description of the flux-tube fragmentation process for hadron production and hadronization in high-energymore » $e^+e^-$ annihilations and $pp$ collisions, the rapidity-space-time ordering and the local conservation laws of charge, flavor, and momentum provide a set of powerful tools that may allow the reconstruction of the space-time dynamics of quarks and mesons in exclusive measurements of produced hadrons, on an event-by-event basis. We propose procedures to reconstruct the space-time dynamics from event-by-event exclusive hadron data to exhibit explicitly the ordered chain of hadrons produced in a flux tube fragmentation. As a supplementary tool, we infer the average space-time coordinates of the $q$-$$\\bar q$$ pair production vertices from the $$\\pi^-$$ rapidity distribution data obtained by the NA61/SHINE Collaboration in $pp$ collisions at $$\\sqrt{s}$$ = 6.3 to 17.3 GeV.« less

Quick-look guide to the crustal dynamics project's data information system

NASA Technical Reports Server (NTRS)

Noll, Carey E.; Behnke, Jeanne M.; Linder, Henry G.

1987-01-01

Described are the contents of the Crustal Dynamics Project Data Information System (DIS) and instructions on the use of this facility. The main purpose of the DIS is to store all geodetic data products acquired by the Project in a central data bank and to maintain information about the archive of all Project-related data. Access and use of the DIS menu-driven system is described as well as procedures for contacting DIS staff and submitting data requests.

Universal versus platelet reactivity assay-driven use of P2Y12 inhibitors in acute coronary syndrome patients: cost-effectiveness analyses for six European perspectives.

PubMed

Coleman, Craig I; Limone, Brendan L

2014-01-01

Platelet reactivity assays (PRAs) can predict patients' likely response to clopidogrel. As ticagrelor and prasugrel are typically considered first-line agents for acute coronary syndrome in Europe, we assessed the cost-effectiveness of universal compared to PRA-driven selection of these agents. A Markov model was used to calculate five-year costs (2013£/€), quality-adjusted life-years and incremental cost-effectiveness ratios (ICERs) for one-year of universal ticagrelor or prasugrel (given to all) compared to each agents' corresponding PRA-driven strategy (ticagrelor/prasugrel in those with high platelet reactivity [HPR, >208 on the VerifyNow P2Y12 assay], others given generic clopidogrel). We assumed patients had their index event at 65-70 years of age and had a 42.7% incidence of HPR 24-48 hours post-revascularisation. The analysis was conducted from the perspective of six countries (France, Germany, Italy, Spain, the Netherlands and United Kingdom) and used a one-year cycle length. Event data for P2Y12 inhibitors were taken from multinational randomised trials and adjusted using country-specific epidemiologic data. Neither universal ticagrelor nor prasugrel were found to be cost-effective (all ICERs >40,250€ or £36,600/QALY) compared to their corresponding PRA-driven strategies in any of the countries evaluated. Results were sensitive to differences in P2Y12 Inhibitors costs and drug-specific relative risks of major adverse cardiac events. Monte Carlo simulation suggested universal ticagrelor or prasugrel were cost-effective in only 25-44% and 11-17% of 10,000 iterations compared to their respective PRA-driven strategies, when applying a willingness-to-pay threshold = €30,000 or £20,000/QALY. In conclusion, the universal use of newer P2Y12 inhibitors is not likely cost-effective compared to PRA-driven strategies.

Origin of the pulse-like signature of shallow long-period volcano seismicity

USGS Publications Warehouse

Chouet, Bernard A.; Dawson, Phillip B.

2016-01-01

Short-duration, pulse-like long-period (LP) events are a characteristic type of seismicity accompanying eruptive activity at Mount Etna in Italy in 2004 and 2008 and at Turrialba Volcano in Costa Rica and Ubinas Volcano in Peru in 2009. We use the discrete wave number method to compute the free surface response in the near field of a rectangular tensile crack embedded in a homogeneous elastic half space and to gain insights into the origin of the LP pulses. Two source models are considered, including (1) a vertical fluid-driven crack and (2) a unilateral tensile rupture growing at a fixed sub-Rayleigh velocity with constant opening on a vertical crack. We apply cross correlation to the synthetics and data to demonstrate that a fluid-driven crack provides a natural explanation for these data with realistic source sizes and fluid properties. Our modeling points to shallow sources (<1 km depth), whose signatures are representative of the Rayleigh pulse sampled at epicentral distances >∼1 km. While a slow-rupture failure provides another potential model for these events, the synthetics and resulting fits to the data are not optimal in this model compared to a fluid-driven source. We infer that pulse-like LP signatures are parts of the continuum of responses produced by shallow fluid-driven sources in volcanoes.

Synergistic effects of the components of global change: Increased vegetation dynamics in open, forest-steppe grasslands driven by wildfires and year-to-year precipitation differences

PubMed Central

Aszalós, Réka; Lengyel, Attila

2017-01-01

Climate change and land use change are two major elements of human-induced global environmental change. In temperate grasslands and woodlands, increasing frequency of extreme weather events like droughts and increasing severity of wildfires has altered the structure and dynamics of vegetation. In this paper, we studied the impact of wildfires and the year-to-year differences in precipitation on species composition changes in semi-arid grasslands of a forest-steppe complex ecosystem which has been partially disturbed by wildfires. Particularly, we investigated both how long-term compositional dissimilarity changes and species richness are affected by year-to-year precipitation differences on burnt and unburnt areas. Study sites were located in central Hungary, in protected areas characterized by partially-burnt, juniper-poplar forest-steppe complexes of high biodiversity. Data were used from two long-term monitoring sites in the Kiskunság National Park, both characterized by the same habitat complex. We investigated the variation in species composition as a function of time using distance decay methodology. In each sampling area, compositional dissimilarity increased with the time elapsed between the sampling events, and species richness differences increased with increasing precipitation differences between consecutive years. We found that both the long-term compositional dissimilarity, and the year-to-year changes in species richness were higher in the burnt areas than in the unburnt ones. The long-term compositional dissimilarities were mostly caused by perennial species, while the year-to-year changes of species richness were driven by annual and biennial species. As the effect of the year-to-year variation in precipitation was more pronounced in the burnt areas, we conclude that canopy removal by wildfires and extreme inter-annual variability of precipitation, two components of global environmental change, act in a synergistic way. They enhance the effect of one another, resulting in greater long-term and year-to-year changes in the composition of grasslands. PMID:29149208

Future projections of insured losses in the German private building sector following the A1B climatic change scenario

NASA Astrophysics Data System (ADS)

Held, H.; Gerstengarbe, F.-W.; Hattermann, F.; Pinto, J. G.; Ulbrich, U.; Böhm, U.; Born, K.; Büchner, M.; Donat, M. G.; Kücken, M.; Leckebusch, G. C.; Nissen, K.; Nocke, T.; Österle, H.; Pardowitz, T.; Werner, P. C.; Burghoff, O.; Broecker, U.; Kubik, A.

2012-04-01

We present an overview of a complementary-approaches impact project dealing with the consequences of climate change for the natural hazard branch of the insurance industry in Germany. The project was conducted by four academic institutions together with the German Insurance Association (GDV) and finalized in autumn 2011. A causal chain is modeled that goes from global warming projections over regional meteorological impacts to regional economic losses for private buildings, hereby fully covering the area of Germany. This presentation will focus on wind storm related losses, although the method developed had also been applied in part to hail and flood impact losses. For the first time, the GDV supplied their collected set of insurance cases, dating back for decades, for such an impact study. These data were used to calibrate and validate event-based damage functions which in turn were driven by three different types of regional climate models to generate storm loss projections. The regional models were driven by a triplet of ECHAM5 experiments following the A1B scenario which were found representative in the recent ENSEMBLES intercomparison study. In our multi-modeling approach we used two types of regional climate models that conceptually differ at maximum: a dynamical model (CCLM) and a statistical model based on the idea of biased bootstrapping (STARS). As a third option we pursued a hybrid approach (statistical-dynamical downscaling). For the assessment of climate change impacts, the buildings' infrastructure and their economic value is kept at current values. For all three approaches, a significant increase of average storm losses and extreme event return levels in the German private building sector is found for future decades assuming an A1B-scenario. However, the three projections differ somewhat in terms of magnitude and regional differentiation. We have developed a formalism that allows us to express the combined effect of multi-source uncertainty on return levels within the framework of a generalized Pareto distribution.

Synergistic effects of the components of global change: Increased vegetation dynamics in open, forest-steppe grasslands driven by wildfires and year-to-year precipitation differences.

PubMed

Kertész, Miklós; Aszalós, Réka; Lengyel, Attila; Ónodi, Gábor

2017-01-01

Climate change and land use change are two major elements of human-induced global environmental change. In temperate grasslands and woodlands, increasing frequency of extreme weather events like droughts and increasing severity of wildfires has altered the structure and dynamics of vegetation. In this paper, we studied the impact of wildfires and the year-to-year differences in precipitation on species composition changes in semi-arid grasslands of a forest-steppe complex ecosystem which has been partially disturbed by wildfires. Particularly, we investigated both how long-term compositional dissimilarity changes and species richness are affected by year-to-year precipitation differences on burnt and unburnt areas. Study sites were located in central Hungary, in protected areas characterized by partially-burnt, juniper-poplar forest-steppe complexes of high biodiversity. Data were used from two long-term monitoring sites in the Kiskunság National Park, both characterized by the same habitat complex. We investigated the variation in species composition as a function of time using distance decay methodology. In each sampling area, compositional dissimilarity increased with the time elapsed between the sampling events, and species richness differences increased with increasing precipitation differences between consecutive years. We found that both the long-term compositional dissimilarity, and the year-to-year changes in species richness were higher in the burnt areas than in the unburnt ones. The long-term compositional dissimilarities were mostly caused by perennial species, while the year-to-year changes of species richness were driven by annual and biennial species. As the effect of the year-to-year variation in precipitation was more pronounced in the burnt areas, we conclude that canopy removal by wildfires and extreme inter-annual variability of precipitation, two components of global environmental change, act in a synergistic way. They enhance the effect of one another, resulting in greater long-term and year-to-year changes in the composition of grasslands.

Data-Intensive Science meets Inquiry-Driven Pedagogy: Interactive Big Data Exploration, Threshold Concepts, and Liminality

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Word, Andrea; Nair, Udasysankar

2014-01-01

Threshold concepts in any discipline are the core concepts an individual must understand in order to master a discipline. By their very nature, these concepts are troublesome, irreversible, integrative, bounded, discursive, and reconstitutive. Although grasping threshold concepts can be extremely challenging for each learner as s/he moves through stages of cognitive development relative to a given discipline, the learner's grasp of these concepts determines the extent to which s/he is prepared to work competently and creatively within the field itself. The movement of individuals from a state of ignorance of these core concepts to one of mastery occurs not along a linear path but in iterative cycles of knowledge creation and adjustment in liminal spaces - conceptual spaces through which learners move from the vaguest awareness of concepts to mastery, accompanied by understanding of their relevance, connectivity, and usefulness relative to questions and constructs in a given discipline. For example, challenges in the teaching and learning of atmospheric science can be traced to threshold concepts in fluid dynamics. In particular, Dynamic Meteorology is one of the most challenging courses for graduate students and undergraduates majoring in Atmospheric Science. Dynamic Meteorology introduces threshold concepts - those that prove troublesome for the majority of students but that are essential, associated with fundamental relationships between forces and motion in the atmosphere and requiring the application of basic classical statics, dynamics, and thermodynamic principles to the three dimensionally varying atmospheric structure. With the explosive growth of data available in atmospheric science, driven largely by satellite Earth observations and high-resolution numerical simulations, paradigms such as that of dataintensive science have emerged. These paradigm shifts are based on the growing realization that current infrastructure, tools and processes will not allow us to analyze and fully utilize the complex and voluminous data that is being gathered. In this emerging paradigm, the scientific discovery process is driven by knowledge extracted from large volumes of data. In this presentation, we contend that this paradigm naturally lends to inquiry-driven pedagogy where knowledge is discovered through inductive engagement with large volumes of data rather than reached through traditional, deductive, hypothesis-driven analyses. In particular, data-intensive techniques married with an inductive methodology allow for exploration on a scale that is not possible in the traditional classroom with its typical problem sets and static, limited data samples. In addition, we identify existing gaps and possible solutions for addressing the infrastructure and tools as well as a pedagogical framework through which to implement this inductive approach.

Comparisons of Rig and Engine Dynamic Events in the Compressor of an Axi-Centrifugal Turboshaft Engine

NASA Technical Reports Server (NTRS)

Owen, A. Karl; Mattern, Duane L.; Le, Dzu K.

1996-01-01

Steady state and dynamic data were acquired in a T55-L-712 compressor rig. In addition, a T55-L-12 engine was instrumented and similar data were acquired. Rig and engine stall/surge data were analyzed using modal techniques. This paper compares rig and engine preliminary results for the ground idle (approximately 60% of design speed) point. The results of these analyses indicate both rig and engine dynamic event are preceded by indications of traveling wave energy in front of the compressor face. For both rig and engine, the traveling wave energy contains broad band energy with some prominent narrow peaks and, while the events are similar in many ways, some noticeable differences exist between the results of the analyses of rig data and engine data.

Geography and host species shape the evolutionary dynamics of U genogroup infectious hematopoietic necrosis virus.

PubMed

Black, Allison; Breyta, Rachel; Bedford, Trevor; Kurath, Gael

2016-07-01

Infectious hematopoietic necrosis virus (IHNV) is a negative-sense RNA virus that infects wild and cultured salmonids throughout the Pacific Coastal United States and Canada, from California to Alaska. Although infection of adult fish is usually asymptomatic, juvenile infections can result in high mortality events that impact salmon hatchery programs and commercial aquaculture. We used epidemiological case data and genetic sequence data from a 303 nt portion of the viral glycoprotein gene to study the evolutionary dynamics of U genogroup IHNV in the Pacific Northwestern United States from 1971 to 2013. We identified 114 unique genotypes among 1,219 U genogroup IHNV isolates representing 619 virus detection events. We found evidence for two previously unidentified, broad subgroups within the U genogroup, which we designated 'UC' and 'UP'. Epidemiologic records indicated that UP viruses were detected more frequently in sockeye salmon ( Oncorhynchus nerka ) and in coastal waters of Washington and Oregon, whereas UC viruses were detected primarily in Chinook salmon ( Oncorhynchus tshawytscha ) and steelhead trout ( Oncorhynchus mykiss ) in the Columbia River Basin, which is a large, complex watershed extending throughout much of interior Washington, Oregon, and Idaho. These findings were supported by phylogenetic analysis and by F ST . Ancestral state reconstruction indicated that early UC viruses in the Columbia River Basin initially infected sockeye salmon but then emerged via host shifts into Chinook salmon and steelhead trout sometime during the 1980s. We postulate that the development of these subgroups within U genogroup was driven by selection pressure for viral adaptation to Chinook salmon and steelhead trout within the Columbia River Basin.

Point process modeling and estimation: Advances in the analysis of dynamic neural spiking data

NASA Astrophysics Data System (ADS)

Deng, Xinyi

2016-08-01

A common interest of scientists in many fields is to understand the relationship between the dynamics of a physical system and the occurrences of discrete events within such physical system. Seismologists study the connection between mechanical vibrations of the Earth and the occurrences of earthquakes so that future earthquakes can be better predicted. Astrophysicists study the association between the oscillating energy of celestial regions and the emission of photons to learn the Universe's various objects and their interactions. Neuroscientists study the link between behavior and the millisecond-timescale spike patterns of neurons to understand higher brain functions. Such relationships can often be formulated within the framework of state-space models with point process observations. The basic idea is that the dynamics of the physical systems are driven by the dynamics of some stochastic state variables and the discrete events we observe in an interval are noisy observations with distributions determined by the state variables. This thesis proposes several new methodological developments that advance the framework of state-space models with point process observations at the intersection of statistics and neuroscience. In particular, we develop new methods 1) to characterize the rhythmic spiking activity using history-dependent structure, 2) to model population spike activity using marked point process models, 3) to allow for real-time decision making, and 4) to take into account the need for dimensionality reduction for high-dimensional state and observation processes. We applied these methods to a novel problem of tracking rhythmic dynamics in the spiking of neurons in the subthalamic nucleus of Parkinson's patients with the goal of optimizing placement of deep brain stimulation electrodes. We developed a decoding algorithm that can make decision in real-time (for example, to stimulate the neurons or not) based on various sources of information present in population spiking data. Lastly, we proposed a general three-step paradigm that allows us to relate behavioral outcomes of various tasks to simultaneously recorded neural activity across multiple brain areas, which is a step towards closed-loop therapies for psychological diseases using real-time neural stimulation. These methods are suitable for real-time implementation for content-based feedback experiments.

Scaling Effects of Riparian Peatlands on Stable Isotopes in Runoff and DOC Mobilization

NASA Astrophysics Data System (ADS)

Tetzlaff, D.; Tunaley, C.; Soulsby, C.

2016-12-01

We combined 13 months of daily isotope measurements in stream water with daily DOC and 15 minute FDOM (fluorescent component of dissolved organic matter) data at three nested scales to identify how riparian peatlands generate runoff and influence DOC dynamics in streams. We investigated how runoff generation processes in a small, riparian peatland dominated headwater catchment (0.65 km2) propagate to larger scales (3.2 km2 and 31 km2) with decreasing percentage of riparian peatland coverage. Isotope damping was most pronounced in the 0.65 km2 headwater catchment due to high water storage in the organic soils which encourage tracer mixing. At the largest scale, stream flow and water isotope dynamics showed a more flashy response. The isotopic difference between the sites was most pronounced in the summer months when stream water signatures were enriched. During the winter months, the inter-site difference reduced. The isotopes also revealed evaporative fractionation in the peatland dominated catchment, in particular during summer low flows, which implied high hydrological connectivity in form of constant seepage from the peatlands sustaining high baseflows at the headwater scale. This connectivity resulted in high DOC concentrations at the peatland site during baseflow ( 5 mg l-1). In contrast, at the larger scales, DOC was minimal during low flows ( 2 mg l-1) due to increased groundwater influence and the disconnection between DOC sources and the stream. High frequency data also revealed diel variability during low flows. Insights into event dynamics through the analysis of hysteresis loops showed slight dilution on the rising limb, the strong influence of dry antecedent conditions and a quick recovery between events at the riparian peatland site. Again, these dynamics are driven by the tight coupling and high connectivity of the landscape to the stream. At larger scales, the disconnection between the landscape units increase and the variable connectivity controls runoff generation and DOC dynamics. The results presented here suggest that the processes occurring in riparian peatlands in headwater catchments are less evident at larger scales which may have implications for the larger scale impact of peatland restoration projects.

Scaling effects of riparian peatlands on stable isotopes in runoff and DOC mobilisation

NASA Astrophysics Data System (ADS)

Tunaley, C.; Tetzlaff, D.; Soulsby, C.

2017-06-01

We combined 13 months of daily isotope measurements in stream water with daily DOC and 15 min FDOM (fluorescent component of dissolved organic matter) data at three nested scales to identify how riparian peatlands generate runoff and influence DOC dynamics in streams. We investigated how runoff generation processes in a small, riparian peatland-dominated headwater catchment (0.65 km2) propagate to larger scales (3.2 km2 and 31 km2) with decreasing percentage of riparian peatland coverage. Isotope damping was most pronounced in the 0.65 km2 headwater catchment due to high water storage in the organic soils encouraging tracer mixing. At the largest scale, stream flow and water isotope dynamics showed a more flashy response. The isotopic difference between the sites was most pronounced in the summer months when stream water signatures were enriched. During the winter months, the inter-site difference reduced. The isotopes also revealed evaporative fractionation in the peatland dominated catchment, in particular during summer low flows, which implied high hydrological connectivity in the form of constant seepage from the peatlands sustaining high baseflows at the headwater scale. This connectivity resulted in high DOC concentrations at the peatland site during baseflow (∼5 mg l-1). In contrast, at the larger scales, DOC was minimal during low flows (∼2 mg l-1) due to increased groundwater influence and the disconnection between DOC sources and the stream. High frequency data also revealed diel variability during low flows. Insights into event dynamics through the analysis of hysteresis loops showed slight dilution on the rising limb, the strong influence of dry antecedent conditions and a quick recovery between events at the riparian peatland site. Again, these dynamics are driven by the tight coupling and high connectivity of the landscape to the stream. At larger scales, the disconnection between the landscape units increases and the variable connectivity controls runoff generation and DOC dynamics. The results presented here suggest that the processes occurring in riparian peatlands in headwater catchments are less evident at larger scales which may have implications for the larger scale impact of peatland restoration projects.

Turbulent Dynamics of Epithelial Cell Cultures

NASA Astrophysics Data System (ADS)

Blanch-Mercader, C.; Yashunsky, V.; Garcia, S.; Duclos, G.; Giomi, L.; Silberzan, P.

2018-05-01

We investigate the large length and long time scales collective flows and structural rearrangements within in vitro human bronchial epithelial cell (HBEC) cultures. Activity-driven collective flows result in ensembles of vortices randomly positioned in space. By analyzing a large population of vortices, we show that their area follows an exponential law with a constant mean value and their rotational frequency is size independent, both being characteristic features of the chaotic dynamics of active nematic suspensions. Indeed, we find that HBECs self-organize in nematic domains of several cell lengths. Nematic defects are found at the interface between domains with a total number that remains constant due to the dynamical balance of nucleation and annihilation events. The mean velocity fields in the vicinity of defects are well described by a hydrodynamic theory of extensile active nematics.

Attribution of Disturbances Causing Tree Mortality for the Continental U.S.

NASA Astrophysics Data System (ADS)

Wang, M.; Xu, C.; Allen, C. D.; McDowell, N. G.

2016-12-01

Broad-scale tree mortality has been frequently reported and documented to increase with warming climate and human activities. However, there is so far no general method to quantify the relative contributions of different disturbances on observed broad-scale tree mortality. In this study, we presented a framework to investigate the contribution of various disturbances causing tree mortality for 2000-2014 in the continental US. Our work is based on the high-resolution forest-loss data developed by Hansen et al. (2013). Firstly, fire-driven mortality was determined using the data from Monitoring Trends in Burn Severity (MTBS) project. Secondly, a landscape-pattern-recognition approach focusing on the differences of boundary complexity caused by natural and anthropogenic disturbances was developed to attribute harvest-driven mortality patches. Then, a drought threshold was determined through conducting an intensive literature survey for attribution of drought-driven mortality. Our results showed that we can correctly attribute 85% harvest-driven mortality as compared to Forest Inventory and Analysis (FIA) data. Based on Evaporative Stress Index (ESI), our literature survey suggests that most mortality events happened at extreme drought (37.7%), then severe (31.4%) and moderate (23.4%) drought. In total, 92.6% of drought-induced mortality events observed during 2000-2014 occurred at drought conditions of moderate or worse with corresponding ESI values ranging from -0.9 -2.49. Therefore, -0.9 will be used as the threshold to attribute drought-driven tree mortality. Overall, these results imply a great potential for using these methods to identify and attribute disturbances driving tree death at broad spatial scales.

Comments on event driven animation

NASA Technical Reports Server (NTRS)

Gomez, Julian E.

1987-01-01

Event driven animation provides a general method of describing controlling values for various computer animation techniques. A definition and comments are provided on genralizing motion description with events. Additional comments are also provided about the implementation of twixt.

Driven fragmentation of granular gases.

PubMed

Cruz Hidalgo, Raúl; Pagonabarraga, Ignacio

2008-06-01

The dynamics of homogeneously heated granular gases which fragment due to particle collisions is analyzed. We introduce a kinetic model which accounts for correlations induced at the grain collisions and analyze both the kinetics and relevant distribution functions these systems develop. The work combines analytical and numerical studies based on direct simulation Monte Carlo calculations. A broad family of fragmentation probabilities is considered, and its implications for the system kinetics are discussed. We show that generically these driven materials evolve asymptotically into a dynamical scaling regime. If the fragmentation probability tends to a constant, the grain number diverges at a finite time, leading to a shattering singularity. If the fragmentation probability vanishes, then the number of grains grows monotonously as a power law. We consider different homogeneous thermostats and show that the kinetics of these systems depends weakly on both the grain inelasticity and driving. We observe that fragmentation plays a relevant role in the shape of the velocity distribution of the particles. When the fragmentation is driven by local stochastic events, the long velocity tail is essentially exponential independently of the heating frequency and the breaking rule. However, for a Lowe-Andersen thermostat, numerical evidence strongly supports the conjecture that the scaled velocity distribution follows a generalized exponential behavior f(c) approximately exp(-cn) , with n approximately 1.2 , regarding less the fragmentation mechanisms.

Complex Pattern Formation from Current-Driven Dynamics of Single-Layer Epitaxial Islands on Crystalline Conducting Substrates

NASA Astrophysics Data System (ADS)

Kumar, Ashish; Dasgupta, Dwaipayan; Maroudas, Dimitrios

We report a systematic study of complex pattern formation resulting from the driven dynamics of single-layer homoepitaxial islands on face-centered cubic (FCC) crystalline conducting substrate surfaces under the action of an externally applied electric field. The analysis is based on an experimentally validated nonlinear model of mass transport via island edge atomic diffusion, which also accounts for edge diffusional anisotropy. We analyze the morphological stability and simulate the field-driven evolution of rounded islands for an electric field oriented along the fast diffusion direction. For larger than critical island sizes on {110} and {100} FCC substrates, we show that multiple necking instabilities generate complex island patterns, including void-containing islands, mediated by sequences of breakup and coalescence events and distributed symmetrically with respect to the electric field direction. We analyze the dependence of the formed patterns on the original island size and on the duration of application of the external field. Starting from a single large rounded island, we characterize the evolution of the number of daughter islands and their average size and uniformity. The analysis reveals that the pattern formation kinetics follows a universal scaling relation. Division of Materials Sciences & Engineering, Office of Basic Energy Sciences, U.S. Department of Energy (Award No.: DE-FG02-07ER46407).

Data Albums: An Event Driven Search, Aggregation and Curation Tool for Earth Science

NASA Technical Reports Server (NTRS)

Ramachandran, Rahul; Kulkarni, Ajinkya; Maskey, Manil; Bakare, Rohan; Basyal, Sabin; Li, Xiang; Flynn, Shannon

2014-01-01

One of the largest continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available. Approaches used in Earth science research such as case study analysis and climatology studies involve gathering discovering and gathering diverse data sets and information to support the research goals. Research based on case studies involves a detailed description of specific weather events using data from different sources, to characterize physical processes in play for a specific event. Climatology-based research tends to focus on the representativeness of a given event, by studying the characteristics and distribution of a large number of events. This allows researchers to generalize characteristics such as spatio-temporal distribution, intensity, annual cycle, duration, etc. To gather relevant data and information for case studies and climatology analysis is both tedious and time consuming. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the datasets of interest can obtain the specific files they need using these systems. However, in cases where researchers are interested in studying a significant event, they have to manually assemble a variety of datasets relevant to it by searching the different distributed data systems. In these cases, a search process needs to be organized around the event rather than observing instruments. In addition, the existing data systems assume users have sufficient knowledge regarding the domain vocabulary to be able to effectively utilize their catalogs. These systems do not support new or interdisciplinary researchers who may be unfamiliar with the domain terminology. This paper presents a specialized search, aggregation and curation tool for Earth science to address these existing challenges. The search tool automatically creates curated "Data Albums", aggregated collections of information related to a specific science topic or event, containing links to relevant data files (granules) from different instruments; tools and services for visualization and analysis; and information about the event contained in news reports, images or videos to supplement research analysis. Curation in the tool is driven via an ontology based relevancy ranking algorithm to filter out non-relevant information and data.

Indicators of ecosystem function identify alternate states in the sagebrush steppe.

PubMed

Kachergis, Emily; Rocca, Monique E; Fernandez-Gimenez, Maria E

2011-10-01

Models of ecosystem change that incorporate nonlinear dynamics and thresholds, such as state-and-transition models (STMs), are increasingly popular tools for land management decision-making. However, few models are based on systematic collection and documentation of ecological data, and of these, most rely solely on structural indicators (species composition) to identify states and transitions. As STMs are adopted as an assessment framework throughout the United States, finding effective and efficient ways to create data-driven models that integrate ecosystem function and structure is vital. This study aims to (1) evaluate the utility of functional indicators (indicators of rangeland health, IRH) as proxies for more difficult ecosystem function measurements and (2) create a data-driven STM for the sagebrush steppe of Colorado, USA, that incorporates both ecosystem structure and function. We sampled soils, plant communities, and IRH at 41 plots with similar clayey soils but different site histories to identify potential states and infer the effects of management practices and disturbances on transitions. We found that many IRH were correlated with quantitative measures of functional indicators, suggesting that the IRH can be used to approximate ecosystem function. In addition to a reference state that functions as expected for this soil type, we identified four biotically and functionally distinct potential states, consistent with the theoretical concept of alternate states. Three potential states were related to management practices (chemical and mechanical shrub treatments and seeding history) while one was related only to ecosystem processes (erosion). IRH and potential states were also related to environmental variation (slope, soil texture), suggesting that there are environmental factors within areas with similar soils that affect ecosystem dynamics and should be noted within STMs. Our approach generated an objective, data-driven model of ecosystem dynamics for rangeland management. Our findings suggest that the IRH approximate ecosystem processes and can distinguish between alternate states and communities and identify transitions when building data-driven STMs. Functional indicators are a simple, efficient way to create data-driven models that are consistent with alternate state theory. Managers can use them to improve current model-building methods and thus apply state-and-transition models more broadly for land management decision-making.

Fishing Quotas, Induced Allee Effect, and Fluctuation-Driven Extinction.

PubMed

Hastings, Harold M; Radin, Michael; Wiandt, Tamas

2017-01-01

We explore the potential of modifications to standard fishery models (for example Gordon-Schafer-Munro) to help understand events such as the collapse of the North Atlantic cod fishery. In particular we find that quota-driven and similar harvesting strategies induce an effective strong Allee effect (collapse if the population falls below a critical level). In the presence of environmental noise, fish population dynamics is similar to a random walk with (non-linear) drift. The expected survival time (first passage time to collapse) is shown to depend sensitively upon the amount of environmental noise and size of the 'safe zone' between the deterministic steady state population and the critical population level at which the system collapses; more precisely it is exponential in the cube of the size of the safe zone divided by the variance of the noise process. Similar scaling can be expected for more survival in more general systems with multiple steady states. Our calculations imply an amplification effect under which small increases in harvest yield large decreases in expected survival time, and one should be cautious in changes in harvesting, especially in fisheries with poor or limited data and fisheries affected by climate change.

Large Deformation Dynamic Bending of Composite Beams

NASA Technical Reports Server (NTRS)

Derian, E. J.; Hyer, M. W.

1986-01-01

Studies were conducted on the large deformation response of composite beams subjected to a dynamic axial load. The beams were loaded with a moderate eccentricity to promote bending. The study was primarily experimental but some finite element results were obtained. Both the deformation and the failure of the beams were of interest. The static response of the beams was also studied to determine potential differences between the static and dynamic failure. Twelve different laminate types were tested. The beams tested were 23 in. by 2 in. and generally 30 plies thick. The beams were loaded dynamically with a gravity-driven impactor traveling at 19.6 ft/sec and quasi-static tests were conducted on identical beams in a displacement controlled manner. For laminates of practical interest, the failure modes under static and dynamic loadings were identical. Failure in most of the laminate types occurred in a single event involving 40% to 50% of the plies. However, failure in laminates with 300 or 150 off-axis plies occurred in several events. All laminates exhibited bimodular elastic properties. The compressive flexural moduli in some laminates was measured to be 1/2 the tensile flexural modulus. No simple relationship could be found among the measured ultimate failure strains of the different laminate types. Using empirically determined flexural properties, a finite element analysis was reasonably accurate in predicting the static and dynamic deformation response.

Weather and extremes in the last Millennium - a challenge for climate modelling

NASA Astrophysics Data System (ADS)

Raible, Christoph C.; Blumer, Sandro R.; Gomez-Navarro, Juan J.; Lehner, Flavio

2015-04-01

Changes in the climate mean state are expected to influence society, but the socio-economic sensitivity to extreme events might be even more severe. Whether or not the current frequency and severity of extreme events is a unique characteristic of anthropogenic-driven climate change can be assessed by putting the observed changes in a long-term perspective. In doing so, early instrumental series and proxy archives are a rich source to investigate also extreme events, in particular during the last millennium, yet they suffer from spatial and temporal scarcity. Therefore, simulations with coupled general circulation models (GCMs) could fill such gaps and help in deepening our process understanding. In this study, an overview of past and current efforts as well as challenges in modelling paleo weather and extreme events is presented. Using simulations of the last millennium we investigate extreme midlatitude cyclone characteristics, precipitation, and their connection to large-scale atmospheric patterns in the North Atlantic European region. In cold climate states such as the Maunder Minimum, the North Atlantic Oscillation (NAO) is found to be predominantly in its negative phase. In this sense, simulations of different models agree with proxy findings for this period. However, some proxy data available for this period suggests an increase in storminess during this period, which could be interpreted as a positive phase of the NAO - a superficial contradiction. The simulated cyclones are partly reduced over Europe, which is consistent with the aforementioned negative phase of the NAO. However, as the meridional temperature gradient is increased during this period - which constitutes a source of low-level baroclincity - they also intensify. This example illustrates how model simulations could be used to improve our proxy interpretation and to gain additional process understanding. Nevertheless, there are also limitations associated with climate modeling efforts to simulate the last millennium. In particular, these models still struggle to properly simulate atmospheric blocking events, an important dynamical feature for dry conditions during summer times. Finally, new and promising ways in improving past climate modelling are briefly introduced. In particular, the use of dynamical downscaling is a powerful tool to bridge the gap between the coarsely resolved GCMs and characteristics of the regional climate, which is potentially recorded in proxy archives. In particular, the representation of extreme events could be improved by dynamical downscaling as processes are better resolved than GCMs.

The Role of Self-Organized Criticality in the Substorm Phenomenon and its Relation to Localized Reconnection in the Magnetospheric Plasma Sheet

NASA Technical Reports Server (NTRS)

Klimas, Alex J.; Valdivia, J. A.; Vassiliadis, D.; Baker, D. N.; Hesse, M.; Takalo, J.

1999-01-01

Evidence is presented that suggests there is a significant self-organized criticality (SOC) component in the dynamics of substorms in the magnetosphere. Observations of BBFs, fast flows, localized dipolarizations, plasma turbulence, etc. are taken to show that multiple localized reconnection sites provide the basic avalanche phenomenon in the establishment of SOC in the plasma sheet. First results are presented from a continuing plasma physical study of this avalanche process. A one-dimensional resistive MHD model of a magnetic field reversal is discussed. Resistivity, in this model, is self-consistently generated in response to the excitation of an idealized current-driven instability. When forced by convection of magnetic flux into the field reversal region, the model yields rapid magnetic field annihilation through a dynamic behavior that is shown to exhibit many of the characteristics of SOC. Over a large range of forcing strengths, the annihilation rate is shown to self-adjust to balance the rate at which flux is convected into the reversal region. Several analogies to magnetotail dynamics are discussed: (1) It is shown that the presence of a localized criticality in the model produces a remarkable stability in the global configuration of the field reversal while simultaneously exciting extraordinarily dynamic internal evolution. (2) Under steady forcing, it is shown that a loading-unloading cycle may arise that, as a consequence of the global stability, is quasi-periodic and, therefore, predictable despite the presence of internal turbulence in the field distribution. Indeed, it is shown that the global loading-unloading cycle is a consequence of the internal turbulence. (3) It is shown that, under steady, strong forcing the loading-unloading cycle vanishes. Instead, a recovery from a single unloading persists indefinitely. The field reversal is globally very steady while internally it is very dynamic as field annihilation goes on at the rate necessary to match the strong forcing. From this result we speculate that steady magnetospheric convection events result when the plasma sheet has been driven close to criticality over an extended spatial domain. During these events, we would expect to find localized reconnection sites distributed over the spatial domain of near criticality and we would expect to find plasma sheet transport in that domain to be closely related to that of BBF and fast flow events.

Joint probabilities of extreme precipitation and wind gusts in Germany

NASA Astrophysics Data System (ADS)

von Waldow, H.; Martius, O.

2012-04-01

Extreme meteorological events such as storms, heavy rain, floods, droughts and heat waves can have devastating consequences for human health, infrastructure and ecosystems. Concomitantly occurring extreme events might interact synergistically to produce a particularly hazardous impact. The joint occurrence of droughts and heat waves, for example, can have a very different impact on human health and ecosystems both in quantity and quality, than just one of the two extreme events. The co-occurrence of certain types of extreme events is plausible from physical and dynamical considerations, for example heavy precipitation and high wind speeds in the pathway of strong extratropical cyclones. The winter storm Kyrill not only caused wind gust speeds well in excess of 30 m/s across Europe, but also brought 24 h precipitation sums greater than the mean January accumulations in some regions. However, the existence of such compound risks is currently not accounted for by insurance companies, who assume independence of extreme weather events to calculate their premiums. While there are established statistical methods to model the extremes of univariate meteorological variables, the modelling of multidimensional extremes calls for an approach that is tailored to the specific problem at hand. A first step involves defining extreme bivariate wind/precipitation events. Because precipitation and wind gusts caused by the same cyclone or convective cell do not occur at exactly the same location and at the same time, it is necessary to find a sound definition of "extreme compound event" for this case. We present a data driven method to choose appropriate time and space intervals that define "concomitance" for wind and precipitation extremes. Based on station data of wind speed and gridded precipitation data, we arrive at time and space intervals that compare well with the typical time and space scales of extratropical cyclones, i.e. a maximum time lag of 1 day and a maximum distance of about 300 km between associated wind and rain events. After modelling extreme precipitation and wind separately, we explore the practicability of characterising their joint distribution using a bivariate threshold excess model. In particular, we present different dependence measures and report about the computational feasibility and available computer codes.

Hyperswitch Network For Hypercube Computer

NASA Technical Reports Server (NTRS)

Chow, Edward; Madan, Herbert; Peterson, John

1989-01-01

Data-driven dynamic switching enables high speed data transfer. Proposed hyperswitch network based on mixed static and dynamic topologies. Routing header modified in response to congestion or faults encountered as path established. Static topology meets requirement if nodes have switching elements that perform necessary routing header revisions dynamically. Hypercube topology now being implemented with switching element in each computer node aimed at designing very-richly-interconnected multicomputer system. Interconnection network connects great number of small computer nodes, using fixed hypercube topology, characterized by point-to-point links between nodes.

Competition between Visual Events Modulates the Influence of Salience during Free-Viewing of Naturalistic Videos

PubMed Central

Nardo, Davide; Console, Paola; Reverberi, Carlo; Macaluso, Emiliano

2016-01-01

In daily life the brain is exposed to a large amount of external signals that compete for processing resources. The attentional system can select relevant information based on many possible combinations of goal-directed and stimulus-driven control signals. Here, we investigate the behavioral and physiological effects of competition between distinctive visual events during free-viewing of naturalistic videos. Nineteen healthy subjects underwent functional magnetic resonance imaging (fMRI) while viewing short video-clips of everyday life situations, without any explicit goal-directed task. Each video contained either a single semantically-relevant event on the left or right side (Lat-trials), or multiple distinctive events in both hemifields (Multi-trials). For each video, we computed a salience index to quantify the lateralization bias due to stimulus-driven signals, and a gaze index (based on eye-tracking data) to quantify the efficacy of the stimuli in capturing attention to either side. Behaviorally, our results showed that stimulus-driven salience influenced spatial orienting only in presence of multiple competing events (Multi-trials). fMRI results showed that the processing of competing events engaged the ventral attention network, including the right temporoparietal junction (R TPJ) and the right inferior frontal cortex. Salience was found to modulate activity in the visual cortex, but only in the presence of competing events; while the orienting efficacy of Multi-trials affected activity in both the visual cortex and posterior parietal cortex (PPC). We conclude that in presence of multiple competing events, the ventral attention system detects semantically-relevant events, while regions of the dorsal system make use of saliency signals to select relevant locations and guide spatial orienting. PMID:27445760

Variability of Slip Behavior in Simulations of Dynamic Rupture Interaction With Stronger Fault Patches Over Long-Term Deformation Histories

NASA Astrophysics Data System (ADS)

Lapusta, N.; Liu, Y.

2007-12-01

Heterogeneity in fault properties can have significant effect on dynamic rupture propagation and aseismic slip. It is often assumed that a fixed heterogeneity would have similar effect on fault slip throughout the slip history. We investigate dynamic rupture interaction with a fault patch of higher normal stress over several earthquake cycles in a three-dimensional model. We find that the influence of the heterogeneity on dynamic events has significant variation and depends on prior slip history. We consider a planar strike-slip fault governed by rate and state friction and driven by slow tectonic loading on deeper extension of the fault. The 30 km by 12 km velocity-weakening region, which is potentially seismogenic, is surrounded by steady-state velocity-strengthening region. The normal stress is constant over the fault, except in a circular patch of 2 km in diameter located in the seismogenic region, where normal stress is higher than on the rest of the fault. Our simulations employ the methodology developed by Lapusta and Liu (AGU, 2006), which is able to resolve both dynamic and quasi-static stages of spontaneous slip accumulation in a single computational procedure. The initial shear stress is constant on the fault, except in a small area where it is higher and where the first large dynamic event initiates. For patches with 20%, 40%, 60% higher normal stress, the first event has significant dynamic interaction with the patch, creating a rupture speed decrease followed by a supershear burst and larger slip around the patch. Hence, in the first event, the patch acts as a seismic asperity. For the case of 100% higher stress, the rupture is not able to break the patch in the first event. In subsequent dynamic events, the behavior depends on the strength of heterogeneity. For the patch with 20% higher normal stress, dynamic rupture in subsequent events propagates through the patch without any noticeable perturbation in rupture speed or slip. In particular, supershear propagation and additional slip accumulation around the patch are never repeated in the simulated history of the fault, and the patch stops manifesting itself as a seismic asperity. This is due to higher shear stress that is established at the patch after the first earthquake cycle. For patches with higher normal stress, shear stress redistribution also occurs, but it is less effective. The patches with 40% and 60% higher normal stress continue to affect rupture speed and fault slip in some of subsequent events, although the effect is much diminished with respect to the first event. For example, there are no supershear bursts. The patch with 100% higher normal stress is first broken in the second large event, and it retains significant influence on rupture speed and slip throughout the fault history, occasionally resulting in supershear bursts. Additional slip complexity emerges for patches with 40% and higher normal stress contrast. Since higher normal stress corresponds to a smaller nucleation size, nucleation of some events moves from the rheological transitions (where nucleation occurs in the cases with no stronger patch and with the patch of 20% higher normal stress) to the patches of higher normal stress. The patches nucleate both large, model-spanning, events, and small events that arrest soon after exiting the patch. Hence not every event that originates at the location of a potential seismic asperity is destined to be large, as its subsequent propagation is significantly influenced by the state of stress outside the patch.

Evaluation of the Event Driven Phenology Model Coupled with the VegET Evapotranspiration Model Through Comparisons with Reference Datasets in a Spatially Explicit Manner

NASA Technical Reports Server (NTRS)

Kovalskyy, V.; Henebry, G. M.; Adusei, B.; Hansen, M.; Roy, D. P.; Senay, G.; Mocko, D. M.

2011-01-01

A new model coupling scheme with remote sensing data assimilation was developed for estimation of daily actual evapotranspiration (ET). The scheme represents a mix of the VegET, a physically based model to estimate ET from a water balance, and an event driven phenology model (EDPM), where the EDPM is an empirically derived crop specific model capable of producing seasonal trajectories of canopy attributes. In this experiment, the scheme was deployed in a spatially explicit manner within the croplands of the Northern Great Plains. The evaluation was carried out using 2007-2009 land surface forcing data from the North American Land Data Assimilation System (NLDAS) and crop maps derived from remotely sensed data of NASA's Moderate Resolution Imaging Spectroradiometer (MODIS). We compared the canopy parameters produced by the phenology model with normalized difference vegetation index (NDVI) data derived from the MODIS nadir bi-directional reflectance distribution function (BRDF) adjusted reflectance (NBAR) product. The expectations of the EDPM performance in prognostic mode were met, producing determination coefficient (r2) of 0.8 +/-.0.15. Model estimates of NDVI yielded root mean square error (RMSE) of 0.1 +/-.0.035 for the entire study area. Retrospective correction of canopy dynamics with MODIS NDVI brought the errors down to just below 10% of observed data range. The ET estimates produced by the coupled scheme were compared with ones from the MODIS land product suite. The expected r2=0.7 +/-.15 and RMSE = 11.2 +/-.4 mm per 8 days were met and even exceeded by the coupling scheme0 functioning in both prognostic and retrospective modes. Minor setbacks of the EDPM and VegET performance (r2 about 0.5 and additional 30 % of RMSR) were found on the peripheries of the study area and attributed to the insufficient EDPM training and to spatially varying accuracy of crop maps. Overall the experiment provided sufficient evidence of soundness and robustness of the EDPM and VegET coupling scheme, assuring its potential for spatially explicit applications.

Quasi-static and dynamic magnetic tension forces in arched, line-tied magnetic flux ropes

DOE Office of Scientific and Technical Information (OSTI.GOV)

Myers, C. E.; Yamada, M.; Ji, H.

Solar eruptions are often driven by magnetohydrodynamic instabilities such as the torus and kink instabilities that act on line-tied magnetic flux ropes. We designed our recent laboratory experiments to study these eruptive instabilities which have demonstrated the key role of both dynamic (Myers et al 2015 Nature 528 526) and quasi-static (Myers et al 2016 Phys. Plasmas 23 112102) magnetic tension forces in contributing to the equilibrium and stability of line-tied magnetic flux ropes. In our paper, we synthesize these laboratory results and explore the relationship between the dynamic and quasi-static tension forces. And while the quasi-static tension force ismore » found to contribute to the flux rope equilibrium in a number of regimes, the dynamic tension force is substantial mostly in the so-called failed torus regime where magnetic self-organization events prevent the flux rope from erupting.« less

Quasi-static and dynamic magnetic tension forces in arched, line-tied magnetic flux ropes

DOE PAGES

Myers, C. E.; Yamada, M.; Ji, H.; ...

2016-11-22

Solar eruptions are often driven by magnetohydrodynamic instabilities such as the torus and kink instabilities that act on line-tied magnetic flux ropes. We designed our recent laboratory experiments to study these eruptive instabilities which have demonstrated the key role of both dynamic (Myers et al 2015 Nature 528 526) and quasi-static (Myers et al 2016 Phys. Plasmas 23 112102) magnetic tension forces in contributing to the equilibrium and stability of line-tied magnetic flux ropes. In our paper, we synthesize these laboratory results and explore the relationship between the dynamic and quasi-static tension forces. And while the quasi-static tension force ismore » found to contribute to the flux rope equilibrium in a number of regimes, the dynamic tension force is substantial mostly in the so-called failed torus regime where magnetic self-organization events prevent the flux rope from erupting.« less

DOE Office of Scientific and Technical Information (OSTI.GOV)

Johnson, Paul A.

Nonlinear dynamics induced by seismic sources and seismic waves are common in Earth. Observations range from seismic strong ground motion (the most damaging aspect of earthquakes), intense near-source effects, and distant nonlinear effects from the source that have important consequences. The distant effects include dynamic earthquake triggering-one of the most fascinating topics in seismology today-which may be elastically nonlinearly driven. Dynamic earthquake triggering is the phenomenon whereby seismic waves generated from one earthquake trigger slip events on a nearby or distant fault. Dynamic triggering may take place at distances thousands of kilometers from the triggering earthquake, and includes triggering ofmore » the entire spectrum of slip behaviors currently identified. These include triggered earthquakes and triggered slow, silent-slip during which little seismic energy is radiated. It appears that the elasticity of the fault gouge-the granular material located between the fault blocks-is key to the triggering phenomenon.« less

A data driven control method for structure vibration suppression

NASA Astrophysics Data System (ADS)

Xie, Yangmin; Wang, Chao; Shi, Hang; Shi, Junwei

2018-02-01

High radio-frequency space applications have motivated continuous research on vibration suppression of large space structures both in academia and industry. This paper introduces a novel data driven control method to suppress vibrations of flexible structures and experimentally validates the suppression performance. Unlike model-based control approaches, the data driven control method designs a controller directly from the input-output test data of the structure, without requiring parametric dynamics and hence free of system modeling. It utilizes the discrete frequency response via spectral analysis technique and formulates a non-convex optimization problem to obtain optimized controller parameters with a predefined controller structure. Such approach is then experimentally applied on an end-driving flexible beam-mass structure. The experiment results show that the presented method can achieve competitive disturbance rejections compared to a model-based mixed sensitivity controller under the same design criterion but with much less orders and design efforts, demonstrating the proposed data driven control is an effective approach for vibration suppression of flexible structures.

Signature-based search for delayed photons in exclusive photon plus missing transverse energy events from pp¯ collisions with s=1.96TeV

NASA Astrophysics Data System (ADS)

Aaltonen, T.; Amerio, S.; Amidei, D.; Anastassov, A.; Annovi, A.; Antos, J.; Apollinari, G.; Appel, J. A.; Arisawa, T.; Artikov, A.; Asaadi, J.; Ashmanskas, W.; Auerbach, B.; Aurisano, A.; Azfar, F.; Badgett, W.; Bae, T.; Barbaro-Galtieri, A.; Barnes, V. E.; Barnett, B. A.; Barria, P.; Bartos, P.; Bauce, M.; Bedeschi, F.; Behari, S.; Bellettini, G.; Bellinger, J.; Benjamin, D.; Beretvas, A.; Bhatti, A.; Bland, K. R.; Blumenfeld, B.; Bocci, A.; Bodek, A.; Bortoletto, D.; Boudreau, J.; Boveia, A.; Brigliadori, L.; Bromberg, C.; Brucken, E.; Budagov, J.; Budd, H. S.; Burkett, K.; Busetto, G.; Bussey, P.; Butti, P.; Buzatu, A.; Calamba, A.; Camarda, S.; Campanelli, M.; Canelli, F.; Carls, B.; Carlsmith, D.; Carosi, R.; Carrillo, S.; Casal, B.; Casarsa, M.; Castro, A.; Catastini, P.; Cauz, D.; Cavaliere, V.; Cavalli-Sforza, M.; Cerri, A.; Cerrito, L.; Chen, Y. C.; Chertok, M.; Chiarelli, G.; Chlachidze, G.; Cho, K.; Chokheli, D.; Ciocci, M. A.; Clark, A.; Clarke, C.; Convery, M. E.; Conway, J.; Corbo, M.; Cordelli, M.; Cox, C. A.; Cox, D. J.; Cremonesi, M.; Cruz, D.; Cuevas, J.; Culbertson, R.; d'Ascenzo, N.; Datta, M.; De Barbaro, P.; Demortier, L.; Deninno, M.; d'Errico, M.; Devoto, F.; Di Canto, A.; Di Ruzza, B.; Dittmann, J. R.; D'Onofrio, M.; Donati, S.; Dorigo, M.; Driutti, A.; Ebina, K.; Edgar, R.; Elagin, A.; Erbacher, R.; Errede, S.; Esham, B.; Eusebi, R.; Farrington, S.; Fernández Ramos, J. P.; Field, R.; Flanagan, G.; Forrest, R.; Franklin, M.; Freeman, J. C.; Frisch, H.; Funakoshi, Y.; Garfinkel, A. F.; Garosi, P.; Gerberich, H.; Gerchtein, E.; Giagu, S.; Giakoumopoulou, V.; Gibson, K.; Ginsburg, C. M.; Giokaris, N.; Giromini, P.; Giurgiu, G.; Glagolev, V.; Glenzinski, D.; Gold, M.; Goldin, D.; Golossanov, A.; Gomez, G.; Gomez-Ceballos, G.; Goncharov, M.; González López, O.; Gorelov, I.; Goshaw, A. T.; Goulianos, K.; Gramellini, E.; Grinstein, S.; Grosso-Pilcher, C.; Group, R. C.; Guimaraes da Costa, J.; Hahn, S. R.; Han, J. Y.; Happacher, F.; Hara, K.; Hare, M.; Harr, R. F.; Harrington-Taber, T.; Hatakeyama, K.; Hays, C.; Heinrich, J.; Herndon, M.; Hocker, A.; Hong, Z.; Hopkins, W.; Hou, S.; Hughes, R. E.; Husemann, U.; Hussein, M.; Huston, J.; Introzzi, G.; Iori, M.; Ivanov, A.; James, E.; Jang, D.; Jayatilaka, B.; Jeon, E. J.; Jindariani, S.; Jones, M.; Joo, K. K.; Jun, S. Y.; Junk, T. R.; Kambeitz, M.; Kamon, T.; Karchin, P. E.; Kasmi, A.; Kato, Y.; Ketchum, W.; Keung, J.; Kilminster, B.; Kim, D. H.; Kim, H. S.; Kim, J. E.; Kim, M. J.; Kim, S. B.; Kim, S. H.; Kim, Y. J.; Kim, Y. K.; Kimura, N.; Kirby, M.; Knoepfel, K.; Kondo, K.; Kong, D. J.; Konigsberg, J.; Kotwal, A. V.; Kreps, M.; Kroll, J.; Kruse, M.; Kuhr, T.; Kurata, M.; Laasanen, A. T.; Lammel, S.; Lancaster, M.; Lannon, K.; Latino, G.; Lee, H. S.; Lee, J. S.; Leo, S.; Leone, S.; Lewis, J. D.; Limosani, A.; Lipeles, E.; Lister, A.; Liu, H.; Liu, Q.; Liu, T.; Lockwitz, S.; Loginov, A.; Lucà, A.; Lucchesi, D.; Lueck, J.; Lujan, P.; Lukens, P.; Lungu, G.; Lys, J.; Lysak, R.; Madrak, R.; Maestro, P.; Malik, S.; Manca, G.; Manousakis-Katsikakis, A.; Margaroli, F.; Marino, P.; Martínez, M.; Matera, K.; Mattson, M. E.; Mazzacane, A.; Mazzanti, P.; McNulty, R.; Mehta, A.; Mehtala, P.; Mesropian, C.; Miao, T.; Mietlicki, D.; Mitra, A.; Miyake, H.; Moed, S.; Moggi, N.; Moon, C. S.; Moore, R.; Morello, M. J.; Mukherjee, A.; Muller, Th.; Murat, P.; Mussini, M.; Nachtman, J.; Nagai, Y.; Naganoma, J.; Nakano, I.; Napier, A.; Nett, J.; Neu, C.; Nigmanov, T.; Nodulman, L.; Noh, S. Y.; Norniella, O.; Oakes, L.; Oh, S. H.; Oh, Y. D.; Oksuzian, I.; Okusawa, T.; Orava, R.; Ortolan, L.; Pagliarone, C.; Palencia, E.; Palni, P.; Papadimitriou, V.; Parker, W.; Pauletta, G.; Paulini, M.; Paus, C.; Phillips, T. J.; Piacentino, G.; Pianori, E.; Pilot, J.; Pitts, K.; Plager, C.; Pondrom, L.; Poprocki, S.; Potamianos, K.; Pranko, A.; Prokoshin, F.; Ptohos, F.; Punzi, G.; Ranjan, N.; Redondo Fernández, I.; Renton, P.; Rescigno, M.; Rimondi, F.; Ristori, L.; Robson, A.; Rodriguez, T.; Rolli, S.; Ronzani, M.; Roser, R.; Rosner, J. L.; Ruffini, F.; Ruiz, A.; Russ, J.; Rusu, V.; Sakumoto, W. K.; Sakurai, Y.; Santi, L.; Sato, K.; Saveliev, V.; Savoy-Navarro, A.; Schlabach, P.; Schmidt, E. E.; Schwarz, T.; Scodellaro, L.; Scuri, F.; Seidel, S.; Seiya, Y.; Semenov, A.; Sforza, F.; Shalhout, S. Z.; Shears, T.; Shepard, P. F.; Shimojima, M.; Shochet, M.; Shreyber-Tecker, I.; Simonenko, A.; Sinervo, P.; Sliwa, K.; Smith, J. R.; Snider, F. D.; Song, H.; Sorin, V.; Stancari, M.; Denis, R. St.; Stelzer, B.; Stelzer-Chilton, O.; Stentz, D.; Strologas, J.; Sudo, Y.; Sukhanov, A.; Suslov, I.; Takemasa, K.; Takeuchi, Y.; Tang, J.; Tecchio, M.; Teng, P. K.; Thom, J.; Thomson, E.; Thukral, V.; Toback, D.; Tokar, S.; Tollefson, K.; Tomura, T.; Tonelli, D.; Torre, S.; Torretta, D.; Totaro, P.; Trovato, M.; Ukegawa, F.; Uozumi, S.; Vázquez, F.; Velev, G.; Vellidis, C.; Vernieri, C.; Vidal, M.; Vilar, R.; Vizán, J.; Vogel, M.; Volpi, G.; Wagner, P.; Wallny, R.; Wang, S. M.; Warburton, A.; Waters, D.; Wester, W. C., III; Whiteson, D.; Wicklund, A. B.; Wilbur, S.; Williams, H. H.; Wilson, J. S.; Wilson, P.; Winer, B. L.; Wittich, P.; Wolbers, S.; Wolfe, H.; Wright, T.; Wu, X.; Wu, Z.; Yamamoto, K.; Yamato, D.; Yang, T.; Yang, U. K.; Yang, Y. C.; Yao, W.-M.; Yeh, G. P.; Yi, K.; Yoh, J.; Yorita, K.; Yoshida, T.; Yu, G. B.; Yu, I.; Zanetti, A. M.; Zeng, Y.; Zhou, C.; Zucchelli, S.

2013-08-01

We present the first signature-based search for delayed photons using an exclusive photon plus missing transverse energy final state. Events are reconstructed in a data sample from the CDF II detector corresponding to 6.3fb-1 of integrated luminosity from s=1.96TeV proton-antiproton collisions. Candidate events are selected if they contain a photon with an arrival time in the detector larger than expected from a promptly produced photon. The mean number of events from standard model sources predicted by the data-driven background model based on the photon timing distribution is 286±24. A total of 322 events are observed. A p value of 12% is obtained, showing consistency of the data with standard model predictions.

Reducing usage of the computational resources by event driven approach to model predictive control

NASA Astrophysics Data System (ADS)

Misik, Stefan; Bradac, Zdenek; Cela, Arben

2017-08-01

This paper deals with a real-time and optimal control of dynamic systems while also considers the constraints which these systems might be subject to. Main objective of this work is to propose a simple modification of the existing Model Predictive Control approach to better suit needs of computational resource-constrained real-time systems. An example using model of a mechanical system is presented and the performance of the proposed method is evaluated in a simulated environment.

Avalanche correlations in the martensitic transition of a Cu-Zn-Al shape memory alloy: analysis of acoustic emission and calorimetry.

PubMed

Baró, Jordi; Martín-Olalla, José-María; Romero, Francisco Javier; Gallardo, María Carmen; Salje, Ekhard K H; Vives, Eduard; Planes, Antoni

2014-03-26

The existence of temporal correlations during the intermittent dynamics of a thermally driven structural phase transition is studied in a Cu-Zn-Al alloy. The sequence of avalanches is observed by means of two techniques: acoustic emission and high sensitivity calorimetry. Both methods reveal the existence of event clustering in a way that is equivalent to the Omori correlations between aftershocks in earthquakes as are commonly used in seismology.

How MAG4 Improves Space Weather Forecasting

NASA Technical Reports Server (NTRS)

Falconer, David; Khazanov, Igor; Barghouty, Nasser

2013-01-01

Dangerous space weather is driven by solar flares and Coronal Mass Ejection (CMEs). Forecasting flares and CMEs is the first step to forecasting either dangerous space weather or All Clear. MAG4 (Magnetogram Forecast), developed originally for NASA/SRAG (Space Radiation Analysis Group), is an automated program that analyzes magnetograms from the HMI (Helioseismic and Magnetic Imager) instrument on NASA SDO (Solar Dynamics Observatory), and automatically converts the rate (or probability) of major flares (M- and X-class), Coronal Mass Ejections (CMEs), and Solar Energetic Particle Events.

A Unified Model of Geostrophic Adjustment and Frontogenesis

NASA Astrophysics Data System (ADS)

Taylor, John; Shakespeare, Callum

2013-11-01

Fronts, or regions with strong horizontal density gradients, are ubiquitous and dynamically important features of the ocean and atmosphere. In the ocean, fronts are associated with enhanced air-sea fluxes, turbulence, and biological productivity, while atmospheric fronts are associated with some of the most extreme weather events. Here, we describe a new mathematical framework for describing the formation of fronts, or frontogenesis. This framework unifies two classical problems in geophysical fluid dynamics, geostrophic adjustment and strain-driven frontogenesis, and provides a number of important extensions beyond previous efforts. The model solutions closely match numerical simulations during the early stages of frontogenesis, and provide a means to describe the development of turbulence at mature fronts.

Spurting Plasma

NASA Image and Video Library

2014-06-16

A stream of plasma burst out from the sun, but since it lacked enough force to break away, most of it fell back into the sun (May 27, 2014). This eruption was minor and such events occur almost every day on the sun and suggest the kind of dynamic activity being driven by powerful magnetic forces near the sun's surface. Credit: NASA/Goddard/Solar Dynamics Observatory NASA image use policy. NASA Goddard Space Flight Center enables NASA’s mission through four scientific endeavors: Earth Science, Heliophysics, Solar System Exploration, and Astrophysics. Goddard plays a leading role in NASA’s accomplishments by contributing compelling scientific knowledge to advance the Agency’s mission. Follow us on Twitter Like us on Facebook Find us on Instagram

Data-Driven Modeling of Complex Systems by means of a Dynamical ANN

NASA Astrophysics Data System (ADS)

Seleznev, A.; Mukhin, D.; Gavrilov, A.; Loskutov, E.; Feigin, A.

2017-12-01

The data-driven methods for modeling and prognosis of complex dynamical systems become more and more popular in various fields due to growth of high-resolution data. We distinguish the two basic steps in such an approach: (i) determining the phase subspace of the system, or embedding, from available time series and (ii) constructing an evolution operator acting in this reduced subspace. In this work we suggest a novel approach combining these two steps by means of construction of an artificial neural network (ANN) with special topology. The proposed ANN-based model, on the one hand, projects the data onto a low-dimensional manifold, and, on the other hand, models a dynamical system on this manifold. Actually, this is a recurrent multilayer ANN which has internal dynamics and capable of generating time series. Very important point of the proposed methodology is the optimization of the model allowing us to avoid overfitting: we use Bayesian criterion to optimize the ANN structure and estimate both the degree of evolution operator nonlinearity and the complexity of nonlinear manifold which the data are projected on. The proposed modeling technique will be applied to the analysis of high-dimensional dynamical systems: Lorenz'96 model of atmospheric turbulence, producing high-dimensional space-time chaos, and quasi-geostrophic three-layer model of the Earth's atmosphere with the natural orography, describing the dynamics of synoptical vortexes as well as mesoscale blocking systems. The possibility of application of the proposed methodology to analyze real measured data is also discussed. The study was supported by the Russian Science Foundation (grant #16-12-10198).

Event Driven Messaging with Role-Based Subscriptions

NASA Technical Reports Server (NTRS)

Bui, Tung; Bui, Bach; Malhotra, Shantanu; Chen, Fannie; Kim, rachel; Allen, Christopher; Luong, Ivy; Chang, George; Zendejas, Silvino; Sadaqathulla, Syed

2009-01-01

Event Driven Messaging with Role-Based Subscriptions (EDM-RBS) is a framework integrated into the Service Management Database (SMDB) to allow for role-based and subscription-based delivery of synchronous and asynchronous messages over JMS (Java Messaging Service), SMTP (Simple Mail Transfer Protocol), or SMS (Short Messaging Service). This allows for 24/7 operation with users in all parts of the world. The software classifies messages by triggering data type, application source, owner of data triggering event (mission), classification, sub-classification and various other secondary classifying tags. Messages are routed to applications or users based on subscription rules using a combination of the above message attributes. This program provides a framework for identifying connected users and their applications for targeted delivery of messages over JMS to the client applications the user is logged into. EDMRBS provides the ability to send notifications over e-mail or pager rather than having to rely on a live human to do it. It is implemented as an Oracle application that uses Oracle relational database management system intrinsic functions. It is configurable to use Oracle AQ JMS API or an external JMS provider for messaging. It fully integrates into the event-logging framework of SMDB (Subnet Management Database).

Attrition in NRG Oncology's Radiation-Based Clinical Trials.

PubMed

Ulrich, Connie M; Deshmukh, Snehal; Pugh, Stephanie L; Hanlon, Alexandra; Grady, Christine; Watkins Bruner, Deborah; Curran, Walter

2018-05-10

To determine individual, organizational, and protocol-specific factors associated with attrition in NRG Oncology's radiation-based clinical trials. This retrospective analysis included 27,443 patients representing 134 NRG Oncology's radiation-based clinical trials .trials with primary efficacy results published from 1985-2011. Trials were separated on the basis of the primary endpoint (fixed time vs event driven). The cumulative incidence approach was used to estimate time to attrition, and cause-specific Cox proportional hazards models were used to assess factors associated with attrition. Most patients (69%) were enrolled in an event-driven trial (n = 18,809), while 31% were enrolled in a fixed-time trial (n = 8634). Median follow-up time for patients enrolled in fixed-time trials was 4.1 months and 37.2 months for patients enrolled in event-driven trials. Fixed time trials with a duration < 6 months had a 5 month attrition rate of 4.3% (95% confidence interval [CI]: 3.4%, 5.5%) and those with a duration ≥ 6 months had a 1 year attrition rate of 1.6% (95% CI: 1.2, 2.1). Event-driven trials had 1- and 5-year attrition rates of 0.5% (95% CI: 0.4%, 0.6%) and 13.6% (95% CI: 13.1%, 14.1%), respectively. Younger age, female gender, and Zubrod performance status >0 were associated with greater attrition as were enrollment by institutions in the West and South regions and participation in fixed-time trials. Attrition in clinical trials can have a negative effect on trial outcomes. Data on factors associated with attrition can help guide the development of strategies to enhance retention. These strategies should focus on patient characteristics associated with attrition in both fixed-time and event-driven trials as well as in differing geographic regions of the country. Copyright © 2018. Published by Elsevier Inc.

Event-driven simulations of nonlinear integrate-and-fire neurons.

PubMed

Tonnelier, Arnaud; Belmabrouk, Hana; Martinez, Dominique

2007-12-01

Event-driven strategies have been used to simulate spiking neural networks exactly. Previous work is limited to linear integrate-and-fire neurons. In this note, we extend event-driven schemes to a class of nonlinear integrate-and-fire models. Results are presented for the quadratic integrate-and-fire model with instantaneous or exponential synaptic currents. Extensions to conductance-based currents and exponential integrate-and-fire neurons are discussed.

MAX - An advanced parallel computer for space applications

NASA Technical Reports Server (NTRS)

Lewis, Blair F.; Bunker, Robert L.

1991-01-01

MAX is a fault-tolerant multicomputer hardware and software architecture designed to meet the needs of NASA spacecraft systems. It consists of conventional computing modules (computers) connected via a dual network topology. One network is used to transfer data among the computers and between computers and I/O devices. This network's topology is arbitrary. The second network operates as a broadcast medium for operating system synchronization messages and supports the operating system's Byzantine resilience. A fully distributed operating system supports multitasking in an asynchronous event and data driven environment. A large grain dataflow paradigm is used to coordinate the multitasking and provide easy control of concurrency. It is the basis of the system's fault tolerance and allows both static and dynamical location of tasks. Redundant execution of tasks with software voting of results may be specified for critical tasks. The dataflow paradigm also supports simplified software design, test and maintenance. A unique feature is a method for reliably patching code in an executing dataflow application.

Analysis/test correlation using VAWT-SDS on a step-relaxation test for the rotating Sandia 34 m test bed

DOE Office of Scientific and Technical Information (OSTI.GOV)

Argueello, J.G.; Dohrmann, C.R.; Carne, T.G.

The combined analysis/test effort described in this paper compares predictions with measured data from a step-relaxation test in the absence of significant wind-driven aerodynamic loading. The process described here is intended to illustrate a method for validation of time domain codes for structural analysis of wind turbine structures. Preliminary analyses were performed to investigate the transient dynamic response that the rotating Sandia 34 m Vertical Axis Wind Turbine (VAWT) would undergo when one of the two blades was excited by step-relaxation. The calculations served two purposes. The first was for pretest planning to evaluate the relative importance of the variousmore » forces that would be acting on the structure during the test and to determine if the applied force in the step-relaxation would be sufficient to produce an excitation that was distinguishable from that produced by the aerodynamic loads. The second was to provide predictions that could subsequently be compared to the data from the test. The test was carried out specifically to help in the validation of the time-domain structural dynamics code, VAWT-SDS, which predicts the dynamic response of VAWTs subject to transient events. Post-test comparisons with the data were performed and showed a qualitative agreement between pretest predictions and measured response. However, they also showed that there was significantly more damping in the measurements than included in the predictions. Efforts to resolve this difference, including post-test analyses, were undertaken and are reported herein. The overall effort described in this paper represents a major step in the process of arriving at a validated structural dynamics code.« less

Fluctuation-Driven Neural Dynamics Reproduce Drosophila Locomotor Patterns

PubMed Central

Cruchet, Steeve; Gustafson, Kyle; Benton, Richard; Floreano, Dario

2015-01-01

The neural mechanisms determining the timing of even simple actions, such as when to walk or rest, are largely mysterious. One intriguing, but untested, hypothesis posits a role for ongoing activity fluctuations in neurons of central action selection circuits that drive animal behavior from moment to moment. To examine how fluctuating activity can contribute to action timing, we paired high-resolution measurements of freely walking Drosophila melanogaster with data-driven neural network modeling and dynamical systems analysis. We generated fluctuation-driven network models whose outputs—locomotor bouts—matched those measured from sensory-deprived Drosophila. From these models, we identified those that could also reproduce a second, unrelated dataset: the complex time-course of odor-evoked walking for genetically diverse Drosophila strains. Dynamical models that best reproduced both Drosophila basal and odor-evoked locomotor patterns exhibited specific characteristics. First, ongoing fluctuations were required. In a stochastic resonance-like manner, these fluctuations allowed neural activity to escape stable equilibria and to exceed a threshold for locomotion. Second, odor-induced shifts of equilibria in these models caused a depression in locomotor frequency following olfactory stimulation. Our models predict that activity fluctuations in action selection circuits cause behavioral output to more closely match sensory drive and may therefore enhance navigation in complex sensory environments. Together these data reveal how simple neural dynamics, when coupled with activity fluctuations, can give rise to complex patterns of animal behavior. PMID:26600381

The Orion GN and C Data-Driven Flight Software Architecture for Automated Sequencing and Fault Recovery

NASA Technical Reports Server (NTRS)

King, Ellis; Hart, Jeremy; Odegard, Ryan

2010-01-01

The Orion Crew Exploration Vehicle (CET) is being designed to include significantly more automation capability than either the Space Shuttle or the International Space Station (ISS). In particular, the vehicle flight software has requirements to accommodate increasingly automated missions throughout all phases of flight. A data-driven flight software architecture will provide an evolvable automation capability to sequence through Guidance, Navigation & Control (GN&C) flight software modes and configurations while maintaining the required flexibility and human control over the automation. This flexibility is a key aspect needed to address the maturation of operational concepts, to permit ground and crew operators to gain trust in the system and mitigate unpredictability in human spaceflight. To allow for mission flexibility and reconfrgurability, a data driven approach is being taken to load the mission event plan as well cis the flight software artifacts associated with the GN&C subsystem. A database of GN&C level sequencing data is presented which manages and tracks the mission specific and algorithm parameters to provide a capability to schedule GN&C events within mission segments. The flight software data schema for performing automated mission sequencing is presented with a concept of operations for interactions with ground and onboard crew members. A prototype architecture for fault identification, isolation and recovery interactions with the automation software is presented and discussed as a forward work item.

Event-by-event elliptic flow fluctuations from PHOBOS

DOE PAGES

Wosiek, Barbara; Alver, B.; Back, B. B.; ...

2009-04-01

Recently PHOBOS has focused on the study of fluctuations and correlations in particle production in heavy-ion collisions at the highest energies delivered by the Relativistic Heavy Ion Collider (RHIC). In this report, we present results on event-by-event elliptic flow fluctuations in Au + Au collisions at √s NN =200 GeV. A data-driven method was used to estimate the dominant contribution from non-flow correlations. Over the broad range of collision centralities, the observed large elliptic flow fluctuations are in agreement with the fluctuations in the initial source eccentricity.

Event-by-Event Elliptic Flow Fluctuations from PHOBOS

NASA Astrophysics Data System (ADS)

Wosiek, B.; Alver, B.; Back, B. B.; Baker, M. D.; Ballintijn, M.; Barton, D. S.; Betts, R. R.; Bickley, A. A.; Bindel, R.; Busza, W.; Carroll, A.; Chai, Z.; Chetluru, V.; Decowski, M. P.; García, E.; Gburek, T.; George, N.; Gulbrandsen, K.; Halliwell, C.; Hamblen, J.; Harnarine, I.; Hauer, M.; Henderson, C.; Hofman, D. J.; Hollis, R. S.; Hołyński, R.; Holzman, B.; Iordanova, A.; Johnson, E.; Kane, J. L.; Khan, N.; Kulinich, P.; Kuo, C. M.; Li, W.; Lin, W. T.; Loizides, C.; Manly, S.; Mignerey, A. C.; Nouicer, R.; Olszewski, A.; Pak, R.; Reed, C.; Richardson, E.; Roland, C.; Roland, G.; Sagerer, J.; Seals, H.; Sedykh, I.; Smith, C. E.; Stankiewicz, M. A.; Steinberg, P.; Stephans, G. S. F.; Sukhanov, A.; Szostak, A.; Tonjes, M. B.; Trzupek, A.; Vale, C.; van Nieuwenhuizen, G. J.; Vaurynovich, S. S.; Verdier, R.; Veres, G. I.; Walters, P.; Wenger, E.; Willhelm, D.; Wolfs, F. L. H.; Woźniak, K.; Wyngaardt, S.; Wysłouch, B.

2009-04-01

Recently PHOBOS has focused on the study of fluctuations and correlations in particle production in heavy-ion collisions at the highest energies delivered by the Relativistic Heavy Ion Collider (RHIC). In this report, we present results on event-by-event elliptic flow fluctuations in (Au+Au) collisions at sqrt {sNN}=200 GeV. A data-driven method was used to estimate the dominant contribution from non-flow correlations. Over the broad range of collision centralities, the observed large elliptic flow fluctuations are in agreement with the fluctuations in the initial source eccentricity.

Unemployment and inflation dynamics prior to the economic downturn of 2007-2008.

PubMed

Guastello, Stephen J; Myers, Adam

2009-10-01

This article revisits a long-standing theoretical issue as to whether a "natural rate" of unemployment exists in the sense of an exogenously driven fixed-point Walrasian equilibrium or attractor, or whether more complex dynamics such as hysteresis or chaos characterize an endogenous dynamical process instead. The same questions are posed regarding a possible natural rate of inflation along with an investigation of the actual relationship between inflation and unemployment for which extent theories differ. Time series of unemployment and inflation for US data - were analyzed using the exponential model series and nonlinear regression for capturing Lyapunov exponents and transfer effects from other variables. The best explanation for unemployment was that it is a chaotic variable that is driven in part by inflation. The best explanation for inflation is that it is also a chaotic variable driven in part by unemployment and the prices of treasury bills. Estimates of attractors' epicenters were calculated in lieu of classical natural rates.

A new method for quantifying and modeling large scale surface water inundation dynamics and key drivers using multiple time series of Earth observation and river flow data. A case study for Australia's Murray-Darling Basin

NASA Astrophysics Data System (ADS)

Heimhuber, Valentin; Tulbure, Mirela G.; Broich, Mark

2017-04-01

Periodically inundated surface water (SW) areas such as floodplains are hotspots of biodiversity and provide a broad range of ecosystem services but have suffered alarming declines in recent history. Large scale flooding events govern the dynamics of these areas and are a critical component of the terrestrial water cycle, but their propagation through river systems and the corresponding long term SW dynamics remain poorly quantified on continental or global scales. In this research, we used an unprecedented Landsat-based time series of SW maps (1986-2011), to develop statistical inundation models and quantify the role of driver variables across the Murray-Darling Basin (MDB) (1 million square-km), which is Australia's bread basket and subject to competing demands over limited water resources. We fitted generalized additive models (GAM) between SW extent as the dependent variable and river flow data from 68 gauges, spatial time series of rainfall (P; interpolated gauge data), evapotranspiration (ET; AWRA-L land surface model) and soil moisture (SM; active passive microwave satellite remote sensing) as predictor variables. We used a fully directed and connected river network (Australian Geofabric) in combination with ancillary data, to develop a spatial modeling framework consisting of 18,521 individual modeling units. We then fitted individual models for all modeling units, which were made up of 10x10 km grid cells split into floodplain, floodplain-lake and non-floodplain areas, depending on the type of water body and its hydrologic connectivity to a gauged river. We applied the framework to quantify flood propagation times for all major river and floodplain systems across the MDB, which were in good accordance with observed travel times. After incorporating these flow lag times into the models, average goodness of fit was high across floodplains and floodplain-lake modeling units (r-squared > 0.65), which were primarily driven by river flow, and lower for non-floodplain areas (r-squared > 0.24), which were primarily driven by local rainfall. Our results indicate that local climate conditions (i.e. P, ET, SM) had more influence on SW dynamics in the northern compared to the southern MDB and were the most influential in the least regulated and most extended floodplains in the north-west. We also applied the statistical models of two floodplain areas with contrasting flooding regimes to predict SW extents of cloud-affected time steps in the Landsat time series during the large 2010 floods with high validated accuracy (r-squared > 0.97). Our findings illustrate that integrating multi-decadal time series of Earth observation data and in situ measurements with statistical modeling techniques can provide cost-effective tools for improving the management of limited SW resources and floods. The data-driven method is applicable to other large river basins and provides statistical models that can predict SW extent for cloud-affected Landsat observations or during the peak of floods and hence, allows a more detailed quantification of the dynamics of large floods compared to existing approaches. Future research will investigate the potential of image fusion techniques (i.e. ESTARFM) for improving the quantification of rapid changes in SW distribution by combining MODIS and Landsat imagery.

Dynamics of origination and extinction in the marine fossil record

PubMed Central

Alroy, John

2008-01-01

The discipline-wide effort to database the fossil record at the occurrence level has made it possible to estimate marine invertebrate extinction and origination rates with much greater accuracy. The new data show that two biotic mechanisms have hastened recoveries from mass extinctions and confined diversity to a relatively narrow range over the past 500 million years (Myr). First, a drop in diversity of any size correlates with low extinction rates immediately afterward, so much so that extinction would almost come to a halt if diversity dropped by 90%. Second, very high extinction rates are followed by equally high origination rates. The two relationships predict that the rebound from the current mass extinction will take at least 10 Myr, and perhaps 40 Myr if it rivals the Permo-Triassic catastrophe. Regardless, any large event will result in a dramatic ecological and taxonomic restructuring of the biosphere. The data also confirm that extinction and origination rates both declined through the Phanerozoic and that several extinctions in addition to the Permo-Triassic event were particularly severe. However, the trend may be driven by taxonomic biases and the rates vary in accord with a simple log normal distribution, so there is no sharp distinction between background and mass extinctions. Furthermore, the lack of any significant autocorrelation in the data is inconsistent with macroevolutionary theories of periodicity or self-organized criticality. PMID:18695240

Learning maximum entropy models from finite-size data sets: A fast data-driven algorithm allows sampling from the posterior distribution.

PubMed

Ferrari, Ulisse

2016-08-01

Maximum entropy models provide the least constrained probability distributions that reproduce statistical properties of experimental datasets. In this work we characterize the learning dynamics that maximizes the log-likelihood in the case of large but finite datasets. We first show how the steepest descent dynamics is not optimal as it is slowed down by the inhomogeneous curvature of the model parameters' space. We then provide a way for rectifying this space which relies only on dataset properties and does not require large computational efforts. We conclude by solving the long-time limit of the parameters' dynamics including the randomness generated by the systematic use of Gibbs sampling. In this stochastic framework, rather than converging to a fixed point, the dynamics reaches a stationary distribution, which for the rectified dynamics reproduces the posterior distribution of the parameters. We sum up all these insights in a "rectified" data-driven algorithm that is fast and by sampling from the parameters' posterior avoids both under- and overfitting along all the directions of the parameters' space. Through the learning of pairwise Ising models from the recording of a large population of retina neurons, we show how our algorithm outperforms the steepest descent method.

Dynamic data driven bidirectional reflectance distribution function measurement system

NASA Astrophysics Data System (ADS)

Nauyoks, Stephen E.; Freda, Sam; Marciniak, Michael A.

2014-09-01

The bidirectional reflectance distribution function (BRDF) is a fitted distribution function that defines the scatter of light off of a surface. The BRDF is dependent on the directions of both the incident and scattered light. Because of the vastness of the measurement space of all possible incident and reflected directions, the calculation of BRDF is usually performed using a minimal amount of measured data. This may lead to poor fits and uncertainty in certain regions of incidence or reflection. A dynamic data driven application system (DDDAS) is a concept that uses an algorithm on collected data to influence the collection space of future data acquisition. The authors propose a DDD-BRDF algorithm that fits BRDF data as it is being acquired and uses on-the-fly fittings of various BRDF models to adjust the potential measurement space. In doing so, it is hoped to find the best model to fit a surface and the best global fit of the BRDF with a minimum amount of collection space.

Time-frequency analysis of the EEG mu rhythm as a measure of sensorimotor integration in the later stages of swallowing.

PubMed

Cuellar, M; Harkrider, A W; Jenson, D; Thornton, D; Bowers, A; Saltuklaroglu, T

2016-07-01

Electroencephalography (EEG) was used to map the temporal dynamics of sensorimotor integration relative to the strength and timing of muscular activity during swallowing. 64-channel EEG data and surface electromyographic (sEMG) data were recorded from 25 neurologically-healthy adults during swallowing and tongue-tapping. Events were demarcated so that sensorimotor activity primarily from the pharyngeal and esophageal phases of swallowing could be compared to activity resulting from tongue tapping. Independent component analysis identified bilateral clusters of sensorimotor mu components localized to the premotor and primary motor cortices as well as an infrahyoid myogenic cluster. Subsequent event-related spectral perturbations (ERSP) analyses showed event-related desynchronization (ERD) in the spectral power in the alpha (8-13Hz) and beta (15-25Hz) frequency bands of the mu clusters in both tasks. Mu ERD was stronger during swallowing when compared to tongue tapping (pFDR<.05) and the differences in sensorimotor processing between conditions was greater in the right hemisphere than the left, suggesting stronger right hemisphere lateralization for swallowing than tongue-tapping. Mu activity was interpreted as representing a normal feed forward and feedback driven sensorimotor loop during the later stages of swallowing. Results support further use of this novel neuroimaging technique to concurrently map neural and muscle activity during swallowing in clinical populations using EEG. Copyright © 2016 International Federation of Clinical Neurophysiology. Published by Elsevier Ireland Ltd. All rights reserved.

Signatures of the impact of flare-ejected plasma on the photosphere of a sunspot light bridge

NASA Astrophysics Data System (ADS)

Felipe, T.; Collados, M.; Khomenko, E.; Rajaguru, S. P.; Franz, M.; Kuckein, C.; Asensio Ramos, A.

2017-12-01

Aims: We investigate the properties of a sunspot light bridge, focusing on the changes produced by the impact of a plasma blob ejected from a C-class flare. Methods: We observed a sunspot in active region NOAA 12544 using spectropolarimetric raster maps of the four Fe I lines around 15 655 Å with the GREGOR Infrared Spectrograph, narrow-band intensity images sampling the Fe I 6173 Å line with the GREGOR Fabry-Pérot Interferometer, and intensity broad-band images in G-band and Ca II H-band with the High-resolution Fast Imager. All these instruments are located at the GREGOR telescope at the Observatorio del Teide, Tenerife, Spain. The data cover the time before, during, and after the flare event. The analysis is complemented with Atmospheric Imaging Assembly and Helioseismic and Magnetic Imager data from the Solar Dynamics Observatory. The physical parameters of the atmosphere at differents heights were inferred using spectral-line inversion techniques. Results: We identify photospheric and chromospheric brightenings, heating events, and changes in the Stokes profiles associated with the flare eruption and the subsequent arrival of the plasma blob to the light bridge, after traveling along an active region loop. Conclusions: The measurements suggest that these phenomena are the result of reconnection events driven by the interaction of the plasma blob with the magnetic field topology of the light bridge. Movies attached to Figs. 1 and 3 are available at http://www.aanda.org

Foreshocks and delayed triggering of the 2016 MW7.1 Te Araroa earthquake and dynamic reinvigoration of its aftershock sequence by the MW7.8 Kaikōura earthquake, New Zealand

NASA Astrophysics Data System (ADS)

Warren-Smith, Emily; Fry, Bill; Kaneko, Yoshihiro; Chamberlain, Calum J.

2018-01-01

We analyze the preparatory period of the September 2016 MW7.1 Te Araroa foreshock-mainshock sequence in the Northern Hikurangi margin, New Zealand, and subsequent reinvigoration of Te Araroa aftershocks driven by a large distant earthquake (the November 2016 MW7.8 Kaikōura earthquake). By adopting a matched-filter detection workflow using 582 well-defined template events, we generate an improved foreshock and aftershock catalog for the Te Araroa sequence (>8,000 earthquakes over 66 d). Templates characteristic of the MW7.1 sequence (including the mainshock template) detect several highly correlating events (ML2.5-3.5) starting 12 min after a MW5.7 foreshock. These pre-cursory events occurred within ∼1 km of the mainshock and migrate bilaterally, suggesting precursory slip was triggered by the foreshock on the MW7.1 fault patch prior to mainshock failure. We extend our matched-filter routine to examine the interactions between high dynamic stresses resulting from passing surface waves of the November 2016 MW7.8 Kaikōura earthquake, and the evolution of the Te Araroa aftershock sequence. We observe a sudden spike in moment release of the aftershock sequence immediately following peak dynamic Coulomb stresses of 50-150 kPa on the MW7.1 fault plane. The triggered increase in moment release culminated in a MW5.1 event, immediately followed by a ∼3 h temporal stress shadow. Our observations document the preparatory period of a major subduction margin earthquake following a significant foreshock, and quantify dynamic reinvigoration of a distant on-going major aftershock sequence amid a period of temporal clustering of seismic activity in New Zealand.

Tick exposure and extreme climate events impact survival and threaten the persistence of a long-lived lizard.

PubMed

Jones, Alice R; Bull, C Michael; Brook, Barry W; Wells, Konstans; Pollock, Kenneth H; Fordham, Damien A

2016-03-01

Assessing the impacts of multiple, often synergistic, stressors on the population dynamics of long-lived species is becoming increasingly important due to recent and future global change. Tiliqua rugosa (sleepy lizard) is a long-lived skink (>30 years) that is adapted to survive in semi-arid environments with varying levels of parasite exposure and highly seasonal food availability. We used an exhaustive database of 30 years of capture-mark-recapture records to quantify the impacts of both parasite exposure and environmental conditions on the lizard's survival rates and long-term population dynamics. Lizard abundance was relatively stable throughout the study period; however, there were changing patterns in adult and juvenile apparent survival rates, driven by spatial and temporal variation in levels of tick exposure and temporal variation in environmental conditions. Extreme weather events during the winter and spring seasons were identified as important environmental drivers of survival. Climate models predict a dramatic increase in the frequency of extreme hot and dry winter and spring seasons in our South Australian study region; from a contemporary probability of 0.17 up to 0.47-0.83 in 2080 depending on the emissions scenario. Our stochastic population model projections showed that these future climatic conditions will induce a decline in the abundance of this long-lived reptile of up to 67% within 30 years from 2080, under worst case scenario modelling. The results have broad implications for future work investigating the drivers of population dynamics and persistence. We highlight the importance of long-term data sets and accounting for synergistic impacts between multiple stressors. We show that predicted increases in the frequency of extreme climate events have the potential to considerably and negatively influence a long-lived species, which might previously have been assumed to be resilient to environmental perturbations. © 2015 The Authors. Journal of Animal Ecology © 2015 British Ecological Society.

Gelation-driven component selection in the generation of constitutional dynamic hydrogels based on guanine-quartet formation

PubMed Central

Sreenivasachary, Nampally; Lehn, Jean-Marie

2005-01-01

The guanosine hydrazide 1 yields a stable supramolecular hydrogel based on the formation of a guanine quartet (G-quartet) in presence of metal cations. The effect of various parameters (concentration, nature of metal ion, and temperature) on the properties of this gel has been studied. Proton NMR spectroscopy is shown to allow a molecular characterization of the gelation process. Hydrazide 1 and its assemblies can be reversibly decorated by acylhydrazone formation with various aldehydes, resulting in formation of highly viscous dynamic hydrogels. When a mixture of aldehydes is used, the dynamic system selects the aldehyde that leads to the most stable gel. Mixing hydrazides 1, 9 and aldehydes 6, 8 in 1:1:1:1 ratio generated a constitutional dynamic library containing the four acylhydrazone derivatives A, B, C, and D. The library constitution displayed preferential formation of the acylhydrazone B that yields the strongest gel. Thus, gelation redirects the acylhydrazone distribution in the dynamic library as guanosine hydrazide 1 scavenges preferentially aldehyde 8, under the pressure of gelation because of the collective interactions in the assemblies of G-quartets B, despite the strong preference of the competing hydrazide 9 for 8. Gel formation and component selection are thermoreversible. The process amounts to gelation-driven self-organization with component selection and amplification in constitutional dynamic hydrogels based on G-quartet formation and reversible covalent connections. The observed self-organization and component selection occur by means of a multilevel self-assembly involving three dynamic processes, two of supramolecular and one of reversible covalent nature. They extend constitutional dynamic chemistry to phase-organization and phase-transition events. PMID:15840720

Gelation-driven component selection in the generation of constitutional dynamic hydrogels based on guanine-quartet formation.

PubMed

Sreenivasachary, Nampally; Lehn, Jean-Marie

2005-04-26

The guanosine hydrazide 1 yields a stable supramolecular hydrogel based on the formation of a guanine quartet (G-quartet) in presence of metal cations. The effect of various parameters (concentration, nature of metal ion, and temperature) on the properties of this gel has been studied. Proton NMR spectroscopy is shown to allow a molecular characterization of the gelation process. Hydrazide 1 and its assemblies can be reversibly decorated by acylhydrazone formation with various aldehydes, resulting in formation of highly viscous dynamic hydrogels. When a mixture of aldehydes is used, the dynamic system selects the aldehyde that leads to the most stable gel. Mixing hydrazides 1, 9 and aldehydes 6, 8 in 1:1:1:1 ratio generated a constitutional dynamic library containing the four acylhydrazone derivatives A, B, C, and D. The library constitution displayed preferential formation of the acylhydrazone B that yields the strongest gel. Thus, gelation redirects the acylhydrazone distribution in the dynamic library as guanosine hydrazide 1 scavenges preferentially aldehyde 8, under the pressure of gelation because of the collective interactions in the assemblies of G-quartets B, despite the strong preference of the competing hydrazide 9 for 8. Gel formation and component selection are thermoreversible. The process amounts to gelation-driven self-organization with component selection and amplification in constitutional dynamic hydrogels based on G-quartet formation and reversible covalent connections. The observed self-organization and component selection occur by means of a multilevel self-assembly involving three dynamic processes, two of supramolecular and one of reversible covalent nature. They extend constitutional dynamic chemistry to phase-organization and phase-transition events.

Decadal-scale climate drivers for glacial dynamics in Glacier National Park, Montana, USA

USGS Publications Warehouse

Pederson, G.T.; Fagre, D.B.; Gray, S.T.; Graumlich, L.J.

2004-01-01

Little Ice Age (14th-19th centuries A.D.) glacial maxima and 20th century retreat have been well documented in Glacier National Park, Montana, USA. However, the influence of regional and Pacific Basin driven climate variability on these events is poorly understood. We use tree-ring reconstructions of North Pacific surface temperature anomalies and summer drought as proxies for winter glacial accumulation and summer ablation, respectively, over the past three centuries. These records show that the 1850's glacial maximum was likely produced by ???70 yrs of cool/wet summers coupled with high snowpack. Post 1850, glacial retreat coincides with an extended period (>50 yr) of summer drought and low snowpack culminating in the exceptional events of 1917 to 1941 when retreat rates for some glaciers exceeded 100 m/yr. This research highlights potential local and ocean-based drivers of glacial dynamics, and difficulties in separating the effects of global climate change from regional expressions of decadal-scale climate variability. Copyright 2004 by the American Geophysical Union.

Initial Results from an Energy-Aware Airborne Dynamic, Data-Driven Application System Performing Sampling in Coherent Boundary-Layer Structures

NASA Astrophysics Data System (ADS)

Frew, E.; Argrow, B. M.; Houston, A. L.; Weiss, C.

2014-12-01

The energy-aware airborne dynamic, data-driven application system (EA-DDDAS) performs persistent sampling in complex atmospheric conditions by exploiting wind energy using the dynamic data-driven application system paradigm. The main challenge for future airborne sampling missions is operation with tight integration of physical and computational resources over wireless communication networks, in complex atmospheric conditions. The physical resources considered here include sensor platforms, particularly mobile Doppler radar and unmanned aircraft, the complex conditions in which they operate, and the region of interest. Autonomous operation requires distributed computational effort connected by layered wireless communication. Onboard decision-making and coordination algorithms can be enhanced by atmospheric models that assimilate input from physics-based models and wind fields derived from multiple sources. These models are generally too complex to be run onboard the aircraft, so they need to be executed in ground vehicles in the field, and connected over broadband or other wireless links back to the field. Finally, the wind field environment drives strong interaction between the computational and physical systems, both as a challenge to autonomous path planning algorithms and as a novel energy source that can be exploited to improve system range and endurance. Implementation details of a complete EA-DDDAS will be provided, along with preliminary flight test results targeting coherent boundary-layer structures.

Characteristics of storms driving wave-induced seafloor mobility on the U.S. East Coast continental shelf

USGS Publications Warehouse

Dalyander, P. Soupy; Butman, Bradford

2015-01-01

This study investigates the relationship between spatial and temporal patterns of wave-driven sediment mobility events on the U.S. East Coast continental shelf and the characteristics of the storms responsible for them. Mobility events, defined as seafloor wave stress exceedance of the critical stress of 0.35 mm diameter sand (0.2160 Pa) for 12 or more hours, were identified from surface wave observations at National Data Buoy Center buoys in the Middle Atlantic Bight (MAB) and South Atlantic Bight (SAB) over the period of 1997-2007. In water depths ranging from 36-48 m, there were 4-9 mobility events/year of 1-2 days duration. Integrated wave stress during events (IWAVES) was used as a combined metric of wave-driven mobility intensity and duration. In the MAB, over 67% of IWAVES was caused by extratropical storms, while in the SAB, greater than 66% of IWAVES was caused by tropical storms. On average, mobility events were caused by waves generated by storms located 800+ km away. Far-field hurricanes generated swell 2-4 days before the waves caused mobility on the shelf. Throughout most of the SAB, mobility events were driven by storms to the south, east, and west. In the MAB and near Cape Hatteras, winds from more northerly storms and low-pressure extratropical systems in the mid-western U.S. also drove mobility events. Waves generated by storms off the SAB generated mobility events along the entire U.S. East Coast shelf north to Cape Cod, while Cape Hatteras shielded the SAB area from swell originating to the north offshore of the MAB.

Modeling radiation belt electron dynamics during GEM challenge intervals with the DREAM3D diffusion model

NASA Astrophysics Data System (ADS)

Tu, Weichao; Cunningham, G. S.; Chen, Y.; Henderson, M. G.; Camporeale, E.; Reeves, G. D.

2013-10-01

a response to the Geospace Environment Modeling (GEM) "Global Radiation Belt Modeling Challenge," a 3D diffusion model is used to simulate the radiation belt electron dynamics during two intervals of the Combined Release and Radiation Effects Satellite (CRRES) mission, 15 August to 15 October 1990 and 1 February to 31 July 1991. The 3D diffusion model, developed as part of the Dynamic Radiation Environment Assimilation Model (DREAM) project, includes radial, pitch angle, and momentum diffusion and mixed pitch angle-momentum diffusion, which are driven by dynamic wave databases from the statistical CRRES wave data, including plasmaspheric hiss, lower-band, and upper-band chorus. By comparing the DREAM3D model outputs to the CRRES electron phase space density (PSD) data, we find that, with a data-driven boundary condition at Lmax = 5.5, the electron enhancements can generally be explained by radial diffusion, though additional local heating from chorus waves is required. Because the PSD reductions are included in the boundary condition at Lmax = 5.5, our model captures the fast electron dropouts over a large L range, producing better model performance compared to previous published results. Plasmaspheric hiss produces electron losses inside the plasmasphere, but the model still sometimes overestimates the PSD there. Test simulations using reduced radial diffusion coefficients or increased pitch angle diffusion coefficients inside the plasmasphere suggest that better wave models and more realistic radial diffusion coefficients, both inside and outside the plasmasphere, are needed to improve the model performance. Statistically, the results show that, with the data-driven outer boundary condition, including radial diffusion and plasmaspheric hiss is sufficient to model the electrons during geomagnetically quiet times, but to best capture the radiation belt variations during active times, pitch angle and momentum diffusion from chorus waves are required.

Model and system learners, optimal process constructors and kinetic theory-based goal-oriented design: A new paradigm in materials and processes informatics

NASA Astrophysics Data System (ADS)

Abisset-Chavanne, Emmanuelle; Duval, Jean Louis; Cueto, Elias; Chinesta, Francisco

2018-05-01

Traditionally, Simulation-Based Engineering Sciences (SBES) has relied on the use of static data inputs (model parameters, initial or boundary conditions, … obtained from adequate experiments) to perform simulations. A new paradigm in the field of Applied Sciences and Engineering has emerged in the last decade. Dynamic Data-Driven Application Systems [9, 10, 11, 12, 22] allow the linkage of simulation tools with measurement devices for real-time control of simulations and applications, entailing the ability to dynamically incorporate additional data into an executing application, and in reverse, the ability of an application to dynamically steer the measurement process. It is in that context that traditional "digital-twins" are giving raise to a new generation of goal-oriented data-driven application systems, also known as "hybrid-twins", embracing models based on physics and models exclusively based on data adequately collected and assimilated for filling the gap between usual model predictions and measurements. Within this framework new methodologies based on model learners, machine learning and kinetic goal-oriented design are defining a new paradigm in materials, processes and systems engineering.

A Control System and Streaming DAQ Platform with Image-Based Trigger for X-ray Imaging

NASA Astrophysics Data System (ADS)

Stevanovic, Uros; Caselle, Michele; Cecilia, Angelica; Chilingaryan, Suren; Farago, Tomas; Gasilov, Sergey; Herth, Armin; Kopmann, Andreas; Vogelgesang, Matthias; Balzer, Matthias; Baumbach, Tilo; Weber, Marc

2015-06-01

High-speed X-ray imaging applications play a crucial role for non-destructive investigations of the dynamics in material science and biology. On-line data analysis is necessary for quality assurance and data-driven feedback, leading to a more efficient use of a beam time and increased data quality. In this article we present a smart camera platform with embedded Field Programmable Gate Array (FPGA) processing that is able to stream and process data continuously in real-time. The setup consists of a Complementary Metal-Oxide-Semiconductor (CMOS) sensor, an FPGA readout card, and a readout computer. It is seamlessly integrated in a new custom experiment control system called Concert that provides a more efficient way of operating a beamline by integrating device control, experiment process control, and data analysis. The potential of the embedded processing is demonstrated by implementing an image-based trigger. It records the temporal evolution of physical events with increased speed while maintaining the full field of view. The complete data acquisition system, with Concert and the smart camera platform was successfully integrated and used for fast X-ray imaging experiments at KIT's synchrotron radiation facility ANKA.

Correlated seed failure as an environmental veto to synchronize reproduction of masting plants.

PubMed

Bogdziewicz, Michał; Steele, Michael A; Marino, Shealyn; Crone, Elizabeth E

2018-07-01

Variable, synchronized seed production, called masting, is a widespread reproductive strategy in plants. Resource dynamics, pollination success, and, as described here, environmental veto are possible proximate mechanisms driving masting. We explored the environmental veto hypothesis, which assumes that reproductive synchrony is driven by external factors preventing reproduction in some years, by extending the resource budget model of masting with correlated reproductive failure. We ran this model across its parameter space to explore how key parameters interact to drive seeding dynamics. Next, we parameterized the model based on 16 yr of seed production data for populations of red (Quercus rubra) and white (Quercus alba) oaks. We used these empirical models to simulate seeding dynamics, and compared simulated time series with patterns observed in the field. Simulations showed that resource dynamics and reproduction failure can produce masting even in the absence of pollen coupling. In concordance with this, in both oaks, among-year variation in resource gain and correlated reproductive failure were necessary and sufficient to reproduce masting, whereas pollen coupling, although present, was not necessary. Reproductive failure caused by environmental veto may drive large-scale synchronization without density-dependent pollen limitation. Reproduction-inhibiting weather events are prevalent in ecosystems, making described mechanisms likely to operate in many systems. © 2018 The Authors New Phytologist © 2018 New Phytologist Trust.

Dynamic Water Storage during Flash Flood Events in the Mountainous Area of Rio de Janeiro/Brazil - Case study: Piabanha River Basin

NASA Astrophysics Data System (ADS)

Araujo, L.; Silva, F. P. D.; Moreira, D. M.; Vásquez P, I. L.; Justi da Silva, M. G. A.; Fernandes, N.; Rotunno Filho, O. C.

2017-12-01

Flash floods are characterized by a rapid rise in water levels, high flow rates and large amounts of debris. Several factors have relevance to the occurrence of these phenomena, including high precipitation rates, terrain slope, soil saturation degree, vegetation cover, soil type, among others. In general, the greater the precipitation intensity, the more likely is the occurrence of a significant increase in flow rate. Particularly on steep and rocky plains or heavily urbanized areas, relatively small rain rates can trigger a flash flood event. In addition, high rain rates in short time intervals can temporarily saturate the surface soil layer acting as waterproofing and favoring the occurrence of greater runoff rates due to non-infiltration of rainwater into the soil. Thus, although precipitation is considered the most important factor for flooding, the interaction between rainfall and the soil can sometimes be of greater importance. In this context, this work investigates the dynamic storage of water associated with flash flood events for Quitandinha river watershed, a tributary of Piabanha river, occurred between 2013 and 2014, by means of water balance analyses applied to three watersheds of varying magnitudes (9.25 km², 260 km² and 429 km²) along the rainy season under different time steps (hourly and daily) using remotely sensed and observational precipitation data. The research work is driven by the hypothesis of a hydrologically active bedrock layer, as the watershed is located in a humid region, having intemperate (fractured) rock layer, just below a shallow soil layer, in the higher part of the basin where steep slopes prevail. The results showed a delay of the variation of the dynamic storage in relation to rainfall peaks and water levels. Such behavior indicates that the surface soil layer, which is not very thick in the region, becomes rapidly saturated along rainfall events. Subsequently, the water infiltrates into the rocky layer and the water storage in the fractured bedrock assumes significant role due to its corresponding release to streams as storm flows.

Spatially and time-resolved magnetization dynamics driven by spin-orbit torques

NASA Astrophysics Data System (ADS)

Baumgartner, Manuel; Garello, Kevin; Mendil, Johannes; Avci, Can Onur; Grimaldi, Eva; Murer, Christoph; Feng, Junxiao; Gabureac, Mihai; Stamm, Christian; Acremann, Yves; Finizio, Simone; Wintz, Sebastian; Raabe, Jörg; Gambardella, Pietro

2017-10-01

Current-induced spin-orbit torques are one of the most effective ways to manipulate the magnetization in spintronic devices, and hold promise for fast switching applications in non-volatile memory and logic units. Here, we report the direct observation of spin-orbit-torque-driven magnetization dynamics in Pt/Co/AlOx dots during current pulse injection. Time-resolved X-ray images with 25 nm spatial and 100 ps temporal resolution reveal that switching is achieved within the duration of a subnanosecond current pulse by the fast nucleation of an inverted domain at the edge of the dot and propagation of a tilted domain wall across the dot. The nucleation point is deterministic and alternates between the four dot quadrants depending on the sign of the magnetization, current and external field. Our measurements reveal how the magnetic symmetry is broken by the concerted action of the damping-like and field-like spin-orbit torques and the Dzyaloshinskii-Moriya interaction, and show that reproducible switching events can be obtained for over 1012 reversal cycles.

The steady-state mosaic of disturbance and succession across an old-growth Central Amazon forest landscape.

PubMed

Chambers, Jeffrey Q; Negron-Juarez, Robinson I; Marra, Daniel Magnabosco; Di Vittorio, Alan; Tews, Joerg; Roberts, Dar; Ribeiro, Gabriel H P M; Trumbore, Susan E; Higuchi, Niro

2013-03-05

Old-growth forest ecosystems comprise a mosaic of patches in different successional stages, with the fraction of the landscape in any particular state relatively constant over large temporal and spatial scales. The size distribution and return frequency of disturbance events, and subsequent recovery processes, determine to a large extent the spatial scale over which this old-growth steady state develops. Here, we characterize this mosaic for a Central Amazon forest by integrating field plot data, remote sensing disturbance probability distribution functions, and individual-based simulation modeling. Results demonstrate that a steady state of patches of varying successional age occurs over a relatively large spatial scale, with important implications for detecting temporal trends on plots that sample a small fraction of the landscape. Long highly significant stochastic runs averaging 1.0 Mg biomass⋅ha(-1)⋅y(-1) were often punctuated by episodic disturbance events, resulting in a sawtooth time series of hectare-scale tree biomass. To maximize the detection of temporal trends for this Central Amazon site (e.g., driven by CO2 fertilization), plots larger than 10 ha would provide the greatest sensitivity. A model-based analysis of fractional mortality across all gap sizes demonstrated that 9.1-16.9% of tree mortality was missing from plot-based approaches, underscoring the need to combine plot and remote-sensing methods for estimating net landscape carbon balance. Old-growth tropical forests can exhibit complex large-scale structure driven by disturbance and recovery cycles, with ecosystem and community attributes of hectare-scale plots exhibiting continuous dynamic departures from a steady-state condition.

Lateral baroclinic forcing enhances sediment transport from shallows to channel in an estuary

USGS Publications Warehouse

Lacy, Jessica R.; Gladding, Steve; Brand, Andreas; Collignon, Audric; Stacey, Mark

2014-01-01

We investigate the dynamics governing exchange of sediment between estuarine shallows and the channel based on field measurements at eight stations spanning the interface between the channel and the extensive eastern shoals of South San Francisco Bay. The study site is characterized by longitudinally homogeneous bathymetry and a straight channel, with friction more important than the Coriolis forcing. Data were collected for 3 weeks in the winter and 4 weeks in the late summer of 2009, to capture a range of hydrologic and meteorologic conditions. The greatest sediment transport from shallows to channel occurred during a pair of strong, late-summer wind events, with westerly winds exceeding 10 m/s for more than 24 h. A combination of wind-driven barotropic return flow and lateral baroclinic circulation caused the transport. The lateral density gradient was produced by differences in temperature and suspended sediment concentration (SSC). During the wind events, SSC-induced vertical density stratification limited turbulent mixing at slack tides in the shallows, increasing the potential for two-layer exchange. The temperature- and SSC-induced lateral density gradient was comparable in strength to salinity-induced gradients in South Bay produced by seasonal freshwater inflows, but shorter in duration. In the absence of a lateral density gradient, suspended sediment flux at the channel slope was directed towards the shallows, both in winter and during summer sea breeze conditions, indicating the importance of baroclinically driven exchange to supply of sediment from the shallows to the channel in South San Francisco Bay and systems with similar bathymetry.

DOE Office of Scientific and Technical Information (OSTI.GOV)

Costa, Liborio I., E-mail: liborio78@gmail.com

A new Markov Chain Monte Carlo method for simulating the dynamics of particle systems characterized by hard-core interactions is introduced. In contrast to traditional Kinetic Monte Carlo approaches, where the state of the system is associated with minima in the energy landscape, in the proposed method, the state of the system is associated with the set of paths traveled by the atoms and the transition probabilities for an atom to be displaced are proportional to the corresponding velocities. In this way, the number of possible state-to-state transitions is reduced to a discrete set, and a direct link between the Montemore » Carlo time step and true physical time is naturally established. The resulting rejection-free algorithm is validated against event-driven molecular dynamics: the equilibrium and non-equilibrium dynamics of hard disks converge to the exact results with decreasing displacement size.« less

Evaluating Aquatic Life Benefits of Reducing Nutrient Loading ...

EPA Pesticide Factsheets

Theoretical linkages between excess nutrient loading, nutrient-enhanced community metabolism (i.e., production and respiration), and hypoxia in estuaries are well-understood. In seasonally-stratified estuaries and coastal systems (e.g., Chesapeake Bay, northern Gulf of Mexico), hypoxia is predominantly seasonal, such that the spatial extent indicates potential aquatic life impacts. However, in relatively small and shallow Gulf of Mexico bays and bayous, hypoxia frequently occurs episodically or on a diel basis. This study utilized continuous DO monitoring and 3-D hydrodynamic (Environmental Fluid Dynamics Code) and water quality (Water Quality Analysis Simulation Program) models to examine physical and biological controls on DO dynamics and ecosystem metabolism in Weeks Bay, AL. Observed vertical DO gradients varied on a diel basis, with larger amplitude variations at depth relative to the surface, underscoring the importance of benthic production and respiration as a driver of ecosystem metabolism in shallow estuaries. Hydrodynamic and water quality models simulated seasonal and event-driven dynamics, but struggled to resolve the amplitude of daily DO fluctuations, particularly in bottom waters. Using these data in conjunction with the 10-year continuous O2 record from Weeks Bay, we applied empirical relationships and simple scaling relations to predict how reducing nutrient loading may affect the frequency, severity and duration of hypoxia. We further applied

Extreme learning machine for reduced order modeling of turbulent geophysical flows.

PubMed

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Extreme learning machine for reduced order modeling of turbulent geophysical flows

NASA Astrophysics Data System (ADS)

San, Omer; Maulik, Romit

2018-04-01

We investigate the application of artificial neural networks to stabilize proper orthogonal decomposition-based reduced order models for quasistationary geophysical turbulent flows. An extreme learning machine concept is introduced for computing an eddy-viscosity closure dynamically to incorporate the effects of the truncated modes. We consider a four-gyre wind-driven ocean circulation problem as our prototype setting to assess the performance of the proposed data-driven approach. Our framework provides a significant reduction in computational time and effectively retains the dynamics of the full-order model during the forward simulation period beyond the training data set. Furthermore, we show that the method is robust for larger choices of time steps and can be used as an efficient and reliable tool for long time integration of general circulation models.

Extreme climate events counteract the effects of climate and land-use changes in Alpine treelines

PubMed Central

Barros, Ceres; Guéguen, Maya; Douzet, Rolland; Carboni, Marta; Boulangeat, Isabelle; Zimmermann, Niklaus E.; Münkemüller, Tamara; Thuiller, Wilfried

2017-01-01

Summary 1. Climate change and extreme events, such as drought, threaten ecosystems worldwide and in particular mountain ecosystems, where species often live at their environmental tolerance limits. In the European Alps, plant communities are also influenced by land-use abandonment leading to woody encroachment of subalpine and alpine grasslands. 2. In this study, we explored how the forest–grassland ecotone of Alpine treelines will respond to gradual climate warming, drought events and land-use change in terms of forest expansion rates, taxonomic diversity and functional composition. We used a previously validated dynamic vegetation model, FATE-HD, parameterised for plant communities in the Ecrins National Park in the French Alps. 3. Our results showed that intense drought counteracted the forest expansion at higher elevations driven by land-use abandonment and climate change, especially when combined with high drought frequency (occurring every 2 or less than 2 years). 4. Furthermore, intense and frequent drought accelerated the rates of taxonomic change and resulted in overall higher taxonomic spatial heterogeneity of the ecotone than would be expected under gradual climate and land-use changes only. 5. Synthesis and applications. The results from our model show that intense and frequent drought counteracts forest expansion driven by climate and land-use changes in the forest–grassland ecotone of Alpine treelines. We argue that land-use planning must consider the effects of extreme events, such as drought, as well as climate and land-use changes, since extreme events might interfere with trends predicted under gradual climate warming and agricultural abandonment. PMID:28670002

Nonlinear Electrostatic Steepening of Whistler Waves: The Guiding Factors and Dynamics in Inhomogeneous Systems

NASA Astrophysics Data System (ADS)

Agapitov, O.; Drake, J. F.; Vasko, I.; Mozer, F. S.; Artemyev, A.; Krasnoselskikh, V.; Angelopoulos, V.; Wygant, J.; Reeves, G. D.

2018-03-01

Whistler mode chorus waves are particularly important in outer radiation belt dynamics due to their key role in controlling the acceleration and scattering of electrons over a very wide energy range. The efficiency of wave-particle resonant interactions is defined by whistler wave properties which have been described by the approximation of plane linear waves propagating through the cold plasma of the inner magnetosphere. However, recent observations of extremely high-amplitude whistlers suggest the importance of nonlinear wave-particle interactions for the dynamics of the outer radiation belt. Oblique chorus waves observed in the inner magnetosphere often exhibit drastically nonsinusoidal (with significant power in the higher harmonics) waveforms of the parallel electric field, presumably due to the feedback from hot resonant electrons. We have considered the nature and properties of such nonlinear whistler waves observed by the Van Allen Probes and Time History of Events and Macroscale Interactions define during Substorms in the inner magnetosphere, and we show that the significant enhancement of the wave electrostatic component can result from whistler wave coupling with the beam-driven electrostatic mode through the resonant interaction with hot electron beams. Being modulated by a whistler wave, the electron beam generates a driven electrostatic mode significantly enhancing the parallel electric field of the initial whistler wave. We confirm this mechanism using a self-consistent particle-in-cell simulation. The nonlinear electrostatic component manifests properties of the beam-driven electron acoustic mode and can be responsible for effective electron acceleration in the inhomogeneous magnetic field.

Lateral-directional aerodynamic characteristics of light, twin-engine, propeller driven airplanes

NASA Technical Reports Server (NTRS)

Wolowicz, C. H.; Yancey, R. B.

1972-01-01

Analytical procedures and design data for predicting the lateral-directional static and dynamic stability and control characteristics of light, twin engine, propeller driven airplanes for propeller-off and power-on conditions are reported. Although the consideration of power effects is limited to twin engine airplanes, the propeller-off considerations are applicable to single engine airplanes as well. The procedures are applied to a twin engine, propeller driven, semi-low-wing airplane in the clean configuration through the linear lift range. The calculated derivative characteristics are compared with wind tunnel and flight data. Included in the calculated characteristics are the spiral mode, roll mode, and Dutch roll mode over the speed range of the airplane.

Solar wind influence on Jupiter's magnetosphere and aurora

NASA Astrophysics Data System (ADS)

Vogt, Marissa; Gyalay, Szilard; Withers, Paul

2016-04-01

Jupiter's magnetosphere is often said to be rotationally driven, with strong centrifugal stresses due to large spatial scales and a rapid planetary rotation period. For example, the main auroral emission at Jupiter is not due to the magnetosphere-solar wind interaction but is driven by a system of corotation enforcement currents that arises to speed up outflowing Iogenic plasma. Additionally, processes like tail reconnection are also thought to be driven, at least in part, by processes internal to the magnetosphere. While the solar wind is generally expected to have only a small influence on Jupiter's magnetosphere and aurora, there is considerable observational evidence that the solar wind does affect the magnetopause standoff distance, auroral radio emissions, and the position and brightness of the UV auroral emissions. We will report on the results of a comprehensive, quantitative study of the influence of the solar wind on various magnetospheric data sets measured by the Galileo mission from 1996 to 2003. Using the Michigan Solar Wind Model (mSWiM) to predict the solar wind conditions upstream of Jupiter, we have identified intervals of high and low solar wind dynamic pressure. We can use this information to quantify how a magnetospheric compression affects the magnetospheric field configuration, which in turn will affect the ionospheric mapping of the main auroral emission. We also consider whether there is evidence that reconnection events occur preferentially during certain solar wind conditions or that the solar wind modulates the quasi-periodicity seen in the magnetic field dipolarizations and flow bursts.

Baseline Preferences for Daily, Event-Driven, or Periodic HIV Pre-Exposure Prophylaxis among Gay and Bisexual Men in the PRELUDE Demonstration Project.

PubMed

Vaccher, Stefanie J; Gianacas, Christopher; Templeton, David J; Poynten, Isobel M; Haire, Bridget G; Ooi, Catriona; Foster, Rosalind; McNulty, Anna; Grulich, Andrew E; Zablotska, Iryna B

2017-01-01

The effectiveness of daily pre-exposure prophylaxis (PrEP) is well established. However, there has been increasing interest in non-daily dosing schedules among gay and bisexual men (GBM). This paper explores preferences for PrEP dosing schedules among GBM at baseline in the PRELUDE demonstration project. Individuals at high-risk of HIV were enrolled in a free PrEP demonstration project in New South Wales, Australia, between November 2014 and April 2016. At baseline, they completed an online survey containing detailed behavioural, demographic, and attitudinal questions, including their ideal way to take PrEP: daily (one pill taken every day), event-driven (pills taken only around specific risk events), or periodic (daily dosing during periods of increased risk). Overall, 315 GBM (98% of study sample) provided a preferred PrEP dosing schedule at baseline. One-third of GBM expressed a preference for non-daily PrEP dosing: 20% for event-driven PrEP, and 14% for periodic PrEP. Individuals with a trade/vocational qualification were more likely to prefer periodic to daily PrEP [adjusted odds ratio (aOR) = 4.58, 95% confidence intervals (95% CI): (1.68, 12.49)], compared to individuals whose highest level of education was high school. Having an HIV-positive main regular partner was associated with strong preference for daily, compared to event-driven PrEP [aOR = 0.20, 95% CI: (0.04, 0.87)]. Participants who rated themselves better at taking medications were more likely to prefer daily over periodic PrEP [aOR = 0.39, 95% CI: (0.20, 0.76)]. Individuals' preferences for PrEP schedules are associated with demographic and behavioural factors that may impact on their ability to access health services and information about PrEP and patterns of HIV risk. At the time of data collection, there were limited data available about the efficacy of non-daily PrEP schedules, and clinicians only recommended daily PrEP to study participants. Further research investigating how behaviours and PrEP preferences change correspondingly over time is needed. ClinicalTrials.gov NCT02206555. Registered 28 July 2014.

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks

PubMed Central

Zhang, Gongxuan; Wang, Yongli; Wang, Tianshu

2018-01-01

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach. PMID:29734718

A New Path-Constrained Rendezvous Planning Approach for Large-Scale Event-Driven Wireless Sensor Networks.

PubMed

Vajdi, Ahmadreza; Zhang, Gongxuan; Zhou, Junlong; Wei, Tongquan; Wang, Yongli; Wang, Tianshu

2018-05-04

We study the problem of employing a mobile-sink into a large-scale Event-Driven Wireless Sensor Networks (EWSNs) for the purpose of data harvesting from sensor-nodes. Generally, this employment improves the main weakness of WSNs that is about energy-consumption in battery-driven sensor-nodes. The main motivation of our work is to address challenges which are related to a network’s topology by adopting a mobile-sink that moves in a predefined trajectory in the environment. Since, in this fashion, it is not possible to gather data from sensor-nodes individually, we adopt the approach of defining some of the sensor-nodes as Rendezvous Points (RPs) in the network. We argue that RP-planning in this case is a tradeoff between minimizing the number of RPs while decreasing the number of hops for a sensor-node that needs data transformation to the related RP which leads to minimizing average energy consumption in the network. We address the problem by formulating the challenges and expectations as a Mixed Integer Linear Programming (MILP). Henceforth, by proving the NP-hardness of the problem, we propose three effective and distributed heuristics for RP-planning, identifying sojourn locations, and constructing routing trees. Finally, experimental results prove the effectiveness of our approach.

Data-driven sensor placement from coherent fluid structures

NASA Astrophysics Data System (ADS)

Manohar, Krithika; Kaiser, Eurika; Brunton, Bingni W.; Kutz, J. Nathan; Brunton, Steven L.

2017-11-01

Optimal sensor placement is a central challenge in the prediction, estimation and control of fluid flows. We reinterpret sensor placement as optimizing discrete samples of coherent fluid structures for full state reconstruction. This permits a drastic reduction in the number of sensors required for faithful reconstruction, since complex fluid interactions can often be described by a small number of coherent structures. Our work optimizes point sensors using the pivoted matrix QR factorization to sample coherent structures directly computed from flow data. We apply this sampling technique in conjunction with various data-driven modal identification methods, including the proper orthogonal decomposition (POD) and dynamic mode decomposition (DMD). In contrast to POD-based sensors, DMD demonstrably enables the optimization of sensors for prediction in systems exhibiting multiple scales of dynamics. Finally, reconstruction accuracy from pivot sensors is shown to be competitive with sensors obtained using traditional computationally prohibitive optimization methods.

Four-Dimensional Imaging of T Cells in Kidney Transplant Rejection.

PubMed

Hughes, Andrew D; Lakkis, Fadi G; Oberbarnscheidt, Martin H

2018-06-01

Kidney transplantation is the treatment of choice for ESRD but is complicated by the response of the recipient's immune system to nonself histocompatibility antigens on the graft, resulting in rejection. Multiphoton intravital microscopy, referred to as four-dimensional imaging because it records dynamic events in three-dimensional tissue volumes, has emerged as a powerful tool to study immunologic processes in living animals. Here, we will review advances in understanding the complex mechanisms of T cell-mediated rejection made possible by four-dimensional imaging of mouse renal allografts. We will summarize recent data showing that activated (effector) T cell migration to the graft is driven by cognate antigen presented by dendritic cells that surround and penetrate peritubular capillaries, and that T cell-dendritic cell interactions persist in the graft over time, maintaining the immune response in the tissue. Copyright © 2018 by the American Society of Nephrology.

Long livestock farming history and human landscape shaping revealed by lake sediment DNA.

PubMed

Giguet-Covex, Charline; Pansu, Johan; Arnaud, Fabien; Rey, Pierre-Jérôme; Griggo, Christophe; Gielly, Ludovic; Domaizon, Isabelle; Coissac, Eric; David, Fernand; Choler, Philippe; Poulenard, Jérôme; Taberlet, Pierre

2014-01-01

The reconstruction of human-driven, Earth-shaping dynamics is important for understanding past human/environment interactions and for helping human societies that currently face global changes. However, it is often challenging to distinguish the effects of the climate from human activities on environmental changes. Here we evaluate an approach based on DNA metabarcoding used on lake sediments to provide the first high-resolution reconstruction of plant cover and livestock farming history since the Neolithic Period. By comparing these data with a previous reconstruction of erosive event frequency, we show that the most intense erosion period was caused by deforestation and overgrazing by sheep and cowherds during the Late Iron Age and Roman Period. Tracking plants and domestic mammals using lake sediment DNA (lake sedDNA) is a new, promising method for tracing past human practices, and it provides a new outlook of the effects of anthropogenic factors on landscape-scale changes.

Information-theoretic model selection for optimal prediction of stochastic dynamical systems from data

NASA Astrophysics Data System (ADS)

Darmon, David

2018-03-01

In the absence of mechanistic or phenomenological models of real-world systems, data-driven models become necessary. The discovery of various embedding theorems in the 1980s and 1990s motivated a powerful set of tools for analyzing deterministic dynamical systems via delay-coordinate embeddings of observations of their component states. However, in many branches of science, the condition of operational determinism is not satisfied, and stochastic models must be brought to bear. For such stochastic models, the tool set developed for delay-coordinate embedding is no longer appropriate, and a new toolkit must be developed. We present an information-theoretic criterion, the negative log-predictive likelihood, for selecting the embedding dimension for a predictively optimal data-driven model of a stochastic dynamical system. We develop a nonparametric estimator for the negative log-predictive likelihood and compare its performance to a recently proposed criterion based on active information storage. Finally, we show how the output of the model selection procedure can be used to compare candidate predictors for a stochastic system to an information-theoretic lower bound.

A Database of Tornado Events as Perceived by the USArray Transportable Array Network

NASA Astrophysics Data System (ADS)

Tytell, J. E.; Vernon, F.; Reyes, J. C.

2015-12-01

Over the course of the deployment of Earthscope's USArray Transportable Array (TA) network there have numerous tornado events that have occurred within the changing footprint of its network. The Array Network Facility based in San Diego, California, has compiled a database of these tornado events based on data provided by the NOAA Storm Prediction Center (SPC). The SPC data itself consists of parameters such as start-end point track data for each event, maximum EF intensities, and maximum track widths. Our database is Antelope driven and combines these data from the SPC with detailed station information from the TA network. We are now able to list all available TA stations during any specific tornado event date and also provide a single calculated "nearest" TA station per individual tornado event. We aim to provide this database as a starting resource for those with an interest in investigating tornado signatures within surface pressure and seismic response data. On a larger scale, the database may be of particular interest to the infrasound research community

Nanocluster building blocks of artificial square spin ice: Stray-field studies of thermal dynamics

DOE Office of Scientific and Technical Information (OSTI.GOV)

Pohlit, Merlin, E-mail: pohlit@physik.uni-frankfurt.de; Porrati, Fabrizio; Huth, Michael

We present measurements of the thermal dynamics of a Co-based single building block of an artificial square spin ice fabricated by focused electron-beam-induced deposition. We employ micro-Hall magnetometry, an ultra-sensitive tool to study the stray field emanating from magnetic nanostructures, as a new technique to access the dynamical properties during the magnetization reversal of the spin-ice nanocluster. The obtained hysteresis loop exhibits distinct steps, displaying a reduction of their “coercive field” with increasing temperature. Therefore, thermally unstable states could be repetitively prepared by relatively simple temperature and field protocols allowing one to investigate the statistics of their switching behavior withinmore » experimentally accessible timescales. For a selected switching event, we find a strong reduction of the so-prepared states' “survival time” with increasing temperature and magnetic field. Besides the possibility to control the lifetime of selected switching events at will, we find evidence for a more complex behavior caused by the special spin ice arrangement of the macrospins, i.e., that the magnetic reversal statistically follows distinct “paths” most likely driven by thermal perturbation.« less

Hybridized Kibble-Zurek scaling in the driven critical dynamics across an overlapping critical region

NASA Astrophysics Data System (ADS)

Zhai, Liang-Jun; Wang, Huai-Yu; Yin, Shuai

2018-04-01

The conventional Kibble-Zurek scaling describes the scaling behavior in the driven dynamics across a single critical region. In this paper, we study the driven dynamics across an overlapping critical region, in which a critical region (Region A) is overlaid by another critical region (Region B). We develop a hybridized Kibble-Zurek scaling (HKZS) to characterize the scaling behavior in the driven process. According to the HKZS, the driven dynamics in the overlapping region can be described by the critical theories for both Region A and Region B simultaneously. This results in a constraint on the scaling function in the overlapping critical region. We take the quantum Ising chain in an imaginary longitudinal field as an example. In this model, the critical region of the Yang-Lee edge singularity and the critical region of the ferromagnetic-paramagnetic phase transition overlap with each other. We numerically confirm the HKZS by simulating the driven dynamics in this overlapping critical region. The HKZSs in other models are also discussed.

Changing climate and nutrient transfers: Evidence from high temporal resolution concentration-flow dynamics in headwater catchments.

PubMed

Ockenden, M C; Deasy, C E; Benskin, C McW H; Beven, K J; Burke, S; Collins, A L; Evans, R; Falloon, P D; Forber, K J; Hiscock, K M; Hollaway, M J; Kahana, R; Macleod, C J A; Reaney, S M; Snell, M A; Villamizar, M L; Wearing, C; Withers, P J A; Zhou, J G; Haygarth, P M

2016-04-01

We hypothesise that climate change, together with intensive agricultural systems, will increase the transfer of pollutants from land to water and impact on stream health. This study builds, for the first time, an integrated assessment of nutrient transfers, bringing together a) high-frequency data from the outlets of two surface water-dominated, headwater (~10km(2)) agricultural catchments, b) event-by-event analysis of nutrient transfers, c) concentration duration curves for comparison with EU Water Framework Directive water quality targets, d) event analysis of location-specific, sub-daily rainfall projections (UKCP, 2009), and e) a linear model relating storm rainfall to phosphorus load. These components, in combination, bring innovation and new insight into the estimation of future phosphorus transfers, which was not available from individual components. The data demonstrated two features of particular concern for climate change impacts. Firstly, the bulk of the suspended sediment and total phosphorus (TP) load (greater than 90% and 80% respectively) was transferred during the highest discharge events. The linear model of rainfall-driven TP transfers estimated that, with the projected increase in winter rainfall (+8% to +17% in the catchments by 2050s), annual event loads might increase by around 9% on average, if agricultural practices remain unchanged. Secondly, events following dry periods of several weeks, particularly in summer, were responsible for high concentrations of phosphorus, but relatively low loads. The high concentrations, associated with low flow, could become more frequent or last longer in the future, with a corresponding increase in the length of time that threshold concentrations (e.g. for water quality status) are exceeded. The results suggest that in order to build resilience in stream health and help mitigate potential increases in diffuse agricultural water pollution due to climate change, land management practices should target controllable risk factors, such as soil nutrient status, soil condition and crop cover. Copyright © 2015 Elsevier B.V. All rights reserved.

An investigation of Martian and terrestrial dust devils

NASA Astrophysics Data System (ADS)

Ringrose, Timothy John

2004-10-01

It is the purpose of this work to provide an insight into the theoretical and practical dynamics of dust devils and how they are detected remotely from orbit or in situ on planetary surfaces. There is particular interest in the detection of convective vortices on Mars; this has been driven by involvement in the development of the Beagle 2 Environmental Sensor Suite. This suite of sensors is essentially a martian weather station and will be the first planetary lander experiment specifically looking for the presence of dust devils on Mars. Dust devils are characterised by their visible dusty core and intense rotation. The physics of particle motion, including dust lofting and the rotational dynamics within convective vortices are explained and modelled. This modelling has helped in identifying dust devils in meteorological data from both terrestrial and martian investigations. An automated technique for dust devil detection using meteorological data has been developed. This technique searches data looking for the specific vortex signature as well as detecting other transient events. This method has been tested on both terrestrial and martian data with surprising results. 38 possible convective vortices were detected in the first 60 sols of the Viking Lander 2 meteorological data. Tests were also carried out on data from a terrestrial dust devil campaign, which provided conclusive evidence from visual observations of the reliability of this technique. A considerable amount of this work does focus on terrestrial vortices. This is to aid in the understanding of dust devils, specifically how, why and when they form. Both laboratory and terrestrial fieldwork is investigated, providing useful data on the general structure of dust devils.

Mercury dynamics in a San Francisco estuary tidal wetland: assessing dynamics using in situ measurements

USGS Publications Warehouse

Bergamaschi, Brian A.; Fleck, Jacob A.; Downing, Bryan D.; Boss, Emmanuel; Pellerin, Brian A.; Ganju, Neil K.; Schoellhamer, David H.; Byington, Amy A.; Heim, Wesley A.; Stephenson, Mark; Fujii, Roger

2012-01-01

We used high-resolution in situ measurements of turbidity and fluorescent dissolved organic matter (FDOM) to quantitatively estimate the tidally driven exchange of mercury (Hg) between the waters of the San Francisco estuary and Browns Island, a tidal wetland. Turbidity and FDOM—representative of particle-associated and filter-passing Hg, respectively—together predicted 94 % of the observed variability in measured total mercury concentration in unfiltered water samples (UTHg) collected during a single tidal cycle in spring, fall, and winter, 2005–2006. Continuous in situ turbidity and FDOM data spanning at least a full spring-neap period were used to generate UTHg concentration time series using this relationship, and then combined with water discharge measurements to calculate Hg fluxes in each season. Wetlands are generally considered to be sinks for sediment and associated mercury. However, during the three periods of monitoring, Browns Island wetland did not appreciably accumulate Hg. Instead, gradual tidally driven export of UTHg from the wetland offset the large episodic on-island fluxes associated with high wind events. Exports were highest during large spring tides, when ebbing waters relatively enriched in FDOM, dissolved organic carbon (DOC), and filter-passing mercury drained from the marsh into the open waters of the estuary. On-island flux of UTHg, which was largely particle-associated, was highest during strong winds coincident with flood tides. Our results demonstrate that processes driving UTHg fluxes in tidal wetlands encompass both the dissolved and particulate phases and multiple timescales, necessitating longer term monitoring to adequately quantify fluxes.

Shenandoah National Park Phenology Project-Weather data collection, description, and processing

USGS Publications Warehouse

Jones, John W.; Aiello, Danielle P.; Osborne, Jesse D.

2010-01-01

The weather data described in this document are being collected as part of a U.S. Geological Survey (USGS) study of changes in Shenandoah National Park (SNP) landscape phenology (Jones and Osbourne, 2008). Phenology is the study of the timing of biological events, such as annual plant flowering and seasonal bird migration. These events are partially driven by changes in temperature and precipitation; therefore, phenology studies how these events may reflect changes in climate. Landscape phenology is the study of changes in biological events over broad areas and assemblages of vegetation. To study climate-change relations over broad areas (at landscape scale), the timing and amount of annual tree leaf emergence, maximum foliage, and leaf fall for forested areas are of interest. To better link vegetation changes with climate, weather data are necessary. This report documents weather-station data collection and processing procedures used in the Shenandoah National Park Phenology Project.

Snow multivariable data assimilation for hydrological predictions in Alpine sites

NASA Astrophysics Data System (ADS)

Piazzi, Gaia; Thirel, Guillaume; Campo, Lorenzo; Gabellani, Simone; Stevenin, Hervè

2017-04-01

Snowpack dynamics (snow accumulation and ablation) strongly impacts on hydrological processes in Alpine areas. During the winter season the presence of snow cover (snow accumulation) reduces the drainage in the basin with a resulting lower watershed time of concentration in case of possible rainfall events. Moreover, the release of the significant water volume stored in winter (snowmelt) considerably contributes to the total discharge during the melting period. Therefore when modeling hydrological processes in snow-dominated catchments the quality of predictions deeply depends on how the model succeeds in catching snowpack dynamics. The integration of a hydrological model with a snow module allows improving predictions of river discharges. Besides the well-known modeling limitations (uncertainty in parameterizations; possible errors affecting both meteorological forcing data and initial conditions; approximations in boundary conditions), there are physical factors that make an exhaustive reconstruction of snow dynamics complicated: snow intermittence in space and time, stratification and slow phenomena like metamorphism processes, uncertainty in snowfall evaluation, wind transportation, etc. Data Assimilation (DA) techniques provide an objective methodology to combine several independent snow-related data sources (model simulations, ground-based measurements and remote sensed observations) in order to obtain the most likely estimate of snowpack state. This study presents SMASH (Snow Multidata Assimilation System for Hydrology), a multi-layer snow dynamic model strengthened by a multivariable DA framework for hydrological purposes. The model is physically based on mass and energy balances and can be used to reproduce the main physical processes occurring within the snowpack: accumulation, density dynamics, melting, sublimation, radiative balance, heat and mass exchanges. The model is driven by observed forcing meteorological data (air temperature, wind velocity, relative air humidity, precipitation and incident solar radiation) to provide a complete estimate of snowpack state. The implementation of a DA scheme enables to assimilate simultaneously ground-based observations of different snow-related variables (snow depth, snow density, surface temperature and albedo). SMASH performances are evaluated by using observed data supplied by meteorological stations located in three experimental Alpine sites: Col de Porte (1325 m, France); Torgnon (2160 m, Italy); Weissfluhjoch (2540 m, Switzerland). A comparison analysis between the resulting performaces of Particle Filter and Ensemble Kalman Filter schemes is shown.

Computer Series, 70.

ERIC Educational Resources Information Center

Moore, John W.

1986-01-01

Describes: (1) spreadheet programs (including VisiCalc) for experiments; (2) event-driven data acquisition (using ADALAB with an Acculab Infrared Spectometer); (3) microcomputer-controlled cyclic voltammetry; (4) inexpensive computerized experiments; (5) the "KC? Discoverer" program; and (6) MOLDOT (space-filling perspective diagrams of…

Dynamics of bloggers’ communities: Bipartite networks from empirical data and agent-based modeling

NASA Astrophysics Data System (ADS)

Mitrović, Marija; Tadić, Bosiljka

2012-11-01

We present an analysis of the empirical data and the agent-based modeling of the emotional behavior of users on the Web portals where the user interaction is mediated by posted comments, like Blogs and Diggs. We consider the dataset of discussion-driven popular Diggs, in which all comments are screened by machine-learning emotion detection in the text, to determine positive and negative valence (attractiveness and aversiveness) of each comment. By mapping the data onto a suitable bipartite network, we perform an analysis of the network topology and the related time-series of the emotional comments. The agent-based model is then introduced to simulate the dynamics and to capture the emergence of the emotional behaviors and communities. The agents are linked to posts on a bipartite network, whose structure evolves through their actions on the posts. The emotional states (arousal and valence) of each agent fluctuate in time, subject to the current contents of the posts to which the agent is exposed. By an agent’s action on a post its current emotions are transferred to the post. The model rules and the key parameters are inferred from the considered empirical data to ensure their realistic values and mutual consistency. The model assumes that the emotional arousal over posts drives the agent’s action. The simulations are preformed for the case of constant flux of agents and the results are analyzed in full analogy with the empirical data. The main conclusions are that the emotion-driven dynamics leads to long-range temporal correlations and emergent networks with community structure, that are comparable with the ones in the empirical system of popular posts. In view of pure emotion-driven agents actions, this type of comparisons provide a quantitative measure for the role of emotions in the dynamics on real blogs. Furthermore, the model reveals the underlying mechanisms which relate the post popularity with the emotion dynamics and the prevalence of negative emotions (critique). We also demonstrate how the community structure is tuned by varying a relevant parameter in the model. All data used in these works are fully anonymized.

Intelligent fuzzy controller for event-driven real time systems

NASA Technical Reports Server (NTRS)

Grantner, Janos; Patyra, Marek; Stachowicz, Marian S.

1992-01-01

Most of the known linguistic models are essentially static, that is, time is not a parameter in describing the behavior of the object's model. In this paper we show a model for synchronous finite state machines based on fuzzy logic. Such finite state machines can be used to build both event-driven, time-varying, rule-based systems and the control unit section of a fuzzy logic computer. The architecture of a pipelined intelligent fuzzy controller is presented, and the linguistic model is represented by an overall fuzzy relation stored in a single rule memory. A VLSI integrated circuit implementation of the fuzzy controller is suggested. At a clock rate of 30 MHz, the controller can perform 3 MFLIPS on multi-dimensional fuzzy data.

Simulation of the Tsunami Resulting from the M 9.2 2004 Sumatra-Andaman Earthquake - Dynamic Rupture vs. Seismic Inversion Source Model

NASA Astrophysics Data System (ADS)

Vater, Stefan; Behrens, Jörn

2017-04-01

Simulations of historic tsunami events such as the 2004 Sumatra or the 2011 Tohoku event are usually initialized using earthquake sources resulting from inversion of seismic data. Also, other data from ocean buoys etc. is sometimes included in the derivation of the source model. The associated tsunami event can often be well simulated in this way, and the results show high correlation with measured data. However, it is unclear how the derived source model compares to the particular earthquake event. In this study we use the results from dynamic rupture simulations obtained with SeisSol, a software package based on an ADER-DG discretization solving the spontaneous dynamic earthquake rupture problem with high-order accuracy in space and time. The tsunami model is based on a second-order Runge-Kutta discontinuous Galerkin (RKDG) scheme on triangular grids and features a robust wetting and drying scheme for the simulation of inundation events at the coast. Adaptive mesh refinement enables the efficient computation of large domains, while at the same time it allows for high local resolution and geometric accuracy. The results are compared to measured data and results using earthquake sources based on inversion. With the approach of using the output of actual dynamic rupture simulations, we can estimate the influence of different earthquake parameters. Furthermore, the comparison to other source models enables a thorough comparison and validation of important tsunami parameters, such as the runup at the coast. This work is part of the ASCETE (Advanced Simulation of Coupled Earthquake and Tsunami Events) project, which aims at an improved understanding of the coupling between the earthquake and the generated tsunami event.

Geography and host species shape the evolutionary dynamics of U genogroup infectious hematopoietic necrosis virus

USGS Publications Warehouse

Black, Allison; Breyta, Rachel; Bedford, Trevor; Kurath, Gael

2016-01-01

Infectious hematopoietic necrosis virus (IHNV) is a negative-sense RNA virus that infects wild and cultured salmonids throughout the Pacific Coastal United States and Canada, from California to Alaska. Although infection of adult fish is usually asymptomatic, juvenile infections can result in high mortality events that impact salmon hatchery programs and commercial aquaculture. We used epidemiological case data and genetic sequence data from a 303 nt portion of the viral glycoprotein gene to study the evolutionary dynamics of U genogroup IHNV in the Pacific Northwestern United States from 1971 to 2013. We identified 114 unique genotypes among 1,219 U genogroup IHNV isolates representing 619 virus detection events. We found evidence for two previously unidentified, broad subgroups within the U genogroup, which we designated ‘UC’ and ‘UP’. Epidemiologic records indicated that UP viruses were detected more frequently in sockeye salmon (Oncorhynchus nerka) and in coastal waters of Washington and Oregon, whereas UC viruses were detected primarily in Chinook salmon (Oncorhynchus tshawytscha) and steelhead trout (Oncorhynchus mykiss) in the Columbia River Basin, which is a large, complex watershed extending throughout much of interior Washington, Oregon, and Idaho. These findings were supported by phylogenetic analysis and by FST. Ancestral state reconstruction indicated that early UC viruses in the Columbia River Basin initially infected sockeye salmon but then emerged via host shifts into Chinook salmon and steelhead trout sometime during the 1980s. We postulate that the development of these subgroups within U genogroup was driven by selection pressure for viral adaptation to Chinook salmon and steelhead trout within the Columbia River Basin.

A Discrete Events Delay Differential System Model for Transmission of Vancomycin-Resistant Enterococcus (VRE) in Hospitals

DTIC Science & Technology

2010-09-19

estimated directly form the surveillance data Infection control measures were implemented in the form of health care worker hand - hygiene before and after...hospital infections , is used to motivate possibilities of modeling nosocomial infec- tion dynamics. This is done in the context of hospital monitoring and...model development. Key Words: Delay equations, discrete events, nosocomial infection dynamics, surveil- lance data, inverse problems, parameter

A multispacecraft event study of Pc5 ultralow-frequency waves in the magnetosphere and their external drivers

DOE PAGES

Wang, Chih-Ping; Thorne, Richard; Liu, Terry Z.; ...

2017-05-09

We investigate a quiet time event of magnetospheric Pc5 ultralow-frequency (ULF) waves and their likely external drivers using multiple spacecraft observations. Enhancements of electric and magnetic field perturbations in two narrow frequency bands, 1.5–2 mHz and 3.5–4 mHz, were observed over a large radial distance range from r ~ 5 to 11 RE. During the first half of this event, perturbations were mainly observed in the transverse components and only in the 3.5–4 mHz band. In comparison, enhancements were stronger during the second half in both transverse and compressional components and in both frequency bands. No indication of field linemore » resonances was found for these magnetic field perturbations. Perturbations in these two bands were also observed in the magnetosheath, but not in the solar wind dynamic pressure perturbations. For the first interval, good correlations between the flow perturbations in the magnetosphere and magnetosheath and an indirect signature for Kelvin-Helmholtz (K-H) vortices suggest K-H surface waves as the driver. For the second interval, good correlations are found between the magnetosheath dynamic pressure perturbations, magnetopause deformation, and magnetospheric waves, all in good correspondence to interplanetary magnetic field (IMF) discontinuities. The characteristics of these perturbations can be explained by being driven by foreshock perturbations resulting from these IMF discontinuities. This event shows that even during quiet periods, K-H-unstable magnetopause and ion foreshock perturbations can combine to create a highly dynamic magnetospheric ULF wave environment« less

A multispacecraft event study of Pc5 ultralow-frequency waves in the magnetosphere and their external drivers

DOE Office of Scientific and Technical Information (OSTI.GOV)

Wang, Chih-Ping; Thorne, Richard; Liu, Terry Z.

We investigate a quiet time event of magnetospheric Pc5 ultralow-frequency (ULF) waves and their likely external drivers using multiple spacecraft observations. Enhancements of electric and magnetic field perturbations in two narrow frequency bands, 1.5–2 mHz and 3.5–4 mHz, were observed over a large radial distance range from r ~ 5 to 11 RE. During the first half of this event, perturbations were mainly observed in the transverse components and only in the 3.5–4 mHz band. In comparison, enhancements were stronger during the second half in both transverse and compressional components and in both frequency bands. No indication of field linemore » resonances was found for these magnetic field perturbations. Perturbations in these two bands were also observed in the magnetosheath, but not in the solar wind dynamic pressure perturbations. For the first interval, good correlations between the flow perturbations in the magnetosphere and magnetosheath and an indirect signature for Kelvin-Helmholtz (K-H) vortices suggest K-H surface waves as the driver. For the second interval, good correlations are found between the magnetosheath dynamic pressure perturbations, magnetopause deformation, and magnetospheric waves, all in good correspondence to interplanetary magnetic field (IMF) discontinuities. The characteristics of these perturbations can be explained by being driven by foreshock perturbations resulting from these IMF discontinuities. This event shows that even during quiet periods, K-H-unstable magnetopause and ion foreshock perturbations can combine to create a highly dynamic magnetospheric ULF wave environment« less

Dynamic Data-Driven UAV Network for Plume Characterization

DTIC Science & Technology

2016-05-23

data collection where simulations and measurements become a symbiotic feedback control system where simulations inform measurement locations and the...and measurements become a symbiotic feedback control system where simulations inform measurement locations and the measured data augments simulations...data analysis techniques with mobile sensor data collection where simulations and measurements become a symbiotic feedback control system where

Spatiotemporal Organization of Energy Release Events in the Quiet Solar Corona

NASA Technical Reports Server (NTRS)

Uritsky, Vadim M.; Davila, Joseph M.

2014-01-01

Using data from the STEREO and SOHO spacecraft, we show that temporal organization of energy release events in the quiet solar corona is close to random, in contrast to the clustered behavior of flaring times in solar active regions. The locations of the quiet-Sun events follow the meso- and supergranulation pattern of the underling photosphere. Together with earlier reports of the scale-free event size statistics, our findings suggest that quiet solar regions responsible for bulk coronal heating operate in a driven self-organized critical state, possibly involving long-range Alfvenic interactions.

Numerical Investigations of Capabilities and Limits of Photospheric Data Driven Magnetic Flux Emergence

NASA Astrophysics Data System (ADS)

Linton, Mark; Leake, James; Schuck, Peter W.

2016-05-01

The magnetic field of the solar atmosphere is the primary driver of solar activity. Understanding the magnetic state of the solar atmosphere is therefore of key importance to predicting solaractivity. One promising means of studying the magnetic atmosphere is to dynamically build up and evolve this atmosphere from the time evolution of the magnetic field at the photosphere, where it can be measured with current solar vector magnetograms at high temporal and spatial resolution.We report here on a series of numerical experiments investigating the capabilities and limits of magnetohydrodynamical simulations of such a process, where a magnetic corona is dynamically built up and evolved from a time series of synthetic photospheric data. These synthetic data are composed of photospheric slices taken from self consistent convection zone to corona simulations of flux emergence. The driven coronae are then quantitatively compared against the coronae of the original simulations. We investigate and report on the fidelity of these driven simulations, both as a function of the emergence timescale of the magnetic flux, and as a function of the driving cadence of the input data.This work was supported by the Chief of Naval Research and the NASA Living with a Star and Heliophysics Supporting Research programs.

Dynamic modeling and motion simulation for a winged hybrid-driven underwater glider

NASA Astrophysics Data System (ADS)

Wang, Shu-Xin; Sun, Xiu-Jun; Wang, Yan-Hui; Wu, Jian-Guo; Wang, Xiao-Ming

2011-03-01

PETREL, a winged hybrid-driven underwater glider is a novel and practical marine survey platform which combines the features of legacy underwater glider and conventional AUV (autonomous underwater vehicle). It can be treated as a multi-rigid-body system with a floating base and a particular hydrodynamic profile. In this paper, theorems on linear and angular momentum are used to establish the dynamic equations of motion of each rigid body and the effect of translational and rotational motion of internal masses on the attitude control are taken into consideration. In addition, due to the unique external shape with fixed wings and deflectable rudders and the dual-drive operation in thrust and glide modes, the approaches of building dynamic model of conventional AUV and hydrodynamic model of submarine are introduced, and the tailored dynamic equations of the hybrid glider are formulated. Moreover, the behaviors of motion in glide and thrust operation are analyzed based on the simulation and the feasibility of the dynamic model is validated by data from lake field trials.

Contrasting Heat Budget Dynamics During Two La Niña Marine Heat Wave Events Along Northwestern Australia

NASA Astrophysics Data System (ADS)

Xu, Jiangtao; Lowe, Ryan J.; Ivey, Gregory N.; Jones, Nicole L.; Zhang, Zhenling

2018-02-01

Two marine heat wave events along Western Australia (WA) during the alternate austral summer periods of 2010/2011 and 2012/2013, both linked to La Niña conditions, severely impacted marine ecosystems over more than 12° of latitude, which included the unprecedented bleaching of many coral reefs. Although these two heat waves were forced by similar large-scale climate drivers, the warming patterns differed substantially between events. The central coast of WA (south of 22°S) experienced greater warming in 2010/2011, whereas the northwestern coast of WA experienced greater warming in 2012/2013. To investigate how oceanic and atmospheric heat exchange processes drove these different spatial patterns, an analysis of the ocean heat budget was conducted by integrating remote sensing observations, in situ mooring data, and a high-resolution (˜1 km) ocean circulation model (Regional Ocean Modeling System). The results revealed substantial spatial differences in the relative contributions made by heat advection and air-sea heat exchange between the two heat wave events. During 2010/2011, anomalous warming driven by heat advection was present throughout the region but was much stronger south of 22°S where the poleward-flowing Leeuwin Current strengthens. During 2012/2013, air-sea heat exchange had a much more positive (warming) influence on sea surface temperatures (especially in the northwest), and when combined with a more positive contribution of heat advection in the north, this can explain the regional differences in warming between these two La Niña-associated marine heat wave events.

Precipitation event tracking reveals that precipitation characteristics respond differently under seasonal, interannual, and anthropogenic forcing

NASA Astrophysics Data System (ADS)

Chen, C.; Chang, W.; Kong, W.; Wang, J.; Kotamarthi, V. R.; Stein, M.; Moyer, E. J.

2017-12-01

Change in precipitation characteristics is an especially concerning potential impact of climate change, and both model and observational studies suggest that increases in precipitation intensity are likely. However, studies to date have focused on mean accumulated precipitation rather than on the characteristics of individual events. We report here on a study using a novel rainstorm identification tracking algorithm (Chang et al. 2016) that allows evaluating changes in spatio-temporal characteristics of events. We analyze high-resolution precipitation from dynamically downscaled regional climate simulations over the continental U.S. (WRF driven by CCSM4) of present and future climate conditions. We show that precipitation events show distinct characteristic changes for natural seasonal and interannual variations and for anthropogenic greenhouse-gas forcing. In all cases, wetter seasons/years/future climate states are associated with increased precipitation intensity, but other precipitation characteristics respond differently to the different drivers. For example, under anthropogenic forcing, future wetter climate states involve smaller individual event sizes (partially offsetting their increased intensity). Under natural variability, however, wetter years involve larger mean event sizes. Event identification and tracking algorithms thus allow distinguishing drivers of different types of precipitation changes, and in relating those changes to large-scale processes.

InfoSymbiotics/DDDAS - The power of Dynamic Data Driven Applications Systems for New Capabilities in Environmental -, Geo-, and Space- Sciences

NASA Astrophysics Data System (ADS)

Darema, F.

2016-12-01

InfoSymbiotics/DDDAS embodies the power of Dynamic Data Driven Applications Systems (DDDAS), a concept whereby an executing application model is dynamically integrated, in a feed-back loop, with the real-time data-acquisition and control components, as well as other data sources of the application system. Advanced capabilities can be created through such new computational approaches in modeling and simulations, and in instrumentation methods, and include: enhancing the accuracy of the application model; speeding-up the computation to allow faster and more comprehensive models of a system, and create decision support systems with the accuracy of full-scale simulations; in addition, the notion of controlling instrumentation processes by the executing application results in more efficient management of application-data and addresses challenges of how to architect and dynamically manage large sets of heterogeneous sensors and controllers, an advance over the static and ad-hoc ways of today - with DDDAS these sets of resources can be managed adaptively and in optimized ways. Large-Scale-Dynamic-Data encompasses the next wave of Big Data, and namely dynamic data arising from ubiquitous sensing and control in engineered, natural, and societal systems, through multitudes of heterogeneous sensors and controllers instrumenting these systems, and where opportunities and challenges at these "large-scales" relate not only to data size but the heterogeneity in data, data collection modalities, fidelities, and timescales, ranging from real-time data to archival data. In tandem with this important dimension of dynamic data, there is an extended view of Big Computing, which includes the collective computing by networked assemblies of multitudes of sensors and controllers, this range from the high-end to the real-time seamlessly integrated and unified, and comprising the Large-Scale-Big-Computing. InfoSymbiotics/DDDAS engenders transformative impact in many application domains, ranging from the nano-scale to the terra-scale and to the extra-terra-scale. The talk will address opportunities for new capabilities together with corresponding research challenges, with illustrative examples from several application areas including environmental sciences, geosciences, and space sciences.

Studies of climate dynamics with innovative global-model simulations

NASA Astrophysics Data System (ADS)

Shi, Xiaoming

Climate simulations with different degrees of idealization are essential for the development of our understanding of the climate system. Studies in this dissertation employ carefully designed global-model simulations for the goal of gaining theoretical and conceptual insights into some problems of climate dynamics. Firstly, global warming-induced changes in extreme precipitation are investigated using a global climate model with idealized geography. The precipitation changes over an idealized north-south mid-latitude mountain barrier at the western margin of an otherwise flat continent are studied. The intensity of the 40 most intense events on the western slopes increases by about ~4°C of surface warming. In contrast, the intensity of the top 40 events on the eastern mountain slopes increases at about ~6°C. This higher sensitivity is due to enhanced ascent during the eastern-slope events, which can be explained in terms of linear mountain-wave theory relating to global warming-induced changes in the upper-tropospheric static stability and the tropopause level. Dominated by different dynamical factors, changes in the intensity of extreme precipitation events over plains and oceans might differ from changes over mountains. So the response of extreme precipitation over mountains and flat areas are further compared using larger data sets of simulated extreme events over the two types of surfaces. It is found that the sensitivity of extreme precipitation to increases in global mean surface temperature is 3% per °C lower over mountains than over the oceans or the plains. The difference in sensitivity among these regions is not due to thermodynamic effects, but rather to differences between the gravity-wave dynamics governing vertical velocities over the mountains and the cyclone dynamics governing vertical motions over the oceans and plains. The strengthening of latent heating in the storms over oceans and plains leads to stronger ascent in the warming climate. Motivated by the fact that natural variability of the atmosphere could obscure the signal of anthropogenic warming on time scales of years to decades, the large scale variability of the atmosphere is also studied. Analysis using simulations in the Community Earth System Model Large Ensemble project reveals that the Northern Annular Mode (NAM) does not have a stable spatial pattern when 50-year long segments of data are used to calculate it. Some segments of data result in NAM-like variability with a very strong North Pacific center of action, while in some others it exhibits a more symmetric structure, with North Pacific and Euro-Atlantic centers of comparable strength. Perhaps somewhat puzzling, the NAM's North Pacific center of action is found to have a strengthening trend under anthropogenic warming. Lastly, the large-scale character of an atmosphere in rotating Radiative-Convective Equilibrium (RCE) is studied, using a global atmospheric model with prescribed globally uniform sea surface temperature and no insolation. In such an equilibrium state, numerous tropical cyclone-like vortices develop in the extratropics, which move slowly poleward and westward. The typical spacing of simulated tropical cyclone-like vortices is comparable to the Rossby radius of deformation, while the production of available potential energy is at a scale slightly smaller than that of the vortices. It is hypothesized that the growth of tropical cyclone-like vortices is driven by the self-aggregation of convection, while baroclinic instability destabilizes any vortices that grow significantly larger than the deformation radius. A weak Hadley circulation dominates in the deep tropics, and an eastward-propagating wavenumber one MJO-like mode with a period of 30 to 40 days develops along the equator.

Extreme climatic events change the dynamics and invasibility of semi-arid annual plant communities.

PubMed

Jiménez, Milagros A; Jaksic, Fabian M; Armesto, Juan J; Gaxiola, Aurora; Meserve, Peter L; Kelt, Douglas A; Gutiérrez, Julio R

2011-12-01

Extreme climatic events represent disturbances that change the availability of resources. We studied their effects on annual plant assemblages in a semi-arid ecosystem in north-central Chile. We analysed 130 years of precipitation data using generalised extreme-value distribution to determine extreme events, and multivariate techniques to analyse 20 years of plant cover data of 34 native and 11 exotic species. Extreme drought resets the dynamics of the system and renders it susceptible to invasion. On the other hand, by favouring native annuals, moderately wet events change species composition and allow the community to be resilient to extreme drought. The probability of extreme drought has doubled over the last 50 years. Therefore, investigations on the interaction of climate change and biological invasions are relevant to determine the potential for future effects on the dynamics of semi-arid annual plant communities. 2011 Blackwell Publishing Ltd/CNRS.

Ultrafast chemical reactions in shocked nitromethane probed with dynamic ellipsometry and transient absorption spectroscopy.

PubMed

Brown, Kathryn E; McGrane, Shawn D; Bolme, Cynthia A; Moore, David S

2014-04-10

Initiation of the shock driven chemical reactions and detonation of nitromethane (NM) can be sensitized by the addition of a weak base; however, the chemical mechanism by which sensitization occurs remains unclear. We investigated the shock driven chemical reaction in NM and in NM sensitized with diethylenetriamine (DETA), using a sustained 300 ps shock driven by a chirped Ti:sapphire laser. We measured the solutions' visible transient absorption spectra and measured interface particle and shock velocities of the nitromethane solutions using ultrafast dynamic ellipsometry. We found there to be a volume-increasing reaction that takes place around interface particle velocity up = 2.4 km/s and up = 2.2 km/s for neat NM and NM with 5% DETA, respectively. The rate at which transient absorption increases is similar in all mixtures, but with decreasing induction times for solutions with increasing DETA concentrations. This result supports the hypothesis that the chemical reaction mechanisms for shocked NM and NM with DETA are the same. Data from shocked NM are compared to literature experimental and theoretical data.

Antarctic icequakes triggered by the 2010 Maule earthquake in Chile

NASA Astrophysics Data System (ADS)

Peng, Zhigang; Walter, Jacob I.; Aster, Richard C.; Nyblade, Andrew; Wiens, Douglas A.; Anandakrishnan, Sridhar

2014-09-01

Seismic waves from distant, large earthquakes can almost instantaneously trigger shallow micro-earthquakes and deep tectonic tremor as they pass through Earth's crust. Such remotely triggered seismic activity mostly occurs in tectonically active regions. Triggered seismicity is generally considered to reflect shear failure on critically stressed fault planes and is thought to be driven by dynamic stress perturbations from both Love and Rayleigh types of surface seismic wave. Here we analyse seismic data from Antarctica in the six hours leading up to and following the 2010 Mw 8.8 Maule earthquake in Chile. We identify many high-frequency seismic signals during the passage of the Rayleigh waves generated by the Maule earthquake, and interpret them as small icequakes triggered by the Rayleigh waves. The source locations of these triggered icequakes are difficult to determine owing to sparse seismic network coverage, but the triggered events generate surface waves, so are probably formed by near-surface sources. Our observations are consistent with tensile fracturing of near-surface ice or other brittle fracture events caused by changes in volumetric strain as the high-amplitude Rayleigh waves passed through. We conclude that cryospheric systems can be sensitive to large distant earthquakes.

Supervised dictionary learning for inferring concurrent brain networks.

PubMed

Zhao, Shijie; Han, Junwei; Lv, Jinglei; Jiang, Xi; Hu, Xintao; Zhao, Yu; Ge, Bao; Guo, Lei; Liu, Tianming

2015-10-01

Task-based fMRI (tfMRI) has been widely used to explore functional brain networks via predefined stimulus paradigm in the fMRI scan. Traditionally, the general linear model (GLM) has been a dominant approach to detect task-evoked networks. However, GLM focuses on task-evoked or event-evoked brain responses and possibly ignores the intrinsic brain functions. In comparison, dictionary learning and sparse coding methods have attracted much attention recently, and these methods have shown the promise of automatically and systematically decomposing fMRI signals into meaningful task-evoked and intrinsic concurrent networks. Nevertheless, two notable limitations of current data-driven dictionary learning method are that the prior knowledge of task paradigm is not sufficiently utilized and that the establishment of correspondences among dictionary atoms in different brains have been challenging. In this paper, we propose a novel supervised dictionary learning and sparse coding method for inferring functional networks from tfMRI data, which takes both of the advantages of model-driven method and data-driven method. The basic idea is to fix the task stimulus curves as predefined model-driven dictionary atoms and only optimize the other portion of data-driven dictionary atoms. Application of this novel methodology on the publicly available human connectome project (HCP) tfMRI datasets has achieved promising results.

Collectivity in Small Collision Systems: An Initial-State Perspective

DOE PAGES

Schlichting, Sören; Tribedy, Prithwish

2016-01-01

Measurements of multiparticle correlations in the collisions of small systems such as p+p, p/d/ 3 He+A show striking similarity to the observations in heavy-ion collisions. A number of observables measured in the high-multiplicity events of these systems resemble features that are attributed to collectivity driven by hydrodynamics. However, alternative explanations based on initial-state dynamics are able to describe many characteristic features of these measurements. In this brief review, we highlight some of the recent developments and outstanding issues in this direction.

Vacancy-stabilized crystalline order in hard cubes

PubMed Central

Smallenburg, Frank; Filion, Laura; Marechal, Matthieu; Dijkstra, Marjolein

2012-01-01

We examine the effect of vacancies on the phase behavior and structure of systems consisting of hard cubes using event-driven molecular dynamics and Monte Carlo simulations. We find a first-order phase transition between a fluid and a simple cubic crystal phase that is stabilized by a surprisingly large number of vacancies, reaching a net vacancy concentration of approximately 6.4% near bulk coexistence. Remarkably, we find that vacancies increase the positional order in the system. Finally, we show that the vacancies are delocalized and therefore hard to detect. PMID:23012241

Are Brazil nuts attractive?

PubMed

Sanders, Duncan A; Swift, Michael R; Bowley, R M; King, P J

2004-11-12

We present event-driven simulation results for single and multiple intruders in a vertically vibrated granular bed. Under our vibratory conditions, the mean vertical position of a single intruder is governed primarily by a buoyancylike effect. Multiple intruders also exhibit buoyancy governed behavior; however, multiple neutrally buoyant intruders cluster spontaneously and undergo horizontal segregation. These effects can be understood by considering the dynamics of two neutrally buoyant intruders. We have measured an attractive force between such intruders which has a range of five intruder diameters, and we provide a mechanistic explanation for the origins of this force.

The relationship between processing style, trauma memory processes, and the development of posttraumatic stress symptoms in children and adolescents.

PubMed

McKinnon, Anna; Brewer, Neil; Cameron, Kate; Nixon, Reginald D V

2017-12-01

Data-driven processing, peri-event fear, and trauma memory characteristics are hypothesised to play a core role in the development of Posttraumatic Stress Disorder. We assessed the relationships between these characteristics and Posttraumatic Stress (PTS) symptoms in a sample of youth. Study 1 (N = 36, 7-16 years), involved a sample of children who had undergone a stressful orthopaedic procedure. One week later they answered a series of probed recall questions about the trauma (assessed for accuracy by comparison to a video) and reported on their PTS symptoms. They also rated confidence in their probed recall answers to assess meta-cognitive monitoring of their memory for the trauma. In Study 2, a sample of injured children (N = 57, 7-16 years) were assessed within 1-month of a visit to an Emergency Department, and then at 3-month follow-up. They answered probed recall questions, made confidence ratings, and completed measures of data-driven processing, peri-event fear, PTS and associated psychopathology. Memories were verified using witness accounts. Studies 1 and 2 did not find an association between PTS symptoms and trauma memory accuracy or confidence. In Studies 1 and 2 data-driven processing predicted PTS symptoms. The studies had modest samples sizes and there were ceiling effects for some accuracy and confidence items. Data-driven processing at the time of a trauma was associated with PTS symptoms after accounting for fear at the time of the trauma. Accuracy of recall for trauma memories was not significantly related to PTS symptoms. No decisive conclusion could be drawn regarding the relation between confidence in trauma memories and PTS symptoms. Copyright © 2017 Elsevier Ltd. All rights reserved.

Alternating event processes during lifetimes: population dynamics and statistical inference.

PubMed

Shinohara, Russell T; Sun, Yifei; Wang, Mei-Cheng

2018-01-01

In the literature studying recurrent event data, a large amount of work has been focused on univariate recurrent event processes where the occurrence of each event is treated as a single point in time. There are many applications, however, in which univariate recurrent events are insufficient to characterize the feature of the process because patients experience nontrivial durations associated with each event. This results in an alternating event process where the disease status of a patient alternates between exacerbations and remissions. In this paper, we consider the dynamics of a chronic disease and its associated exacerbation-remission process over two time scales: calendar time and time-since-onset. In particular, over calendar time, we explore population dynamics and the relationship between incidence, prevalence and duration for such alternating event processes. We provide nonparametric estimation techniques for characteristic quantities of the process. In some settings, exacerbation processes are observed from an onset time until death; to account for the relationship between the survival and alternating event processes, nonparametric approaches are developed for estimating exacerbation process over lifetime. By understanding the population dynamics and within-process structure, the paper provide a new and general way to study alternating event processes.

A data-driven prediction method for fast-slow systems

NASA Astrophysics Data System (ADS)

Groth, Andreas; Chekroun, Mickael; Kondrashov, Dmitri; Ghil, Michael

2016-04-01

In this work, we present a prediction method for processes that exhibit a mixture of variability on low and fast scales. The method relies on combining empirical model reduction (EMR) with singular spectrum analysis (SSA). EMR is a data-driven methodology for constructing stochastic low-dimensional models that account for nonlinearity and serial correlation in the estimated noise, while SSA provides a decomposition of the complex dynamics into low-order components that capture spatio-temporal behavior on different time scales. Our study focuses on the data-driven modeling of partial observations from dynamical systems that exhibit power spectra with broad peaks. The main result in this talk is that the combination of SSA pre-filtering with EMR modeling improves, under certain circumstances, the modeling and prediction skill of such a system, as compared to a standard EMR prediction based on raw data. Specifically, it is the separation into "fast" and "slow" temporal scales by the SSA pre-filtering that achieves the improvement. We show, in particular that the resulting EMR-SSA emulators help predict intermittent behavior such as rapid transitions between specific regions of the system's phase space. This capability of the EMR-SSA prediction will be demonstrated on two low-dimensional models: the Rössler system and a Lotka-Volterra model for interspecies competition. In either case, the chaotic dynamics is produced through a Shilnikov-type mechanism and we argue that the latter seems to be an important ingredient for the good prediction skills of EMR-SSA emulators. Shilnikov-type behavior has been shown to arise in various complex geophysical fluid models, such as baroclinic quasi-geostrophic flows in the mid-latitude atmosphere and wind-driven double-gyre ocean circulation models. This pervasiveness of the Shilnikow mechanism of fast-slow transition opens interesting perspectives for the extension of the proposed EMR-SSA approach to more realistic situations.

Data-driven model for the assessment of Mycobacterium tuberculosis transmission in evolving demographic structures

PubMed Central

Arregui, Sergio; Marinova, Dessislava; Sanz, Joaquín

2018-01-01

In the case of tuberculosis (TB), the capabilities of epidemic models to produce quantitatively robust forecasts are limited by multiple hindrances. Among these, understanding the complex relationship between disease epidemiology and populations’ age structure has been highlighted as one of the most relevant. TB dynamics depends on age in multiple ways, some of which are traditionally simplified in the literature. That is the case of the heterogeneities in contact intensity among different age strata that are common to all airborne diseases, but still typically neglected in the TB case. Furthermore, while demographic structures of many countries are rapidly aging, demographic dynamics are pervasively ignored when modeling TB spreading. In this work, we present a TB transmission model that incorporates country-specific demographic prospects and empirical contact data around a data-driven description of TB dynamics. Using our model, we find that the inclusion of demographic dynamics is followed by an increase in the burden levels predicted for the next decades in the areas of the world that are most hit by the disease today. Similarly, we show that considering realistic patterns of contacts among individuals in different age strata reshapes the transmission patterns reproduced by the models, a result with potential implications for the design of age-focused epidemiological interventions. PMID:29563223

Modeling Dynamic Evolution of Online Friendship Network

NASA Astrophysics Data System (ADS)

Wu, Lian-Ren; Yan, Qiang

2012-10-01

In this paper, we study the dynamic evolution of friendship network in SNS (Social Networking Site). Our analysis suggests that an individual joining a community depends not only on the number of friends he or she has within the community, but also on the friendship network generated by those friends. In addition, we propose a model which is based on two processes: first, connecting nearest neighbors; second, strength driven attachment mechanism. The model reflects two facts: first, in the social network it is a universal phenomenon that two nodes are connected when they have at least one common neighbor; second, new nodes connect more likely to nodes which have larger weights and interactions, a phenomenon called strength driven attachment (also called weight driven attachment). From the simulation results, we find that degree distribution P(k), strength distribution P(s), and degree-strength correlation are all consistent with empirical data.

Potential of turbidity monitoring for real time control of pollutant discharge in sewers during rainfall events.

PubMed

Lacour, C; Joannis, C; Gromaire, M-C; Chebbo, G

2009-01-01

Turbidity sensors can be used to continuously monitor the evolution of pollutant mass discharge. For two sites within the Paris combined sewer system, continuous turbidity, conductivity and flow data were recorded at one-minute time intervals over a one-year period. This paper is intended to highlight the variability in turbidity dynamics during wet weather. For each storm event, turbidity response aspects were analysed through different classifications. The correlation between classification and common parameters, such as the antecedent dry weather period, total event volume per impervious hectare and both the mean and maximum hydraulic flow for each event, was also studied. Moreover, the dynamics of flow and turbidity signals were compared at the event scale. No simple relation between turbidity responses, hydraulic flow dynamics and the chosen parameters was derived from this effort. Knowledge of turbidity dynamics could therefore potentially improve wet weather management, especially when using pollution-based real-time control (P-RTC) since turbidity contains information not included in hydraulic flow dynamics and not readily predictable from such dynamics.

Sea surface temperature anomalies driven by oceanic local forcing in the Brazil-Malvinas Confluence

NASA Astrophysics Data System (ADS)

da Silveira, Isabel Porto; Pezzi, Luciano Ponzi

2014-03-01

Sea surface temperature (SST) anomaly events in the Brazil-Malvinas Confluence (BMC) were investigated through wavelet analysis and numerical modeling. Wavelet analysis was applied to recognize the main spectral signals of SST anomaly events in the BMC and in the Drake Passage as a first attempt to link middle and high latitudes. The numerical modeling approach was used to clarify the local oceanic dynamics that drive these anomalies. Wavelet analysis pointed to the 8-12-year band as the most energetic band representing remote forcing between high to middle latitudes. Other frequencies observed in the BMC wavelet analysis indicate that part of its variability could also be forced by low-latitude events, such as El Niño. Numerical experiments carried out for the years of 1964 and 1992 (cold and warm El Niño-Southern Oscillation (ENSO) phases) revealed two distinct behaviors that produced negative and positive sea surface temperature anomalies on the BMC region. The first behavior is caused by northward cold flow, Río de la Plata runoff, and upwelling processes. The second behavior is driven by a southward excursion of the Brazil Current (BC) front, alterations in Río de la Plata discharge rates, and most likely by air-sea interactions. Both episodes are characterized by uncoupled behavior between the surface and deeper layers.

Diffusion and interactions of interstitials in hard-sphere interstitial solid solutions

NASA Astrophysics Data System (ADS)

van der Meer, Berend; Lathouwers, Emma; Smallenburg, Frank; Filion, Laura

2017-12-01

Using computer simulations, we study the dynamics and interactions of interstitial particles in hard-sphere interstitial solid solutions. We calculate the free-energy barriers associated with their diffusion for a range of size ratios and densities. By applying classical transition state theory to these free-energy barriers, we predict the diffusion coefficients, which we find to be in good agreement with diffusion coefficients as measured using event-driven molecular dynamics simulations. These results highlight that transition state theory can capture the interstitial dynamics in the hard-sphere model system. Additionally, we quantify the interactions between the interstitials. We find that, apart from excluded volume interactions, the interstitial-interstitial interactions are almost ideal in our system. Lastly, we show that the interstitial diffusivity can be inferred from the large-particle fluctuations alone, thus providing an empirical relationship between the large-particle fluctuations and the interstitial diffusivity.

Ballistic aggregation in systems of inelastic particles: Cluster growth, structure, and aging

NASA Astrophysics Data System (ADS)

Paul, Subhajit; Das, Subir K.

2017-07-01

We study far-from-equilibrium dynamics in models of freely cooling granular gas and ballistically aggregating compact clusters. For both the cases, from event-driven molecular dynamics simulations, we have presented detailed results on structure and dynamics in space dimensions d =1 and 2. Via appropriate analyses it has been confirmed that the ballistic aggregation mechanism applies in d =1 granular gases as well. Aging phenomena for this mechanism, in both the dimensions, have been studied via the two-time density autocorrelation function. This quantity is demonstrated to exhibit scaling property similar to that in the standard phase transition kinetics. The corresponding functional forms have been quantified and the outcomes have been discussed in connection with the structural properties. Our results on aging establish a more complete equivalence between the granular gas and the ballistic aggregation models in d =1 .

Numerical Investigations of Capabilities and Limits of Photospheric Data Driven Magnetic Flux Emergence

NASA Astrophysics Data System (ADS)

Linton, M.; Leake, J. E.; Schuck, P. W.

2016-12-01

The magnetic field of the solar atmosphere is the primary driver of solar activity. Understanding the magnetic state of the solar atmosphere is therefore of key importance to predicting solar activity. One promising means of studying the magnetic atmosphere is to dynamically build up and evolve this atmosphere from the time evolution of emerging magnetic field at the photosphere, where it can be measured with current solar vector magnetograms at high temporal and spatial resolution. We report here on a series of numerical experiments investigating the capabilities and limits of magnetohydrodynamical simulations of such a process, where a magnetic corona is dynamically built up and evolved from a time series of synthetic photospheric data. These synthetic data are composed of photospheric slices taken from self consistent convection zone to corona simulations of flux emergence. The driven coronae are then quantitatively compared against the coronae of the original simulations. We investigate and report on the fidelity of these driven simulations, both as a function of the emergence timescale of the magnetic flux, and as a function of the driving cadence of the input data. These investigations will then be used to outline future prospects and challenges for using observed photospheric data to drive such solar atmospheric simulations. This work was supported by the Chief of Naval Research and the NASA Living with a Star and Heliophysics Supporting Research programs.

Cortical subnetwork dynamics during human language tasks.

PubMed

Collard, Maxwell J; Fifer, Matthew S; Benz, Heather L; McMullen, David P; Wang, Yujing; Milsap, Griffin W; Korzeniewska, Anna; Crone, Nathan E

2016-07-15

Language tasks require the coordinated activation of multiple subnetworks-groups of related cortical interactions involved in specific components of task processing. Although electrocorticography (ECoG) has sufficient temporal and spatial resolution to capture the dynamics of event-related interactions between cortical sites, it is difficult to decompose these complex spatiotemporal patterns into functionally discrete subnetworks without explicit knowledge of each subnetwork's timing. We hypothesized that subnetworks corresponding to distinct components of task-related processing could be identified as groups of interactions with co-varying strengths. In this study, five subjects implanted with ECoG grids over language areas performed word repetition and picture naming. We estimated the interaction strength between each pair of electrodes during each task using a time-varying dynamic Bayesian network (tvDBN) model constructed from the power of high gamma (70-110Hz) activity, a surrogate for population firing rates. We then reduced the dimensionality of this model using principal component analysis (PCA) to identify groups of interactions with co-varying strengths, which we term functional network components (FNCs). This data-driven technique estimates both the weight of each interaction's contribution to a particular subnetwork, and the temporal profile of each subnetwork's activation during the task. We found FNCs with temporal and anatomical features consistent with articulatory preparation in both tasks, and with auditory and visual processing in the word repetition and picture naming tasks, respectively. These FNCs were highly consistent between subjects with similar electrode placement, and were robust enough to be characterized in single trials. Furthermore, the interaction patterns uncovered by FNC analysis correlated well with recent literature suggesting important functional-anatomical distinctions between processing external and self-produced speech. Our results demonstrate that subnetwork decomposition of event-related cortical interactions is a powerful paradigm for interpreting the rich dynamics of large-scale, distributed cortical networks during human cognitive tasks. Copyright © 2016 Elsevier Inc. All rights reserved.

DNA-Binding Kinetics Determines the Mechanism of Noise-Induced Switching in Gene Networks

PubMed Central

Tse, Margaret J.; Chu, Brian K.; Roy, Mahua; Read, Elizabeth L.

2015-01-01

Gene regulatory networks are multistable dynamical systems in which attractor states represent cell phenotypes. Spontaneous, noise-induced transitions between these states are thought to underlie critical cellular processes, including cell developmental fate decisions, phenotypic plasticity in fluctuating environments, and carcinogenesis. As such, there is increasing interest in the development of theoretical and computational approaches that can shed light on the dynamics of these stochastic state transitions in multistable gene networks. We applied a numerical rare-event sampling algorithm to study transition paths of spontaneous noise-induced switching for a ubiquitous gene regulatory network motif, the bistable toggle switch, in which two mutually repressive genes compete for dominant expression. We find that the method can efficiently uncover detailed switching mechanisms that involve fluctuations both in occupancies of DNA regulatory sites and copy numbers of protein products. In addition, we show that the rate parameters governing binding and unbinding of regulatory proteins to DNA strongly influence the switching mechanism. In a regime of slow DNA-binding/unbinding kinetics, spontaneous switching occurs relatively frequently and is driven primarily by fluctuations in DNA-site occupancies. In contrast, in a regime of fast DNA-binding/unbinding kinetics, switching occurs rarely and is driven by fluctuations in levels of expressed protein. Our results demonstrate how spontaneous cell phenotype transitions involve collective behavior of both regulatory proteins and DNA. Computational approaches capable of simulating dynamics over many system variables are thus well suited to exploring dynamic mechanisms in gene networks. PMID:26488666

Adaptive Anchoring Model: How Static and Dynamic Presentations of Time Series Influence Judgments and Predictions.

PubMed

Kusev, Petko; van Schaik, Paul; Tsaneva-Atanasova, Krasimira; Juliusson, Asgeir; Chater, Nick

2018-01-01

When attempting to predict future events, people commonly rely on historical data. One psychological characteristic of judgmental forecasting of time series, established by research, is that when people make forecasts from series, they tend to underestimate future values for upward trends and overestimate them for downward ones, so-called trend-damping (modeled by anchoring on, and insufficient adjustment from, the average of recent time series values). Events in a time series can be experienced sequentially (dynamic mode), or they can also be retrospectively viewed simultaneously (static mode), not experienced individually in real time. In one experiment, we studied the influence of presentation mode (dynamic and static) on two sorts of judgment: (a) predictions of the next event (forecast) and (b) estimation of the average value of all the events in the presented series (average estimation). Participants' responses in dynamic mode were anchored on more recent events than in static mode for all types of judgment but with different consequences; hence, dynamic presentation improved prediction accuracy, but not estimation. These results are not anticipated by existing theoretical accounts; we develop and present an agent-based model-the adaptive anchoring model (ADAM)-to account for the difference between processing sequences of dynamically and statically presented stimuli (visually presented data). ADAM captures how variation in presentation mode produces variation in responses (and the accuracy of these responses) in both forecasting and judgment tasks. ADAM's model predictions for the forecasting and judgment tasks fit better with the response data than a linear-regression time series model. Moreover, ADAM outperformed autoregressive-integrated-moving-average (ARIMA) and exponential-smoothing models, while neither of these models accounts for people's responses on the average estimation task. Copyright © 2017 The Authors. Cognitive Science published by Wiley Periodicals, Inc. on behalf of Cognitive Science Society.

The Structural Consequences of Big Data-Driven Education.

PubMed

Zeide, Elana

2017-06-01

Educators and commenters who evaluate big data-driven learning environments focus on specific questions: whether automated education platforms improve learning outcomes, invade student privacy, and promote equality. This article puts aside separate unresolved-and perhaps unresolvable-issues regarding the concrete effects of specific technologies. It instead examines how big data-driven tools alter the structure of schools' pedagogical decision-making, and, in doing so, change fundamental aspects of America's education enterprise. Technological mediation and data-driven decision-making have a particularly significant impact in learning environments because the education process primarily consists of dynamic information exchange. In this overview, I highlight three significant structural shifts that accompany school reliance on data-driven instructional platforms that perform core school functions: teaching, assessment, and credentialing. First, virtual learning environments create information technology infrastructures featuring constant data collection, continuous algorithmic assessment, and possibly infinite record retention. This undermines the traditional intellectual privacy and safety of classrooms. Second, these systems displace pedagogical decision-making from educators serving public interests to private, often for-profit, technology providers. They constrain teachers' academic autonomy, obscure student evaluation, and reduce parents' and students' ability to participate or challenge education decision-making. Third, big data-driven tools define what "counts" as education by mapping the concepts, creating the content, determining the metrics, and setting desired learning outcomes of instruction. These shifts cede important decision-making to private entities without public scrutiny or pedagogical examination. In contrast to the public and heated debates that accompany textbook choices, schools often adopt education technologies ad hoc. Given education's crucial impact on individual and collective success, educators and policymakers must consider the implications of data-driven education proactively and explicitly.

Turbulence-driven anisotropic electron tail generation during magnetic reconnection

NASA Astrophysics Data System (ADS)

DuBois, A. M.; Scherer, A.; Almagri, A. F.; Anderson, J. K.; Pandya, M. D.; Sarff, J. S.

2018-05-01

Magnetic reconnection (MR) plays an important role in particle transport, energization, and acceleration in space, astrophysical, and laboratory plasmas. In the Madison Symmetric Torus reversed field pinch, discrete MR events release large amounts of energy from the equilibrium magnetic field, a fraction of which is transferred to electrons and ions. Previous experiments revealed an anisotropic electron tail that favors the perpendicular direction and is symmetric in the parallel. New profile measurements of x-ray emission show that the tail distribution is localized near the magnetic axis, consistent modeling of the bremsstrahlung emission. The tail appears first near the magnetic axis and then spreads radially, and the dynamics in the anisotropy and diffusion are discussed. The data presented imply that the electron tail formation likely results from a turbulent wave-particle interaction and provides evidence that high energy electrons are escaping the core-localized region through pitch angle scattering into the parallel direction, followed by stochastic parallel transport to the plasma edge. New measurements also show a strong correlation between high energy x-ray measurements and tearing mode dynamics, suggesting that the coupling between core and edge tearing modes is essential for energetic electron tail formation.

On the nonlinear forced response of the North Atlantic atmosphere to meridional shifts of the Gulf Stream path

NASA Astrophysics Data System (ADS)

Seo, H.; Kwon, Y. O.; Joyce, T. M.; Ummenhofer, C.

2016-12-01

This study examines the North Atlantic atmospheric circulation response to the meridional shift of Gulf Stream path using a large-ensemble, high-resolution, and hemispheric-scale WRF simulations. The model is forced with wintertime SST anomalies derived from a wide range of Gulf Stream shift scenarios. The key result of the model experiments, supported in part by an independent analysis of a reanalysis data set, is that the large-scale, quasi-steady North Atlantic circulation response is unambiguously nonlinear about the sign and amplitude of chosen SST anomalies. This nonlinear response prevails over the weak linear response and resembles the negative North Atlantic Oscillation, the leading intrinsic mode of variability in the model and the observations. Further analysis of the associated dynamics reveals that the nonlinear responses are accompanied by the anomalous southward shift of the North Atlantic eddy-driven jet stream, which is reinforced nearly equally by the high-frequency transient eddy feedback and the low-frequency high-latitude wave breaking events. The result highlights the importance of the intrinsically nonlinear transient eddy dynamics and eddy-mean flow interactions in generating the nonlinear forced response to the meridional shift in the Gulf Stream.

Confirmation of Small Dynamical and Stellar Masses for Extreme Emission Line Galaxies at z Approx. 2

NASA Technical Reports Server (NTRS)

Maseda, Michael V.; van Der Wel, Arjen; da Cunha, Elisabete; Rix, Hans-Walter; Pacifici, Camilla; Momcheva, Ivelina; Brammer, Gabriel B.; Franx, Marijn; van Dokkum, Pieter; Bell, Eric F.;

100J Pulsed Laser Shock Driver for Dynamic Compression Research

NASA Astrophysics Data System (ADS)

Wang, X.; Sethian, J.; Bromage, J.; Fochs, S.; Broege, D.; Zuegel, J.; Roides, R.; Cuffney, R.; Brent, G.; Zweiback, J.; Currier, Z.; D'Amico, K.; Hawreliak, J.; Zhang, J.; Rigg, P. A.; Gupta, Y. M.

2017-06-01

Logos Technologies and the Laboratory for Laser Energetics (LLE, University of Rochester) - in partnership with Washington State University - have designed, built and deployed a one of a kind 100J pulsed UV (351 nm) laser system to perform real-time, x-ray diffraction and imaging experiments in laser-driven compression experiments at the Dynamic Compression Sector (DCS) at the Advanced Photon Source, Argonne National Laboratory. The laser complements the other dynamic compression drivers at DCS. The laser system features beam smoothing for 2-d spatially uniform loading of samples and four, highly reproducible, temporal profiles (total pulse duration: 5-15 ns) to accommodate a wide variety of scientific needs. Other pulse shapes can be achieved as the experimental needs evolve. Timing of the laser pulse is highly precise (<200 ps) to allow accurate synchronization of the x-rays with the dynamic compression event. Details of the laser system, its operating parameters, and representative results will be presented. Work supported by DOE/NNSA.

Comparison of crash rates and rear-end striking crashes among novice teens and experienced adults using the SHRP2 Naturalistic Driving Study.

PubMed

Seacrist, Thomas; Belwadi, Aditya; Prabahar, Abhiti; Chamberlain, Samuel; Megariotis, James; Loeb, Helen

2016-09-01

Motor vehicle crashes are the leading cause of death for teens. Previous teen and adult crash rates have been based upon fatal crashes, police-reported crashes, and estimated miles driven. Large-scale naturalistic driving studies offer the opportunity to compute crash rates using a reliable methodology to capture crashes and driving exposure. The Strategic Highway Research Program 2 (SHRP2) Naturalistic Driving Study contains extensive real-world data on teen and adult driving. This article presents findings on the crash rates of novice teen and experienced adult drivers in naturalistic crashes. A subset from the SHRP2 database consisting of 539 crash events for novice teens (16-19 years, n = 549) and experienced adults (35-54 years, n = 591) was used. Onboard instrumentation such as scene cameras, accelerometers, and Global Positioning System logged time series data at 10 Hz. Scene videos were reviewed for all events to identify rear-end striking crashes. Dynamic variables such as acceleration and velocity were analyzed for rear-end striking events. Number of crashes, crash rates, rear-end striking crash severity, and rear-end striking impact velocity were compared between novice teens and experienced adults. Video review of the SHRP2 crashes identified significantly more crashes (P < 0.01) and rear-end striking crashes (P < 0.01) among the teen group than among the adult group. This yielded crash rates of 30.0 crashes per million miles driven for novice teens compared to 5.3 crashes per million miles driven for experienced adults. The crash rate ratio for teens vs. adults was 5.7. The rear-end striking crash rate was 13.5 and 1.8 per million miles driven for novice teens and experienced adults, respectively. The rear-end striking crash rate ratio for teens vs. adults was 7.5. The rear-end striking crash severity measured by the accelerometers was greater (P < 0.05) for the teen group (1.8 ± 0.9 g; median = 1.6 g) than for the adult group (1.1 ± 0.4 g; median = 1.0 g), suggesting that teen crashes tend to be more serious than adult crashes. Increased rear-end striking impact velocity (P < 0.01) was also observed for novice teens (18.8 ± 13.2 mph; median = 18.9 mph) compared to experienced adults (3.3 ± 1.2 mph; median = 2.8 mph). To our knowledge, this is the first study to compare crash rates between teens and adults using a large-scale naturalistic driving database. Unlike previous crash rates, the reported rates reliably control for crash type and driving exposure. These results conform to previous findings that novice teens exhibit increased crash rates compared to experienced adults.

Thermospheric dynamics during November 21-22, 1981 - Dynamics Explorer measurements and thermospheric general circulation model predictions

NASA Technical Reports Server (NTRS)

Roble, R. G.; Killeen, T. L.; Spencer, N. W.; Heelis, R. A.; Reiff, P. H.

1988-01-01

Time-dependent aurora and magnetospheric convection parameterizations have been derived from solar wind and aurora particle data for November 21-22, 1981, and are used to drive the auroral and magnetospheric convection models that are embedded in the National Center for Atmospheric Research thermospheric general circulation model (TGCM). Neutral wind speeds and transition boundaries between the midlatitude solar-driven circulation and the high-latitude magnetospheric convection-driven circulation are examined on an orbit-by-orbit basis. The results show that TGCM-calculated winds and reversal boundary locations are in generally good agreement with Dynamics Explorer 2 measurements for the orbits studied. This suggests that, at least for this particular period of relatively moderate geomagnetic activity, the TGCM parameterizations on the eveningside of the auroral oval and polar cap are adequate.

Unveiling causal activity of complex networks

NASA Astrophysics Data System (ADS)

Williams-García, Rashid V.; Beggs, John M.; Ortiz, Gerardo

2017-07-01

We introduce a novel tool for analyzing complex network dynamics, allowing for cascades of causally-related events, which we call causal webs (c-webs), to be separated from other non-causally-related events. This tool shows that traditionally-conceived avalanches may contain mixtures of spatially-distinct but temporally-overlapping cascades of events, and dynamical disorder or noise. In contrast, c-webs separate these components, unveiling previously hidden features of the network and dynamics. We apply our method to mouse cortical data with resulting statistics which demonstrate for the first time that neuronal avalanches are not merely composed of causally-related events. The original version of this article was uploaded to the arXiv on March 17th, 2016 [1].

Episodic inflation events at Akutan Volcano, Alaska, during 2005-2017

NASA Astrophysics Data System (ADS)

Ji, Kang Hyeun; Yun, Sang-Ho; Rim, Hyoungrea

2017-08-01

Detection of weak volcano deformation helps constrain characteristics of eruption cycles. We have developed a signal detection technique, called the Targeted Projection Operator (TPO), to monitor surface deformation with Global Positioning System (GPS) data. We have applied the TPO to GPS data collected at Akutan Volcano from June 2005 to March 2017 and detected four inflation events that occurred in 2008, 2011, 2014, and 2016 with inflation rates of about 8-22 mm/yr above the background trend at a near-source site AV13. Numerical modeling suggests that the events should be driven by closely located sources or a single source in a shallow magma chamber at a depth of about 4 km. The inflation events suggest that magma has episodically accumulated in a shallow magma chamber.

The semantics of verbs in the dissolution and development of language.

PubMed

Lahey, M; Feier, C D

1982-03-01

Evidence of the dissolution (DL) of verbs was examined in the written logs kept daily for 4 1/2 years by a woman (Mrs. W) who suffered from cerebral atrophy of unknown origin. Results were compared with similar analyses of written samples obtained from elementary school children (CWL), from normal adults (AWL) and from the literature on early oral language development (COL). The major finding of this study was that the sequence of the dissolution of verbs, in terms of the meanings expressed, mirrored the sequence of early acquisition. In the DL data reported here, Mrs. W continued to write about dynamic events after she ceased writing about stative events; in COL, children talk about dynamic events before stative events. Based on the AWL and CWL data, frequency of use is rejected as an explanation for the dominance and stability of dynamic relations in DL. Rather, it is suggested that the expression of dynamic relations may be less complex than the expression of stative relations due to possible differences in imagery and implication, but particularly due to the linguistic contexts in which each can be expressed.

Data-driven outbreak forecasting with a simple nonlinear growth model

PubMed Central

Lega, Joceline; Brown, Heidi E.

2016-01-01

Recent events have thrown the spotlight on infectious disease outbreak response. We developed a data-driven method, EpiGro, which can be applied to cumulative case reports to estimate the order of magnitude of the duration, peak and ultimate size of an ongoing outbreak. It is based on a surprisingly simple mathematical property of many epidemiological data sets, does not require knowledge or estimation of disease transmission parameters, is robust to noise and to small data sets, and runs quickly due to its mathematical simplicity. Using data from historic and ongoing epidemics, we present the model. We also provide modeling considerations that justify this approach and discuss its limitations. In the absence of other information or in conjunction with other models, EpiGro may be useful to public health responders. PMID:27770752

Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems.

PubMed

Wu, Weiwei; Li, Huafan; Shan, Feng; Zhao, Yingchao

2017-12-20

Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm.

Optimal Rate Schedules with Data Sharing in Energy Harvesting Communication Systems

PubMed Central

Wu, Weiwei; Li, Huafan; Shan, Feng; Zhao, Yingchao

2017-01-01

Despite the abundant research on energy-efficient rate scheduling polices in energy harvesting communication systems, few works have exploited data sharing among multiple applications to further enhance the energy utilization efficiency, considering that the harvested energy from environments is limited and unstable. In this paper, to overcome the energy shortage of wireless devices at transmitting data to a platform running multiple applications/requesters, we design rate scheduling policies to respond to data requests as soon as possible by encouraging data sharing among data requests and reducing the redundancy. We formulate the problem as a transmission completion time minimization problem under constraints of dynamical data requests and energy arrivals. We develop offline and online algorithms to solve this problem. For the offline setting, we discover the relationship between two problems: the completion time minimization problem and the energy consumption minimization problem with a given completion time. We first derive the optimal algorithm for the min-energy problem and then adopt it as a building block to compute the optimal solution for the min-completion-time problem. For the online setting without future information, we develop an event-driven online algorithm to complete the transmission as soon as possible. Simulation results validate the efficiency of the proposed algorithm. PMID:29261135

Resonance of a fluid-driven crack: radiation properties and implications for the source of long-period events and harmonic tremor.

USGS Publications Warehouse

Chouet, B.

1988-01-01

A dynamic source model is presented, in which a 3-D crack containing a viscous compressible fluid is excited into resonance by an impulsive pressure transient applied over a small area DELTA S of the crack surface. The crack excitation depends critically on two dimensionless parameters called the crack stiffness and viscous damping loss. According to the model, the long-period event and harmonic tremor share the same source but differ in the boundary conditions for fluid flow and in the triggering mechanism setting up the resonance of the source, the former being viewed as the impulse response of the tremor generating system and the later representing the excitation due to more complex forcing functions.-from Author

Final Report for Dynamic Models for Causal Analysis of Panel Data. Approaches to the Censoring Problem in Analysis of Event Histories. Part III, Chapter 2.

ERIC Educational Resources Information Center

Tuma, Nancy Brandon; Hannan, Michael T.

The document, part of a series of chapters described in SO 011 759, considers the problem of censoring in the analysis of event-histories (data on dated events, including dates of change from one qualitative state to another). Censoring refers to the lack of information on events that occur before or after the period for which data are available.…

Automated adaptive inference of phenomenological dynamical models.

PubMed

Daniels, Bryan C; Nemenman, Ilya

2015-08-21

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved.

Automated adaptive inference of phenomenological dynamical models

PubMed Central

Daniels, Bryan C.; Nemenman, Ilya

2015-01-01

Dynamics of complex systems is often driven by large and intricate networks of microscopic interactions, whose sheer size obfuscates understanding. With limited experimental data, many parameters of such dynamics are unknown, and thus detailed, mechanistic models risk overfitting and making faulty predictions. At the other extreme, simple ad hoc models often miss defining features of the underlying systems. Here we develop an approach that instead constructs phenomenological, coarse-grained models of network dynamics that automatically adapt their complexity to the available data. Such adaptive models produce accurate predictions even when microscopic details are unknown. The approach is computationally tractable, even for a relatively large number of dynamical variables. Using simulated data, it correctly infers the phase space structure for planetary motion, avoids overfitting in a biological signalling system and produces accurate predictions for yeast glycolysis with tens of data points and over half of the interacting species unobserved. PMID:26293508

Demonstration of successful malaria forecasts for Botswana using an operational seasonal climate model

NASA Astrophysics Data System (ADS)

MacLeod, Dave A.; Jones, Anne; Di Giuseppe, Francesca; Caminade, Cyril; Morse, Andrew P.

2015-04-01

The severity and timing of seasonal malaria epidemics is strongly linked with temperature and rainfall. Advance warning of meteorological conditions from seasonal climate models can therefore potentially anticipate unusually strong epidemic events, building resilience and adapting to possible changes in the frequency of such events. Here we present validation of a process-based, dynamic malaria model driven by hindcasts from a state-of-the-art seasonal climate model from the European Centre for Medium-Range Weather Forecasts. We validate the climate and malaria models against observed meteorological and incidence data for Botswana over the period 1982-2006 the longest record of observed incidence data which has been used to validate a modeling system of this kind. We consider the impact of climate model biases, the relationship between climate and epidemiological predictability and the potential for skillful malaria forecasts. Forecast skill is demonstrated for upper tercile malaria incidence for the Botswana malaria season (January-May), using forecasts issued at the start of November; the forecast system anticipates six out of the seven upper tercile malaria seasons in the observational period. The length of the validation time series gives confidence in the conclusion that it is possible to make reliable forecasts of seasonal malaria risk, forming a key part of a health early warning system for Botswana and contributing to efforts to adapt to climate change.

Integrating complex business processes for knowledge-driven clinical decision support systems.

PubMed

Kamaleswaran, Rishikesan; McGregor, Carolyn

2012-01-01

This paper presents in detail the component of the Complex Business Process for Stream Processing framework that is responsible for integrating complex business processes to enable knowledge-driven Clinical Decision Support System (CDSS) recommendations. CDSSs aid the clinician in supporting the care of patients by providing accurate data analysis and evidence-based recommendations. However, the incorporation of a dynamic knowledge-management system that supports the definition and enactment of complex business processes and real-time data streams has not been researched. In this paper we discuss the process web service as an innovative method of providing contextual information to a real-time data stream processing CDSS.

Extended-Range Prediction with Low-Dimensional, Stochastic-Dynamic Models: A Data-driven Approach

DTIC Science & Technology

2012-09-30

characterization of extratropical storms and extremes and link these to LFV modes. Mingfang Ting, Yochanan Kushnir, Andrew W. Robertson...simulating and predicting a wide range of climate phenomena including ENSO, tropical Atlantic sea surface temperatures (SSTs), storm track variability...into empirical prediction models. Use observations to improve low-order dynamical MJO models. Adam Sobel, Daehyun Kim. Extratropical variability

Machine Learning-based discovery of closures for reduced models of dynamical systems

NASA Astrophysics Data System (ADS)

Pan, Shaowu; Duraisamy, Karthik

2017-11-01

Despite the successful application of machine learning (ML) in fields such as image processing and speech recognition, only a few attempts has been made toward employing ML to represent the dynamics of complex physical systems. Previous attempts mostly focus on parameter calibration or data-driven augmentation of existing models. In this work we present a ML framework to discover closure terms in reduced models of dynamical systems and provide insights into potential problems associated with data-driven modeling. Based on exact closure models for linear system, we propose a general linear closure framework from viewpoint of optimization. The framework is based on trapezoidal approximation of convolution term. Hyperparameters that need to be determined include temporal length of memory effect, number of sampling points, and dimensions of hidden states. To circumvent the explicit specification of memory effect, a general framework inspired from neural networks is also proposed. We conduct both a priori and posteriori evaluations of the resulting model on a number of non-linear dynamical systems. This work was supported in part by AFOSR under the project ``LES Modeling of Non-local effects using Statistical Coarse-graining'' with Dr. Jean-Luc Cambier as the technical monitor.

JPL Big Data Technologies for Radio Astronomy

NASA Astrophysics Data System (ADS)

Jones, Dayton L.; D'Addario, L. R.; De Jong, E. M.; Mattmann, C. A.; Rebbapragada, U. D.; Thompson, D. R.; Wagstaff, K.

2014-04-01

During the past three years the Jet Propulsion Laboratory has been working on several technologies to deal with big data challenges facing next-generation radio arrays, among other applications. This program has focused on the following four areas: 1) We are investigating high-level ASIC architectures that reduce power consumption for cross-correlation of data from large interferometer arrays by one to two orders of magnitude. The cost of operations for the Square Kilometre Array (SKA), which may be dominated by the cost of power for data processing, is a serious concern. A large improvement in correlator power efficiency could have a major positive impact. 2) Data-adaptive algorithms (machine learning) for real-time detection and classification of fast transient signals in high volume data streams are being developed and demonstrated. Studies of the dynamic universe, particularly searches for fast (<< 1 second) transient events, require that data be analyzed rapidly and with robust RFI rejection. JPL, in collaboration with the International Center for Radio Astronomy Research in Australia, has developed a fast transient search system for eventual deployment on ASKAP. In addition, a real-time transient detection experiment is now running continuously and commensally on NRAO's Very Long Baseline Array. 3) Scalable frameworks for data archiving, mining, and distribution are being applied to radio astronomy. A set of powerful open-source Object Oriented Data Technology (OODT) tools is now available through Apache. OODT was developed at JPL for Earth science data archives, but it is proving to be useful for radio astronomy, planetary science, health care, Earth climate, and other large-scale archives. 4) We are creating automated, event-driven data visualization tools that can be used to extract information from a wide range of complex data sets. Visualization of complex data can be improved through algorithms that detect events or features of interest and autonomously generate images or video to display those features. This work has been carried out at the Jet Propulsion Laboratory, California Institute of Technology, under contract with the National Aeronautics and Space Administration.

Nontrivial behavior of water in the vicinity and inside lipid bilayers as probed by molecular dynamics simulations.

PubMed

Krylov, Nikolay A; Pentkovsky, Vladimir M; Efremov, Roman G

2013-10-22

The atomic-scale diffusion of water in the presence of several lipid bilayers mimicking biomembranes is characterized via unconstrained molecular dynamics (MD) simulations. Although the overall water dynamics corresponds well to literature data, namely, the efficient braking near polar head groups of lipids, a number of interesting and biologically relevant details observed in this work have not been sufficiently discussed so far; for instance, the fact that waters "sense" the membrane unexpectedly early, before water density begins to decrease. In this "transitional zone" the velocity distributions of water and their H-bonding patterns deviate from those in the bulk solution. The boundaries of this zone are well preserved even despite the local (<1 nm size) perturbation of the lipid bilayer, thus indicating a decoupling of the surface and bulk dynamics of water. This is in excellent agreement with recent experimental data. Near the membrane surface, water movement becomes anisotropic, that is, solvent molecules preferentially move outward the bilayer. Deep in the membrane interior, the velocities can even exceed those in the bulk solvent and undergo large-scale fluctuations. The analysis of MD trajectories of individual waters in the middle part of the acyl chain region of lipids reveals a number of interesting rare phenomena, such as the fast (ca. 50 ps) breakthrough across the membrane or long-time (up to 750 ps) "roaming" between lipid leaflets. The analysis of these events was accomplished to delineate the mechanisms of spontaneous water permeation inside the hydrophobic membrane core. It was shown that such nontrivial dynamics of water in an "alien" environment is driven by the dynamic heterogeneities of the local bilayer structure and the formation of transient atomic-scale "defects" in it. The picture observed in lipid bilayers is drastically different from that in a primitive membrane mimic, a hydrated cyclohexane slab. The possible biological impact of such phenomena in equilibrated lipid bilayers is discussed.

Structural stability, microbial biomass and community composition of sediments affected by the hydric dynamics of an urban stormwater infiltration basin. Dynamics of physical and microbial characteristics of stormwater sediment.

PubMed

Badin, Anne Laure; Monier, Armelle; Volatier, Laurence; Geremia, Roberto A; Delolme, Cécile; Bedell, Jean-Philippe

2011-05-01

The sedimentary layer deposited at the surface of stormwater infiltration basins is highly organic and multicontaminated. It undergoes considerable moisture content fluctuations due to the drying and inundation cycles (called hydric dynamics) of these basins. Little is known about the microflora of the sediments and its dynamics; hence, the purpose of this study is to describe the physicochemical and biological characteristics of the sediments at different hydric statuses of the infiltration basin. Sediments were sampled at five time points following rain events and dry periods. They were characterized by physical (aggregation), chemical (nutrients and heavy metals), and biological (total, bacterial and fungal biomasses, and genotypic fingerprints of total bacterial and fungal communities) parameters. Data were processed using statistical analyses which indicated that heavy metal (1,841 μg/g dry weight (DW)) and organic matter (11%) remained stable through time. By contrast, aggregation, nutrient content (NH₄⁺, 53-717 μg/g DW), pH (6.9-7.4), and biological parameters were shown to vary with sediment water content and sediment biomass, and were higher consecutive to stormwater flows into the basin (up to 7 mg C/g DW) than during dry periods (0.6 mg C/g DW). Coinertia analysis revealed that the structure of the bacterial communities is driven by the hydric dynamics of the infiltration basin, although no such trend was found for fungal communities. Hydric dynamics more than rain events appear to be more relevant for explaining variations of aggregation, microbial biomass, and shift in the microbial community composition. We concluded that the hydric dynamics of stormwater infiltration basins greatly affects the structural stability of the sedimentary layer, the biomass of the microbial community living in it and its dynamics. The decrease in aggregation consecutive to rewetting probably enhances access to organic matter (OM), explaining the consecutive release of NH₄⁺, the bloom of the microbial biomass, and the change in structure of the bacterial community. These results open new perspectives for basin management since the risk of OM and pollutant transfer to the aquifer is greatly affected by alternating dry and flood periods.

Modeling the Fluid Withdraw and Injection Induced Earthquakes

NASA Astrophysics Data System (ADS)

Meng, C.

2016-12-01

We present an open source numerical code, Defmod, that allows one to model the induced seismicity in an efficient and standalone manner. The fluid withdraw and injection induced earthquake has been a great concern to the industries including oil/gas, wastewater disposal and CO2 sequestration. Being able to numerically model the induced seismicity is long desired. To do that, one has to consider at lease two processes, a steady process that describes the inducing and aseismic stages before and in between the seismic events, and an abrupt process that describes the dynamic fault rupture accompanied by seismic energy radiations during the events. The steady process can be adequately modeled by a quasi-static model, while the abrupt process has to be modeled by a dynamic model. In most of the published modeling works, only one of these processes is considered. The geomechanicists and reservoir engineers are focused more on the quasi-static modeling, whereas the geophysicists and seismologists are focused more on the dynamic modeling. The finite element code Defmod combines these two models into a hybrid model that uses the failure criterion and frictional laws to adaptively switch between the (quasi-)static and dynamic states. The code is capable of modeling episodic fault rupture driven by quasi-static loading, e.g. due to reservoir fluid withdraw and/or injection, and by dynamic loading, e.g. due to the foregoing earthquakes. We demonstrate a case study for the 2013 Azle earthquake.

A Spiking Neural Simulator Integrating Event-Driven and Time-Driven Computation Schemes Using Parallel CPU-GPU Co-Processing: A Case Study.

PubMed

Naveros, Francisco; Luque, Niceto R; Garrido, Jesús A; Carrillo, Richard R; Anguita, Mancia; Ros, Eduardo

2015-07-01

Time-driven simulation methods in traditional CPU architectures perform well and precisely when simulating small-scale spiking neural networks. Nevertheless, they still have drawbacks when simulating large-scale systems. Conversely, event-driven simulation methods in CPUs and time-driven simulation methods in graphic processing units (GPUs) can outperform CPU time-driven methods under certain conditions. With this performance improvement in mind, we have developed an event-and-time-driven spiking neural network simulator suitable for a hybrid CPU-GPU platform. Our neural simulator is able to efficiently simulate bio-inspired spiking neural networks consisting of different neural models, which can be distributed heterogeneously in both small layers and large layers or subsystems. For the sake of efficiency, the low-activity parts of the neural network can be simulated in CPU using event-driven methods while the high-activity subsystems can be simulated in either CPU (a few neurons) or GPU (thousands or millions of neurons) using time-driven methods. In this brief, we have undertaken a comparative study of these different simulation methods. For benchmarking the different simulation methods and platforms, we have used a cerebellar-inspired neural-network model consisting of a very dense granular layer and a Purkinje layer with a smaller number of cells (according to biological ratios). Thus, this cerebellar-like network includes a dense diverging neural layer (increasing the dimensionality of its internal representation and sparse coding) and a converging neural layer (integration) similar to many other biologically inspired and also artificial neural networks.

Event-driven contrastive divergence for spiking neuromorphic systems.

PubMed

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2013-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality.

Event-driven contrastive divergence for spiking neuromorphic systems

PubMed Central

Neftci, Emre; Das, Srinjoy; Pedroni, Bruno; Kreutz-Delgado, Kenneth; Cauwenberghs, Gert

2014-01-01

Restricted Boltzmann Machines (RBMs) and Deep Belief Networks have been demonstrated to perform efficiently in a variety of applications, such as dimensionality reduction, feature learning, and classification. Their implementation on neuromorphic hardware platforms emulating large-scale networks of spiking neurons can have significant advantages from the perspectives of scalability, power dissipation and real-time interfacing with the environment. However, the traditional RBM architecture and the commonly used training algorithm known as Contrastive Divergence (CD) are based on discrete updates and exact arithmetics which do not directly map onto a dynamical neural substrate. Here, we present an event-driven variation of CD to train a RBM constructed with Integrate & Fire (I&F) neurons, that is constrained by the limitations of existing and near future neuromorphic hardware platforms. Our strategy is based on neural sampling, which allows us to synthesize a spiking neural network that samples from a target Boltzmann distribution. The recurrent activity of the network replaces the discrete steps of the CD algorithm, while Spike Time Dependent Plasticity (STDP) carries out the weight updates in an online, asynchronous fashion. We demonstrate our approach by training an RBM composed of leaky I&F neurons with STDP synapses to learn a generative model of the MNIST hand-written digit dataset, and by testing it in recognition, generation and cue integration tasks. Our results contribute to a machine learning-driven approach for synthesizing networks of spiking neurons capable of carrying out practical, high-level functionality. PMID:24574952

Seismic source parameters of the induced seismicity at The Geysers geothermal area, California, by a generalized inversion approach

NASA Astrophysics Data System (ADS)

Picozzi, Matteo; Oth, Adrien; Parolai, Stefano; Bindi, Dino; De Landro, Grazia; Amoroso, Ortensia

2017-04-01

The accurate determination of stress drop, seismic efficiency and how source parameters scale with earthquake size is an important for seismic hazard assessment of induced seismicity. We propose an improved non-parametric, data-driven strategy suitable for monitoring induced seismicity, which combines the generalized inversion technique together with genetic algorithms. In the first step of the analysis the generalized inversion technique allows for an effective correction of waveforms for the attenuation and site contributions. Then, the retrieved source spectra are inverted by a non-linear sensitivity-driven inversion scheme that allows accurate estimation of source parameters. We therefore investigate the earthquake source characteristics of 633 induced earthquakes (ML 2-4.5) recorded at The Geysers geothermal field (California) by a dense seismic network (i.e., 32 stations of the Lawrence Berkeley National Laboratory Geysers/Calpine surface seismic network, more than 17.000 velocity records). We find for most of the events a non-selfsimilar behavior, empirical source spectra that requires ωγ source model with γ > 2 to be well fitted and small radiation efficiency ηSW. All these findings suggest different dynamic rupture processes for smaller and larger earthquakes, and that the proportion of high frequency energy radiation and the amount of energy required to overcome the friction or for the creation of new fractures surface changes with the earthquake size. Furthermore, we observe also two distinct families of events with peculiar source parameters that, in one case suggests the reactivation of deep structures linked to the regional tectonics, while in the other supports the idea of an important role of steeply dipping fault in the fluid pressure diffusion.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics

NASA Astrophysics Data System (ADS)

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Sparse dynamical Boltzmann machine for reconstructing complex networks with binary dynamics.

PubMed

Chen, Yu-Zhong; Lai, Ying-Cheng

2018-03-01

Revealing the structure and dynamics of complex networked systems from observed data is a problem of current interest. Is it possible to develop a completely data-driven framework to decipher the network structure and different types of dynamical processes on complex networks? We develop a model named sparse dynamical Boltzmann machine (SDBM) as a structural estimator for complex networks that host binary dynamical processes. The SDBM attains its topology according to that of the original system and is capable of simulating the original binary dynamical process. We develop a fully automated method based on compressive sensing and a clustering algorithm to construct the SDBM. We demonstrate, for a variety of representative dynamical processes on model and real world complex networks, that the equivalent SDBM can recover the network structure of the original system and simulates its dynamical behavior with high precision.

Impact of intermediate and high energy nuclear data on the neutronic safety parameters of MYRRHA accelerator driven system

NASA Astrophysics Data System (ADS)

Stankovskiy, Alexey; Çelik, Yurdunaz; Eynde, Gert Van den

2017-09-01

Perturbation of external neutron source can cause significant local power changes transformed into undesired safety-related events in an accelerator driven system. Therefore for the accurate design of MYRRHA sub-critical core it is important to evaluate the uncertainty of power responses caused by the uncertainties in nuclear reaction models describing the particle transport from primary proton energy down to the evaluated nuclear data table range. The calculations with a set of models resulted in quite low uncertainty on the local power caused by significant perturbation of primary neutron yield from proton interactions with lead and bismuth isotopes. The considered accidental event of prescribed proton beam shape loss causes drastic increase in local power but does not practically change the total core thermal power making this effect difficult to detect. In the same time the results demonstrate a correlation between perturbed local power responses in normal operation and misaligned beam conditions indicating that generation of covariance data for proton and neutron induced neutron multiplicities for lead and bismuth isotopes is needed to obtain reliable uncertainties for local power responses.

Flash flood warning based on fully dynamic hydrology modelling

NASA Astrophysics Data System (ADS)

Pejanovic, Goran; Petkovic, Slavko; Cvetkovic, Bojan; Nickovic, Slobodan

2016-04-01

Numerical hydrologic modeling has achieved limited success in the past due to, inter alia, lack of adequate input data. Over the last decade, data availability has improved substantially. For modelling purposes, high-resolution data on topography, river routing, and land cover and soil features have meanwhile become available, as well as the observations such as radar precipitation information. In our study, we have implemented the HYPROM model (Hydrology Prognostic Model) to predict a flash flood event at a smaller-scale basin in Southern Serbia. HYPROM is based on the full set of governing equations for surface hydrological dynamics, in which momentum components, along with the equation of mass continuity, are used as full prognostic equations. HYPROM also includes a river routing module serving as a collector for the extra surface water. Such approach permits appropriate representation of different hydrology scales ranging from flash floods to flows of large and slow river basins. The use of full governing equations, if not appropriately parameterized, may lead to numerical instability systems when the surface water in a model is vanishing. To resolve these modelling problems, an unconditionally stable numerical scheme and a method for height redistribution avoiding shortwave height noise have been developed in HYPROM, which achieve numerical convergence of u, v and h when surface water disappears. We have applied HYPROM, driven by radar-estimated precipitation, to predict flash flooding occurred over smaller and medium-size river basins. Two torrential rainfall cases have been simulated to check the accuracy of the model: the exceptional flooding of May 2014 in Western Serbia, and the convective flash flood of January 2015 in Southern Serbia. The second episode has been successfully predicted by HYPROM in terms of timing and intensity six hours before the event occurred. Such flash flood warning system is in preparation to be operationally implemented in the Republic Hydrometeorological Service of Serbia.

Measurement of the top quark-pair production cross section with ATLAS in pp collisions at $$\\sqrt{s}=7$$  TeV

DOE PAGES

Aad, G.; Abbott, B.; Abdallah, J.; ...

2011-03-01

A measurement of the production cross-section for top quark pairs (more » $$t\\bar{t}$$) in pp collisions at √s =7 TeVis presented using data recorded with the ATLAS detector at the Large Hadron Collider. Events are selected in two different topologies: single lepton (electron e or muon μ) with large missing transverse energy and at least four jets, and dilepton (ee, μμ or eμ) with large missing transverse energy and at least two jets. In a data sample of 2.9 pb –1, 37 candidate events are observed in the single-lepton topology and 9 events in the dilepton topology. The corresponding expected backgrounds from non-$$t\\bar{t}$$Standard Model processes are estimated using data-driven methods and determined to be 12.2 ± 3.9 events and 2.5± 0.6 events, respectively.« less

Subsynchronous instability of a geared centrifugal compressor of overhung design

NASA Technical Reports Server (NTRS)

Hudson, J. H.; Wittman, L. J.

1980-01-01

The original design analysis and shop test data are presented for a three stage (poster) air compressor with impellers mounted on the extensions of a twin pinion gear, and driven by an 8000 hp synchronous motor. Also included are field test data, subsequent rotor dynamics analysis, modifications, and final rotor behavior. A subsynchronous instability existed on a geared, overhung rotor. State-of-the-art rotor dynamics analysis techniques provided a reasonable analytical model of the rotor. A bearing modification arrived at analytically eliminated the instability.

Modeling the Pineapple Express phenomenon via Multivariate Extreme Value Theory

NASA Astrophysics Data System (ADS)

Weller, G.; Cooley, D. S.

2011-12-01

The pineapple express (PE) phenomenon is responsible for producing extreme winter precipitation events in the coastal and mountainous regions of the western United States. Because the PE phenomenon is also associated with warm temperatures, the heavy precipitation and associated snowmelt can cause destructive flooding. In order to study impacts, it is important that regional climate models from NARCCAP are able to reproduce extreme precipitation events produced by PE. We define a daily precipitation quantity which captures the spatial extent and intensity of precipitation events produced by the PE phenomenon. We then use statistical extreme value theory to model the tail dependence of this quantity as seen in an observational data set and each of the six NARCCAP regional models driven by NCEP reanalysis. We find that most NCEP-driven NARCCAP models do exhibit tail dependence between daily model output and observations. Furthermore, we find that not all extreme precipitation events are pineapple express events, as identified by Dettinger et al. (2011). The synoptic-scale atmospheric processes that drive extreme precipitation events produced by PE have only recently begun to be examined. Much of the current work has focused on pattern recognition, rather than quantitative analysis. We use daily mean sea-level pressure (MSLP) fields from NCEP to develop a "pineapple express index" for extreme precipitation, which exhibits tail dependence with our observed precipitation quantity for pineapple express events. We build a statistical model that connects daily precipitation output from the WRFG model, daily MSLP fields from NCEP, and daily observed precipitation in the western US. Finally, we use this model to simulate future observed precipitation based on WRFG output driven by the CCSM model, and our pineapple express index derived from future CCSM output. Our aim is to use this model to develop a better understanding of the frequency and intensity of extreme precipitation events produced by PE under climate change.

Data-driven modelling of vertical dynamic excitation of bridges induced by people running

NASA Astrophysics Data System (ADS)

Racic, Vitomir; Morin, Jean Benoit

2014-02-01

With increasingly popular marathon events in urban environments, structural designers face a great deal of uncertainty when assessing dynamic performance of bridges occupied and dynamically excited by people running. While the dynamic loads induced by pedestrians walking have been intensively studied since the infamous lateral sway of the London Millennium Bridge in 2000, reliable and practical descriptions of running excitation are still very rare and limited. This interdisciplinary study has addressed the issue by bringing together a database of individual running force signals recorded by two state-of-the-art instrumented treadmills and two attempts to mathematically describe the measurements. The first modelling strategy is adopted from the available design guidelines for human walking excitation of structures, featuring perfectly periodic and deterministic characterisation of pedestrian forces presentable via Fourier series. This modelling approach proved to be inadequate for running loads due to the inherent near-periodic nature of the measured signals, a great inter-personal randomness of the dominant Fourier amplitudes and the lack of strong correlation between the amplitudes and running footfall rate. Hence, utilising the database established and motivated by the existing models of wind and earthquake loading, speech recognition techniques and a method of replicating electrocardiogram signals, this paper finally presents a numerical generator of random near-periodic running force signals which can reliably simulate the measurements. Such a model is an essential prerequisite for future quality models of dynamic loading induced by individuals, groups and crowds running under a wide range of conditions, such as perceptibly vibrating bridges and different combinations of visual, auditory and tactile cues.

Inpatient-Derived Vital Sign Parameters Implementation: An Initiative to Decrease Alarm Burden.

PubMed

Kipps, Alaina K; Poole, Sarah F; Slaney, Cheryl; Feehan, Shannon; Longhurst, Christopher A; Sharek, Paul J; Goel, Veena V

2017-08-01

To implement data-driven vital sign parameters to reduce bedside monitor alarm burden. Single-center, quality-improvement initiative with historical controls assessing the impact of age-based, inpatient-derived heart rate (HR) and respiratory rate (RR) parameters on a 20-bed acute care ward that serves primarily pediatric cardiology patients. The primary outcome was the number of alarms per monitored bed day (MBD) with the aim to decrease the alarms per MBD. Balancing measures included the frequency of missed rapid response team activations, acute respiratory code events, and cardiorespiratory arrest events in the unit with the new vital sign parameters. The median number of all cardiorespiratory monitor alarms per MBD decreased by 21% from 52 (baseline period) to 41 (postintervention period) ( P < .001). This included a 17% decrease in the median HR alarms (9-7.5 per MBD) and a 53% drop in RR alarms (16.8-8.0 per MBD). There were 57 rapid response team activations, 8 acute respiratory code events, and no cardiorespiratory arrest events after the implementation of the new parameters. An evaluation of HRs and RRs recorded at the time of the event revealed that all patients with HRs and/or RRs out of range per former default parameters would also be out of range with the new parameters. Implementation of data-driven HR and iteratively derived RR parameters safely decreased the total alarm frequency by 21% in a pediatric acute care unit. Copyright © 2017 by the American Academy of Pediatrics.

Skyalert: a Platform for Event Understanding and Dissemination

NASA Astrophysics Data System (ADS)

Williams, Roy; Drake, A. J.; Djorgovski, S. G.; Donalek, C.; Graham, M. J.; Mahabal, A.

2010-01-01

Skyalert.org is an event repository, web interface, and event-oriented workflow architecture that can be used in many different ways for handling astronomical events that are encoded as VOEvent. It can be used as a remote application (events in the cloud) or installed locally. Some applications are: Dissemination of events with sophisticated discrimination (trigger), using email, instant message, RSS, twitter, etc; Authoring interface for survey-generated events, follow-up observations, and other event types; event streams can be put into the skyalert.org repository, either public or private, or into a local inbstallation of Skyalert; Event-driven software components to fetch archival data, for data-mining and classification of events; human interface to events though wiki, comments, and circulars; use of the "notices and circulars" model, where machines make the notices in real time and people write the interpretation later; Building trusted, automated decisions for automated follow-up observation, and the information infrastructure for automated follow-up with DC3 and HTN telescope schedulers; Citizen science projects such as artifact detection and classification; Query capability for past events, including correlations between different streams and correlations with existing source catalogs; Event metadata structures and connection to the global registry of the virtual observatory.

Pulsating Magnetic Reconnection Driven by Three-Dimensional Flux-Rope Interactions.

PubMed

Gekelman, W; De Haas, T; Daughton, W; Van Compernolle, B; Intrator, T; Vincena, S

2016-06-10

The dynamics of magnetic reconnection is investigated in a laboratory experiment consisting of two magnetic flux ropes, with currents slightly above the threshold for the kink instability. The evolution features periodic bursts of magnetic reconnection. To diagnose this complex evolution, volumetric three-dimensional data were acquired for both the magnetic and electric fields, allowing key field-line mapping quantities to be directly evaluated for the first time with experimental data. The ropes interact by rotating about each other and periodically bouncing at the kink frequency. During each reconnection event, the formation of a quasiseparatrix layer (QSL) is observed in the magnetic field between the flux ropes. Furthermore, a clear correlation is demonstrated between the quasiseparatrix layer and enhanced values of the quasipotential computed by integrating the parallel electric field along magnetic field lines. These results provide clear evidence that field lines passing through the quasiseparatrix layer are undergoing reconnection and give a direct measure of the nonlinear reconnection rate. The measurements suggest that the parallel electric field within the QSL is supported predominantly by electron pressure; however, resistivity may play a role.

Transition probability, dynamic regimes, and the critical point of financial crisis

NASA Astrophysics Data System (ADS)

Tang, Yinan; Chen, Ping

2015-07-01

An empirical and theoretical analysis of financial crises is conducted based on statistical mechanics in non-equilibrium physics. The transition probability provides a new tool for diagnosing a changing market. Both calm and turbulent markets can be described by the birth-death process for price movements driven by identical agents. The transition probability in a time window can be estimated from stock market indexes. Positive and negative feedback trading behaviors can be revealed by the upper and lower curves in transition probability. Three dynamic regimes are discovered from two time periods including linear, quasi-linear, and nonlinear patterns. There is a clear link between liberalization policy and market nonlinearity. Numerical estimation of a market turning point is close to the historical event of the US 2008 financial crisis.

A Digital Ecosystem of Diabetes Data and Technology: Services, Systems, and Tools Enabled by Wearables, Sensors, and Apps.

PubMed

Heintzman, Nathaniel D

2015-12-20

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes "connected" technologies--such as smartphone apps, and wearable devices and sensors--which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in "the cloud" for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care. © 2015 Diabetes Technology Society.

A Digital Ecosystem of Diabetes Data and Technology

PubMed Central

Heintzman, Nathaniel D.

2015-01-01

The management of type 1 diabetes (T1D) ideally involves regimented measurement of various health signals; constant interpretation of diverse kinds of data; and consistent cohesion between patients, caregivers, and health care professionals (HCPs). In the context of myriad factors that influence blood glucose dynamics for each individual patient (eg, medication, activity, diet, stress, sleep quality, hormones, environment), such coordination of self-management and clinical care is a great challenge, amplified by the routine unavailability of many types of data thought to be useful in diabetes decision-making. While much remains to be understood about the physiology of diabetes and blood glucose dynamics at the level of the individual, recent and emerging medical and consumer technologies are helping the diabetes community to take great strides toward truly personalized, real-time, data-driven management of this chronic disease. This review describes “connected” technologies—such as smartphone apps, and wearable devices and sensors—which comprise part of a new digital ecosystem of data-driven tools that can link patients and their care teams for precision management of diabetes. These connected technologies are rich sources of physiologic, behavioral, and contextual data that can be integrated and analyzed in “the cloud” for research into personal models of glycemic dynamics, and employed in a multitude of applications for mobile health (mHealth) and telemedicine in diabetes care. PMID:26685994

Symmetry breaking, phase separation and anomalous fluctuations in driven granular gas

NASA Astrophysics Data System (ADS)

Meerson, Baruch; Pöschel, Thorsten; Sasorov, Pavel V.; Schwager, Thomas

2003-03-01

What is the role of noise, caused by the discrete nature of particles, in granular dynamics? We address this question by considering a simple driven granular system: an ensemble of nearly elastically colliding hard spheres in a rectangular box, driven by a rapidly vibrating side wall at zero gravity. The elementary state of this system is a strip of enhanced particle density away from the driving wall. Granular hydrodynamics (GHD) predicts a symmetry breaking instability of this state, when the aspect ratio of the confining box exceeds a threshold value, while the average density of the gas is within a ``spinodal interval". At large aspect ratios this instability leads to phase separation similar to that in van der Waals gas. In the present work (see cond-mat/0208286) we focus on the system behavior around the threshold of the symmetry-breaking instability. We put GHD into a quantitative test by performing extensive event-driven molecular dynamic simulations in 2D. Please watch the movies of the simulations at http://summa.physik.hu-berlin.de/ kies/HD/. We found that the supercritical bifurcation curve, predicted by GHD, agrees with the simulations well below and well above the instability threshold. In a wide region of aspect ratios around the threshold the system is dominated by fluctuations. We checked that the fluctuation strength goes down when the number of particles increases. However, fluctuations remain strong (and the critical region wide) even for as many as 4 ot 10^4 particles. We conclude by suggesting that fluctuations may put a severe limitation on the validity of continuum theories of granular flow in systems with a moderately large number of particles.

Event-Based Tone Mapping for Asynchronous Time-Based Image Sensor

PubMed Central

Simon Chane, Camille; Ieng, Sio-Hoi; Posch, Christoph; Benosman, Ryad B.

2016-01-01

The asynchronous time-based neuromorphic image sensor ATIS is an array of autonomously operating pixels able to encode luminance information with an exceptionally high dynamic range (>143 dB). This paper introduces an event-based methodology to display data from this type of event-based imagers, taking into account the large dynamic range and high temporal accuracy that go beyond available mainstream display technologies. We introduce an event-based tone mapping methodology for asynchronously acquired time encoded gray-level data. A global and a local tone mapping operator are proposed. Both are designed to operate on a stream of incoming events rather than on time frame windows. Experimental results on real outdoor scenes are presented to evaluate the performance of the tone mapping operators in terms of quality, temporal stability, adaptation capability, and computational time. PMID:27642275

BMI cyberworkstation: enabling dynamic data-driven brain-machine interface research through cyberinfrastructure.

PubMed

Zhao, Ming; Rattanatamrong, Prapaporn; DiGiovanna, Jack; Mahmoudi, Babak; Figueiredo, Renato J; Sanchez, Justin C; Príncipe, José C; Fortes, José A B

2008-01-01

Dynamic data-driven brain-machine interfaces (DDDBMI) have great potential to advance the understanding of neural systems and improve the design of brain-inspired rehabilitative systems. This paper presents a novel cyberinfrastructure that couples in vivo neurophysiology experimentation with massive computational resources to provide seamless and efficient support of DDDBMI research. Closed-loop experiments can be conducted with in vivo data acquisition, reliable network transfer, parallel model computation, and real-time robot control. Behavioral experiments with live animals are supported with real-time guarantees. Offline studies can be performed with various configurations for extensive analysis and training. A Web-based portal is also provided to allow users to conveniently interact with the cyberinfrastructure, conducting both experimentation and analysis. New motor control models are developed based on this approach, which include recursive least square based (RLS) and reinforcement learning based (RLBMI) algorithms. The results from an online RLBMI experiment shows that the cyberinfrastructure can successfully support DDDBMI experiments and meet the desired real-time requirements.

A Data-Driven Diagnostic Framework for Wind Turbine Structures: A Holistic Approach

PubMed Central

Bogoevska, Simona; Spiridonakos, Minas; Chatzi, Eleni; Dumova-Jovanoska, Elena; Höffer, Rudiger

2017-01-01

The complex dynamics of operational wind turbine (WT) structures challenges the applicability of existing structural health monitoring (SHM) strategies for condition assessment. At the center of Europe’s renewable energy strategic planning, WT systems call for implementation of strategies that may describe the WT behavior in its complete operational spectrum. The framework proposed in this paper relies on the symbiotic treatment of acting environmental/operational variables and the monitored vibration response of the structure. The approach aims at accurate simulation of the temporal variability characterizing the WT dynamics, and subsequently at the tracking of the evolution of this variability in a longer-term horizon. The bi-component analysis tool is applied on long-term data, collected as part of continuous monitoring campaigns on two actual operating WT structures located in different sites in Germany. The obtained data-driven structural models verify the potential of the proposed strategy for development of an automated SHM diagnostic tool. PMID:28358346

The Impact of Heterogeneity and Awareness in Modeling Epidemic Spreading on Multiplex Networks

PubMed Central

Scatà, Marialisa; Di Stefano, Alessandro; Liò, Pietro; La Corte, Aurelio

2016-01-01

In the real world, dynamic processes involving human beings are not disjoint. To capture the real complexity of such dynamics, we propose a novel model of the coevolution of epidemic and awareness spreading processes on a multiplex network, also introducing a preventive isolation strategy. Our aim is to evaluate and quantify the joint impact of heterogeneity and awareness, under different socioeconomic conditions. Considering, as case study, an emerging public health threat, Zika virus, we introduce a data-driven analysis by exploiting multiple sources and different types of data, ranging from Big Five personality traits to Google Trends, related to different world countries where there is an ongoing epidemic outbreak. Our findings demonstrate how the proposed model allows delaying the epidemic outbreak and increasing the resilience of nodes, especially under critical economic conditions. Simulation results, using data-driven approach on Zika virus, which has a growing scientific research interest, are coherent with the proposed analytic model. PMID:27848978

Economic and environmental impacts of alternative transportation technologies.

DOT National Transportation Integrated Search

2013-04-01

This project has focused on comparing alternative transportation technologies in terms of their : environmental and economic impacts. The research is data-driven and quantitative, and examines the : dynamics of impact. We have developed new theory an...

Air-Breathing Hypersonic Vehicle Tracking Control Based on Adaptive Dynamic Programming.

PubMed

Mu, Chaoxu; Ni, Zhen; Sun, Changyin; He, Haibo

2017-03-01

In this paper, we propose a data-driven supplementary control approach with adaptive learning capability for air-breathing hypersonic vehicle tracking control based on action-dependent heuristic dynamic programming (ADHDP). The control action is generated by the combination of sliding mode control (SMC) and the ADHDP controller to track the desired velocity and the desired altitude. In particular, the ADHDP controller observes the differences between the actual velocity/altitude and the desired velocity/altitude, and then provides a supplementary control action accordingly. The ADHDP controller does not rely on the accurate mathematical model function and is data driven. Meanwhile, it is capable to adjust its parameters online over time under various working conditions, which is very suitable for hypersonic vehicle system with parameter uncertainties and disturbances. We verify the adaptive supplementary control approach versus the traditional SMC in the cruising flight, and provide three simulation studies to illustrate the improved performance with the proposed approach.

Data-driven monitoring for stochastic systems and its application on batch process

NASA Astrophysics Data System (ADS)

Yin, Shen; Ding, Steven X.; Haghani Abandan Sari, Adel; Hao, Haiyang

2013-07-01

Batch processes are characterised by a prescribed processing of raw materials into final products for a finite duration and play an important role in many industrial sectors due to the low-volume and high-value products. Process dynamics and stochastic disturbances are inherent characteristics of batch processes, which cause monitoring of batch processes a challenging problem in practice. To solve this problem, a subspace-aided data-driven approach is presented in this article for batch process monitoring. The advantages of the proposed approach lie in its simple form and its abilities to deal with stochastic disturbances and process dynamics existing in the process. The kernel density estimation, which serves as a non-parametric way of estimating the probability density function, is utilised for threshold calculation. An industrial benchmark of fed-batch penicillin production is finally utilised to verify the effectiveness of the proposed approach.

Learning-Based Adaptive Optimal Tracking Control of Strict-Feedback Nonlinear Systems.

PubMed

Gao, Weinan; Jiang, Zhong-Ping; Weinan Gao; Zhong-Ping Jiang; Gao, Weinan; Jiang, Zhong-Ping

2018-06-01

This paper proposes a novel data-driven control approach to address the problem of adaptive optimal tracking for a class of nonlinear systems taking the strict-feedback form. Adaptive dynamic programming (ADP) and nonlinear output regulation theories are integrated for the first time to compute an adaptive near-optimal tracker without any a priori knowledge of the system dynamics. Fundamentally different from adaptive optimal stabilization problems, the solution to a Hamilton-Jacobi-Bellman (HJB) equation, not necessarily a positive definite function, cannot be approximated through the existing iterative methods. This paper proposes a novel policy iteration technique for solving positive semidefinite HJB equations with rigorous convergence analysis. A two-phase data-driven learning method is developed and implemented online by ADP. The efficacy of the proposed adaptive optimal tracking control methodology is demonstrated via a Van der Pol oscillator with time-varying exogenous signals.

Transformation of Context-dependent Sensory Dynamics into Motor Behavior

PubMed Central

Latorre, Roberto; Levi, Rafael; Varona, Pablo

2013-01-01

The intrinsic dynamics of sensory networks play an important role in the sensory-motor transformation. In this paper we use conductance based models and electrophysiological recordings to address the study of the dual role of a sensory network to organize two behavioral context-dependent motor programs in the mollusk Clione limacina. We show that: (i) a winner take-all dynamics in the gravimetric sensory network model drives the typical repetitive rhythm in the wing central pattern generator (CPG) during routine swimming; (ii) the winnerless competition dynamics of the same sensory network organizes the irregular pattern observed in the wing CPG during hunting behavior. Our model also shows that although the timing of the activity is irregular, the sequence of the switching among the sensory cells is preserved whenever the same set of neurons are activated in a given time window. These activation phase locks in the sensory signals are transformed into specific events in the motor activity. The activation phase locks can play an important role in motor coordination driven by the intrinsic dynamics of a multifunctional sensory organ. PMID:23459114

Quantifying Ubiquitin Signaling

PubMed Central

Ordureau, Alban; Münch, Christian; Harper, J. Wade

2015-01-01

Ubiquitin (UB)-driven signaling systems permeate biology, and are often integrated with other types of post-translational modifications (PTMs), most notably phosphorylation. Flux through such pathways is typically dictated by the fractional stoichiometry of distinct regulatory modifications and protein assemblies as well as the spatial organization of pathway components. Yet, we rarely understand the dynamics and stoichiometry of rate-limiting intermediates along a reaction trajectory. Here, we review how quantitative proteomic tools and enrichment strategies are being used to quantify UB-dependent signaling systems, and to integrate UB signaling with regulatory phosphorylation events. A key regulatory feature of ubiquitylation is that the identity of UB chain linkage types can control downstream processes. We also describe how proteomic and enzymological tools can be used to identify and quantify UB chain synthesis and linkage preferences. The emergence of sophisticated quantitative proteomic approaches will set a new standard for elucidating biochemical mechanisms of UB-driven signaling systems. PMID:26000850

Novel Hybrid Scheduling Technique for Sensor Nodes with Mixed Criticality Tasks.

PubMed

Micea, Mihai-Victor; Stangaciu, Cristina-Sorina; Stangaciu, Valentin; Curiac, Daniel-Ioan

2017-06-26

Sensor networks become increasingly a key technology for complex control applications. Their potential use in safety- and time-critical domains has raised the need for task scheduling mechanisms specially adapted to sensor node specific requirements, often materialized in predictable jitter-less execution of tasks characterized by different criticality levels. This paper offers an efficient scheduling solution, named Hybrid Hard Real-Time Scheduling (H²RTS), which combines a static, clock driven method with a dynamic, event driven scheduling technique, in order to provide high execution predictability, while keeping a high node Central Processing Unit (CPU) utilization factor. From the detailed, integrated schedulability analysis of the H²RTS, a set of sufficiency tests are introduced and demonstrated based on the processor demand and linear upper bound metrics. The performance and correct behavior of the proposed hybrid scheduling technique have been extensively evaluated and validated both on a simulator and on a sensor mote equipped with ARM7 microcontroller.

Spatial Extent of Wave-Supported Fluid Mud on the Waipaoa Continental Margin

NASA Astrophysics Data System (ADS)

Hale, R. P.; Ogston, A. S.; Walsh, J. P.; Orpin, A. R.

2013-12-01

Data from acoustic and optical sensors provide a powerful tool to connect near-bed water-column processes with the deposits they generate. Ideally, the product of water-column and seabed interactions can then be applied more broadly to understand systems as a whole, in both space and time. Recent observational research has allowed for an improved understanding of shelf sediment-transport dynamics in many coastal systems, including the dynamic Waipaoa Sedimentary System (WSS), on the east coast of the north island of New Zealand. This narrow shelf (~20 km) on an active continental margin is subject to strong environmental forcings in the form of high waves (>5 m), strong currents (>50 cm/s), and frequent floods of the Waipaoa River, which delivers an average of 15 MT of sediment to Poverty Bay and the coastal environment each year. A year-long study of the WSS during 2010-2011 combined observational data from instrumented tripods at three locations on the continental shelf, with repeat sediment cores collected in four-month intervals, to identify and assess the mechanisms of cross- and off-shelf sediment transport. Observational data identified that cross-shelf sediment transport is stochastic, typically driven by high-wave events, with 40% of the net annual cross-shelf flux for one tripod location occurring during a single wave-supported fluid mud (WSFM) in July 2010. Fortunately, this event was recorded in the instrument data, and the resulting deposit was plainly visible in x-radiograph images. This particular WSFM was observed in x-radiographs collected as deep as ~50 m, and as far as ~28 km from the mouth of the Waipaoa River, and is more prevalent on the northern portion of the shelf. A critical water depth is not the only criteria for WSFM deposition, as some shallower areas on the southern shelf, which were subject to high bed stress, show no evidence of WSFM in this event, while cores collected in deeper areas (e.g. lower bed stress) on the northern shelf did observe WSFM. Interestingly, several cores on the southern shelf do appear to preserve evidence of previous wave-reworking of the seabed. It appears that the presence of a river plume and associated sediment, as well as the direction in which it is advected, are instrumental in WSFM generation.

Data-driven parameterization of the generalized Langevin equation

DOE PAGES

Lei, Huan; Baker, Nathan A.; Li, Xiantao

2016-11-29

We present a data-driven approach to determine the memory kernel and random noise of the generalized Langevin equation. To facilitate practical implementations, we parameterize the kernel function in the Laplace domain by a rational function, with coefficients directly linked to the equilibrium statistics of the coarse-grain variables. Further, we show that such an approximation can be constructed to arbitrarily high order. Within these approximations, the generalized Langevin dynamics can be embedded in an extended stochastic model without memory. We demonstrate how to introduce the stochastic noise so that the fluctuation-dissipation theorem is exactly satisfied.

Three-dimensional particle tracking velocimetry using dynamic vision sensors

NASA Astrophysics Data System (ADS)

Borer, D.; Delbruck, T.; Rösgen, T.

2017-12-01

A fast-flow visualization method is presented based on tracking neutrally buoyant soap bubbles with a set of neuromorphic cameras. The "dynamic vision sensors" register only the changes in brightness with very low latency, capturing fast processes at a low data rate. The data consist of a stream of asynchronous events, each encoding the corresponding pixel position, the time instant of the event and the sign of the change in logarithmic intensity. The work uses three such synchronized cameras to perform 3D particle tracking in a medium sized wind tunnel. The data analysis relies on Kalman filters to associate the asynchronous events with individual tracers and to reconstruct the three-dimensional path and velocity based on calibrated sensor information.

Vertical velocity and turbulence aspects during Mistral events as observed by UHF wind profilers

NASA Astrophysics Data System (ADS)

Caccia, J.; Guénard, V.; Benech, B.; Campistron, B.; Drobinski, P.

2004-11-01

The general purpose of this paper is to experimentally study mesoscale dynamical aspects of the Mistral in the coastal area located at the exit of the Rhône-valley. The Mistral is a northerly low-level flow blowing in southern France along the Rhône-valley axis, located between the French Alps and the Massif Central, towards the Mediterranean Sea. The experimental data are obtained by UHF wind profilers deployed during two major field campaigns, MAP (Mesoscale Alpine Program) in autumn 1999, and ESCOMPTE (Expérience sur Site pour COntraindre les Modèles de Pollution atmosphériques et de Transports d'Emission) in summer 2001. Thanks to the use of the time evolution of the vertical profile of the horizontal wind vector, recent works have shown that the dynamics of the Mistral is highly dependent on the season because of the occurrence of specific synoptic patterns. In addition, during summer, thermal forcing leads to a combination of sea breeze with Mistral and weaker Mistral due to the enhanced friction while, during autumn, absence of convective turbulence leads to substantial acceleration as low-level jets are generated in the stably stratified planetary boundary layer. At the exit of the Rhône valley, the gap flow dynamics dominates, whereas at the lee of the Alps, the dynamics is driven by the relative contribution of "flow around" and "flow over" mechanisms, upstream of the Alps. This paper analyses vertical velocity and turbulence, i.e. turbulent dissipation rate, with data obtained by the same UHF wind profilers during the same Mistral events. In autumn, the motions are found to be globally and significantly subsident, which is coherent for a dry, cold and stable flow approaching the sea, and the turbulence is found to be of pure dynamical origin (wind shears and mountain/lee wave breaking), which is coherent with non-convective situations. In summer, due to the ground heating and to the interactions with thermal circulation, the vertical motions are less pronounced and no longer have systematic subsident charateristics. In addition, those vertical motions are found to be much less developed during the nighttimes because of the stabilization of the nocturnal planetary boundary layer due to a ground cooling. The enhanced turbulent dissipation-rate values found at lower levels during the afternoons of weak Mistral cases are consistent with the installation of the summer convective boundary layer and show that, as expected in weaker Mistral events, the convection is the preponderant factor for the turbulence generation. On the other hand, for stronger cases, such a convective boundary layer installation is perturbed by the Mistral.

The impact of multi-decadal sub-surface circulation changes on sea surface chlorophyll patterns in the tropical Pacific

NASA Astrophysics Data System (ADS)

Schollaert Uz, S.; Busalacchi, A. J.; Smith, T. M.; Evans, M. N.; Brown, C.; Hackert, E. C.; Wang, X.

2016-12-01

The tropical Pacific is a region of strong forcing where physical oceanography primarily controls biological variability over the seasonal to interannual time scales observed since dedicated ocean color satellite remote sensing began in 1997. To quantify how multi-decadal, climate-scale changes impact marine biological dynamics, we used the correlation with sea-surface temperature and height to reconstruct a 50-year time series of surface chlorophyll concentrations. The reconstruction demonstrates greatest skill away from the coast and within 10o of the equator where chlorophyll variance is greatest and primarily associated with El Niño Southern Oscillation (ENSO) dynamics and secondarily associated with decadal variability. We observe significant basin-wide differences between east and central Pacific events when the El Niño events are strong: chlorophyll increases with La Niña and decreases with El Niño, with larger declines east of 180o for remotely-forced east Pacific events and west of 180o for locally-forced central Pacific events. Chlorophyll variations also reflect the physical dynamics of Pacific decadal variability with small but significant differences between cool and warm eras: consistent with advection variability west of 180o and likely driven by subsurface changes in the nutricline depth between 110-140oW. Comparisons with output from a fully-coupled biogeochemical model support the hypothesis that this anomalous region is controlled by lower frequency changes in subsurface circulation patterns that transport nutrients to the surface. Basin-wide chlorophyll distributions exhibiting spatial heterogeneity in response to multi-decadal climate forcing imply similar long-term changes in phytoplankton productivity, with implications for the marine food web and the ocean's role as a carbon sink.

Notification Event Architecture for Traveler Screening: Predictive Traveler Screening Using Event Driven Business Process Management

ERIC Educational Resources Information Center

Lynch, John Kenneth

2013-01-01

Using an exploratory model of the 9/11 terrorists, this research investigates the linkages between Event Driven Business Process Management (edBPM) and decision making. Although the literature on the role of technology in efficient and effective decision making is extensive, research has yet to quantify the benefit of using edBPM to aid the…

Event-Driven X-Ray CCD Detectors for High Energy Astrophysics

NASA Technical Reports Server (NTRS)

Ricker, George R.

2004-01-01

A viewgraph presentation describing the Event-Driven X- Ray CCD (EDCCD) detector system for high energy astrophysics is presented. The topics include: 1) EDCCD: Description and Advantages; 2) Summary of Grant Activity Carried Out; and 3) EDCCD Test System.

The evolution of meaning: spatio-temporal dynamics of visual object recognition.

PubMed

Clarke, Alex; Taylor, Kirsten I; Tyler, Lorraine K

2011-08-01

Research on the spatio-temporal dynamics of visual object recognition suggests a recurrent, interactive model whereby an initial feedforward sweep through the ventral stream to prefrontal cortex is followed by recurrent interactions. However, critical questions remain regarding the factors that mediate the degree of recurrent interactions necessary for meaningful object recognition. The novel prediction we test here is that recurrent interactivity is driven by increasing semantic integration demands as defined by the complexity of semantic information required by the task and driven by the stimuli. To test this prediction, we recorded magnetoencephalography data while participants named living and nonliving objects during two naming tasks. We found that the spatio-temporal dynamics of neural activity were modulated by the level of semantic integration required. Specifically, source reconstructed time courses and phase synchronization measures showed increased recurrent interactions as a function of semantic integration demands. These findings demonstrate that the cortical dynamics of object processing are modulated by the complexity of semantic information required from the visual input.

Analyzing the Possibility of Dynamic Earthquake Triggering in Socorro, New Mexico

NASA Astrophysics Data System (ADS)

Morton, E.; Bilek, S. L.

2011-12-01

The release of energy during an earthquake changes the stress state and seismicity both locally and remotely. Far-field stress changes can lead to triggered earthquakes coinciding with the arrival of the surface waves. This dynamic triggering is found to occur in a variety of tectonic settings, but in particular magmatic regions. Here we test whether the Socorro Magma Body region in central New Mexico hosts triggered seismicity. Preliminary inspection of continuous network data in central New Mexico suggested a local triggered event with the passage of surface waves from an MW 6.9 event in 2009. For a more comprehensive view, we examine data from 379 earthquakes MW ≥ 6.0 between January 15, 2008 to March 13, 2010 recorded on the EarthScope USArray Transportable Network stations located within New Mexico and providing more dense coverage for better detectability. Waveforms from twenty EarthScope stations were windowed around the time of the large event, high-pass filtered at 5 Hz to remove low frequency signals and analyzed to detect high frequency triggered local earthquakes. For each possible trigger detected, waveforms from nine short-period stations in the Socorro Seismic Network were added to aid in locating the events. In the time period analyzed, twelve triggered events were detected. Only one of these events, on August 30, 2009, corresponded to the arrival of surface waves, occurring about a minute after their arrival. The majority of the triggered events occur well after the arrival of the surface waves, indicating that they are either independent of the main shock or the result of delayed dynamic triggering. Delayed dynamic triggering can occur hours or days after the passage of surface waves, and are marked by an increase in seismicity relative to background. Only one of the events, on September 18, 2009, occurred within the Socorro Magma Body area. The rest of these events occur spread throughout New Mexico. The widely spread distribution of possibly triggered events and the low ratio of triggers to main shocks indicates that the rifted magmatic region above the Socorro Magma Body is not particularly susceptible to dynamic triggering from remote main shocks. The lack of direct correspondence to a seismic phase can mean that the detected events may be independent (not triggered events), or the result of delayed dynamic triggering. A comparison to randomly chosen waveforms within the time period as background will reveal if the possible events are a result of delayed dynamic triggering or part of the background.

Multiplex Recurrence Networks

NASA Astrophysics Data System (ADS)

Eroglu, Deniz; Marwan, Norbert

2017-04-01

The complex nature of a variety of phenomena in physical, biological, or earth sciences is driven by a large number of degrees of freedom which are strongly interconnected. Although the evolution of such systems is described by multivariate time series (MTS), so far research mostly focuses on analyzing these components one by one. Recurrence based analyses are powerful methods to understand the underlying dynamics of a dynamical system and have been used for many successful applications including examples from earth science, economics, or chemical reactions. The backbone of these techniques is creating the phase space of the system. However, increasing the dimension of a system requires increasing the length of the time series in order get significant and reliable results. This requirement is one of the challenges in many disciplines, in particular in palaeoclimate, thus, it is not easy to create a phase space from measured MTS due to the limited number of available obervations (samples). To overcome this problem, we suggest to create recurrence networks from each component of the system and combine them into a multiplex network structure, the multiplex recurrence network (MRN). We test the MRN by using prototypical mathematical models and demonstrate its use by studying high-dimensional palaeoclimate dynamics derived from pollen data from the Bear Lake (Utah, US). By using the MRN, we can distinguish typical climate transition events, e.g., such between Marine Isotope Stages.

Simulating Flying Insects Using Dynamics and Data-Driven Noise Modeling to Generate Diverse Collective Behaviors

PubMed Central

Ren, Jiaping; Wang, Xinjie; Manocha, Dinesh

2016-01-01

We present a biologically plausible dynamics model to simulate swarms of flying insects. Our formulation, which is based on biological conclusions and experimental observations, is designed to simulate large insect swarms of varying densities. We use a force-based model that captures different interactions between the insects and the environment and computes collision-free trajectories for each individual insect. Furthermore, we model the noise as a constructive force at the collective level and present a technique to generate noise-induced insect movements in a large swarm that are similar to those observed in real-world trajectories. We use a data-driven formulation that is based on pre-recorded insect trajectories. We also present a novel evaluation metric and a statistical validation approach that takes into account various characteristics of insect motions. In practice, the combination of Curl noise function with our dynamics model is used to generate realistic swarm simulations and emergent behaviors. We highlight its performance for simulating large flying swarms of midges, fruit fly, locusts and moths and demonstrate many collective behaviors, including aggregation, migration, phase transition, and escape responses. PMID:27187068

Cooperation guided by the coexistence of imitation dynamics and aspiration dynamics in structured populations

NASA Astrophysics Data System (ADS)

Xu, Kuangyi; Li, Kun; Cong, Rui; Wang, Long

2017-02-01

In the framework of the evolutionary game theory, two fundamentally different mechanisms, the imitation process and the aspiration-driven dynamics, can be adopted by players to update their strategies. In the former case, individuals imitate the strategy of a more successful peer, while in the latter case individuals change their strategies based on a comparison of payoffs they collect in the game to their own aspiration levels. Here we explore how cooperation evolves for the coexistence of these two dynamics. Intriguingly, cooperation reaches its lowest level when a certain moderate fraction of individuals pick aspiration-level-driven rule while the others choose pairwise comparison rule. Furthermore, when individuals can adjust their update rules besides their strategies, either imitation dynamics or aspiration-driven dynamics will finally take over the entire population, and the stationary cooperation level is determined by the outcome of competition between these two dynamics. We find that appropriate synergetic effects and moderate aspiration level boost the fixation probability of aspiration-driven dynamics most effectively. Our work may be helpful in understanding the cooperative behavior induced by the coexistence of imitation dynamics and aspiration dynamics in the society.

The fluid events model: Predicting continuous task action change.

PubMed

Radvansky, Gabriel A; D'Mello, Sidney; Abbott, Robert G; Morgan, Brent; Fike, Karl; Tamplin, Andrea K

2015-01-01

The fluid events model is a behavioural model aimed at predicting the likelihood that people will change their actions in ongoing, interactive events. From this view, not only are people responding to aspects of the environment, but they are also basing responses on prior experiences. The fluid events model is an attempt to predict the likelihood that people will shift the type of actions taken within an event on a trial-by-trial basis, taking into account both event structure and experience-based factors. The event-structure factors are: (a) changes in event structure, (b) suitability of the current action to the event, and (c) time on task. The experience-based factors are: (a) whether a person has recently shifted actions, (b) how often a person has shifted actions, (c) whether there has been a dip in performance, and (d) a person's propensity to switch actions within the current task. The model was assessed using data from a series of tasks in which a person was producing responses to events. These were two stimulus-driven figure-drawing studies, a conceptually driven decision-making study, and a probability matching study using a standard laboratory task. This analysis predicted trial-by-trial action switching in a person-independent manner with an average accuracy of 70%, which reflects a 34% improvement above chance. In addition, correlations between overall switch rates and actual switch rates were remarkably high (mean r = .98). The experience-based factors played a more major role than the event-structure factors, but this might be attributable to the nature of the tasks.

Empirical relationships between soil moisture, albedo, and the planetary boundary layer height: a two-layer bucket model approach

NASA Astrophysics Data System (ADS)

Sanchez-Mejia, Z. M.; Papuga, S. A.

2013-12-01

In semiarid regions, where water resources are limited and precipitation dynamics are changing, understanding land surface-atmosphere interactions that regulate the coupled soil moisture-precipitation system is key for resource management and planning. We present a modeling approach to study soil moisture and albedo controls on planetary boundary layer height (PBLh). We used data from the Santa Rita Creosote Ameriflux site and Tucson Airport atmospheric sounding to generate empirical relationships between soil moisture, albedo and PBLh. We developed empirical relationships and show that at least 50% of the variation in PBLh can be explained by soil moisture and albedo. Then, we used a stochastically driven two-layer bucket model of soil moisture dynamics and our empirical relationships to model PBLh. We explored soil moisture dynamics under three different mean annual precipitation regimes: current, increase, and decrease, to evaluate at the influence on soil moisture on land surface-atmospheric processes. While our precipitation regimes are simple, they represent future precipitation regimes that can influence the two soil layers in our conceptual framework. For instance, an increase in annual precipitation, could impact on deep soil moisture and atmospheric processes if precipitation events remain intense. We observed that the response of soil moisture, albedo, and the PBLh will depend not only on changes in annual precipitation, but also on the frequency and intensity of this change. We argue that because albedo and soil moisture data are readily available at multiple temporal and spatial scales, developing empirical relationships that can be used in land surface - atmosphere applications are of great value.

Data-driven forecasting of high-dimensional chaotic systems with long short-term memory networks.

PubMed

Vlachas, Pantelis R; Byeon, Wonmin; Wan, Zhong Y; Sapsis, Themistoklis P; Koumoutsakos, Petros

2018-05-01

We introduce a data-driven forecasting method for high-dimensional chaotic systems using long short-term memory (LSTM) recurrent neural networks. The proposed LSTM neural networks perform inference of high-dimensional dynamical systems in their reduced order space and are shown to be an effective set of nonlinear approximators of their attractor. We demonstrate the forecasting performance of the LSTM and compare it with Gaussian processes (GPs) in time series obtained from the Lorenz 96 system, the Kuramoto-Sivashinsky equation and a prototype climate model. The LSTM networks outperform the GPs in short-term forecasting accuracy in all applications considered. A hybrid architecture, extending the LSTM with a mean stochastic model (MSM-LSTM), is proposed to ensure convergence to the invariant measure. This novel hybrid method is fully data-driven and extends the forecasting capabilities of LSTM networks.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models.

PubMed

Henriques, David; Villaverde, Alejandro F; Rocha, Miguel; Saez-Rodriguez, Julio; Banga, Julio R

2017-02-01

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM's ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge.

Data-driven reverse engineering of signaling pathways using ensembles of dynamic models

PubMed Central

Henriques, David; Villaverde, Alejandro F.; Banga, Julio R.

2017-01-01

Despite significant efforts and remarkable progress, the inference of signaling networks from experimental data remains very challenging. The problem is particularly difficult when the objective is to obtain a dynamic model capable of predicting the effect of novel perturbations not considered during model training. The problem is ill-posed due to the nonlinear nature of these systems, the fact that only a fraction of the involved proteins and their post-translational modifications can be measured, and limitations on the technologies used for growing cells in vitro, perturbing them, and measuring their variations. As a consequence, there is a pervasive lack of identifiability. To overcome these issues, we present a methodology called SELDOM (enSEmbLe of Dynamic lOgic-based Models), which builds an ensemble of logic-based dynamic models, trains them to experimental data, and combines their individual simulations into an ensemble prediction. It also includes a model reduction step to prune spurious interactions and mitigate overfitting. SELDOM is a data-driven method, in the sense that it does not require any prior knowledge of the system: the interaction networks that act as scaffolds for the dynamic models are inferred from data using mutual information. We have tested SELDOM on a number of experimental and in silico signal transduction case-studies, including the recent HPN-DREAM breast cancer challenge. We found that its performance is highly competitive compared to state-of-the-art methods for the purpose of recovering network topology. More importantly, the utility of SELDOM goes beyond basic network inference (i.e. uncovering static interaction networks): it builds dynamic (based on ordinary differential equation) models, which can be used for mechanistic interpretations and reliable dynamic predictions in new experimental conditions (i.e. not used in the training). For this task, SELDOM’s ensemble prediction is not only consistently better than predictions from individual models, but also often outperforms the state of the art represented by the methods used in the HPN-DREAM challenge. PMID:28166222

Transition Manifolds of Complex Metastable Systems: Theory and Data-Driven Computation of Effective Dynamics.

PubMed

Bittracher, Andreas; Koltai, Péter; Klus, Stefan; Banisch, Ralf; Dellnitz, Michael; Schütte, Christof

2018-01-01

We consider complex dynamical systems showing metastable behavior, but no local separation of fast and slow time scales. The article raises the question of whether such systems exhibit a low-dimensional manifold supporting its effective dynamics. For answering this question, we aim at finding nonlinear coordinates, called reaction coordinates, such that the projection of the dynamics onto these coordinates preserves the dominant time scales of the dynamics. We show that, based on a specific reducibility property, the existence of good low-dimensional reaction coordinates preserving the dominant time scales is guaranteed. Based on this theoretical framework, we develop and test a novel numerical approach for computing good reaction coordinates. The proposed algorithmic approach is fully local and thus not prone to the curse of dimension with respect to the state space of the dynamics. Hence, it is a promising method for data-based model reduction of complex dynamical systems such as molecular dynamics.

Complex Pattern Formation from Current-Driven Dynamics of Single-Layer Homoepitaxial Islands on Crystalline Conducting Substrates

NASA Astrophysics Data System (ADS)

Kumar, Ashish; Dasgupta, Dwaipayan; Maroudas, Dimitrios

2017-07-01

We report a systematic study of complex pattern formation resulting from the driven dynamics of single-layer homoepitaxial islands on surfaces of face-centered-cubic (fcc) crystalline conducting substrates under the action of an externally applied electric field. The analysis is based on an experimentally validated nonlinear model of mass transport via island edge atomic diffusion, which also accounts for edge diffusional anisotropy. We analyze the morphological stability and simulate the field-driven evolution of rounded islands for an electric field oriented along the fast edge diffusion direction. For larger-than-critical island sizes on {110 } and {100 } fcc substrates, we show that multiple necking instabilities generate complex island patterns, including not-simply-connected void-containing islands mediated by sequences of breakup and coalescence events and distributed symmetrically with respect to the electric field direction. We analyze the dependence of the formed patterns on the original island size and on the duration of application of the external field. Starting from a single large rounded island, we characterize the evolution of the number of daughter islands and their average size and uniformity. The evolution of the average island size follows a universal power-law scaling relation, and the evolution of the total edge length of the islands in the complex pattern follows Kolmogorov-Johnson-Mehl-Avrami kinetics. Our study makes a strong case for the use of electric fields, as precisely controlled macroscopic forcing, toward surface patterning involving complex nanoscale features.

Climate forcing for dynamics of dissolved inorganic nutrients at Palmer Station, Antarctica: An interdecadal (1993-2013) analysis

NASA Astrophysics Data System (ADS)

Kim, Hyewon; Doney, Scott C.; Iannuzzi, Richard A.; Meredith, Michael P.; Martinson, Douglas G.; Ducklow, Hugh W.

2016-09-01

We analyzed 20 years (1993-2013) of observations of dissolved inorganic macronutrients (nitrate, N; phosphate, P; and silicate, Si) and chlorophyll a (Chl) at Palmer Station, Antarctica (64.8°S, 64.1°W) to elucidate how large-scale climate and local physical forcing affect the interannual variability in the seasonal phytoplankton bloom and associated drawdown of nutrients. The leading modes of nutrients (N, P, and Si empirical orthogonal functions 1, EOF1) represent overall negative anomalies throughout growing seasons, showing a mixed signal of variability in the initial levels and drawdown thereafter (low-frequency dynamics). The second most common seasonal patterns of nitrate and phosphate (N and P EOF2) capture prolonged drawdown events during December-March, which are correlated to Chl EOF1. Si EOF2 captures a drawdown event during November-December, which is correlated to Chl EOF2. These different drawdown patterns are shaped by different sets of physical and climate forcing mechanisms. N and P drawdown events during December-March are influenced by the winter and spring Southern Annular Mode (SAM) phase, where nutrient utilization is enhanced in a stabilized upper water column as a consequence of SAM-driven winter sea ice and spring wind dynamics. Si drawdown during November-December is influenced by early sea ice retreat, where ice breakup may induce abrupt water column stratification and a subsequent diatom bloom or release of diatom cells from within the sea ice. Our findings underscore that seasonal nutrient dynamics in the coastal WAP are coupled to large-scale climate forcing and related physics, understanding of which may enable improved projections of biogeochemical responses to climate change.

Coral population dynamics across consecutive mass mortality events.

PubMed

Riegl, Bernhard; Purkis, Sam

2015-11-01

Annual coral mortality events due to increased atmospheric heat may occur regularly from the middle of the century and are considered apocalyptic for coral reefs. In the Arabian/Persian Gulf, this situation has already occurred and population dynamics of four widespread corals (Acropora downingi, Porites harrisoni, Dipsastrea pallida, Cyphastrea micropthalma) were examined across the first-ever occurrence of four back-to-back mass mortality events (2009-2012). Mortality was driven by diseases in 2009, bleaching and subsequent diseases in 2010/2011/2012. 2009 reduced P. harrisoni cover and size, the other events increasingly reduced overall cover (2009: -10%; 2010: -20%; 2011: -20%; 2012: -15%) and affected all examined species. Regeneration was only observed after the first disturbance. P. harrisoni and A. downingi severely declined from 2010 due to bleaching and subsequent white syndromes, while D. pallida and P. daedalea declined from 2011 due to bleaching and black-band disease. C. microphthalma cover was not affected. In all species, most large corals were lost while fission due to partial tissue mortality bolstered small size classes. This general shrinkage led to a decrease of coral cover and a dramatic reduction of fecundity. Transition matrices for disturbed and undisturbed conditions were evaluated as Life Table Response Experiment and showed that C. microphthalma changed the least in size-class dynamics and fecundity, suggesting they were 'winners'. In an ordered 'degradation cascade', impacts decreased from the most common to the least common species, leading to step-wise removal of previously dominant species. A potentially permanent shift from high- to low-coral cover with different coral community and size structure can be expected due to the demographic dynamics resultant from the disturbances. Similarities to degradation of other Caribbean and Pacific reefs are discussed. As comparable environmental conditions and mortality patterns must be expected worldwide, demographic collapse of many other coral populations may soon be widespread. © 2015 John Wiley & Sons Ltd.

Predicting sea-level rise vulnerability of terrestrial habitat and wildlife of the Northwestern Hawaiian Islands

USGS Publications Warehouse

Reynolds, Michelle H.; Berkowitz, Paul; Courtot, Karen N.; Krause, Crystal M.; Reynolds, Michelle H.; Berkowitz, Paul; Courtot, Karen N.; Krause, Crystal M.

2012-01-01

If current climate change trends continue, rising sea levels may inundate low-lying islands across the globe, placing island biodiversity at risk. Recent models predict a rise of approximately one meter (1 m) in global sea level by 2100, with larger increases possible in areas of the Pacific Ocean. Pacific Islands are unique ecosystems home to many endangered endemic plant and animal species. The Northwestern Hawaiian Islands (NWHI), which extend 1,930 kilometers (km) beyond the main Hawaiian Islands, are a World Heritage Site and part of the Papahanaumokuakea Marine National Monument. These NWHI support the largest tropical seabird rookery in the world, providing breeding habitat for 21 species of seabirds, 4 endemic land bird species and essential foraging, breeding, or haul-out habitat for other resident and migratory wildlife. In recent years, concern has grown about the increasing vulnerability of the NWHI and their wildlife populations to changing climatic patterns, particularly the uncertainty associated with potential impacts from global sea-level rise (SLR) and storms. In response to the need by managers to adapt future resource protection strategies to climate change variability and dynamic island ecosystems, we have synthesized and down scaled analyses for this important region. This report describes a 2-year study of a remote northwestern Pacific atoll ecosystem and identifies wildlife and habitat vulnerable to rising sea levels and changing climate conditions. A lack of high-resolution topographic data for low-lying islands of the NWHI had previously precluded an extensive quantitative model of the potential impacts of SLR on wildlife habitat. The first chapter (chapter 1) describes the vegetation and topography of 20 islands of Papahanaumokuakea Marine National Monument, the distribution and status of wildlife populations, and the predicted impacts for a range of SLR scenarios. Furthermore, this chapter explores the potential effects of SLR on wildlife breeding habitats for each island. The subsequent chapter (chapter 2) details a study of the Laysan Island ecosystem, describing a quantitative model that incorporates SLR, storm wave, and rising groundwater inundation. Wildlife, storm, and oceanographic data allowed for an assessment of the phenological and spatial vulnerability of Laysan Island's breeding bird species to SLR and storms. Using remote sensing and geospatial techniques, we estimated topography, classified vegetation, modeled SLR, and evaluated a range of climate change scenarios. On the basis of high-resolution airborne data collected during 2010-11 (root-mean-squared error = 0.05-0.18 m), we estimated the maximum elevation of 20 individual islands extending from Kure Atoll to French Frigate Shoals (range: 1.8-39.7 m) and computed the mean elevation (1.7 m, standard deviation 1.1 m) across all low-lying islands. We also analyzed general climate models to describe rainfall and temperature scenarios expected to influence adaptation of some plants and animals for this region. Outcomes for the NWHI predicted an increase in temperature of 1.8-2.6 degrees Celsius (°C) and an annual decrease in precipitation of 24.7-76.3 millimeters (mm) across the NWHI by 2100. Our models of passive SLR (excluding wave-driven effects, erosion, and accretion) showed that approximately 4 percent of the total land area in the NWHI will be lost with scenarios of +1.0 m of SLR and 26 percent will be lost with +2.0 m of SLR. Some atolls are especially vulnerable to SLR. For example, at Pearl and Hermes Atoll our analysis indicated substantial habitat losses with 43 percent of the land area inundated at +1.0 m SLR and 92 percent inundated at +2.0 m SLR. Across the NWHI, seven islands will be completely submerged with +2.0 m SLR. The limited global ranges of some tropical nesting birds make them particularly vulnerable to climate change impacts in the NWHI. Climate change scenarios and potential SLR impacts presented here emphasize the need for early climate change adaptation and mitigation planning, especially for species with limited breeding distributions and/or ranges restricted primarily to the low-lying NWHI: Cyperus pennatiformis var. bryanii, Black-footed Albatross (Phoebastria nigripes), Laysan Albatross (P. immutabilis), Bonin Petrel (Pterodroma hypoleuca), Gray-backed Tern (Onychoprion lunatus), Laysan Teal (Anas laysanensis), Laysan Finch (Telespiza cantans), and Hawaiian monk seal (Monachus schauinslandi). Furthermore, SLR scenarios that include the effects of wave dynamics and groundwater rise may indicate amplified vulnerability to climate change driven habitat loss on low-lying islands. In chapter 2, we incorporated the combined effects of SLR, dynamic wave-driven inundation, and rising groundwater in a quantitative study specifically for the Laysan Island ecosystem. This is the first hydrodynamic model to simulate the combined impacts of SLR and wave-driven inundation in the NWHI. We developed a high-resolution digital elevation model (mean vertical accuracy of 0.32 m) for the island. Then using recent satellite imagery, geospatial models, and historical oceanographic, storm, and biological data we estimated potential inundation extent, habitat loss, and wildlife population impacts for a range of potential SLR scenarios (0.00, +0.50, +1.00, +1.50, and +2.00 m) that may occur over the next century. Additionally, we estimated the carrying capacity of Laysan Island for five species based on the available population monitoring data and described how potential losses in nesting habitat could influence population dynamics for Black-footed Albatross, Laysan Albatross, Red-footed Booby (Sula sula), Laysan Teal, and Laysan Finch. For some other seabird populations (Masked Booby, S. dactylatra; Brown Booby, S. leucogaster; Great Frigatebird, Fregata minor; and Sooty Tern, Onychoprion fuscata), we used recent colony distribution data, land cover maps, and nesting behavior to estimate potential losses of nesting habitat from SLR and wave-driven inundation. We observed far greater potential impacts of SLR to wildlife with the dynamic wave-driven modeling approach than with the passive modeling approach. Depending on SLR scenario and coastal orientation, during storms under a +2.00 m SLR scenario, the wave-driven inundation model predicted three times more inundation than the passive model (17.2 percent of total terrestrial area versus 4.6 percent, respectively). Large-wave events generally added 1 m of water height to passive inundation surfaces, therefore our dynamic models (during storm events) forecasted comparable inundation extents earlier than passive models. Although wave-driven water levels were highest in the northwest quadrant of Laysan Island, the greatest extent of inundation occurred in the southeast where coastal dunes less than 3 m above mean sea level provide little protection from wave-driven inundation. When wave-driven inundation was included in the SLR model for Laysan Island greater nesting habitat loss and potential impacts on wildlife population dynamics were predicted. The consequences of habitat loss due to SLR may be worse for species with colonies in the wave-exposed coastal zones (for example, Black-footed Albatross) and for populations already near the island's carrying capacity (for example, Laysan Teal). Species whose peak incubation and chick-rearing periods coincide with seasonally high wave heights also will be increasingly vulnerable, especially those species nesting on the ground in areas vulnerable to inundation, such as Gray-backed Tern and Black-footed Albatross. Other species that have space for population growth, or are not restricted to a narrow range of habitat types on Laysan (for instance, Sooty Terns), may be less sensitive to habitat loss from SLR over the next century. Our assessments of inundation risk, habitat loss, and wildlife species vulnerability synthesize current knowledge about individual islands and contribute to a broader understanding of the impacts of inundation from SLR and storm-induced waves. Yet, most NWHI and their bird populations lack monitoring data to evaluate adaptations to and impacts of climate change. Exceptions include some data sets from long-term monitoring of wildlife populations, tides, or weather at French Frigate Shoals, Laysan Island, and Midway Atoll. These data sets are potentially valuable baselines, which could be informative for adaptive learning (integrating management and science) to predict, adapt, and mitigate the effects of climate change on NWHI wildlife in the future. This study provides the first quantitative vulnerability assessment for all of the low-lying NWHI, and results identify biological communities, locales, and resident endangered species of Papahanaumokuakea Marine National Monument expected to be at risk from SLR. This report is also intended as a reference for managers and conservation planners, a tool to identify and potentially reduce uncertainty, and a starting place for developing climate change monitoring priorities and future scientific studies.

Event-Driven Simulation and Analysis of an Underwater Acoustic Local Area Network

DTIC Science & Technology

2010-06-01

Successful number of data packets % b. PSUP = Successful number of Utility packets % c. PSB = Successful number of byte Tx. % d. PSPRT = Number of sub...g. PFU = Number of failed utilities Tx failures with time log of failure % h. PTO = Number of Time-outs 55 function [PSDP,PSUP, PSB ,PSPRT,PFP,PFSP...transmitted PSB = 0 ; % Number of Bytes transmitted PSPRT = 0; % Number of sub-packets retransmitted PFP = 0; % Number of failed packets event PFSP

High-resolution dynamical downscaling of the future Alpine climate

NASA Astrophysics Data System (ADS)

Bozhinova, Denica; José Gómez-Navarro, Juan; Raible, Christoph

2017-04-01

The Alpine region and Switzerland is a challenging area for simulating and analysing Global Climate Model (GCM) results. This is mostly due to the combination of a very complex topography and the still rather coarse horizontal resolution of current GCMs, in which not all of the many-scale processes that drive the local weather and climate can be resolved. In our study, the Weather Research and Forecasting (WRF) model is used to dynamically downscale a GCM simulation to a resolution as high as 2 km x 2 km. WRF is driven by initial and boundary conditions produced with the Community Earth System Model (CESM) for the recent past (control run) and until 2100 using the RCP8.5 climate scenario (future run). The control run downscaled with WRF covers the period 1976-2005, while the future run investigates a 20-year-slice simulated for the 2080-2099. We compare the control WRF-CESM simulations to an observational product provided by MeteoSwiss and an additional WRF simulation driven by the ERA-Interim reanalysis, to estimate the bias that is introduced by the extra modelling step of our framework. Several bias-correction methods are evaluated, including a quantile mapping technique, to ameliorate the bias in the control WRF-CESM simulation. In the next step of our study these corrections are applied to our future WRF-CESM run. The resulting downscaled and bias-corrected data is analysed for the properties of precipitation and wind speed in the future climate. Our special interest focuses on the absolute quantities simulated for these meteorological variables as these are used to identify extreme events, such as wind storms and situations that can lead to floods.

A new practice-driven approach to develop software in a cyber-physical system environment

NASA Astrophysics Data System (ADS)

Jiang, Yiping; Chen, C. L. Philip; Duan, Junwei

2016-02-01

Cyber-physical system (CPS) is an emerging area, which cannot work efficiently without proper software handling of the data and business logic. Software and middleware is the soul of the CPS. The software development of CPS is a critical issue because of its complicity in a large scale realistic system. Furthermore, object-oriented approach (OOA) is often used to develop CPS software, which needs some improvements according to the characteristics of CPS. To develop software in a CPS environment, a new systematic approach is proposed in this paper. It comes from practice, and has been evolved from software companies. It consists of (A) Requirement analysis in event-oriented way, (B) architecture design in data-oriented way, (C) detailed design and coding in object-oriented way and (D) testing in event-oriented way. It is a new approach based on OOA; the difference when compared with OOA is that the proposed approach has different emphases and measures in every stage. It is more accord with the characteristics of event-driven CPS. In CPS software development, one should focus on the events more than the functions or objects. A case study of a smart home system is designed to reveal the effectiveness of the approach. It shows that the approach is also easy to be operated in the practice owing to some simplifications. The running result illustrates the validity of this approach.

Designing a dynamic data driven application system for estimating real-time load of dissolved organic carbon in a river

Treesearch

Ying Ouyang

2012-01-01

Understanding the dynamics of naturally occurring dissolved organic carbon (DOC) in a river is central to estimating surface water quality, aquatic carbon cycling, and global climate change. Currently, determination of the DOC in surface water is primarily accomplished by manually collecting samples for laboratory analysis, which requires at least 24 h. In other words...

Dynamic data-driven integrated flare model based on self-organized criticality

NASA Astrophysics Data System (ADS)

Dimitropoulou, M.; Isliker, H.; Vlahos, L.; Georgoulis, M. K.

2013-05-01

Context. We interpret solar flares as events originating in active regions that have reached the self-organized critical state. We describe them with a dynamic integrated flare model whose initial conditions and driving mechanism are derived from observations. Aims: We investigate whether well-known scaling laws observed in the distribution functions of characteristic flare parameters are reproduced after the self-organized critical state has been reached. Methods: To investigate whether the distribution functions of total energy, peak energy, and event duration follow the expected scaling laws, we first applied the previously reported static cellular automaton model to a time series of seven solar vector magnetograms of the NOAA active region 8210 recorded by the Imaging Vector Magnetograph on May 1 1998 between 18:59 UT and 23:16 UT until the self-organized critical state was reached. We then evolved the magnetic field between these processed snapshots through spline interpolation, mimicking a natural driver in our dynamic model. We identified magnetic discontinuities that exceeded a threshold in the Laplacian of the magnetic field after each interpolation step. These discontinuities were relaxed in local diffusion events, implemented in the form of cellular automaton evolution rules. Subsequent interpolation and relaxation steps covered all transitions until the end of the processed magnetograms' sequence. We additionally advanced each magnetic configuration that has reached the self-organized critical state (SOC configuration) by the static model until 50 more flares were triggered, applied the dynamic model again to the new sequence, and repeated the same process sufficiently often to generate adequate statistics. Physical requirements, such as the divergence-free condition for the magnetic field, were approximately imposed. Results: We obtain robust power laws in the distribution functions of the modeled flaring events with scaling indices that agree well with observations. Peak and total flare energy obey single power laws with indices -1.65 ± 0.11 and -1.47 ± 0.13, while the flare duration is best fitted with a double power law (-2.15 ± 0.15 and -3.60 ± 0.09 for the flatter and steeper parts, respectively). Conclusions: We conclude that well-known statistical properties of flares are reproduced after active regions reach the state of self-organized criticality. A significant enhancement of our refined cellular automaton model is that it initiates and further drives the simulation from observed evolving vector magnetograms, thus facilitating energy calculation in physical units, while a separation between MHD and kinetic timescales is possible by assigning distinct MHD timestamps to each interpolation step.

System on chip module configured for event-driven architecture

DOEpatents

Robbins, Kevin; Brady, Charles E.; Ashlock, Tad A.

2017-10-17

A system on chip (SoC) module is described herein, wherein the SoC modules comprise a processor subsystem and a hardware logic subsystem. The processor subsystem and hardware logic subsystem are in communication with one another, and transmit event messages between one another. The processor subsystem executes software actors, while the hardware logic subsystem includes hardware actors, the software actors and hardware actors conform to an event-driven architecture, such that the software actors receive and generate event messages and the hardware actors receive and generate event messages.

Sloshing of Cryogenic Helium Driven by Lateral Impulse/Gravity Gradient-Dominated/or g-Jitter-Dominated Accelerations and Orbital Dynamics

NASA Technical Reports Server (NTRS)

Hung, R. J.; Long, Y. T.; Zu, G. J.

1996-01-01

The coupling of slosh dynamics within a partially filled rotating dewar of superfluid helium 11 with spacecraft orbital dynamics is investigated in response to the environmental disturbances of (a) lateral impulses, (b) gravity gradients and (c) g-jitter forces. The purpose of this study is to investigate how the coupling of helium 11 fluid slosh dynamics driven by three cases of environmental force with spacecraft dynamics can affect the bubble deformations and their associated fluid and spacecraft mass centre fluctuations. The numerical computation of slosh dynamics is based on a rotational frame, while the spacecraft dynamics is associated with a non-rotational frame. Results show that the major contribution of orbital dynamics is driven by coupling with slosh dynamics. Neglecting the effect of slosh dynamics acting on the spacecraft may lead to the wrong results for the development of orbital and attitude control techniques.

Post-rift Tectonic History of the Songliao Basin, NE China: Cooling Events and Post-rift Unconformities Driven by Orogenic Pulses From Plate Boundaries

NASA Astrophysics Data System (ADS)

Song, Ying; Stepashko, Andrei; Liu, Keyu; He, Qingkun; Shen, Chuanbo; Shi, Bingjie; Ren, Jianye

2018-03-01

The classic lithosphere-stretching model predicts that the post-rift evolution of extensional basin should be exclusively controlled by decaying thermal subsidence. However, the stratigraphy of the Songliao Basin in northeastern China shows that the post-rift evolution was punctuated by multiple episodes of uplift and exhumation events, commonly attributed to the response to regional tectonic events, including the far-field compression from plate margins. Three prominent tectonostratigraphic post-rift unconformities are recognized in the Late Cretaceous strata of the basin: T11, T03, and T02. The subsequent Cenozoic history is less constrained due to the incomplete record of younger deposits. In this paper, we utilize detrital apatite fission track (AFT) thermochronology to unravel the enigmatic timing and origin of post-rift unconformities. Relating the AFT results to the unconformities and other geological data, we conclude that in the post-rift stage, the basin experienced a multiepisodic tectonic evolution with four distinct cooling and exhumation events. The thermal history and age pattern document the timing of the unconformities in the Cretaceous succession: the T11 unconformity at 88-86 Ma, the T03 unconformity at 79-75 Ma, and the T02 unconformity at 65-50 Ma. A previously unrecognized Oligocene unconformity is also defined by a 32-24 Ma cooling event. Tectonically, all the cooling episodes were regional, controlled by plate boundary stresses. We propose that Pacific dynamics influenced the wider part of eastern Asia during the Late Cretaceous until Cenozoic, whereas the far-field effects of the Neo-Tethys subduction and collision processes became another tectonic driver in the later Cenozoic.

CLARA: CLAS12 Reconstruction and Analysis Framework

DOE Office of Scientific and Technical Information (OSTI.GOV)

Gyurjyan, Vardan; Matta, Sebastian Mancilla; Oyarzun, Ricardo

2016-11-01

In this paper we present SOA based CLAS12 event Reconstruction and Analyses (CLARA) framework. CLARA design focus is on two main traits: real-time data stream processing, and service-oriented architecture (SOA) in a flow based programming (FBP) paradigm. Data driven and data centric architecture of CLARA presents an environment for developing agile, elastic, multilingual data processing applications. The CLARA framework presents solutions capable of processing large volumes of data interactively and substantially faster than batch systems.

Computational Fluid Dynamics Simulation of Flows in an Oxidation Ditch Driven by a New Surface Aerator.

PubMed

Huang, Weidong; Li, Kun; Wang, Gan; Wang, Yingzhe

2013-11-01

In this article, we present a newly designed inverse umbrella surface aerator, and tested its performance in driving flow of an oxidation ditch. Results show that it has a better performance in driving the oxidation ditch than the original one with higher average velocity and more uniform flow field. We also present a computational fluid dynamics model for predicting the flow field in an oxidation ditch driven by a surface aerator. The improved momentum source term approach to simulate the flow field of the oxidation ditch driven by an inverse umbrella surface aerator was developed and validated through experiments. Four kinds of turbulent models were investigated with the approach, including the standard k - ɛ model, RNG k - ɛ model, realizable k - ɛ model, and Reynolds stress model, and the predicted data were compared with those calculated with the multiple rotating reference frame approach (MRF) and sliding mesh approach (SM). Results of the momentum source term approach are in good agreement with the experimental data, and its prediction accuracy is better than MRF, close to SM. It is also found that the momentum source term approach has lower computational expenses, is simpler to preprocess, and is easier to use.

Most atolls will be uninhabitable by the mid-21st century because of sea-level rise exacerbating wave-driven flooding

USGS Publications Warehouse

Storlazzi, Curt; Gingerich, Stephen B.; van Dongeren, Ap; Cheriton, Olivia; Swarzenski, Peter W.; Quataert, Ellen; Voss, Clifford I.; Field, Donald W.; Annamalai, Hariharasubramanian; Piniak, Greg A.; McCall, Robert T.

2018-01-01

Sea levels are rising, with the highest rates in the tropics, where thousands of low-lying coral atoll islands are located. Most studies on the resilience of these islands to sea-level rise have projected that they will experience minimal inundation impacts until at least the end of the 21st century. However, these have not taken into account the additional hazard of wave-driven overwash or its impact on freshwater availability. We project the impact of sea-level rise and wave-driven flooding on atoll infrastructure and freshwater availability under a variety of climate change scenarios. We show that, on the basis of current greenhouse gas emission rates, the nonlinear interactions between sea-level rise and wave dynamics over reefs will lead to the annual wave-driven overwash of most atoll islands by the mid-21st century. This annual flooding will result in the islands becoming uninhabitable because of frequent damage to infrastructure and the inability of their freshwater aquifers to recover between overwash events. This study provides critical information for understanding the timing and magnitude of climate change impacts on atoll islands that will result in significant, unavoidable geopolitical issues if it becomes necessary to abandon and relocate low-lying island states.