Data base on physical observations of near-Earth asteroids and establishment of a network to coordinate observations of newly discovered near-Earth asteroids

NASA Technical Reports Server (NTRS)

Davis, D. R.; Chapman, C. R.; Campins, H.

1990-01-01

This program consists of two tasks: (1) development of a data base of physical observations of near-earth asteroids and establishment of a network to coordinate observations of newly discovered earth-approaching asteroids; and (2) a simulation of the surface of low-activity comets. Significant progress was made on task one and, and task two was completed during the period covered by this progress report.

The advances in airglow study and observation by the ground-based airglow observation network over China

NASA Astrophysics Data System (ADS)

Xu, Jiyao; Li, Qinzeng; Yuan, Wei; Liu, Xiao; Liu, Weijun; Sun, Longchang

2017-04-01

Ground-based airglow observation networks over China used to study airglow have been established, which contains 15 stations. Some new results were obtained using the networks. For OH airglow observations, firstly, an unusual outbreak of Concentric Gravity Wave (CGW) events were observed by the first no-gap network nearly every night during the first half of August 2013. Combination of the ground imager network with satellites provides multilevel observations of the CGWs from the troposphere to the mesopause region. Secondly, three-year OH airglow images (2012-2014) from Qujing (25.6°N, 103.7°E) were used to study how orographic features of the Tibetan Plateau (TP) affect the geographical distributions of gravity wave (GW) sources. We find the orographic forcings have a significant impact on the gravity wave propagation features. Thirdly, ground-based observations of the OH (9-4, 8-3, 6-2, 5-1, 3-0) band airglow over Xinglong (40°2N, 117°4E) in northern China from 2012 to 2014 are used to calculate rotational temperatures. By comparing the ground-based OH rotational temperature with SABER's observations, five Einstein coefficient datasets are evaluated. We find rotational temperatures determined using any of the available Einstein coefficient datasets have systematic errors. We have obtained a set of optimal Einstein coefficients ratios for rotational temperature derivation using three years data from ground-based OH spectra and SABER temperatures. For the OI 630.0 nm airglow observations, we used three-year (2011-2013) observations of thermospheric winds (at 250 km) by Fabry-Perot interferometers at Xinglong to study the climatology of atmospheric planetary wave-type oscillations (PWTOs) with periods of 4-19 days. We found these PWTOs occur more frequently in the months from May to October. They are consistent with the summertime preference of middle-latitude ionospheric electron density oscillations noted in other studies. By using an all-sky airglow imager at 630.0 nm over Xinglong, we studied the evolution (generation, amplification, and dissipation) of mesoscale field-aligned irregularity structures (FAIs) ( 150 km) associated with a medium-scale traveling ionospheric disturbance (MSTID) event. We also investigates the statistical features of equatorial plasma bubbles (EPBs) using airglow images from 2012 to 2014 from a ground-based network of four imagers in the equatorial region of China.

Distributed Position-Based Consensus of Second-Order Multiagent Systems With Continuous/Intermittent Communication.

PubMed

Song, Qiang; Liu, Fang; Wen, Guanghui; Cao, Jinde; Yang, Xinsong

2017-04-24

This paper considers the position-based consensus in a network of agents with double-integrator dynamics and directed topology. Two types of distributed observer algorithms are proposed to solve the consensus problem by utilizing continuous and intermittent position measurements, respectively, where each observer does not interact with any other observers. For the case of continuous communication between network agents, some convergence conditions are derived for reaching consensus in the network with a single constant delay or multiple time-varying delays on the basis of the eigenvalue analysis and the descriptor method. When the network agents can only obtain intermittent position data from local neighbors at discrete time instants, the consensus in the network without time delay or with nonuniform delays is investigated by using the Wirtinger's inequality and the delayed-input approach. Numerical examples are given to illustrate the theoretical analysis.

The Deep Space Network: An instrument for radio astronomy research

NASA Technical Reports Server (NTRS)

Renzetti, N. A.; Levy, G. S.; Kuiper, T. B. H.; Walken, P. R.; Chandlee, R. C.

1988-01-01

The NASA Deep Space Network operates and maintains the Earth-based two-way communications link for unmanned spacecraft exploring the solar system. It is NASA's policy to also make the Network's facilities available for radio astronomy observations. The Network's microwave communication systems and facilities are being continually upgraded. This revised document, first published in 1982, describes the Network's current radio astronomy capabilities and future capabilities that will be made available by the ongoing Network upgrade. The Bibliography, which includes published papers and articles resulting from radio astronomy observations conducted with Network facilities, has been updated to include papers to May 1987.

Ground-Based Network and Supersite Observations to Complement and Enrich EOS Research

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee; Holben, Brent N.; Welton, Ellsworth J.

2011-01-01

Since 1997 NASA has been successfully launching a series of satellites - the Earth Observing System (EOS) - to intensively study, and gain a better understanding of, the Earth as an integrated system. Space-borne remote sensing observations, however, are often plagued by contamination of surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from atmospheric constituents, the sun, and/or the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft, and satellite datasets. Through numerous participations, particularly but not limited to the EOS remote-sensing/retrieval and validation projects over the years, NASA/GSFC has developed and continuously refined ground-based networks and mobile observatories that proved to be vital in providing high temporal measurements, which complement and enrich the satellite observations. These are: the AERO NET (AErosol RObotic NETwork) a federation of ground-based globally distributed network of spectral sun-sky photometers; the MPLNET (Micro-Pulse Lidar NETwork, a similarly organized network of micro-pulse lidar systems measuring aerosol and cloud vertical structure continuously; and the SMART-COMMIT (Surface-sensing Measurements for Atmospheric Radiative Transfer - Chemical, Optical & Microphysical Measurements of In-situ Troposphere, mobile observatories, a suite of spectral radiometers and in-situ probes acquiring supersite measurements. Most MPLNET sites are collocated with those of AERONET, and both networks always support the deployment of SMART-COMMIT worldwide. These data products follow the data structure of EOS conventions: Level-0, instrument archived raw data; Level-1 (or 1.5), real-time data with no (or limited) quality assurance; Level-2, not real high temporal and spectral resolutions. In this talk, we will present NASA/GSFC groundbased facilities, serving as network or supersite observations, which have been playing key roles in major international research projects over diverse aerosol regimes to complement and enrich the EOS scientific research.

LTAR linkages with other research networks: Capitalizing on network interconnections

USDA-ARS?s Scientific Manuscript database

The USDA ARS Research Unit based at the Jornada Experimental Range outside of Las Cruces, NM, is a member of the USDA’s Long Term Agro-ecosystem Research (LTAR) Network, the National Science Foundation’s Long Term Ecological Research (LTER) Network, the National Ecological Observation Network (NEON)...

Optimal topology to minimizing congestion in connected communication complex network

NASA Astrophysics Data System (ADS)

Benyoussef, M.; Ez-Zahraouy, H.; Benyoussef, A.

In this paper, a new model of the interdependent complex network is proposed, based on two assumptions that (i) the capacity of a node depends on its degree, and (ii) the traffic load depends on the distribution of the links in the network. Based on these assumptions, the presented model proposes a method of connection not based on the node having a higher degree but on the region containing hubs. It is found that the final network exhibits two kinds of degree distribution behavior, depending on the kind and the way of the connection. This study reveals a direct relation between network structure and traffic flow. It is found that pc the point of transition between the free flow and the congested phase depends on the network structure and the degree distribution. Moreover, this new model provides an improvement in the traffic compared to the results found in a single network. The same behavior of degree distribution found in a BA network and observed in the real world is obtained; except for this model, the transition point between the free phase and congested phase is much higher than the one observed in a network of BA, for both static and dynamic protocols.

A Complex Network Approach to Distributional Semantic Models

PubMed Central

Utsumi, Akira

2015-01-01

A number of studies on network analysis have focused on language networks based on free word association, which reflects human lexical knowledge, and have demonstrated the small-world and scale-free properties in the word association network. Nevertheless, there have been very few attempts at applying network analysis to distributional semantic models, despite the fact that these models have been studied extensively as computational or cognitive models of human lexical knowledge. In this paper, we analyze three network properties, namely, small-world, scale-free, and hierarchical properties, of semantic networks created by distributional semantic models. We demonstrate that the created networks generally exhibit the same properties as word association networks. In particular, we show that the distribution of the number of connections in these networks follows the truncated power law, which is also observed in an association network. This indicates that distributional semantic models can provide a plausible model of lexical knowledge. Additionally, the observed differences in the network properties of various implementations of distributional semantic models are consistently explained or predicted by considering the intrinsic semantic features of a word-context matrix and the functions of matrix weighting and smoothing. Furthermore, to simulate a semantic network with the observed network properties, we propose a new growing network model based on the model of Steyvers and Tenenbaum. The idea underlying the proposed model is that both preferential and random attachments are required to reflect different types of semantic relations in network growth process. We demonstrate that this model provides a better explanation of network behaviors generated by distributional semantic models. PMID:26295940

High-Bandwidth Tactical-Network Data Analysis in a High-Performance-Computing (HPC) Environment: Transport Protocol (Transmission Control Protocol/User Datagram Protocol [TCP/UDP]) Analysis

DTIC Science & Technology

2015-09-01

the network Mac8 Medium Access Control ( Mac ) (Ethernet) address observed as destination for outgoing packets subsessionid8 Zero-based index of...15. SUBJECT TERMS tactical networks, data reduction, high-performance computing, data analysis, big data 16. SECURITY CLASSIFICATION OF: 17...Integer index of row cts_deid Device (instrument) Identifier where observation took place cts_collpt Collection point or logical observation point on

Radar signal categorization using a neural network

NASA Technical Reports Server (NTRS)

Anderson, James A.; Gately, Michael T.; Penz, P. Andrew; Collins, Dean R.

1991-01-01

Neural networks were used to analyze a complex simulated radar environment which contains noisy radar pulses generated by many different emitters. The neural network used is an energy minimizing network (the BSB model) which forms energy minima - attractors in the network dynamical system - based on learned input data. The system first determines how many emitters are present (the deinterleaving problem). Pulses from individual simulated emitters give rise to separate stable attractors in the network. Once individual emitters are characterized, it is possible to make tentative identifications of them based on their observed parameters. As a test of this idea, a neural network was used to form a small data base that potentially could make emitter identifications.

Controllability and observability of Boolean networks arising from biology

NASA Astrophysics Data System (ADS)

Li, Rui; Yang, Meng; Chu, Tianguang

2015-02-01

Boolean networks are currently receiving considerable attention as a computational scheme for system level analysis and modeling of biological systems. Studying control-related problems in Boolean networks may reveal new insights into the intrinsic control in complex biological systems and enable us to develop strategies for manipulating biological systems using exogenous inputs. This paper considers controllability and observability of Boolean biological networks. We propose a new approach, which draws from the rich theory of symbolic computation, to solve the problems. Consequently, simple necessary and sufficient conditions for reachability, controllability, and observability are obtained, and algorithmic tests for controllability and observability which are based on the Gröbner basis method are presented. As practical applications, we apply the proposed approach to several different biological systems, namely, the mammalian cell-cycle network, the T-cell activation network, the large granular lymphocyte survival signaling network, and the Drosophila segment polarity network, gaining novel insights into the control and/or monitoring of the specific biological systems.

BoolFilter: an R package for estimation and identification of partially-observed Boolean dynamical systems.

PubMed

Mcclenny, Levi D; Imani, Mahdi; Braga-Neto, Ulisses M

2017-11-25

Gene regulatory networks govern the function of key cellular processes, such as control of the cell cycle, response to stress, DNA repair mechanisms, and more. Boolean networks have been used successfully in modeling gene regulatory networks. In the Boolean network model, the transcriptional state of each gene is represented by 0 (inactive) or 1 (active), and the relationship among genes is represented by logical gates updated at discrete time points. However, the Boolean gene states are never observed directly, but only indirectly and incompletely through noisy measurements based on expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays. The Partially-Observed Boolean Dynamical System (POBDS) signal model is distinct from other deterministic and stochastic Boolean network models in removing the requirement of a directly observable Boolean state vector and allowing uncertainty in the measurement process, addressing the scenario encountered in practice in transcriptomic analysis. BoolFilter is an R package that implements the POBDS model and associated algorithms for state and parameter estimation. It allows the user to estimate the Boolean states, network topology, and measurement parameters from time series of transcriptomic data using exact and approximated (particle) filters, as well as simulate the transcriptomic data for a given Boolean network model. Some of its infrastructure, such as the network interface, is the same as in the previously published R package for Boolean Networks BoolNet, which enhances compatibility and user accessibility to the new package. We introduce the R package BoolFilter for Partially-Observed Boolean Dynamical Systems (POBDS). The BoolFilter package provides a useful toolbox for the bioinformatics community, with state-of-the-art algorithms for simulation of time series transcriptomic data as well as the inverse process of system identification from data obtained with various expression technologies such as cDNA microarrays, RNA-Seq, and cell imaging-based assays.

Operational VGOS Scheduling

NASA Astrophysics Data System (ADS)

Searle, Anthony; Petrachenko, Bill

2016-12-01

The VLBI Global Observing System (VGOS) has been designed to take advantage of advances in data recording speeds and storage capacity, allowing for smaller and faster antennas, wider bandwidths, and shorter observation durations. Here, schedules for a ``realistic" VGOS network, frequency sequences, and expanded source lists are presented using a new source-based scheduling algorithm. The VGOS aim for continuous observations presents new operational challenges. As the source-based strategy is independent of the observing network, there are operational advantages which allow for more flexible scheduling of continuous VLBI observations. Using VieVS, simulations of several schedules are presented and compared with previous VGOS studies.

Comparative evaluation of reverse engineering gene regulatory networks with relevance networks, graphical gaussian models and bayesian networks.

PubMed

Werhli, Adriano V; Grzegorczyk, Marco; Husmeier, Dirk

2006-10-15

An important problem in systems biology is the inference of biochemical pathways and regulatory networks from postgenomic data. Various reverse engineering methods have been proposed in the literature, and it is important to understand their relative merits and shortcomings. In the present paper, we compare the accuracy of reconstructing gene regulatory networks with three different modelling and inference paradigms: (1) Relevance networks (RNs): pairwise association scores independent of the remaining network; (2) graphical Gaussian models (GGMs): undirected graphical models with constraint-based inference, and (3) Bayesian networks (BNs): directed graphical models with score-based inference. The evaluation is carried out on the Raf pathway, a cellular signalling network describing the interaction of 11 phosphorylated proteins and phospholipids in human immune system cells. We use both laboratory data from cytometry experiments as well as data simulated from the gold-standard network. We also compare passive observations with active interventions. On Gaussian observational data, BNs and GGMs were found to outperform RNs. The difference in performance was not significant for the non-linear simulated data and the cytoflow data, though. Also, we did not observe a significant difference between BNs and GGMs on observational data in general. However, for interventional data, BNs outperform GGMs and RNs, especially when taking the edge directions rather than just the skeletons of the graphs into account. This suggests that the higher computational costs of inference with BNs over GGMs and RNs are not justified when using only passive observations, but that active interventions in the form of gene knockouts and over-expressions are required to exploit the full potential of BNs. Data, software and supplementary material are available from http://www.bioss.sari.ac.uk/staff/adriano/research.html

Tropospheric and Lower Stratospheric Temperature Anomalies Based on Global Radiosonde Network Data (1958 - 2005)

DOE Data Explorer

Sterin, Alexander M. [Russian Research Institute for Hydrometeorological Information--World Data Center

2007-01-01

The observed radiosonde data from the Comprehensive Aerological Reference Data Set (CARDS) (Eskridge et al. 1995) were taken as the primary input for obtaining the series. These data were for the global radiosonde observational network through 2001. Since 2002, the AEROSTAB data (uper-air observations obtained through communication channels), collected at RIHMI-WDC in Obninsk, have been used. Both of these data sources were for the global radiosonde observational network. The CARDS data set is known as the most complete collection of radiosonde data.

Fast and accurate detection of spread source in large complex networks.

PubMed

Paluch, Robert; Lu, Xiaoyan; Suchecki, Krzysztof; Szymański, Bolesław K; Hołyst, Janusz A

2018-02-06

Spread over complex networks is a ubiquitous process with increasingly wide applications. Locating spread sources is often important, e.g. finding the patient one in epidemics, or source of rumor spreading in social network. Pinto, Thiran and Vetterli introduced an algorithm (PTVA) to solve the important case of this problem in which a limited set of nodes act as observers and report times at which the spread reached them. PTVA uses all observers to find a solution. Here we propose a new approach in which observers with low quality information (i.e. with large spread encounter times) are ignored and potential sources are selected based on the likelihood gradient from high quality observers. The original complexity of PTVA is O(N α ), where α ∈ (3,4) depends on the network topology and number of observers (N denotes the number of nodes in the network). Our Gradient Maximum Likelihood Algorithm (GMLA) reduces this complexity to O (N 2 log (N)). Extensive numerical tests performed on synthetic networks and real Gnutella network with limitation that id's of spreaders are unknown to observers demonstrate that for scale-free networks with such limitation GMLA yields higher quality localization results than PTVA does.

AstroNet: A Tool Set for Simultaneous, Multi-Site Observations of Astronomical Objects

NASA Technical Reports Server (NTRS)

Chakrabarti, Supriya

1995-01-01

Earth-based, fully automatic "robotic" telescopes have been in routine operation for a number of years. As their number grows and their distribution becomes global, increasing attention is being given to forming networks of various sorts that will allow them, as a group, to make observations 24 hours a day in both hemispheres. We have suggested that telescopes based in space be part of this network. We further suggested that any telescope on this network be capable of asking, almost in real time, that other robotic telescopes perform support observations for them. When a target of opportunity required support observations, the system would determine which telescope(s) in the network would be most appropriate to make the observations and formulate a request to do so. Because the network would be comprised of telescopes located in widely distributed regions, this system would guarantee continuity of observations This report summarizes our efforts under this contract. We proposed to develop a set of data collection and display tools to aid simultaneous observation of astronomical targets from a number of observing sites. We planned to demonstrate the usefulness of this toolset for simultaneous multi-site observation of astronomical targets. Possible candidates or the proposed demonstration included the Extreme Ultraviolet Explorer (EUVE), International Ultraviolet Explorer (IUE), and ALEXIS, sounding rocket experiments. Ground-based observatories operated by the University of California, Berkeley, the Jet Propulsion Laboratory, and Fairborn Observatory in Mesa, Arizona were to be used to demonstrate the proposed concept. Although the demonstration was to have involved astronomical investigations, the tools were to have been applicable to a large number of scientific disciplines. The software tools and systems developed as a result of the work were to have been made available to the scientific community.

Optimization of water-level monitoring networks in the eastern Snake River Plain aquifer using a kriging-based genetic algorithm method

USGS Publications Warehouse

Fisher, Jason C.

2013-01-01

Long-term groundwater monitoring networks can provide essential information for the planning and management of water resources. Budget constraints in water resource management agencies often mean a reduction in the number of observation wells included in a monitoring network. A network design tool, distributed as an R package, was developed to determine which wells to exclude from a monitoring network because they add little or no beneficial information. A kriging-based genetic algorithm method was used to optimize the monitoring network. The algorithm was used to find the set of wells whose removal leads to the smallest increase in the weighted sum of the (1) mean standard error at all nodes in the kriging grid where the water table is estimated, (2) root-mean-squared-error between the measured and estimated water-level elevation at the removed sites, (3) mean standard deviation of measurements across time at the removed sites, and (4) mean measurement error of wells in the reduced network. The solution to the optimization problem (the best wells to retain in the monitoring network) depends on the total number of wells removed; this number is a management decision. The network design tool was applied to optimize two observation well networks monitoring the water table of the eastern Snake River Plain aquifer, Idaho; these networks include the 2008 Federal-State Cooperative water-level monitoring network (Co-op network) with 166 observation wells, and the 2008 U.S. Geological Survey-Idaho National Laboratory water-level monitoring network (USGS-INL network) with 171 wells. Each water-level monitoring network was optimized five times: by removing (1) 10, (2) 20, (3) 40, (4) 60, and (5) 80 observation wells from the original network. An examination of the trade-offs associated with changes in the number of wells to remove indicates that 20 wells can be removed from the Co-op network with a relatively small degradation of the estimated water table map, and 40 wells can be removed from the USGS-INL network before the water table map degradation accelerates. The optimal network designs indicate the robustness of the network design tool. Observation wells were removed from high well-density areas of the network while retaining the spatial pattern of the existing water-table map.

Designing optimal greenhouse gas monitoring networks for Australia

NASA Astrophysics Data System (ADS)

Ziehn, T.; Law, R. M.; Rayner, P. J.; Roff, G.

2016-01-01

Atmospheric transport inversion is commonly used to infer greenhouse gas (GHG) flux estimates from concentration measurements. The optimal location of ground-based observing stations that supply these measurements can be determined by network design. Here, we use a Lagrangian particle dispersion model (LPDM) in reverse mode together with a Bayesian inverse modelling framework to derive optimal GHG observing networks for Australia. This extends the network design for carbon dioxide (CO2) performed by Ziehn et al. (2014) to also minimise the uncertainty on the flux estimates for methane (CH4) and nitrous oxide (N2O), both individually and in a combined network using multiple objectives. Optimal networks are generated by adding up to five new stations to the base network, which is defined as two existing stations, Cape Grim and Gunn Point, in southern and northern Australia respectively. The individual networks for CO2, CH4 and N2O and the combined observing network show large similarities because the flux uncertainties for each GHG are dominated by regions of biologically productive land. There is little penalty, in terms of flux uncertainty reduction, for the combined network compared to individually designed networks. The location of the stations in the combined network is sensitive to variations in the assumed data uncertainty across locations. A simple assessment of economic costs has been included in our network design approach, considering both establishment and maintenance costs. Our results suggest that, while site logistics change the optimal network, there is only a small impact on the flux uncertainty reductions achieved with increasing network size.

A strategic outlook for coordination of ground-based measurement networks of atmospheric state variables and atmospheric composition

NASA Astrophysics Data System (ADS)

Bodeker, G. E.; Thorne, P.; Braathen, G.; De Maziere, M.; Thompson, A. M.; Kurylo, M. J., III

2016-12-01

There are a number of ground-based global observing networks that collectively aim to make key measurements of atmospheric state variables and atmospheric chemical composition. These networks include, but are not limited to:NDACC: Network for the Detection of Atmospheric Composition Change GUAN: GCOS Upper Air Network GRUAN: GCOS Reference Upper Air Network EARLINET: the European Aerosol Research Lidar Network GAW: Global Atmosphere Watch SHADOZ: Southern Hemisphere ADditional OZonesondes TCCON: Total Carbon Column Observing Network BSRN: Baseline Surface Radiation Network While each network brings unique capabilities to the global observing system, there are many instances where the activities and capabilities of the networks overlap. These commonalities across multiple networks can confound funding agencies when allocating scarce financial resources. Overlaps between networks may also result in some duplication of effort and a resultant sub-optimal use of funding resource for the global observing system. While some degree of overlap is useful for quality assurance, it is essential to identify the degree to which one network can take on a specific responsibility on behalf of all other networks to avoid unnecessary duplication, to identify where expertise in any one network may serve other networks, and to develop a long-term strategy for the evolution of these networks that clarifies to funding agencies where new investment is required. This presentation will briefly summarise the key characteristics of each network listed above, adopt a matrix approach to identify commonalities and, in particular, where there may be a danger of duplication of effort, and where gaps between the networks may be compromising the services that these networks are expected to collectively deliver to the global atmospheric and climate science research communities. The presentation will also examine where sharing of data and tools between networks may result in a more efficient delivery of records of essential climate variables to the global research community. There are aspects of underpinning research that are needed across all of these networks, such as laboratory spectroscopy, that often do not receive the attention they deserve. The presentation will also seek to identify where that underpinning research is lacking.

Characterizing networks formed by P. polycephalum

NASA Astrophysics Data System (ADS)

Dirnberger, M.; Mehlhorn, K.

2017-06-01

We present a systematic study of the characteristic vein networks formed by the slime mold P. polycephalum. Our study is based on an extensive set of graph representations of slime mold networks. We analyze a total of 1998 graphs capturing growth and network formation of P. polycephalum as observed in 36 independent, identical, wet-lab experiments. Relying on concepts from graph theory such as face cycles and cuts as well as ideas from percolation theory, we establish a broad collection of individual observables taking into account various complementary aspects of P. polycephalum networks. As a whole, the collection is intended to serve as a specialized knowledge-base providing a comprehensive characterization of P. polycephalum networks. To this end, it contains individual as well as cumulative results for all investigated observables across all available data series, down to the level of single P. polycephalum graphs. Furthermore we include the raw numerical data as well as various plotting and analysis tools to ensure reproducibility and increase the usefulness of the collection. All our results are publicly available in an organized fashion in the slime mold graph repository (Smgr).

Role of Distance-Based Routing in Traffic Dynamics on Mobile Networks

NASA Astrophysics Data System (ADS)

Yang, Han-Xin; Wang, Wen-Xu

2013-06-01

Despite of intensive investigations on transportation dynamics taking place on complex networks with fixed structures, a deep understanding of networks consisting of mobile nodes is challenging yet, especially the lacking of insight into the effects of routing strategies on transmission efficiency. We introduce a distance-based routing strategy for networks of mobile agents toward enhancing the network throughput and the transmission efficiency. We study the transportation capacity and delivering time of data packets associated with mobility and communication ability. Interestingly, we find that the transportation capacity is optimized at moderate moving speed, which is quite different from random routing strategy. In addition, both continuous and discontinuous transitions from free flow to congestions are observed. Degree distributions are explored in order to explain the enhancement of network throughput and other observations. Our work is valuable toward understanding complex transportation dynamics and designing effective routing protocols.

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata

PubMed Central

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-01-01

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research. PMID:28353664

Modeling and Density Estimation of an Urban Freeway Network Based on Dynamic Graph Hybrid Automata.

PubMed

Chen, Yangzhou; Guo, Yuqi; Wang, Ying

2017-03-29

In this paper, in order to describe complex network systems, we firstly propose a general modeling framework by combining a dynamic graph with hybrid automata and thus name it Dynamic Graph Hybrid Automata (DGHA). Then we apply this framework to model traffic flow over an urban freeway network by embedding the Cell Transmission Model (CTM) into the DGHA. With a modeling procedure, we adopt a dual digraph of road network structure to describe the road topology, use linear hybrid automata to describe multi-modes of dynamic densities in road segments and transform the nonlinear expressions of the transmitted traffic flow between two road segments into piecewise linear functions in terms of multi-mode switchings. This modeling procedure is modularized and rule-based, and thus is easily-extensible with the help of a combination algorithm for the dynamics of traffic flow. It can describe the dynamics of traffic flow over an urban freeway network with arbitrary topology structures and sizes. Next we analyze mode types and number in the model of the whole freeway network, and deduce a Piecewise Affine Linear System (PWALS) model. Furthermore, based on the PWALS model, a multi-mode switched state observer is designed to estimate the traffic densities of the freeway network, where a set of observer gain matrices are computed by using the Lyapunov function approach. As an example, we utilize the PWALS model and the corresponding switched state observer to traffic flow over Beijing third ring road. In order to clearly interpret the principle of the proposed method and avoid computational complexity, we adopt a simplified version of Beijing third ring road. Practical application for a large-scale road network will be implemented by decentralized modeling approach and distributed observer designing in the future research.

A Bayesian connectivity-based approach to constructing probabilistic gene regulatory networks.

PubMed

Zhou, Xiaobo; Wang, Xiaodong; Pal, Ranadip; Ivanov, Ivan; Bittner, Michael; Dougherty, Edward R

2004-11-22

We have hypothesized that the construction of transcriptional regulatory networks using a method that optimizes connectivity would lead to regulation consistent with biological expectations. A key expectation is that the hypothetical networks should produce a few, very strong attractors, highly similar to the original observations, mimicking biological state stability and determinism. Another central expectation is that, since it is expected that the biological control is distributed and mutually reinforcing, interpretation of the observations should lead to a very small number of connection schemes. We propose a fully Bayesian approach to constructing probabilistic gene regulatory networks (PGRNs) that emphasizes network topology. The method computes the possible parent sets of each gene, the corresponding predictors and the associated probabilities based on a nonlinear perceptron model, using a reversible jump Markov chain Monte Carlo (MCMC) technique, and an MCMC method is employed to search the network configurations to find those with the highest Bayesian scores to construct the PGRN. The Bayesian method has been used to construct a PGRN based on the observed behavior of a set of genes whose expression patterns vary across a set of melanoma samples exhibiting two very different phenotypes with respect to cell motility and invasiveness. Key biological features have been faithfully reflected in the model. Its steady-state distribution contains attractors that are either identical or very similar to the states observed in the data, and many of the attractors are singletons, which mimics the biological propensity to stably occupy a given state. Most interestingly, the connectivity rules for the most optimal generated networks constituting the PGRN are remarkably similar, as would be expected for a network operating on a distributed basis, with strong interactions between the components.

Tower-Based Greenhouse Gas Measurement Network Design---The National Institute of Standards and Technology North East Corridor Testbed.

PubMed

Lopez-Coto, Israel; Ghosh, Subhomoy; Prasad, Kuldeep; Whetstone, James

2017-09-01

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k -means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Tower-based greenhouse gas measurement network design—The National Institute of Standards and Technology North East Corridor Testbed

NASA Astrophysics Data System (ADS)

Lopez-Coto, Israel; Ghosh, Subhomoy; Prasad, Kuldeep; Whetstone, James

2017-09-01

The North-East Corridor (NEC) Testbed project is the 3rd of three NIST (National Institute of Standards and Technology) greenhouse gas emissions testbeds designed to advance greenhouse gas measurements capabilities. A design approach for a dense observing network combined with atmospheric inversion methodologies is described. The Advanced Research Weather Research and Forecasting Model with the Stochastic Time-Inverted Lagrangian Transport model were used to derive the sensitivity of hypothetical observations to surface greenhouse gas emissions (footprints). Unlike other network design algorithms, an iterative selection algorithm, based on a k-means clustering method, was applied to minimize the similarities between the temporal response of each site and maximize sensitivity to the urban emissions contribution. Once a network was selected, a synthetic inversion Bayesian Kalman filter was used to evaluate observing system performance. We present the performances of various measurement network configurations consisting of differing numbers of towers and tower locations. Results show that an overly spatially compact network has decreased spatial coverage, as the spatial information added per site is then suboptimal as to cover the largest possible area, whilst networks dispersed too broadly lose capabilities of constraining flux uncertainties. In addition, we explore the possibility of using a very high density network of lower cost and performance sensors characterized by larger uncertainties and temporal drift. Analysis convergence is faster with a large number of observing locations, reducing the response time of the filter. Larger uncertainties in the observations implies lower values of uncertainty reduction. On the other hand, the drift is a bias in nature, which is added to the observations and, therefore, biasing the retrieved fluxes.

Proof of Concept in Disrupted Tactical Networking

DTIC Science & Technology

2017-09-01

because of the risk of detection. In this study , we design projectile-based mesh networking prototypes as one potential type of short-living network...to communicate because of the risk of detection. In this study , we design projectile-based mesh networking prototypes as one potential type of short...reader with a background in systems-theory. This study is designed using systems theory and uses systems theory as a lens through which to observe

Networked high-speed auroral observations combined with radar measurements for multi-scale insights

NASA Astrophysics Data System (ADS)

Hirsch, M.; Semeter, J. L.

2015-12-01

Networks of ground-based instruments to study terrestrial aurora for the purpose of analyzing particle precipitation characteristics driving the aurora have been established. Additional funding is pouring into future ground-based auroral observation networks consisting of combinations of tossable, portable, and fixed installation ground-based legacy equipment. Our approach to this problem using the High Speed Tomography (HiST) system combines tightly-synchronized filtered auroral optical observations capturing temporal features of order 10 ms with supporting measurements from incoherent scatter radar (ISR). ISR provides a broader spatial context up to order 100 km laterally on one minute time scales, while our camera field of view (FOV) is chosen to be order 10 km at auroral altitudes in order to capture 100 m scale lateral auroral features. The dual-scale observations of ISR and HiST fine-scale optical observations may be coupled through a physical model using linear basis functions to estimate important ionospheric quantities such as electron number density in 3-D (time, perpendicular and parallel to the geomagnetic field).Field measurements and analysis using HiST and PFISR are presented from experiments conducted at the Poker Flat Research Range in central Alaska. Other multiscale configuration candidates include supplementing networks of all-sky cameras such as THEMIS with co-locations of HiST-like instruments to fuse wide FOV measurements with the fine-scale HiST precipitation characteristic estimates. Candidate models for this coupling include GLOW and TRANSCAR. Future extensions of this work may include incorporating line of sight total electron count estimates from ground-based networks of GPS receivers in a sensor fusion problem.

Sensorless control for permanent magnet synchronous motor using a neural network based adaptive estimator

NASA Astrophysics Data System (ADS)

Kwon, Chung-Jin; Kim, Sung-Joong; Han, Woo-Young; Min, Won-Kyoung

2005-12-01

The rotor position and speed estimation of permanent-magnet synchronous motor(PMSM) was dealt with. By measuring the phase voltages and currents of the PMSM drive, two diagonally recurrent neural network(DRNN) based observers, a neural current observer and a neural velocity observer were developed. DRNN which has self-feedback of the hidden neurons ensures that the outputs of DRNN contain the whole past information of the system even if the inputs of DRNN are only the present states and inputs of the system. Thus the structure of DRNN may be simpler than that of feedforward and fully recurrent neural networks. If the backpropagation method was used for the training of the DRNN the problem of slow convergence arise. In order to reduce this problem, recursive prediction error(RPE) based learning method for the DRNN was presented. The simulation results show that the proposed approach gives a good estimation of rotor speed and position, and RPE based training has requires a shorter computation time compared to backpropagation based training.

Brain parcellation choice affects disease-related topology differences increasingly from global to local network levels.

PubMed

Lord, Anton; Ehrlich, Stefan; Borchardt, Viola; Geisler, Daniel; Seidel, Maria; Huber, Stefanie; Murr, Julia; Walter, Martin

2016-03-30

Network-based analyses of deviant brain function have become extremely popular in psychiatric neuroimaging. Underpinning brain network analyses is the selection of appropriate regions of interest (ROIs). Although ROI selection is fundamental in network analysis, its impact on detecting disease effects remains unclear. We investigated the impact of parcellation choice when comparing results from different studies. We investigated the effects of anatomical (AAL) and literature-based (Dosenbach) parcellation schemes on comparability of group differences in 35 female patients with anorexia nervosa and 35 age- and sex-matched healthy controls. Global and local network properties, including network-based statistics (NBS), were assessed on resting state functional magnetic resonance imaging data obtained at 3T. Parcellation schemes were comparably consistent on global network properties, while NBS and local metrics differed in location, but not metric type. Location of local metric alterations varied for AAL (parietal and cingulate cortices) versus Dosenbach (insula, thalamus) parcellation approaches. However, consistency was observed for the occipital cortex. Patient-specific global network properties can be robustly observed using different parcellation schemes, while graph metrics characterizing impairments of individual nodes vary considerably. Therefore, the impact of parcellation choice on specific group differences varies depending on the level of network organization. Copyright © 2016 Elsevier Ireland Ltd. All rights reserved.

Preserved Network Metrics across Translated Texts

NASA Astrophysics Data System (ADS)

Cabatbat, Josephine Jill T.; Monsanto, Jica P.; Tapang, Giovanni A.

2014-09-01

Co-occurrence language networks based on Bible translations and the Universal Declaration of Human Rights (UDHR) translations in different languages were constructed and compared with random text networks. Among the considered network metrics, the network size, N, the normalized betweenness centrality (BC), and the average k-nearest neighbors, knn, were found to be the most preserved across translations. Moreover, similar frequency distributions of co-occurring network motifs were observed for translated texts networks.

Applying artificial neural networks to predict communication risks in the emergency department.

PubMed

Bagnasco, Annamaria; Siri, Anna; Aleo, Giuseppe; Rocco, Gennaro; Sasso, Loredana

2015-10-01

To describe the utility of artificial neural networks in predicting communication risks. In health care, effective communication reduces the risk of error. Therefore, it is important to identify the predictive factors of effective communication. Non-technical skills are needed to achieve effective communication. This study explores how artificial neural networks can be applied to predict the risk of communication failures in emergency departments. A multicentre observational study. Data were collected between March-May 2011 by observing the communication interactions of 840 nurses with their patients during their routine activities in emergency departments. The tools used for our observation were a questionnaire to collect personal and descriptive data, level of training and experience and Guilbert's observation grid, applying the Situation-Background-Assessment-Recommendation technique to communication in emergency departments. A total of 840 observations were made on the nurses working in the emergency departments. Based on Guilbert's observation grid, the output variables is likely to influence the risk of communication failure were 'terminology'; 'listening'; 'attention' and 'clarity', whereas nurses' personal characteristics were used as input variables in the artificial neural network model. A model based on the multilayer perceptron topology was developed and trained. The receiver operator characteristic analysis confirmed that the artificial neural network model correctly predicted the performance of more than 80% of the communication failures. The application of the artificial neural network model could offer a valid tool to forecast and prevent harmful communication errors in the emergency department. © 2015 John Wiley & Sons Ltd.

Network-based machine learning and graph theory algorithms for precision oncology.

PubMed

Zhang, Wei; Chien, Jeremy; Yong, Jeongsik; Kuang, Rui

2017-01-01

Network-based analytics plays an increasingly important role in precision oncology. Growing evidence in recent studies suggests that cancer can be better understood through mutated or dysregulated pathways or networks rather than individual mutations and that the efficacy of repositioned drugs can be inferred from disease modules in molecular networks. This article reviews network-based machine learning and graph theory algorithms for integrative analysis of personal genomic data and biomedical knowledge bases to identify tumor-specific molecular mechanisms, candidate targets and repositioned drugs for personalized treatment. The review focuses on the algorithmic design and mathematical formulation of these methods to facilitate applications and implementations of network-based analysis in the practice of precision oncology. We review the methods applied in three scenarios to integrate genomic data and network models in different analysis pipelines, and we examine three categories of network-based approaches for repositioning drugs in drug-disease-gene networks. In addition, we perform a comprehensive subnetwork/pathway analysis of mutations in 31 cancer genome projects in the Cancer Genome Atlas and present a detailed case study on ovarian cancer. Finally, we discuss interesting observations, potential pitfalls and future directions in network-based precision oncology.

Graph distance for complex networks

NASA Astrophysics Data System (ADS)

Shimada, Yutaka; Hirata, Yoshito; Ikeguchi, Tohru; Aihara, Kazuyuki

2016-10-01

Networks are widely used as a tool for describing diverse real complex systems and have been successfully applied to many fields. The distance between networks is one of the most fundamental concepts for properly classifying real networks, detecting temporal changes in network structures, and effectively predicting their temporal evolution. However, this distance has rarely been discussed in the theory of complex networks. Here, we propose a graph distance between networks based on a Laplacian matrix that reflects the structural and dynamical properties of networked dynamical systems. Our results indicate that the Laplacian-based graph distance effectively quantifies the structural difference between complex networks. We further show that our approach successfully elucidates the temporal properties underlying temporal networks observed in the context of face-to-face human interactions.

Quantifying structural uncertainty on fault networks using a marked point process within a Bayesian framework

NASA Astrophysics Data System (ADS)

Aydin, Orhun; Caers, Jef Karel

2017-08-01

Faults are one of the building-blocks for subsurface modeling studies. Incomplete observations of subsurface fault networks lead to uncertainty pertaining to location, geometry and existence of faults. In practice, gaps in incomplete fault network observations are filled based on tectonic knowledge and interpreter's intuition pertaining to fault relationships. Modeling fault network uncertainty with realistic models that represent tectonic knowledge is still a challenge. Although methods that address specific sources of fault network uncertainty and complexities of fault modeling exists, a unifying framework is still lacking. In this paper, we propose a rigorous approach to quantify fault network uncertainty. Fault pattern and intensity information are expressed by means of a marked point process, marked Strauss point process. Fault network information is constrained to fault surface observations (complete or partial) within a Bayesian framework. A structural prior model is defined to quantitatively express fault patterns, geometries and relationships within the Bayesian framework. Structural relationships between faults, in particular fault abutting relations, are represented with a level-set based approach. A Markov Chain Monte Carlo sampler is used to sample posterior fault network realizations that reflect tectonic knowledge and honor fault observations. We apply the methodology to a field study from Nankai Trough & Kumano Basin. The target for uncertainty quantification is a deep site with attenuated seismic data with only partially visible faults and many faults missing from the survey or interpretation. A structural prior model is built from shallow analog sites that are believed to have undergone similar tectonics compared to the site of study. Fault network uncertainty for the field is quantified with fault network realizations that are conditioned to structural rules, tectonic information and partially observed fault surfaces. We show the proposed methodology generates realistic fault network models conditioned to data and a conceptual model of the underlying tectonics.

DEFINING THE PLAYERS IN HIGHER-ORDER NETWORKS: PREDICTIVE MODELING FOR REVERSE ENGINEERING FUNCTIONAL INFLUENCE NETWORKS

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

McDermott, Jason E.; Costa, Michelle N.; Stevens, S.L.

A difficult problem that is currently growing rapidly due to the sharp increase in the amount of high-throughput data available for many systems is that of determining useful and informative causative influence networks. These networks can be used to predict behavior given observation of a small number of components, predict behavior at a future time point, or identify components that are critical to the functioning of the system under particular conditions. In these endeavors incorporating observations of systems from a wide variety of viewpoints can be particularly beneficial, but has often been undertaken with the objective of inferring networks thatmore » are generally applicable. The focus of the current work is to integrate both general observations and measurements taken for a particular pathology, that of ischemic stroke, to provide improved ability to produce useful predictions of systems behavior. A number of hybrid approaches have recently been proposed for network generation in which the Gene Ontology is used to filter or enrich network links inferred from gene expression data through reverse engineering methods. These approaches have been shown to improve the biological plausibility of the inferred relationships determined, but still treat knowledge-based and machine-learning inferences as incommensurable inputs. In this paper, we explore how further improvements may be achieved through a full integration of network inference insights achieved through application of the Gene Ontology and reverse engineering methods with specific reference to the construction of dynamic models of transcriptional regulatory networks. We show that integrating two approaches to network construction, one based on reverse-engineering from conditional transcriptional data, one based on reverse-engineering from in situ hybridization data, and another based on functional associations derived from Gene Ontology, using probabilities can improve results of clustering as evaluated by a predictive model of transcriptional expression levels.« less

Predicting clinical outcome of neuroblastoma patients using an integrative network-based approach.

PubMed

Tranchevent, Léon-Charles; Nazarov, Petr V; Kaoma, Tony; Schmartz, Georges P; Muller, Arnaud; Kim, Sang-Yoon; Rajapakse, Jagath C; Azuaje, Francisco

2018-06-07

One of the main current challenges in computational biology is to make sense of the huge amounts of multidimensional experimental data that are being produced. For instance, large cohorts of patients are often screened using different high-throughput technologies, effectively producing multiple patient-specific molecular profiles for hundreds or thousands of patients. We propose and implement a network-based method that integrates such patient omics data into Patient Similarity Networks. Topological features derived from these networks were then used to predict relevant clinical features. As part of the 2017 CAMDA challenge, we have successfully applied this strategy to a neuroblastoma dataset, consisting of genomic and transcriptomic data. In particular, we observe that models built on our network-based approach perform at least as well as state of the art models. We furthermore explore the effectiveness of various topological features and observe, for instance, that redundant centrality metrics can be combined to build more powerful models. We demonstrate that the networks inferred from omics data contain clinically relevant information and that patient clinical outcomes can be predicted using only network topological data. This article was reviewed by Yang-Yu Liu, Tomislav Smuc and Isabel Nepomuceno.

Resting-state networks associated with cognitive processing show more age-related decline than those associated with emotional processing.

PubMed

Nashiro, Kaoru; Sakaki, Michiko; Braskie, Meredith N; Mather, Mara

2017-06-01

Correlations in activity across disparate brain regions during rest reveal functional networks in the brain. Although previous studies largely agree that there is an age-related decline in the "default mode network," how age affects other resting-state networks, such as emotion-related networks, is still controversial. Here we used a dual-regression approach to investigate age-related alterations in resting-state networks. The results revealed age-related disruptions in functional connectivity in all 5 identified cognitive networks, namely the default mode network, cognitive-auditory, cognitive-speech (or speech-related somatosensory), and right and left frontoparietal networks, whereas such age effects were not observed in the 3 identified emotion networks. In addition, we observed age-related decline in functional connectivity in 3 visual and 3 motor/visuospatial networks. Older adults showed greater functional connectivity in regions outside 4 out of the 5 identified cognitive networks, consistent with the dedifferentiation effect previously observed in task-based functional magnetic resonance imaging studies. Both reduced within-network connectivity and increased out-of-network connectivity were correlated with poor cognitive performance, providing potential biomarkers for cognitive aging. Copyright © 2017 Elsevier Inc. All rights reserved.

Remote observing with NASA's Deep Space Network

NASA Astrophysics Data System (ADS)

Kuiper, T. B. H.; Majid, W. A.; Martinez, S.; Garcia-Miro, C.; Rizzo, J. R.

2012-09-01

The Deep Space Network (DSN) communicates with spacecraft as far away as the boundary between the Solar System and the interstellar medium. To make this possible, large sensitive antennas at Canberra, Australia, Goldstone, California, and Madrid, Spain, provide for constant communication with interplanetary missions. We describe the procedures for radioastronomical observations using this network. Remote access to science monitor and control computers by authorized observers is provided by two-factor authentication through a gateway at the Jet Propulsion Laboratory (JPL) in Pasadena. To make such observations practical, we have devised schemes based on SSH tunnels and distributed computing. At the very minimum, one can use SSH tunnels and VNC (Virtual Network Computing, a remote desktop software suite) to control the science hosts within the DSN Flight Operations network. In this way we have controlled up to three telescopes simultaneously. However, X-window updates can be slow and there are issues involving incompatible screen sizes and multi-screen displays. Consequently, we are now developing SSH tunnel-based schemes in which instrument control and monitoring, and intense data processing, are done on-site by the remote DSN hosts while data manipulation and graphical display are done at the observer's host. We describe our approaches to various challenges, our experience with what worked well and lessons learned, and directions for future development.

Design of a mission network system using SpaceWire for scientific payloads onboard the Arase spacecraft

NASA Astrophysics Data System (ADS)

Takashima, Takeshi; Ogawa, Emiko; Asamura, Kazushi; Hikishima, Mitsuru

2018-05-01

Arase is a small scientific satellite program conducted by the Institute of Space and Astronautical Science/Japan Aerospace Exploration Agency, which is dedicated to the detailed study of the radiation belts around Earth through in situ observations. In particular, the goal is to directly observe the interaction between plasma waves and particles, which cause the generation of high-energy electrons. To observe the waves and particles in detail, we must record large volumes of burst data with high transmission rates through onboard mission network systems. For this purpose, we developed a high-speed and highly reliable mission network based on SpaceWire, as well as a new and large memory data recorder equipped with a data search function based on observation time (the time index, TI, is the satellite time starting from when the spacecraft is powered on.) with respect to the orbital data generated in large quantities. By adopting a new transaction concept of a ring topology network with SpaceWire, we could secure a redundant mission network system without using large routers and having to suppress the increase in cable weight. We confirmed that their orbit performs as designed.[Figure not available: see fulltext.

The signaling petri net-based simulator: a non-parametric strategy for characterizing the dynamics of cell-specific signaling networks.

PubMed

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T

2008-02-29

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations.

The Signaling Petri Net-Based Simulator: A Non-Parametric Strategy for Characterizing the Dynamics of Cell-Specific Signaling Networks

PubMed Central

Ruths, Derek; Muller, Melissa; Tseng, Jen-Te; Nakhleh, Luay; Ram, Prahlad T.

2008-01-01

Reconstructing cellular signaling networks and understanding how they work are major endeavors in cell biology. The scale and complexity of these networks, however, render their analysis using experimental biology approaches alone very challenging. As a result, computational methods have been developed and combined with experimental biology approaches, producing powerful tools for the analysis of these networks. These computational methods mostly fall on either end of a spectrum of model parameterization. On one end is a class of structural network analysis methods; these typically use the network connectivity alone to generate hypotheses about global properties. On the other end is a class of dynamic network analysis methods; these use, in addition to the connectivity, kinetic parameters of the biochemical reactions to predict the network's dynamic behavior. These predictions provide detailed insights into the properties that determine aspects of the network's structure and behavior. However, the difficulty of obtaining numerical values of kinetic parameters is widely recognized to limit the applicability of this latter class of methods. Several researchers have observed that the connectivity of a network alone can provide significant insights into its dynamics. Motivated by this fundamental observation, we present the signaling Petri net, a non-parametric model of cellular signaling networks, and the signaling Petri net-based simulator, a Petri net execution strategy for characterizing the dynamics of signal flow through a signaling network using token distribution and sampling. The result is a very fast method, which can analyze large-scale networks, and provide insights into the trends of molecules' activity-levels in response to an external stimulus, based solely on the network's connectivity. We have implemented the signaling Petri net-based simulator in the PathwayOracle toolkit, which is publicly available at http://bioinfo.cs.rice.edu/pathwayoracle. Using this method, we studied a MAPK1,2 and AKT signaling network downstream from EGFR in two breast tumor cell lines. We analyzed, both experimentally and computationally, the activity level of several molecules in response to a targeted manipulation of TSC2 and mTOR-Raptor. The results from our method agreed with experimental results in greater than 90% of the cases considered, and in those where they did not agree, our approach provided valuable insights into discrepancies between known network connectivities and experimental observations. PMID:18463702

Observer-based consensus of networked thrust-propelled vehicles with directed graphs.

PubMed

Cang, Weiye; Li, Zhongkui; Wang, Hanlei

2017-11-01

In this paper, we investigate the consensus problem for networked underactuated thrust-propelled vehicles (TPVs) interacting on directed graphs. We propose distributed observer-based consensus protocols, which avoid the reliance on the measurements of translational velocities and accelerations. Using the input-output analysis, we present necessary and sufficient conditions to ensure that the observer-based protocols can achieve consensus for both the cases without and with constant communication delays, provided that the communication graph contains a directed spanning tree. Simulation examples are finally provided to illustrate the effectiveness of the control schemes. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Controllability and observability analysis for vertex domination centrality in directed networks

NASA Astrophysics Data System (ADS)

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-06-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks.

Controllability and observability analysis for vertex domination centrality in directed networks

PubMed Central

Wang, Bingbo; Gao, Lin; Gao, Yong; Deng, Yue; Wang, Yu

2014-01-01

Topological centrality is a significant measure for characterising the relative importance of a node in a complex network. For directed networks that model dynamic processes, however, it is of more practical importance to quantify a vertex's ability to dominate (control or observe) the state of other vertices. In this paper, based on the determination of controllable and observable subspaces under the global minimum-cost condition, we introduce a novel direction-specific index, domination centrality, to assess the intervention capabilities of vertices in a directed network. Statistical studies demonstrate that the domination centrality is, to a great extent, encoded by the underlying network's degree distribution and that most network positions through which one can intervene in a system are vertices with high domination centrality rather than network hubs. To analyse the interaction and functional dependence between vertices when they are used to dominate a network, we define the domination similarity and detect significant functional modules in glossary and metabolic networks through clustering analysis. The experimental results provide strong evidence that our indices are effective and practical in accurately depicting the structure of directed networks. PMID:24954137

A combined geostatistical-optimization model for the optimal design of a groundwater quality monitoring network

NASA Astrophysics Data System (ADS)

Kolosionis, Konstantinos; Papadopoulou, Maria P.

2017-04-01

Monitoring networks provide essential information for water resources management especially in areas with significant groundwater exploitation due to extensive agricultural activities. In this work, a simulation-optimization framework is developed based on heuristic optimization methodologies and geostatistical modeling approaches to obtain an optimal design for a groundwater quality monitoring network. Groundwater quantity and quality data obtained from 43 existing observation locations at 3 different hydrological periods in Mires basin in Crete, Greece will be used in the proposed framework in terms of Regression Kriging to develop the spatial distribution of nitrates concentration in the aquifer of interest. Based on the existing groundwater quality mapping, the proposed optimization tool will determine a cost-effective observation wells network that contributes significant information to water managers and authorities. The elimination of observation wells that add little or no beneficial information to groundwater level and quality mapping of the area can be obtain using estimations uncertainty and statistical error metrics without effecting the assessment of the groundwater quality. Given the high maintenance cost of groundwater monitoring networks, the proposed tool could used by water regulators in the decision-making process to obtain a efficient network design that is essential.

Strong Sporadic E Occurrence Detected by Ground-Based GNSS

NASA Astrophysics Data System (ADS)

Sun, Wenjie; Ning, Baiqi; Yue, Xinan; Li, Guozhu; Hu, Lianhuan; Chang, Shoumin; Lan, Jiaping; Zhu, Zhengping; Zhao, Biqiang; Lin, Jian

2018-04-01

The ionospheric sporadic E (Es) layer has significant impact on radio wave propagation. The traditional techniques employed for Es layer observation, for example, ionosondes, are not dense enough to resolve the morphology and dynamics of Es layer in spatial distribution. The ground-based Global Navigation Satellite Systems (GNSS) technique is expected to shed light on the understanding of regional strong Es occurrence, owing to the facts that the critical frequency (foEs) of strong Es structure is usually high enough to cause pulse-like disturbances in GNSS total electron content (TEC), and a large number of GNSS receivers have been deployed all over the world. Based on the Chinese ground-based GNSS networks, including the Crustal Movement Observation Network of China and the Beidou Ionospheric Observation Network, a large-scale strong Es event was observed in the middle latitude of China. The strong Es shown as a band-like structure in the southwest-northeast direction extended more than 1,000 km. By making a comparative analysis of Es occurrences identified from the simultaneous observations by ionosondes and GNSS TEC receivers over China middle latitude statistically, we found that GNSS TEC can be well employed to observe strong Es occurrence with a threshold value of foEs, 14 MHz.

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

Liu, Yanhong; Gao, Ping; Bi, Kaifeng

Conducting pathway of percolation network was identified in resistive switching devices (RSDs) with the structure of silver/amorphous silicon/p-type silicon (Ag/a-Si/p-Si) based on its gradual RESET-process and the stochastic complex impedance spectroscopy characteristics (CIS). The formation of the percolation network is attributed to amounts of nanocrystalline Si particles as well as defect sites embedded in a-Si layer, in which the defect sites supply positions for Ag ions to nucleate and grow. The similar percolation network has been only observed in Ag-Ge-Se based RSD before. This report provides a better understanding for electric properties of RSD based on the percolation network.

Equipment Management for Sensor Networks: Linking Physical Infrastructure and Actions to Observational Data

NASA Astrophysics Data System (ADS)

Jones, A. S.; Horsburgh, J. S.; Matos, M.; Caraballo, J.

2015-12-01

Networks conducting long term monitoring using in situ sensors need the functionality to track physical equipment as well as deployments, calibrations, and other actions related to site and equipment maintenance. The observational data being generated by sensors are enhanced if direct linkages to equipment details and actions can be made. This type of information is typically recorded in field notebooks or in static files, which are rarely linked to observations in a way that could be used to interpret results. However, the record of field activities is often relevant to analysis or post-processing of the observational data. We have developed an underlying database schema and deployed a web interface for recording and retrieving information on physical infrastructure and related actions for observational networks. The database schema for equipment was designed as an extension to the Observations Data Model 2 (ODM2), a community-developed information model for spatially discrete, feature based earth observations. The core entities of ODM2 describe location, observed variable, and timing of observations, and the equipment extension contains entities to provide additional metadata specific to the inventory of physical infrastructure and associated actions. The schema is implemented in a relational database system for storage and management with an associated web interface. We designed the web-based tools for technicians to enter and query information on the physical equipment and actions such as site visits, equipment deployments, maintenance, and calibrations. These tools were implemented for the iUTAH (innovative Urban Transitions and Aridregion Hydrosustainability) ecohydrologic observatory, and we anticipate that they will be useful for similar large-scale monitoring networks desiring to link observing infrastructure to observational data to increase the quality of sensor-based data products.

Understanding resilience in industrial symbiosis networks: insights from network analysis.

PubMed

Chopra, Shauhrat S; Khanna, Vikas

2014-08-01

Industrial symbiotic networks are based on the principles of ecological systems where waste equals food, to develop synergistic networks. For example, industrial symbiosis (IS) at Kalundborg, Denmark, creates an exchange network of waste, water, and energy among companies based on contractual dependency. Since most of the industrial symbiotic networks are based on ad-hoc opportunities rather than strategic planning, gaining insight into disruptive scenarios is pivotal for understanding the balance of resilience and sustainability and developing heuristics for designing resilient IS networks. The present work focuses on understanding resilience as an emergent property of an IS network via a network-based approach with application to the Kalundborg Industrial Symbiosis (KIS). Results from network metrics and simulated disruptive scenarios reveal Asnaes power plant as the most critical node in the system. We also observe a decrease in the vulnerability of nodes and reduction in single points of failure in the system, suggesting an increase in the overall resilience of the KIS system from 1960 to 2010. Based on our findings, we recommend design strategies, such as increasing diversity, redundancy, and multi-functionality to ensure flexibility and plasticity, to develop resilient and sustainable industrial symbiotic networks. Copyright © 2014 Elsevier Ltd. All rights reserved.

Network diffusion-based analysis of high-throughput data for the detection of differentially enriched modules

PubMed Central

Bersanelli, Matteo; Mosca, Ettore; Remondini, Daniel; Castellani, Gastone; Milanesi, Luciano

2016-01-01

A relation exists between network proximity of molecular entities in interaction networks, functional similarity and association with diseases. The identification of network regions associated with biological functions and pathologies is a major goal in systems biology. We describe a network diffusion-based pipeline for the interpretation of different types of omics in the context of molecular interaction networks. We introduce the network smoothing index, a network-based quantity that allows to jointly quantify the amount of omics information in genes and in their network neighbourhood, using network diffusion to define network proximity. The approach is applicable to both descriptive and inferential statistics calculated on omics data. We also show that network resampling, applied to gene lists ranked by quantities derived from the network smoothing index, indicates the presence of significantly connected genes. As a proof of principle, we identified gene modules enriched in somatic mutations and transcriptional variations observed in samples of prostate adenocarcinoma (PRAD). In line with the local hypothesis, network smoothing index and network resampling underlined the existence of a connected component of genes harbouring molecular alterations in PRAD. PMID:27731320

Neural-network-observer-based optimal control for unknown nonlinear systems using adaptive dynamic programming

NASA Astrophysics Data System (ADS)

Liu, Derong; Huang, Yuzhu; Wang, Ding; Wei, Qinglai

2013-09-01

In this paper, an observer-based optimal control scheme is developed for unknown nonlinear systems using adaptive dynamic programming (ADP) algorithm. First, a neural-network (NN) observer is designed to estimate system states. Then, based on the observed states, a neuro-controller is constructed via ADP method to obtain the optimal control. In this design, two NN structures are used: a three-layer NN is used to construct the observer which can be applied to systems with higher degrees of nonlinearity and without a priori knowledge of system dynamics, and a critic NN is employed to approximate the value function. The optimal control law is computed using the critic NN and the observer NN. Uniform ultimate boundedness of the closed-loop system is guaranteed. The actor, critic, and observer structures are all implemented in real-time, continuously and simultaneously. Finally, simulation results are presented to demonstrate the effectiveness of the proposed control scheme.

Method and apparatus for in-process sensing of manufacturing quality

DOEpatents

Hartman, Daniel A [Santa Fe, NM; Dave, Vivek R [Los Alamos, NM; Cola, Mark J [Santa Fe, NM; Carpenter, Robert W [Los Alamos, NM

2005-02-22

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining the quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint.

Method and Apparatus for In-Process Sensing of Manufacturing Quality

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

Hartman, D.A.; Dave, V.R.; Cola, M.J.

2005-02-22

A method for determining the quality of an examined weld joint comprising the steps of providing acoustical data from the examined weld joint, and performing a neural network operation on the acoustical data determine the quality of the examined weld joint produced by a friction weld process. The neural network may be trained by the steps of providing acoustical data and observable data from at least one test weld joint, and training the neural network based on the acoustical data and observable data to form a trained neural network so that the trained neural network is capable of determining themore » quality of a examined weld joint based on acoustical data from the examined weld joint. In addition, an apparatus having a housing, acoustical sensors mounted therein, and means for mounting the housing on a friction weld device so that the acoustical sensors do not contact the weld joint. The apparatus may sample the acoustical data necessary for the neural network to determine the quality of a weld joint.« less

Assimilation of spatially sparse in situ soil moisture networks into a continuous model domain

USDA-ARS?s Scientific Manuscript database

Growth in the availability of near-real-time soil moisture observations from ground-based networks has spurred interest in the assimilation of these observations into land surface models via a two-dimensional data assimilation system. However, the design of such systems is currently hampered by our ...

A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

NASA Astrophysics Data System (ADS)

Tan, H.; Chandra, C. V.; Chen, H.

2016-12-01

Rainfall estimation based on radar measurements has been an important topic for a few decades. Generally, radar rainfall estimation is conducted through parametric algorisms such as reflectivity-rainfall relation (i.e., Z-R relation). On the other hand, neural networks are developed for ground rainfall estimation based on radar measurements. This nonparametric method, which takes into account of both radar observations and rainfall measurements from ground rain gauges, has been demonstrated successfully for rainfall rate estimation. However, the neural network-based rainfall estimation is limited in practice due to the model complexity and structure, data quality, as well as different rainfall microphysics. Recently, the deep learning approach has been introduced in pattern recognition and machine learning areas. Compared to traditional neural networks, the deep learning based methodologies have larger number of hidden layers and more complex structure for data representation. Through a hierarchical learning process, the high level structured information and knowledge can be extracted automatically from low level features of the data. In this paper, we introduce a novel deep neural network model for rainfall estimation based on ground polarimetric radar measurements .The model is designed to capture the complex abstractions of radar measurements at different levels using multiple layers feature identification and extraction. The abstractions at different levels can be used independently or fused with other data resource such as satellite-based rainfall products and/or topographic data to represent the rain characteristics at certain location. In particular, the WSR-88DP radar and rain gauge data collected in Dallas - Fort Worth Metroplex and Florida are used extensively to train the model, and for demonstration purposes. Quantitative evaluation of the deep neural network based rainfall products will also be presented, which is based on an independent rain gauge network.

Evaluation of the Horizontal and Vertical Accuracy of GNSS Survey Observations from a Real-Time Network

NASA Astrophysics Data System (ADS)

Allahyari, M.; Olsen, M. J.; Gillins, D. T.; Dennis, M. L.

2016-12-01

Many current surveying standards in the United States require several long-duration, static Global Navigation Satellite System (GNSS) observations to derive high-accuracy geodetic coordinates. However, over the past decade, many entities have established real-time GNSS networks (RTNs), which could reduce the field time for establishing geodetic control from hours to minutes. To evaluate the accuracy of RTN GNSS observations, data collected from two National Geodetic Survey (NGS) surveys in South Carolina and Oregon were studied. The objectives were to: 1) determine the accuracy of a real-time observation as a function of duration; 2) examine the influence of including GLONASS (Russia's version of GPS); 3) compare results using a single base to the full RTN network solution; and 4) assess the effect of baseline length on accuracy. In South Carolina, 360 observations ranging from 5 to 600 seconds were collected on 20 passive marks using RTN and single-base solutions, both with GPS+GLONASS and GPS-only. In Oregon, 18 passive marks were observed from 5 to 900 seconds using GPS-only with the RTN, and with GPS+GLONASS and GPS-only from a single-base. To develop "truth" coordinates, at least 30 hours of static GPS data were also collected on all marks. Each static survey session was post-processed in OPUS-Projects, and the resulting vectors were used to build survey networks that were least-squares adjusted using the NGS software ADJUST. The resulting coordinates provided the basis for evaluating the accuracy of the real-time observations. Results from this study indicate great potential in the use of RTNs for accurate derivation of geodetic coordinates. Both case studies showed an optimal observation duration of 180 seconds. RTN data tended to be more accurate and consistent than single-base data, and GLONASS slightly improved accuracy. A key benefit of GLONASS was the ability to obtain more fixed solutions at longer baseline lengths than single-base solutions.

Atmospheric mercury concentrations observed at ground-based monitoring sites globally distributed in the framework of the GMOS network

NASA Astrophysics Data System (ADS)

Sprovieri, Francesca; Pirrone, Nicola; Bencardino, Mariantonia; D'Amore, Francesco; Carbone, Francesco; Cinnirella, Sergio; Mannarino, Valentino; Landis, Matthew; Ebinghaus, Ralf; Weigelt, Andreas; Brunke, Ernst-Günther; Labuschagne, Casper; Martin, Lynwill; Munthe, John; Wängberg, Ingvar; Artaxo, Paulo; Morais, Fernando; Barbosa, Henrique de Melo Jorge; Brito, Joel; Cairns, Warren; Barbante, Carlo; Diéguez, María del Carmen; Garcia, Patricia Elizabeth; Dommergue, Aurélien; Angot, Helene; Magand, Olivier; Skov, Henrik; Horvat, Milena; Kotnik, Jože; Read, Katie Alana; Mendes Neves, Luis; Gawlik, Bernd Manfred; Sena, Fabrizio; Mashyanov, Nikolay; Obolkin, Vladimir; Wip, Dennis; Feng, Xin Bin; Zhang, Hui; Fu, Xuewu; Ramachandran, Ramesh; Cossa, Daniel; Knoery, Joël; Marusczak, Nicolas; Nerentorp, Michelle; Norstrom, Claus

2016-09-01

Long-term monitoring of data of ambient mercury (Hg) on a global scale to assess its emission, transport, atmospheric chemistry, and deposition processes is vital to understanding the impact of Hg pollution on the environment. The Global Mercury Observation System (GMOS) project was funded by the European Commission (http://www.gmos.eu) and started in November 2010 with the overall goal to develop a coordinated global observing system to monitor Hg on a global scale, including a large network of ground-based monitoring stations, ad hoc periodic oceanographic cruises and measurement flights in the lower and upper troposphere as well as in the lower stratosphere. To date, more than 40 ground-based monitoring sites constitute the global network covering many regions where little to no observational data were available before GMOS. This work presents atmospheric Hg concentrations recorded worldwide in the framework of the GMOS project (2010-2015), analyzing Hg measurement results in terms of temporal trends, seasonality and comparability within the network. Major findings highlighted in this paper include a clear gradient of Hg concentrations between the Northern and Southern hemispheres, confirming that the gradient observed is mostly driven by local and regional sources, which can be anthropogenic, natural or a combination of both.

Agent-Based Modeling of China's Rural-Urban Migration and Social Network Structure.

PubMed

Fu, Zhaohao; Hao, Lingxin

2018-01-15

We analyze China's rural-urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core-periphery method developed from the k -core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period.

Agent-based modeling of China's rural-urban migration and social network structure

NASA Astrophysics Data System (ADS)

Fu, Zhaohao; Hao, Lingxin

2018-01-01

We analyze China's rural-urban migration and endogenous social network structures using agent-based modeling. The agents from census micro data are located in their rural origin with an empirical-estimated prior propensity to move. The population-scale social network is a hybrid one, combining observed family ties and locations of the origin with a parameter space calibrated from census, survey and aggregate data and sampled using a stepwise Latin Hypercube Sampling method. At monthly intervals, some agents migrate and these migratory acts change the social network by turning within-nonmigrant connections to between-migrant-nonmigrant connections, turning local connections to nonlocal connections, and adding among-migrant connections. In turn, the changing social network structure updates migratory propensities of those well-connected nonmigrants who become more likely to move. These two processes iterate over time. Using a core-periphery method developed from the k-core decomposition method, we identify and quantify the network structural changes and map these changes with the migration acceleration patterns. We conclude that network structural changes are essential for explaining migration acceleration observed in China during the 1995-2000 period.

Rumor Diffusion in an Interests-Based Dynamic Social Network

PubMed Central

Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping

2013-01-01

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency. PMID:24453911

Rumor diffusion in an interests-based dynamic social network.

PubMed

Tang, Mingsheng; Mao, Xinjun; Guessoum, Zahia; Zhou, Huiping

2013-01-01

To research rumor diffusion in social friend network, based on interests, a dynamic friend network is proposed, which has the characteristics of clustering and community, and a diffusion model is also proposed. With this friend network and rumor diffusion model, based on the zombie-city model, some simulation experiments to analyze the characteristics of rumor diffusion in social friend networks have been conducted. The results show some interesting observations: (1) positive information may evolve to become a rumor through the diffusion process that people may modify the information by word of mouth; (2) with the same average degree, a random social network has a smaller clustering coefficient and is more beneficial for rumor diffusion than the dynamic friend network; (3) a rumor is spread more widely in a social network with a smaller global clustering coefficient than in a social network with a larger global clustering coefficient; and (4) a network with a smaller clustering coefficient has a larger efficiency.

Attack Vulnerability of Network Controllability

PubMed Central

2016-01-01

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability. PMID:27588941

Attack Vulnerability of Network Controllability.

PubMed

Lu, Zhe-Ming; Li, Xin-Feng

2016-01-01

Controllability of complex networks has attracted much attention, and understanding the robustness of network controllability against potential attacks and failures is of practical significance. In this paper, we systematically investigate the attack vulnerability of network controllability for the canonical model networks as well as the real-world networks subject to attacks on nodes and edges. The attack strategies are selected based on degree and betweenness centralities calculated for either the initial network or the current network during the removal, among which random failure is as a comparison. It is found that the node-based strategies are often more harmful to the network controllability than the edge-based ones, and so are the recalculated strategies than their counterparts. The Barabási-Albert scale-free model, which has a highly biased structure, proves to be the most vulnerable of the tested model networks. In contrast, the Erdős-Rényi random model, which lacks structural bias, exhibits much better robustness to both node-based and edge-based attacks. We also survey the control robustness of 25 real-world networks, and the numerical results show that most real networks are control robust to random node failures, which has not been observed in the model networks. And the recalculated betweenness-based strategy is the most efficient way to harm the controllability of real-world networks. Besides, we find that the edge degree is not a good quantity to measure the importance of an edge in terms of network controllability.

Data center networks and network architecture

NASA Astrophysics Data System (ADS)

Esaki, Hiroshi

2014-02-01

This paper discusses and proposes the architectural framework, which is for data center networks. The data center networks require new technical challenges, and it would be good opportunity to change the functions, which are not need in current and future networks. Based on the observation and consideration on data center networks, this paper proposes; (i) Broadcast-free layer 2 network (i.e., emulation of broadcast at the end-node), (ii) Full-mesh point-to-point pipes, and (iii) IRIDES (Invitation Routing aDvertisement for path Engineering System).

Data Reduction and Control Software for Meteor Observing Stations Based on CCD Video Systems

NASA Technical Reports Server (NTRS)

Madiedo, J. M.; Trigo-Rodriguez, J. M.; Lyytinen, E.

2011-01-01

The SPanish Meteor Network (SPMN) is performing a continuous monitoring of meteor activity over Spain and neighbouring countries. The huge amount of data obtained by the 25 video observing stations that this network is currently operating made it necessary to develop new software packages to accomplish some tasks, such as data reduction and remote operation of autonomous systems based on high-sensitivity CCD video devices. The main characteristics of this software are described here.

Ability of the current global observing network to constrain N2O sources and sinks

NASA Astrophysics Data System (ADS)

Millet, D. B.; Wells, K. C.; Chaliyakunnel, S.; Griffis, T. J.; Henze, D. K.; Bousserez, N.

2014-12-01

The global observing network for atmospheric N2O combines flask and in-situ measurements at ground stations with sustained and campaign-based aircraft observations. In this talk we apply a new global model of N2O (based on GEOS-Chem) and its adjoint to assess the strengths and weaknesses of this network for quantifying N2O emissions. We employ an ensemble of pseudo-observation analyses to evaluate the relative constraints provided by ground-based (surface, tall tower) and airborne (HIPPO, CARIBIC) observations, and the extent to which variability (e.g. associated with pulsing or seasonality of emissions) not captured by the a priori inventory can bias the inferred fluxes. We find that the ground-based and HIPPO datasets each provide a stronger constraint on the distribution of global emissions than does the CARIBIC dataset on its own. Given appropriate initial conditions, we find that our inferred surface fluxes are insensitive to model errors in the stratospheric loss rate of N2O over the timescale of our analysis (2 years); however, the same is not necessarily true for model errors in stratosphere-troposphere exchange. Finally, we examine the a posteriori error reduction distribution to identify priority locations for future N2O measurements.

Networking observers and observatories with remote telescope markup language

NASA Astrophysics Data System (ADS)

Hessman, Frederic V.; Tuparev, Georg; Allan, Alasdair

2006-06-01

Remote Telescope Markup Language (RTML) is an XML-based protocol for the transport of the high-level description of a set of observations to be carried out on a remote, robotic or service telescope. We describe how RTML is being used in a wide variety of contexts: the transport of service and robotic observing requests in the Hands-On Universe TM, ACP, eSTAR, and MONET networks; how RTML is easily combined with other XML protocols for more localized control of telescopes; RTML as a secondary observation report format for the IVOA's VOEvent protocol; the input format for a general-purpose observation simulator; and the observatory-independent means for carrying out request transactions for the international Heterogeneous Telescope Network (HTN).

Results of Joint Observations of Jupiter's Atmosphere by Juno and a Network of Earth-Based Observing Stations

NASA Astrophysics Data System (ADS)

Orton, Glenn; Momary, Thomas; Bolton, Scott; Levin, Steven; Hansen, Candice; Janssen, Michael; Adriani, Alberto; Gladstone, G. Randall; Bagenal, Fran; Ingersoll, Andrew

2017-04-01

The Juno mission has promoted and coordinated a network of Earth-based observations, including both Earth-proximal and ground-based facilities, to extend and enhance observations made by the Juno mission. The spectral region and timeline of all of these observations are summarized in the web site: https://www.missionjuno.swri.edu/planned-observations. Among the earliest of these were observation of Jovian auroral phenomena at X-ray, ultraviolet and infrared wavelengths and measurements of Jovian synchrotron radiation from the Earth simultaneously with the measurement of properties of the upstream solar wind. Other observations of significance to the magnetosphere measured the mass loading from Io by tracking its observed volcanic activity and the opacity of its torus. Observations of Jupiter's neutral atmosphere included observations of reflected sunlight from the near-ultraviolet through the near-infrared and thermal emission from 5 μm through the radio region. The point of these measurements is to relate properties of the deep atmosphere that are the focus of Juno's mission to the state of the "weather layer" at much higher atmospheric levels. These observations cover spectral regions not included in Juno's instrumentation, provide spatial context for Juno's often spatially limited coverage of Jupiter, and they describe the evolution of atmospheric features in time that are measured only once by Juno. We will summarize the results of measurements during the approach phase of the mission that characterized the state of the atmosphere, as well as observations made by Juno and the supporting campaign during Juno's perijoves 1 (2016 August 27), 3 (2016 December 11), 4 (2017 February 2) and possibly "early" results from 5 (2017 March 27). Besides a global network of professional astronomers, the Juno mission also benefited from the enlistment of a network of dedicated amateur astronomers who provided a quasi-continuous picture of the evolution of features observed by Juno's instruments.

Design and implementation of a software package to control a network of robotic observatories

NASA Astrophysics Data System (ADS)

Tuparev, G.; Nicolova, I.; Zlatanov, B.; Mihova, D.; Popova, I.; Hessman, F. V.

2006-09-01

We present a description of a reusable software package able to control a large, heterogeneous network of fully and semi-robotic observatories initially developed to run the MONET network of two 1.2 m telescopes. Special attention is given to the design of a robust, long-term observation scheduler which also allows the trading of observation time and facilities within various networks. The handling of the ``Phase I&II" project-development process, the time-accounting between complex organizational structures, and usability issues for making the package accessible not only to professional astronomers, but also to amateurs and high-school students is discussed. A simple RTML-based solution to link multiple networks is demonstrated.

Template based rotation: A method for functional connectivity analysis with a priori templates☆

PubMed Central

Schultz, Aaron P.; Chhatwal, Jasmeer P.; Huijbers, Willem; Hedden, Trey; van Dijk, Koene R.A.; McLaren, Donald G.; Ward, Andrew M.; Wigman, Sarah; Sperling, Reisa A.

2014-01-01

Functional connectivity magnetic resonance imaging (fcMRI) is a powerful tool for understanding the network level organization of the brain in research settings and is increasingly being used to study large-scale neuronal network degeneration in clinical trial settings. Presently, a variety of techniques, including seed-based correlation analysis and group independent components analysis (with either dual regression or back projection) are commonly employed to compute functional connectivity metrics. In the present report, we introduce template based rotation,1 a novel analytic approach optimized for use with a priori network parcellations, which may be particularly useful in clinical trial settings. Template based rotation was designed to leverage the stable spatial patterns of intrinsic connectivity derived from out-of-sample datasets by mapping data from novel sessions onto the previously defined a priori templates. We first demonstrate the feasibility of using previously defined a priori templates in connectivity analyses, and then compare the performance of template based rotation to seed based and dual regression methods by applying these analytic approaches to an fMRI dataset of normal young and elderly subjects. We observed that template based rotation and dual regression are approximately equivalent in detecting fcMRI differences between young and old subjects, demonstrating similar effect sizes for group differences and similar reliability metrics across 12 cortical networks. Both template based rotation and dual-regression demonstrated larger effect sizes and comparable reliabilities as compared to seed based correlation analysis, though all three methods yielded similar patterns of network differences. When performing inter-network and sub-network connectivity analyses, we observed that template based rotation offered greater flexibility, larger group differences, and more stable connectivity estimates as compared to dual regression and seed based analyses. This flexibility owes to the reduced spatial and temporal orthogonality constraints of template based rotation as compared to dual regression. These results suggest that template based rotation can provide a useful alternative to existing fcMRI analytic methods, particularly in clinical trial settings where predefined outcome measures and conserved network descriptions across groups are at a premium. PMID:25150630

Data Fusion for Earth Science Remote Sensing

NASA Technical Reports Server (NTRS)

Braverman, Amy

2007-01-01

Beginning in 2004, NASA has supported the development of an international network of ground-based remote sensing installations for the measurement of greenhouse gas columns. This collaboration has been successful and is currently used in both carbon cycle investigations and in the efforts to validate the GOSAT space-based column observations of CO2 and CH4. With the support of a grant, this research group has established a network of ground-based column observations that provide an essential link between the satellite observations of CO2, CO, and CH4 and the extensive global in situ surface network. The Total Carbon Column Observing Network (TCCON) was established in 2004. At the time of this report seven sites, employing modern instrumentation, were operational or were expected to be shortly. TCCON is expected to expand. In addition to providing the most direct means of tying the in situ and remote sensing data sets together, TCCON provides a means of testing the retrieval algorithms of SCIAMACHY and GOSAT over the broadest variation in atmospheric state. TCCON provides a critically maintained and long timescale record for identification of temporal drift and spatial bias in the calibration of the space-based sensors. Finally, the global observations from TCCON are improving our understanding of how to use column observations to provide robust estimates of surface exchange of C02 and CH4 in advance of the launch of OCO and GOSAT. TCCON data are being used to better understand the impact of both regional fluxes and long-range transport on gradients in the C02 column. Such knowledge is essential for identifying the tools required to best use the space-based observations. The technical approach and methodology of retrieving greenhouse gas columns from near-IR solar spectra, data quality and process control are described. Additionally, the impact of and relevance to NASA of TCCON and satellite validation and carbon science are addressed.

Fault detection and diagnosis using neural network approaches

NASA Technical Reports Server (NTRS)

Kramer, Mark A.

1992-01-01

Neural networks can be used to detect and identify abnormalities in real-time process data. Two basic approaches can be used, the first based on training networks using data representing both normal and abnormal modes of process behavior, and the second based on statistical characterization of the normal mode only. Given data representative of process faults, radial basis function networks can effectively identify failures. This approach is often limited by the lack of fault data, but can be facilitated by process simulation. The second approach employs elliptical and radial basis function neural networks and other models to learn the statistical distributions of process observables under normal conditions. Analytical models of failure modes can then be applied in combination with the neural network models to identify faults. Special methods can be applied to compensate for sensor failures, to produce real-time estimation of missing or failed sensors based on the correlations codified in the neural network.

Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks.

PubMed

Shen, Yiwen; Hattink, Maarten H N; Samadi, Payman; Cheng, Qixiang; Hu, Ziyiz; Gazman, Alexander; Bergman, Keren

2018-04-16

Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. We present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly network testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 µs control plane latency for data-center and high performance computing platforms.

A neural networks-based hybrid routing protocol for wireless mesh networks.

PubMed

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic-i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance.

A Neural Networks-Based Hybrid Routing Protocol for Wireless Mesh Networks

PubMed Central

Kojić, Nenad; Reljin, Irini; Reljin, Branimir

2012-01-01

The networking infrastructure of wireless mesh networks (WMNs) is decentralized and relatively simple, but they can display reliable functioning performance while having good redundancy. WMNs provide Internet access for fixed and mobile wireless devices. Both in urban and rural areas they provide users with high-bandwidth networks over a specific coverage area. The main problems affecting these networks are changes in network topology and link quality. In order to provide regular functioning, the routing protocol has the main influence in WMN implementations. In this paper we suggest a new routing protocol for WMN, based on good results of a proactive and reactive routing protocol, and for that reason it can be classified as a hybrid routing protocol. The proposed solution should avoid flooding and creating the new routing metric. We suggest the use of artificial logic—i.e., neural networks (NNs). This protocol is based on mobile agent technologies controlled by a Hopfield neural network. In addition to this, our new routing metric is based on multicriteria optimization in order to minimize delay and blocking probability (rejected packets or their retransmission). The routing protocol observes real network parameters and real network environments. As a result of artificial logic intelligence, the proposed routing protocol should maximize usage of network resources and optimize network performance. PMID:22969360

Ocean Environmental Assessment and Adaptive Resource Management within the Framework of IOOS and CLEANER

NASA Astrophysics Data System (ADS)

Bonner, J.; Brezonik, P.; Clesceri, N.; Gouldman, C.; Jamail, R.; Zilkoski, D.

2006-12-01

The Integrated Ocean Observing System (IOOS), established through the efforts of the National Office for Integrated and Sustained Ocean Observations (Oceans.US) provides quality controlled data and information on a routine and continuous basis regarding current and future states of the oceans and Great Lakes at scales from global ocean basins to coastal ecosystems. The seven societal goals of IOOS are outlined in this paper. The Engineering and Geosciences Directorates at the National Science Foundation (NSF) are collaborating in planning the WATERS (WATer Environmental Research System) Network, an outgrowth of earlier, separate initiatives of the two directorates: CLEANER (Collaborative Large-scale Engineering Analysis Network for Environmental Research) and Hydrologic Observatories. WATERS Network is being developed by engineers and scientists in the academic community who recognize the need for an observation and research network to enable better understanding of human-dominated water-environments, their stressors, and the links between them. The WATERS Network model is based on a research framework anchored in a distributed, cyber-based network supporting: 1) data collection; 2) data aggregation; 3) analytical and exploratory tools; and 4) a computational environment supporting predictive modeling and policy analysis on water resource systems. Within IOOS, the U.S. coastal margin is divided into Regional Associations (RAs), organizational units that are conceptually linked through planned data collection and analysis activities for resolving fundamental coastal margin ecosystem questions and addressing RA concerns. Under the WATERS Network scheme, a Coastal Margin Regional Environmental System (RES) for coastal areas would be defined conceptually based on geomorphologic considerations of four major water bodies; Atlantic and Pacific Oceans, Gulf of Mexico, and Laurentian Great Lakes. Within this framework, each coastal margin would operate one or more local environmental field facilities (or observatories). Mutual coordination and collaboration would exist among these coasts through RES interactions based on a cyberinfrastructure supporting all aspects of quantitative analysis. Because the U.S. Ocean Action Plan refers to the creation of a National Water Quality Monitoring Network, a close liaison between IOOS and WATERS Network could be mutually advantageous considering the shared visions, goals and objectives. A focus on activities and initiatives involving sensor and sensor networks for coastal margin observation and assessment would be a specific instance of this liaison, leveraging the infrastructural base of both organizations to maximize resource allocation. This coordinated venture with intelligent environmental systems would include new specialized coastal monitoring networks, and management of near-real-time data, including data assimilation models. An ongoing NSF planning grant aimed at environmental observatory design for coastal margins is a component of the broader WATERS Network planning for collaborative research to support adaptive and sustainable environmental management. We propose a collaborative framework between IOOS and WATERS Network wherein collaborative research will be enabled by cybernetworks to support adaptive and sustainable management of the coastal regions.

The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing/Archival Methodology, and First Results

NASA Technical Reports Server (NTRS)

Blakelee, Richard

1999-01-01

A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measurement Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/MSFC are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.

The Rondonia Lightning Detection Network: Network Description, Science Objectives, Data Processing/Archival Methodology, and First Results

NASA Technical Reports Server (NTRS)

Blakeslee, Rich; Bailey, Jeff; Koshak, Bill

1999-01-01

A four station Advanced Lightning Direction Finder (ALDF) network was recently established in the state of Rondonia in western Brazil through a collaboration of U.S. and Brazilian participants from NASA, INPE, INMET, and various universities. The network utilizes ALDF IMPACT (Improved Accuracy from Combined Technology) sensors to provide cloud-to-ground lightning observations (i.e., stroke/flash locations, signal amplitude, and polarity) using both time-of-arrival and magnetic direction finding techniques. The observations are collected, processed and archived at a central site in Brasilia and at the NASA/ Marshall Space Flight Center (MSFC) in Huntsville, Alabama. Initial, non-quality assured quick-look results are made available in near real-time over the internet. The network will remain deployed for several years to provide ground truth data for the Lightning Imaging Sensor (LIS) on the Tropical Rainfall Measuring Mission (TRMM) satellite which was launched in November 1997. The measurements will also be used to investigate the relationship between the electrical, microphysical and kinematic properties of tropical convection. In addition, the long-term observations from this network will contribute in establishing a regional lightning climatological data base, supplementing other data bases in Brazil that already exist or may soon be implemented. Analytic inversion algorithms developed at NASA/Marshall Space Flight Center (MSFC) are now being applied to the Rondonian ALDF lightning observations to obtain site error corrections and improved location retrievals. The processing methodology and the initial results from an analysis of the first 6 months of network operations will be presented.

Use of NEXRAD radar-based observations for quality control of in-situ rain gauge measurements

NASA Astrophysics Data System (ADS)

Nelson, B. R.; Prat, O.; Leeper, R.

2017-12-01

Rain gauge quality control is an often over looked important step in the archive of historical precipitation estimates. We investigate the possibilities that exist for using ground based radar networks for quality control of rain gauge measurements. This process includes the point to pixel comparisons of the rain gauge measurements with NEXRAD observations. There are two NEXRAD based data sets used for reference; the NCEP stage IV and the NWS MRMS gridded data sets. The NCEP stage IV data set is available at 4km hourly for the period 2002-present and includes the radar-gauge bias adjusted precipitation estimate. The NWS MRMS data set includes several different variables such as reflectivity, radar-only estimates, precipitation flag, and radar-gauge bias adjusted precipitation estimates. The latter product provides for much more information to apply quality control such as identification of precipitation type, identification of storm type and Z-R relation. In addition, some of the variables are available at 5-minute scale. The rain gauge networks that are investigated are the Climate Reference Network (CRN), the Fischer-Porter Hourly Precipitation Database (HPD), and the Hydrometeorological Automated Data System (HADS). The CRN network is available at the 5-minute scale, the HPD network is available at the 15-minute and hourly scale, and HADS is available at the hourly scale. The varying scales present challenges for comparisons. However given the higher resolution radar-based products we identify an optimal combination of rain gauge observations that can be compared to the radar-based information. The quality control process focuses on identifying faulty gauges in direct comparison while a deeper investigation focuses on event-based differences from light rain to extreme storms.

Understanding the Longitudinal Variability of Equatorial Electrodynamics using integrated Ground- and Space-based Observations

NASA Astrophysics Data System (ADS)

Yizengaw, E.; Moldwin, M.; Zesta, E.

2015-12-01

The currently funded African Meridian B-Field Education and Research (AMBER) magnetometer array comprises more than thirteen magnetometers stationed globally in the vicinity of geomagnetic equator. One of the main objectives of AMBER network is to understand the longitudinal variability of equatorial electrodynamics as function of local time, magnetic activity, and season. While providing complete meridian observation in the region and filling the largest land-based gap in global magnetometer coverage, the AMBER array addresses two fundamental areas of space physics: first, the processes governing electrodynamics of the equatorial ionosphere as a function of latitude (or L-shell), local time, longitude, magnetic activity, and season, and second, ULF pulsation strength at low/mid-latitude regions and its connection with equatorial electrojet and density fluctuation. The global AMBER network can also be used to augment observations from space-based instruments, such us the triplet SWARM mission and the upcoming ICON missions. Thus, in coordination with space-based and other ground-based observations, the AMBER magnetometer network provides a great opportunity to understand the electrodynamics that governs equatorial ionosphere motions. In this paper we present the longitudinal variability of the equatorial electrodynamics using the combination of instruments onboard SWARM and C/NOFS satellites and ground-based AMBER network. Both ground- and pace-based observations show stronger dayside and evening sector equatorial electrodynamics in the American and Asian sectors compared to the African sector. On the other hand, the African sector is home to stronger and year-round ionospheric bubbles/irregularities compared to the American and Asian sectors. This raises the question if the evening sector equatorial electrodynamics (vertical drift), which is believed to be the main cause for the enhancement of Rayleigh-Taylor (RT) instability growth rate, is stronger in the American sector and weaker in the African sector - why are the occurrence and amplitude of equatorial irregularities stronger in the African sector?

Individual-Based Ant-Plant Networks: Diurnal-Nocturnal Structure and Species-Area Relationship

PubMed Central

Dáttilo, Wesley; Fagundes, Roberth; Gurka, Carlos A. Q.; Silva, Mara S. A.; Vieira, Marisa C. L.; Izzo, Thiago J.; Díaz-Castelazo, Cecília; Del-Claro, Kleber; Rico-Gray, Victor

2014-01-01

Despite the importance and increasing knowledge of ecological networks, sampling effort and intrapopulation variation has been widely overlooked. Using continuous daily sampling of ants visiting three plant species in the Brazilian Neotropical savanna, we evaluated for the first time the topological structure over 24 h and species-area relationships (based on the number of extrafloral nectaries available) in individual-based ant-plant networks. We observed that diurnal and nocturnal ant-plant networks exhibited the same pattern of interactions: a nested and non-modular pattern and an average level of network specialization. Despite the high similarity in the ants’ composition between the two collection periods, ant species found in the central core of highly interacting species totally changed between diurnal and nocturnal sampling for all plant species. In other words, this “night-turnover” suggests that the ecological dynamics of these ant-plant interactions can be temporally partitioned (day and night) at a small spatial scale. Thus, it is possible that in some cases processes shaping mutualistic networks formed by protective ants and plants may be underestimated by diurnal sampling alone. Moreover, we did not observe any effect of the number of extrafloral nectaries on ant richness and their foraging on such plants in any of the studied ant-plant networks. We hypothesize that competitively superior ants could monopolize individual plants and allow the coexistence of only a few other ant species, however, other alternative hypotheses are also discussed. Thus, sampling period and species-area relationship produces basic information that increases our confidence in how individual-based ant-plant networks are structured, and the need to consider nocturnal records in ant-plant network sampling design so as to decrease inappropriate inferences. PMID:24918750

A disassembly-driven mechanism explains F-actin-mediated chromosome transport in starfish oocytes

PubMed Central

Bun, Philippe; Dmitrieff, Serge; Belmonte, Julio M

2018-01-01

While contraction of sarcomeric actomyosin assemblies is well understood, this is not the case for disordered networks of actin filaments (F-actin) driving diverse essential processes in animal cells. For example, at the onset of meiosis in starfish oocytes a contractile F-actin network forms in the nuclear region transporting embedded chromosomes to the assembling microtubule spindle. Here, we addressed the mechanism driving contraction of this 3D disordered F-actin network by comparing quantitative observations to computational models. We analyzed 3D chromosome trajectories and imaged filament dynamics to monitor network behavior under various physical and chemical perturbations. We found no evidence of myosin activity driving network contractility. Instead, our observations are well explained by models based on a disassembly-driven contractile mechanism. We reconstitute this disassembly-based contractile system in silico revealing a simple architecture that robustly drives chromosome transport to prevent aneuploidy in the large oocyte, a prerequisite for normal embryonic development. PMID:29350616

Modeling epidemics on adaptively evolving networks: A data-mining perspective.

PubMed

Kattis, Assimakis A; Holiday, Alexander; Stoica, Ana-Andreea; Kevrekidis, Ioannis G

2016-01-01

The exploration of epidemic dynamics on dynamically evolving ("adaptive") networks poses nontrivial challenges to the modeler, such as the determination of a small number of informative statistics of the detailed network state (that is, a few "good observables") that usefully summarize the overall (macroscopic, systems-level) behavior. Obtaining reduced, small size accurate models in terms of these few statistical observables--that is, trying to coarse-grain the full network epidemic model to a small but useful macroscopic one--is even more daunting. Here we describe a data-based approach to solving the first challenge: the detection of a few informative collective observables of the detailed epidemic dynamics. This is accomplished through Diffusion Maps (DMAPS), a recently developed data-mining technique. We illustrate the approach through simulations of a simple mathematical model of epidemics on a network: a model known to exhibit complex temporal dynamics. We discuss potential extensions of the approach, as well as possible shortcomings.

The LCOGT NEO Follow-up Network

NASA Astrophysics Data System (ADS)

Lister, Tim; Greenstreet, Sarah; Gomez, Edward; Christensen, Eric J.; Larson, Stephen M.

2016-10-01

The LCOGT NEO Follow-up Network is using the telescopes of the Las Cumbres Observatory Global Telescope Network (LCOGT) and a web-based target selection, scheduling and data reduction system to confirm NEO candidates and characterize radar-targeted known NEOs. Starting in July 2014, the LCOGT NEO Follow-up Network has observed over 3,500 targets and reported more than 16,000 astrometric and photometric measurements to the Minor Planet Center (MPC).The LCOGT NEO Follow-up Network's main aims are to perform confirming follow-up of the large number of NEO candidates and to perform characterization measurements of radar targets to obtain light curves and rotation rates. The NEO candidates come from the NEO surveys such as Catalina, PanSTARRS, ATLAS, NEOWISE and others. In particular, we are targeting objects in the Southern Hemisphere, where the LCOGT NEO Follow-up Network is the largest resource for NEO observations.LCOGT has completed the first phase of the deployment with the installation and commissioning of the nine 1-meter telescopes at McDonald Observatory (Texas), Cerro Tololo (Chile), SAAO (South Africa) and Siding Spring Observatory (Australia). The telescope network has been fully operational since 2014 May, and observations are being executed remotely and robotically. Future expansion to a site at Ali Observatory, Tibet is planned for 2017-2018.We have developed web-based software called NEOexchange which automatically downloads and aggregates NEO candidates from the Minor Planet Center's NEO Confirmation Page, the Arecibo and Goldstone radar target lists and the NASA ARM list. NEOexchange allows the planning and scheduling of observations on the LCOGT Telescope Network and the tracking of the resulting blocks and generated data. We have recently extended the NEOexchange software to include automated data reduction to re-compute the astrometric solution, determine the photometric zeropoint and find moving objects and present these results to the user via the website.We will present results from the LCOGT NEO Follow-up Network and from the development of the NEOexchange software which is used to schedule, analyze and report observations taken with the LCOGT Network.

Autonomous Sensorweb Operations for Integrated Space, In-Situ Monitoring of Volcanic Activity

NASA Technical Reports Server (NTRS)

Chien, Steve A.; Doubleday, Joshua; Kedar, Sharon; Davies, Ashley G.; Lahusen, Richard; Song, Wenzhan; Shirazi, Behrooz; Mandl, Daniel; Frye, Stuart

2010-01-01

We have deployed and demonstrated operations of an integrated space in-situ sensorweb for monitoring volcanic activity. This sensorweb includes a network of ground sensors deployed to the Mount Saint Helens volcano as well as the Earth Observing One spacecraft. The ground operations and space operations are interlinked in that ground-based intelligent event detections can cause the space segment to acquire additional data via observation requests and space-based data acquisitions (thermal imagery) can trigger reconfigurations of the ground network to allocate increased bandwidth to areas of the network best situated to observe the activity. The space-based operations are enabled by an automated mission planning and tasking capability which utilizes several Opengeospatial Consortium (OGC) Sensorweb Enablement (SWE) standards which enable acquiring data, alerts, and tasking using web services. The ground-based segment also supports similar protocols to enable seamless tasking and data delivery. The space-based segment also supports onboard development of data products (thermal summary images indicating areas of activity, quicklook context images, and thermal activity alerts). These onboard developed products have reduced data volume (compared to the complete images) which enables them to be transmitted to the ground more rapidly in engineering channels.

Intelligent Sensing and Classification in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Yu T. (Jade

Wireless ad hoc networks have fundamentally altered today's battlefield, with applications ranging from unmanned air vehicles to randomly deployed sensor networks. Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments.

Pan-Eurasian experiment (PEEX) establishing a process towards high level Pan-Eurasian atmosphere-ecosystem observation networks

NASA Astrophysics Data System (ADS)

Lappalainen, Hanna K.; Petäjä, Tuukka; Zaytzeva, Nina; Viisanen, Yrjö; Kotlyakov, Vladimir; Kasimov, Nikolay; Bondur, Valery; Matvienko, Gennady; Zilitinkevich, Sergej; Kulmala, Markku

2014-05-01

Pan-Eurasian Experiment (PEEX) is a new multidisciplinary research approach aiming at resolving the major uncertainties in the Earth system science and global sustainability questions in the Arctic and boreal Pan-Eurasian regions (Kulmala et al. 2011). The main goal of PEEX Research agenda is to contribute to solving the scientific questions that are specifically important for the Pan-Eurasian region in the coming years, in particular the global climate change and its consequences to nature and human society. Pan Eurasian region represents one the Earth most extensive areas of boreal forest (taiga) and the largest natural wetlands, thus being a significant source area of trace gas emissions, biogenic aerosol particles, and source and sink area for the greenhouse gas (GHG) exchange in a global scale (Guenther et al. 1995, Timkovsky et al. 2010, Tunved et al. 2006, Glagolev et al. 2010). One of the first activities of the PEEX initiative is to establish a process towards high level Pan-Eurasian Observation Networks. Siberian region is currently lacking a coordinated, coherent ground based atmosphere-ecosystem measurement network, which would be crucial component for observing and predicting the effects of climate change in the Northern Pan- Eurasian region The vision of the Pan-Eurasion network will be based on a hierarchical SMEAR-type (Stations Measuring Atmosphere-Ecosystem Interactions) integrated land-atmosphere observation system (Hari et al. 2009). A suite of stations have been selected for the Preliminary Phase of PEEX Observation network. These Preliminary Phase stations includes the SMEAR-type stations in Finland (SMEAR-I-II-II-IV stations), in Estonia (SMEAR-Järviselja) and in China (SMEAR-Nanjing) and selected stations in Russia and ecosystem station network in China. PEEX observation network will fill in the current observational gap in the Siberian region and bring the Siberian observation setup into international context with the with standardized or comparable procedures. It will prove a basis for the long-term continuation of advanced measurements on aerosols, clouds, GHGs and trace gases in Northern Pan- Eurasian area to be operated by PEEX educated technical staff.

Variable-free exploration of stochastic models: a gene regulatory network example.

PubMed

Erban, Radek; Frewen, Thomas A; Wang, Xiao; Elston, Timothy C; Coifman, Ronald; Nadler, Boaz; Kevrekidis, Ioannis G

2007-04-21

Finding coarse-grained, low-dimensional descriptions is an important task in the analysis of complex, stochastic models of gene regulatory networks. This task involves (a) identifying observables that best describe the state of these complex systems and (b) characterizing the dynamics of the observables. In a previous paper [R. Erban et al., J. Chem. Phys. 124, 084106 (2006)] the authors assumed that good observables were known a priori, and presented an equation-free approach to approximate coarse-grained quantities (i.e., effective drift and diffusion coefficients) that characterize the long-time behavior of the observables. Here we use diffusion maps [R. Coifman et al., Proc. Natl. Acad. Sci. U.S.A. 102, 7426 (2005)] to extract appropriate observables ("reduction coordinates") in an automated fashion; these involve the leading eigenvectors of a weighted Laplacian on a graph constructed from network simulation data. We present lifting and restriction procedures for translating between physical variables and these data-based observables. These procedures allow us to perform equation-free, coarse-grained computations characterizing the long-term dynamics through the design and processing of short bursts of stochastic simulation initialized at appropriate values of the data-based observables.

Comparative Network-Based Recovery Analysis and Proteomic Profiling of Neurological Changes in Valproic Acid-Treated Mice

PubMed Central

2013-01-01

Despite its prominence for characterization of complex mixtures, LC–MS/MS frequently fails to identify many proteins. Network-based analysis methods, based on protein–protein interaction networks (PPINs), biological pathways, and protein complexes, are useful for recovering non-detected proteins, thereby enhancing analytical resolution. However, network-based analysis methods do come in varied flavors for which the respective efficacies are largely unknown. We compare the recovery performance and functional insights from three distinct instances of PPIN-based approaches, viz., Proteomics Expansion Pipeline (PEP), Functional Class Scoring (FCS), and Maxlink, in a test scenario of valproic acid (VPA)-treated mice. We find that the most comprehensive functional insights, as well as best non-detected protein recovery performance, are derived from FCS utilizing real biological complexes. This outstrips other network-based methods such as Maxlink or Proteomics Expansion Pipeline (PEP). From FCS, we identified known biological complexes involved in epigenetic modifications, neuronal system development, and cytoskeletal rearrangements. This is congruent with the observed phenotype where adult mice showed an increase in dendritic branching to allow the rewiring of visual cortical circuitry and an improvement in their visual acuity when tested behaviorally. In addition, PEP also identified a novel complex, comprising YWHAB, NR1, NR2B, ACTB, and TJP1, which is functionally related to the observed phenotype. Although our results suggest different network analysis methods can produce different results, on the whole, the findings are mutually supportive. More critically, the non-overlapping information each provides can provide greater holistic understanding of complex phenotypes. PMID:23557376

A link prediction method for heterogeneous networks based on BP neural network

NASA Astrophysics Data System (ADS)

Li, Ji-chao; Zhao, Dan-ling; Ge, Bing-Feng; Yang, Ke-Wei; Chen, Ying-Wu

2018-04-01

Most real-world systems, composed of different types of objects connected via many interconnections, can be abstracted as various complex heterogeneous networks. Link prediction for heterogeneous networks is of great significance for mining missing links and reconfiguring networks according to observed information, with considerable applications in, for example, friend and location recommendations and disease-gene candidate detection. In this paper, we put forward a novel integrated framework, called MPBP (Meta-Path feature-based BP neural network model), to predict multiple types of links for heterogeneous networks. More specifically, the concept of meta-path is introduced, followed by the extraction of meta-path features for heterogeneous networks. Next, based on the extracted meta-path features, a supervised link prediction model is built with a three-layer BP neural network. Then, the solution algorithm of the proposed link prediction model is put forward to obtain predicted results by iteratively training the network. Last, numerical experiments on the dataset of examples of a gene-disease network and a combat network are conducted to verify the effectiveness and feasibility of the proposed MPBP. It shows that the MPBP with very good performance is superior to the baseline methods.

A new modelling approach for zooplankton behaviour

NASA Astrophysics Data System (ADS)

Keiyu, A. Y.; Yamazaki, H.; Strickler, J. R.

We have developed a new simulation technique to model zooplankton behaviour. The approach utilizes neither the conventional artificial intelligence nor neural network methods. We have designed an adaptive behaviour network, which is similar to BEER [(1990) Intelligence as an adaptive behaviour: an experiment in computational neuroethology, Academic Press], based on observational studies of zooplankton behaviour. The proposed method is compared with non- "intelligent" models—random walk and correlated walk models—as well as observed behaviour in a laboratory tank. Although the network is simple, the model exhibits rich behavioural patterns similar to live copepods.

Integrated design of multivariable hydrometric networks using entropy theory with a multiobjective optimization approach

NASA Astrophysics Data System (ADS)

Kim, Y.; Hwang, T.; Vose, J. M.; Martin, K. L.; Band, L. E.

2016-12-01

Obtaining quality hydrologic observations is the first step towards a successful water resources management. While remote sensing techniques have enabled to convert satellite images of the Earth's surface to hydrologic data, the importance of ground-based observations has never been diminished because in-situ data are often highly accurate and can be used to validate remote measurements. The existence of efficient hydrometric networks is becoming more important to obtain as much as information with minimum redundancy. The World Meteorological Organization (WMO) has recommended a guideline for the minimum hydrometric network density based on physiography; however, this guideline is not for the optimum network design but for avoiding serious deficiency from a network. Moreover, all hydrologic variables are interconnected within the hydrologic cycle, while monitoring networks have been designed individually. This study proposes an integrated network design method using entropy theory with a multiobjective optimization approach. In specific, a precipitation and a streamflow networks in a semi-urban watershed in Ontario, Canada were designed simultaneously by maximizing joint entropy, minimizing total correlation, and maximizing conditional entropy of streamflow network given precipitation network. After comparing with the typical individual network designs, the proposed design method would be able to determine more efficient optimal networks by avoiding the redundant stations, in which hydrologic information is transferable. Additionally, four quantization cases were applied in entropy calculations to assess their implications on the station rankings and the optimal networks. The results showed that the selection of quantization method should be considered carefully because the rankings and optimal networks are subject to change accordingly.

Integrated design of multivariable hydrometric networks using entropy theory with a multiobjective optimization approach

NASA Astrophysics Data System (ADS)

Keum, J.; Coulibaly, P. D.

2017-12-01

Obtaining quality hydrologic observations is the first step towards a successful water resources management. While remote sensing techniques have enabled to convert satellite images of the Earth's surface to hydrologic data, the importance of ground-based observations has never been diminished because in-situ data are often highly accurate and can be used to validate remote measurements. The existence of efficient hydrometric networks is becoming more important to obtain as much as information with minimum redundancy. The World Meteorological Organization (WMO) has recommended a guideline for the minimum hydrometric network density based on physiography; however, this guideline is not for the optimum network design but for avoiding serious deficiency from a network. Moreover, all hydrologic variables are interconnected within the hydrologic cycle, while monitoring networks have been designed individually. This study proposes an integrated network design method using entropy theory with a multiobjective optimization approach. In specific, a precipitation and a streamflow networks in a semi-urban watershed in Ontario, Canada were designed simultaneously by maximizing joint entropy, minimizing total correlation, and maximizing conditional entropy of streamflow network given precipitation network. After comparing with the typical individual network designs, the proposed design method would be able to determine more efficient optimal networks by avoiding the redundant stations, in which hydrologic information is transferable. Additionally, four quantization cases were applied in entropy calculations to assess their implications on the station rankings and the optimal networks. The results showed that the selection of quantization method should be considered carefully because the rankings and optimal networks are subject to change accordingly.

Network Approach to Autistic Traits: Group and Subgroup Analyses of ADOS Item Scores

ERIC Educational Resources Information Center

Anderson, George M.; Montazeri, Farhad; de Bildt, Annelies

2015-01-01

A network conceptualization might contribute to understanding the occurrence and interacting nature of behavioral traits in the autism realm. Networks were constructed based on correlations of item scores of the Autism Diagnostic Observation Schedule for Modules 1, 2 and 3 obtained for a group of 477 Dutch individuals with developmental disorders.…

An Observing System Simulation Experiment Approach to Meteorological Network Assessment

NASA Astrophysics Data System (ADS)

Abbasnezhadi, K.; Rasmussen, P. F.; Stadnyk, T.; Boluwade, A.

2016-12-01

A proper knowledge of the spatiotemporal distribution of rainfall is important in order to conduct a mindful investigation of water movement and storage throughout a catchment. Currently, the most accurate precipitation information available for the remote Boreal ecozones of northern Manitoba is coming from the Canadian Precipitation Analysis (CaPA) data assimilation system. Throughout the Churchill River Basin (CRB), CaPA still does not have the proper skill due to the limited number of weather stations. A new approach to experimental network design was investigated based on the concept of Observing System Simulation Experiment (OSSE). The OSSE-based network assessment procedure which simulates the CaPA system provides a scientific and hydrologically significant tool to assess the sensitivity of CaPA precipitation analysis to observation network density throughout the CRB. To simulate CaPA system, synthetic background and station data were simulated, respectively, by adding spatially uncorrelated and correlated Gaussian noises to an assumingly true daily weather field synthesized by a gridded precipitation generator which simulates CaPA data. Given the true reference field on one hand, and a set of pseudo-CaPA analyses associated with different network realizations on the other hand, a WATFLOOD hydrological model was employed to compare the modeled runoff. The simulations showed that as network density increases, the accuracy of CaPA precipitation products improves up to a certain limit beyond which adding more stations to the network does not result in further accuracy.

Steering operational synergies in terrestrial observation networks: opportunity for advancing Earth system dynamics modelling

NASA Astrophysics Data System (ADS)

Baatz, Roland; Sullivan, Pamela L.; Li, Li; Weintraub, Samantha R.; Loescher, Henry W.; Mirtl, Michael; Groffman, Peter M.; Wall, Diana H.; Young, Michael; White, Tim; Wen, Hang; Zacharias, Steffen; Kühn, Ingolf; Tang, Jianwu; Gaillardet, Jérôme; Braud, Isabelle; Flores, Alejandro N.; Kumar, Praveen; Lin, Henry; Ghezzehei, Teamrat; Jones, Julia; Gholz, Henry L.; Vereecken, Harry; Van Looy, Kris

2018-05-01

Advancing our understanding of Earth system dynamics (ESD) depends on the development of models and other analytical tools that apply physical, biological, and chemical data. This ambition to increase understanding and develop models of ESD based on site observations was the stimulus for creating the networks of Long-Term Ecological Research (LTER), Critical Zone Observatories (CZOs), and others. We organized a survey, the results of which identified pressing gaps in data availability from these networks, in particular for the future development and evaluation of models that represent ESD processes, and provide insights for improvement in both data collection and model integration. From this survey overview of data applications in the context of LTER and CZO research, we identified three challenges: (1) widen application of terrestrial observation network data in Earth system modelling, (2) develop integrated Earth system models that incorporate process representation and data of multiple disciplines, and (3) identify complementarity in measured variables and spatial extent, and promoting synergies in the existing observational networks. These challenges lead to perspectives and recommendations for an improved dialogue between the observation networks and the ESD modelling community, including co-location of sites in the existing networks and further formalizing these recommendations among these communities. Developing these synergies will enable cross-site and cross-network comparison and synthesis studies, which will help produce insights around organizing principles, classifications, and general rules of coupling processes with environmental conditions.

The ANTARES observation network

NASA Astrophysics Data System (ADS)

Dogliotti, Ana I.; Ulloa, Osvaldo; Muller-Karger, Frank; Hu, Chuanmin; Murch, Brock; Taylor, Charles; Yuras, Gabriel; Kampel, Milton; Lutz, Vivian; Gaeta, Salvador; Gagliardini, Domingo A.; Garcia, Carlos A. E.; Klein, Eduardo; Helbling, Walter; Varela, Ramon; Barbieri, Elena; Negri, Ruben; Frouin, Robert; Sathyendranath, Shubha; Platt, Trevor

2005-08-01

The ANTARES network seeks to understand the variability of the coastal environment on a continental scale and the local, regional, and global factors and processes that effect this change. The focus are coastal zones of South America and the Caribbean Sea. The initial approach includes developing time series of in situ and satellite-based environmental observations in coastal and oceanic regions. The network is constituted by experts that seek to exchange ideas, develop an infrastructure for mutual logistical and knowledge support, and link in situ time series of observations located around the Americas with real-time and historical satellite-derived time series of relevant products. A major objective is to generate information that will be distributed publicly and openly in the service of coastal ocean research, resource management, science-based policy making and education in the Americas. As a first stage, the network has linked oceanographic time series located in Argentina, Brazil, Chile and Venezuela. The group has also developed an online tool to examine satellite data collected with sensors such as NASA's MODIS. Specifically, continental-scale high-resolution (1 km) maps of chlorophyll and of sea surface temperature are generated and served daily over the web according to specifications of users within the ANTARES network. Other satellite-derived variables will be added as support for the network is solidified. ANTARES serves data and offers simple analysis tools that anyone can use with the ultimate goal of improving coastal assessments, management and policies.

L-GRAAL: Lagrangian graphlet-based network aligner.

PubMed

Malod-Dognin, Noël; Pržulj, Nataša

2015-07-01

Discovering and understanding patterns in networks of protein-protein interactions (PPIs) is a central problem in systems biology. Alignments between these networks aid functional understanding as they uncover important information, such as evolutionary conserved pathways, protein complexes and functional orthologs. A few methods have been proposed for global PPI network alignments, but because of NP-completeness of underlying sub-graph isomorphism problem, producing topologically and biologically accurate alignments remains a challenge. We introduce a novel global network alignment tool, Lagrangian GRAphlet-based ALigner (L-GRAAL), which directly optimizes both the protein and the interaction functional conservations, using a novel alignment search heuristic based on integer programming and Lagrangian relaxation. We compare L-GRAAL with the state-of-the-art network aligners on the largest available PPI networks from BioGRID and observe that L-GRAAL uncovers the largest common sub-graphs between the networks, as measured by edge-correctness and symmetric sub-structures scores, which allow transferring more functional information across networks. We assess the biological quality of the protein mappings using the semantic similarity of their Gene Ontology annotations and observe that L-GRAAL best uncovers functionally conserved proteins. Furthermore, we introduce for the first time a measure of the semantic similarity of the mapped interactions and show that L-GRAAL also uncovers best functionally conserved interactions. In addition, we illustrate on the PPI networks of baker's yeast and human the ability of L-GRAAL to predict new PPIs. Finally, L-GRAAL's results are the first to show that topological information is more important than sequence information for uncovering functionally conserved interactions. L-GRAAL is coded in C++. Software is available at: http://bio-nets.doc.ic.ac.uk/L-GRAAL/. n.malod-dognin@imperial.ac.uk Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press.

A novel interacting multiple model based network intrusion detection scheme

NASA Astrophysics Data System (ADS)

Xin, Ruichi; Venkatasubramanian, Vijay; Leung, Henry

2006-04-01

In today's information age, information and network security are of primary importance to any organization. Network intrusion is a serious threat to security of computers and data networks. In internet protocol (IP) based network, intrusions originate in different kinds of packets/messages contained in the open system interconnection (OSI) layer 3 or higher layers. Network intrusion detection and prevention systems observe the layer 3 packets (or layer 4 to 7 messages) to screen for intrusions and security threats. Signature based methods use a pre-existing database that document intrusion patterns as perceived in the layer 3 to 7 protocol traffics and match the incoming traffic for potential intrusion attacks. Alternately, network traffic data can be modeled and any huge anomaly from the established traffic pattern can be detected as network intrusion. The latter method, also known as anomaly based detection is gaining popularity for its versatility in learning new patterns and discovering new attacks. It is apparent that for a reliable performance, an accurate model of the network data needs to be established. In this paper, we illustrate using collected data that network traffic is seldom stationary. We propose the use of multiple models to accurately represent the traffic data. The improvement in reliability of the proposed model is verified by measuring the detection and false alarm rates on several datasets.

Applications of neural network methods to the processing of earth observation satellite data.

PubMed

Loyola, Diego G

2006-03-01

The new generation of earth observation satellites carries advanced sensors that will gather very precise data for studying the Earth system and global climate. This paper shows that neural network methods can be successfully used for solving forward and inverse remote sensing problems, providing both accurate and fast solutions. Two examples of multi-neural network systems for the determination of cloud properties and for the retrieval of total columns of ozone using satellite data are presented. The developed algorithms based on multi-neural network are currently being used for the operational processing of European atmospheric satellite sensors and will play a key role in related satellite missions planed for the near future.

Passive and Active Analysis in DSR-Based Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Dempsey, Tae; Sahin, Gokhan; Morton, Y. T. (Jade)

Security and vulnerabilities in wireless ad hoc networks have been considered at different layers, and many attack strategies have been proposed, including denial of service (DoS) through the intelligent jamming of the most critical packet types of flows in a network. This paper investigates the effectiveness of intelligent jamming in wireless ad hoc networks using the Dynamic Source Routing (DSR) and TCP protocols and introduces an intelligent classifier to facilitate the jamming of such networks. Assuming encrypted packet headers and contents, our classifier is based solely on the observable characteristics of size, inter-arrival timing, and direction and classifies packets with up to 99.4% accuracy in our experiments. Furthermore, we investigate active analysis, which is the combination of a classifier and intelligent jammer to invoke specific responses from a victim network.

Improved classification of drainage networks using junction angles and secondary tributary lengths

NASA Astrophysics Data System (ADS)

Jung, Kichul; Marpu, Prashanth R.; Ouarda, Taha B. M. J.

2015-06-01

River networks in different regions have distinct characteristics generated by geological processes. These differences enable classification of drainage networks using several measures with many features of the networks. In this study, we propose a new approach that only uses the junction angles with secondary tributary lengths to directly classify different network types. This methodology is based on observations on 50 predefined channel networks. The cumulative distributions of secondary tributary lengths for different ranges of junction angles are used to obtain the descriptive values that are defined using a power-law representation. The averages of the values for the known networks are used to represent the classes, and any unclassified network can be classified based on the similarity of the representative values to those of the known classes. The methodology is applied to 10 networks in the United Arab Emirates and Oman and five networks in the USA, and the results are validated using the classification obtained with other methods.

A Network Model of Observation and Imitation of Speech

PubMed Central

Mashal, Nira; Solodkin, Ana; Dick, Anthony Steven; Chen, E. Elinor; Small, Steven L.

2012-01-01

Much evidence has now accumulated demonstrating and quantifying the extent of shared regional brain activation for observation and execution of speech. However, the nature of the actual networks that implement these functions, i.e., both the brain regions and the connections among them, and the similarities and differences across these networks has not been elucidated. The current study aims to characterize formally a network for observation and imitation of syllables in the healthy adult brain and to compare their structure and effective connectivity. Eleven healthy participants observed or imitated audiovisual syllables spoken by a human actor. We constructed four structural equation models to characterize the networks for observation and imitation in each of the two hemispheres. Our results show that the network models for observation and imitation comprise the same essential structure but differ in important ways from each other (in both hemispheres) based on connectivity. In particular, our results show that the connections from posterior superior temporal gyrus and sulcus to ventral premotor, ventral premotor to dorsal premotor, and dorsal premotor to primary motor cortex in the left hemisphere are stronger during imitation than during observation. The first two connections are implicated in a putative dorsal stream of speech perception, thought to involve translating auditory speech signals into motor representations. Thus, the current results suggest that flow of information during imitation, starting at the posterior superior temporal cortex and ending in the motor cortex, enhances input to the motor cortex in the service of speech execution. PMID:22470360

An Aerosol Extinction-to-Backscatter Ratio Database Derived from the NASA Micro-Pulse Lidar Network: Applications for Space-based Lidar Observations

NASA Technical Reports Server (NTRS)

Welton, Ellsworth J.; Campbell, James R.; Spinhime, James D.; Berkoff, Timothy A.; Holben, Brent; Tsay, Si-Chee; Bucholtz, Anthony

2004-01-01

Backscatter lidar signals are a function of both backscatter and extinction. Hence, these lidar observations alone cannot separate the two quantities. The aerosol extinction-to-backscatter ratio, S, is the key parameter required to accurately retrieve extinction and optical depth from backscatter lidar observations of aerosol layers. S is commonly defined as 4*pi divided by the product of the single scatter albedo and the phase function at 180-degree scattering angle. Values of S for different aerosol types are not well known, and are even more difficult to determine when aerosols become mixed. Here we present a new lidar-sunphotometer S database derived from Observations of the NASA Micro-Pulse Lidar Network (MPLNET). MPLNET is a growing worldwide network of eye-safe backscatter lidars co-located with sunphotometers in the NASA Aerosol Robotic Network (AERONET). Values of S for different aerosol species and geographic regions will be presented. A framework for constructing an S look-up table will be shown. Look-up tables of S are needed to calculate aerosol extinction and optical depth from space-based lidar observations in the absence of co-located AOD data. Applications for using the new S look-up table to reprocess aerosol products from NASA's Geoscience Laser Altimeter System (GLAS) will be discussed.

Stock market index prediction using neural networks

NASA Astrophysics Data System (ADS)

Komo, Darmadi; Chang, Chein-I.; Ko, Hanseok

1994-03-01

A neural network approach to stock market index prediction is presented. Actual data of the Wall Street Journal's Dow Jones Industrial Index has been used for a benchmark in our experiments where Radial Basis Function based neural networks have been designed to model these indices over the period from January 1988 to Dec 1992. A notable success has been achieved with the proposed model producing over 90% prediction accuracies observed based on monthly Dow Jones Industrial Index predictions. The model has also captured both moderate and heavy index fluctuations. The experiments conducted in this study demonstrated that the Radial Basis Function neural network represents an excellent candidate to predict stock market index.

Social networks dynamics revealed by temporal analysis: An example in a non-human primate (Macaca sylvanus) in "La Forêt des Singes".

PubMed

Sosa, Sebastian; Zhang, Peng; Cabanes, Guénaël

2017-06-01

This study applied a temporal social network analysis model to describe three affiliative social networks (allogrooming, sleep in contact, and triadic interaction) in a non-human primate species, Macaca sylvanus. Three main social mechanisms were examined to determine interactional patterns among group members, namely preferential attachment (i.e., highly connected individuals are more likely to form new connections), triadic closure (new connections occur via previous close connections), and homophily (individuals interact preferably with others with similar attributes). Preferential attachment was only observed for triadic interaction network. Triadic closure was significant in allogrooming and triadic interaction networks. Finally, gender homophily was seasonal for allogrooming and sleep in contact networks, and observed in each period for triadic interaction network. These individual-based behaviors are based on individual reactions, and their analysis can shed light on the formation of the affiliative networks determining ultimate coalition networks, and how these networks may evolve over time. A focus on individual behaviors is necessary for a global interactional approach to understanding social behavior rules and strategies. When combined, these social processes could make animal social networks more resilient, thus enabling them to face drastic environmental changes. This is the first study to pinpoint some of the processes underlying the formation of a social structure in a non-human primate species, and identify common mechanisms with humans. The approach used in this study provides an ideal tool for further research seeking to answer long-standing questions about social network dynamics. © 2017 Wiley Periodicals, Inc.

EOP and scale from continuous VLBI observing: CONT campaigns to future VGOS networks

NASA Astrophysics Data System (ADS)

MacMillan, D. S.

2017-07-01

Continuous (CONT) VLBI campaigns have been carried out about every 3 years since 2002. The basic idea of these campaigns is to acquire state-of-the-art VLBI data over a continuous time period of about 2 weeks to demonstrate the highest accuracy of which the current VLBI system is capable. In addition, these campaigns support scientific studies such as investigations of high-resolution Earth rotation, reference frame stability, and daily to sub-daily site motions. The size of the CONT networks and the observing data rate have increased steadily since 1994. Performance of these networks based on reference frame scale precision and polar motion/LOD comparison with global navigation satellite system (GNSS) earth orientation parameters (EOP) has been substantially better than the weekly operational R1 and R4 series. The precisions of CONT EOP and scale have improved by more than a factor of two since 2002. Polar motion precision based on the WRMS difference between VLBI and GNSS for the most recent CONT campaigns is at the 30 μas level, which is comparable to that of GNSS. The CONT campaigns are a natural precursor to the planned future VLBI observing networks, which are expected to observe continuously. We compare the performance of the most recent CONT campaigns in 2011 and 2014 with the expected performance of the future VLBI global observing system network using simulations. These simulations indicate that the expected future precision of scale and EOP will be at least 3 times better than the current CONT precision.

Noisy scale-free networks

NASA Astrophysics Data System (ADS)

Scholz, Jan; Dejori, Mathäus; Stetter, Martin; Greiner, Martin

2005-05-01

The impact of observational noise on the analysis of scale-free networks is studied. Various noise sources are modeled as random link removal, random link exchange and random link addition. Emphasis is on the resulting modifications for the node-degree distribution and for a functional ranking based on betweenness centrality. The implications for estimated gene-expressed networks for childhood acute lymphoblastic leukemia are discussed.

Friendship Network and Dental Brushing Behavior among Middle School Students: An Agent Based Modeling Approach.

PubMed

Sadeghipour, Maryam; Khoshnevisan, Mohammad Hossein; Jafari, Afshin; Shariatpanahi, Seyed Peyman

2017-01-01

By using a standard questionnaire, the level of dental brushing frequency was assessed among 201 adolescent female middle school students in Tehran. The initial assessment was repeated after 5 months, in order to observe the dynamics in dental health behavior level. Logistic Regression model was used to evaluate the correlation among individuals' dental health behavior in their social network. A significant correlation on dental brushing habits was detected among groups of friends. This correlation was further spread over the network within the 5 months period. Moreover, it was identified that the average brushing level was improved within the 5 months period. Given that there was a significant correlation between social network's nodes' in-degree value, and brushing level, it was suggested that the observed improvement was partially due to more popularity of individuals with better tooth brushing habit. Agent Based Modeling (ABM) was used to demonstrate the dynamics of dental brushing frequency within a sample of friendship network. Two models with static and dynamic assumptions for the network structure were proposed. The model with dynamic network structure successfully described the dynamics of dental health behavior. Based on this model, on average, every 43 weeks a student changes her brushing habit due to learning from her friends. Finally, three training scenarios were tested by these models in order to evaluate their effectiveness. When training more popular students, considerable improvement in total students' brushing frequency was demonstrated by simulation results.

Nondeducibility-Based Analysis of Cyber-Physical Systems

NASA Astrophysics Data System (ADS)

Gamage, Thoshitha; McMillin, Bruce

Controlling information flow in a cyber-physical system (CPS) is challenging because cyber domain decisions and actions manifest themselves as visible changes in the physical domain. This paper presents a nondeducibility-based observability analysis for CPSs. In many CPSs, the capacity of a low-level (LL) observer to deduce high-level (HL) actions ranges from limited to none. However, a collaborative set of observers strategically located in a network may be able to deduce all the HL actions. This paper models a distributed power electronics control device network using a simple DC circuit in order to understand the effect of multiple observers in a CPS. The analysis reveals that the number of observers required to deduce all the HL actions in a system increases linearly with the number of configurable units. A simple definition of nondeducibility based on the uniqueness of low-level projections is also presented. This definition is used to show that a system with two security domain levels could be considered “nondeducibility secure” if no unique LL projections exist.

Eco-hydrological Wireless Sensor Network and upscaling method research in the Heihe River Basin, China

NASA Astrophysics Data System (ADS)

Jin, Rui; kang, Jian

2017-04-01

Wireless Sensor Networks are recognized as one of most important near-surface components of GEOSS (Global Earth Observation System of Systems), with flourish development of low-cost, robust and integrated data loggers and sensors. A nested eco-hydrological wireless sensor network (EHWSN) was installed in the up- and middle-reaches of the Heihe River Basin, operated to obtain multi-scale observation of soil moisture, soil temperature and land surface temperature from 2012 till now. The spatial distribution of EHWSN was optimally designed based on the geo-statistical theory, with the aim to capture the spatial variations and temporal dynamics of soil moisture and soil temperature, and to produce ground truth at grid scale for validating the related remote sensing products and model simulation in the heterogeneous land surface. In terms of upscaling research, we have developed a set of method to aggregate multi-point WSN observations to grid scale ( 1km), including regression kriging estimation to utilize multi-resource remote sensing auxiliary information, block kriging with homogeneous measurement errors, and bayesian-based upscaling algorithm that utilizes MODIS-derived apparent thermal inertia. All the EHWSN observation are organized as datasets to be freely published at http://westdc.westgis.ac.cn/hiwater. EHWSN integrates distributed observation nodes to achieve an automated, intelligent and remote-controllable network that provides superior integrated, standardized and automated observation capabilities for hydrological and ecological processes research at the basin scale.

Managing cancer care through service delivery networks: The role of professional collaboration in two European cancer networks.

PubMed

Prades, Joan; Morando, Verdiana; Tozzi, Valeria D; Verhoeven, Didier; Germà, Jose R; Borras, Josep M

2017-01-01

Background The study examines two meso-strategic cancer networks, exploring to what extent collaboration can strengthen or hamper network effectiveness. Unlike macro-strategic networks, meso-strategic networks have no hierarchical governance structures nor are they institutionalised within healthcare services' delivery systems. This study aims to analyse the models of professional cooperation and the tools developed for managing clinical practice within two meso-strategic, European cancer networks. Methods Multiple case study design based on the comparative analysis of two cancer networks: Iridium, in Antwerp, Belgium and the Institut Català d'Oncologia in Catalonia, Spain. The case studies applied mixed methods, with qualitative research based on semi-structured interviews ( n = 35) together with case-site observation and material collection. Results The analysis identified four levels of collaborative intensity within medical specialties as well as in multidisciplinary settings, which became both platforms for crosscutting clinical work between hubs' experts and local care teams and the levers for network-based tools development. The organisation of clinical practice relied on professional-based cooperative processes and tiers, lacking vertical integration mechanisms. Conclusions The intensity of professional linkages largely shaped the potential of meso-strategic cancer networks to influence clinical practice organisation. Conversely, the introduction of managerial techniques or network governance structures, without introducing vertical hierarchies, was found to be critical solutions.

A Unified Framework for Complex Networks with Degree Trichotomy Based on Markov Chains.

PubMed

Hui, David Shui Wing; Chen, Yi-Chao; Zhang, Gong; Wu, Weijie; Chen, Guanrong; Lui, John C S; Li, Yingtao

2017-06-16

This paper establishes a Markov chain model as a unified framework for describing the evolution processes in complex networks. The unique feature of the proposed model is its capability in addressing the formation mechanism that can reflect the "trichotomy" observed in degree distributions, based on which closed-form solutions can be derived. Important special cases of the proposed unified framework are those classical models, including Poisson, Exponential, Power-law distributed networks. Both simulation and experimental results demonstrate a good match of the proposed model with real datasets, showing its superiority over the classical models. Implications of the model to various applications including citation analysis, online social networks, and vehicular networks design, are also discussed in the paper.

Optimal Interpolation scheme to generate reference crop evapotranspiration

NASA Astrophysics Data System (ADS)

Tomas-Burguera, Miquel; Beguería, Santiago; Vicente-Serrano, Sergio; Maneta, Marco

2018-05-01

We used an Optimal Interpolation (OI) scheme to generate a reference crop evapotranspiration (ETo) grid, forcing meteorological variables, and their respective error variance in the Iberian Peninsula for the period 1989-2011. To perform the OI we used observational data from the Spanish Meteorological Agency (AEMET) and outputs from a physically-based climate model. To compute ETo we used five OI schemes to generate grids for the five observed climate variables necessary to compute ETo using the FAO-recommended form of the Penman-Monteith equation (FAO-PM). The granularity of the resulting grids are less sensitive to variations in the density and distribution of the observational network than those generated by other interpolation methods. This is because our implementation of the OI method uses a physically-based climate model as prior background information about the spatial distribution of the climatic variables, which is critical for under-observed regions. This provides temporal consistency in the spatial variability of the climatic fields. We also show that increases in the density and improvements in the distribution of the observational network reduces substantially the uncertainty of the climatic and ETo estimates. Finally, a sensitivity analysis of observational uncertainties and network densification suggests the existence of a trade-off between quantity and quality of observations.

Coordination and Integration of Global Ocean Observing through JCOMM

NASA Astrophysics Data System (ADS)

Legler, D. M.; Meldrum, D. T.; Hill, K. L.; Charpentier, E.

2016-02-01

The primary objective of the JCOMM Observations Coordination Group (OCG) is to provide technical coordination to implement fully integrated ocean observing system across the entire marine meteorology and oceanographic community. JCOMM OCG works in partnership with the Global Ocean Observing System, , which focusses on setting observing system requirements and conducting evalutions. JCOMM OCG initially focused on major global observing networks (e.g. Argo profiling floats, moored buoys, ship based observations, sea level stations, reference sites, etc), and is now expanding its horizon in recognition of new observing needs and new technologies/networks (e.g. ocean gliders). Over the next five years the JCOMM OCG is focusing its attention on integration and coordination in four major areas: observing network implementation particularly in response to integrated ocean observing requirements; observing system monitoring and metrics; standards and best practices; and improving integrated data management and access. This presentation will describe the scope and mission of JCOMM OCG; summarize the state of the global ocean observing system; highlight recent successes and resources for the research, prediction, and assessment communities; summarize our plans for the next several years; and suggest engagement opportunities.

MultiWaveLink: An interactive data base for the coordination of multiwavelength and multifacility observations

NASA Technical Reports Server (NTRS)

Cordova, F. A.

1993-01-01

MultiWaveLink is an interactive, computerized data base that was developed to facilitate a multi-wavelength approach to studying astrophysical sources. It can be used to access information about multiwavelenth resources (observers, telescopes, data bases and analysis facilities) or to organize observing campaigns that require either many telescopes operating in different spectral regimes or a network of similar telescopes circumspanning the Earth.

Link predication based on matrix factorization by fusion of multi class organizations of the network.

PubMed

Jiao, Pengfei; Cai, Fei; Feng, Yiding; Wang, Wenjun

2017-08-21

Link predication aims at forecasting the latent or unobserved edges in the complex networks and has a wide range of applications in reality. Almost existing methods and models only take advantage of one class organization of the networks, which always lose important information hidden in other organizations of the network. In this paper, we propose a link predication framework which makes the best of the structure of networks in different level of organizations based on nonnegative matrix factorization, which is called NMF 3 here. We first map the observed network into another space by kernel functions, which could get the different order organizations. Then we combine the adjacency matrix of the network with one of other organizations, which makes us obtain the objective function of our framework for link predication based on the nonnegative matrix factorization. Third, we derive an iterative algorithm to optimize the objective function, which converges to a local optimum, and we propose a fast optimization strategy for large networks. Lastly, we test the proposed framework based on two kernel functions on a series of real world networks under different sizes of training set, and the experimental results show the feasibility, effectiveness, and competitiveness of the proposed framework.

Cancer Transcriptome Dataset Analysis: Comparing Methods of Pathway and Gene Regulatory Network-Based Cluster Identification.

PubMed

Nam, Seungyoon

2017-04-01

Cancer transcriptome analysis is one of the leading areas of Big Data science, biomarker, and pharmaceutical discovery, not to forget personalized medicine. Yet, cancer transcriptomics and postgenomic medicine require innovation in bioinformatics as well as comparison of the performance of available algorithms. In this data analytics context, the value of network generation and algorithms has been widely underscored for addressing the salient questions in cancer pathogenesis. Analysis of cancer trancriptome often results in complicated networks where identification of network modularity remains critical, for example, in delineating the "druggable" molecular targets. Network clustering is useful, but depends on the network topology in and of itself. Notably, the performance of different network-generating tools for network cluster (NC) identification has been little investigated to date. Hence, using gastric cancer (GC) transcriptomic datasets, we compared two algorithms for generating pathway versus gene regulatory network-based NCs, showing that the pathway-based approach better agrees with a reference set of cancer-functional contexts. Finally, by applying pathway-based NC identification to GC transcriptome datasets, we describe cancer NCs that associate with candidate therapeutic targets and biomarkers in GC. These observations collectively inform future research on cancer transcriptomics, drug discovery, and rational development of new analysis tools for optimal harnessing of omics data.

The LCO Follow-up and Characterization Network and AgentNEO Citizen Science Project

NASA Astrophysics Data System (ADS)

Lister, Tim; Greenstreet, Sarah; Gomez, Edward; Christensen, Eric J.; Larson, Stephen M.

2017-10-01

The LCO NEO Follow-up Network is using the telescopes of the Las Cumbres Observatory (LCO) and a web-based target selection, scheduling and data reduction system to confirm NEO candidates and characterize radar-targeted known NEOs. Starting in July 2014, the LCO NEO Follow-up Network has observed over 4,500 targets and reported more than 25,000 astrometric and photometric measurements to the Minor Planet Center.The LCO NEO Follow-up Network's main aims are to perform confirming follow-up of the large number of NEO candidates and to perform characterization measurements of radar targets to obtain light curves and rotation rates. The NEO candidates come from the NEO surveys such as Catalina, PanSTARRS, ATLAS, NEOWISE and others. In particular, we are targeting objects in the Southern Hemisphere, where the LCO NEO Follow-up Network is the largest resource for NEO observations.The first phase of the LCO Network comprises nine 1-meter and seven 0.4-meter telescopes at site at McDonald Observatory (Texas), Cerro Tololo (Chile), SAAO (South Africa) and Siding Spring Observatory (Australia). The network has been fully operational since 2014 May, and observations are being executed remotely and robotically. Additional 0.4-meter telescopes will be deployed in 2017 and 2x1-meter telescopes for a site at Ali Observatory, Tibet are planned for 2018-2019.We have developed web-based software called NEOexchange which automatically downloads and aggregates NEO candidates from the Minor Planet Center's NEO Confirmation Page, the Arecibo and Goldstone radar target lists and the NASA lists. NEOexchange allows the planning and scheduling of observations on the LCO Telescope Network and the tracking of the resulting blocks and generated data. We have extended the NEOexchange software to include automated scheduling and moving object detection, with the results presented to the user via the website.We will present results from the LCO NEO Follow-up Network and from the development of the NEOexchange software which is used to schedule, analyze and report observations taken with the LCO Network. In addition, we describe a Citizen Science project, AgentNEO, which uses LCO data to allow the public to find and learn about asteroids.

Mobile and static sensors in a citizen-based observatory of water

NASA Astrophysics Data System (ADS)

Brauchli, Tristan; Weijs, Steven V.; Lehning, Michael; Huwald, Hendrik

2014-05-01

Understanding and forecasting water resources and components of the water cycle require spatially and temporally resolved observations of numerous water-related variables. Such observations are often obtained from wireless networks of automated weather stations. The "WeSenseIt" project develops a citizen- and community-based observatory of water to improve the water and risk management at the catchment scale and to support decision-making of stakeholders. It is implemented in three case studies addressing various questions related to flood, drought, water resource management, water quality and pollution. Citizens become potential observers and may transmit water-related measurements and information. Combining the use of recent technologies (wireless communication, internet, smartphone) with the development of innovative low cost sensors enables the implementation of heterogeneous observatories, which (a) empower citizens and (b) expand and complement traditional operational sensing networks. With the goal of increasing spatial coverage of observations and decreasing cost for sensors, this study presents the examples of measuring (a) flow velocity in streams using smartphones and (b) sensible heat flux using simple sensors at the nodes of wireless sensor networks.

Further development of the EUMETNET Composite Observing System (EUCOS)

NASA Astrophysics Data System (ADS)

Klink, S.; Dibbern, J.

2009-09-01

EUCOS, which stands for EUMETNET Composite Observing System, is a EUMETNET programme whose main objective is a central management of surface based operational observations on a European-wide scale serving the needs of regional scale NWP. EUMETNET is a consortium of currently 26 national meteorological services in Europe that provides a framework for different operational and developmental co-operative programmes between the services. The work content of the EUCOS Programme includes the management of the operational observing networks, through the E-AMDAR, E-ASAP, E-SURFMAR and E-WINPROF programmes. The coordination of NMSs owned territorial networks (e.g. radiosonde stations and synoptic stations), data quality monitoring, fault reporting and recovery, a studies programme for the evolution of the observing networks and liaison with other organisations like WMO are among the tasks of the programme. The current period of the EUCOS programme has a five year duration (2007-2011) and a two stage approach was proposed in the programme definition. During the transition phase 2007-2008 no new programmatic objectives had been set because amongst others the Space-Terrestrial (S-T) study which investigated the relative contributions of selected space based and ground based observing systems to the forecast skill of global and regional NWP models had to be finalised first. Based on the findings of this study EUCOS currently prepares a redesign of its upper-air network. The original EUCOS upper-air network design was prepared in 2000 in order to define a set of stations serving the common general NWP requirement. Additional considerations were to make it possible to supply a common set of performance standards across the territory of EUMETNET Members and to ensure that the radiosonde network interleaved with AMDAR airports. The EUCOS upper-air network now requires a redesign because of several reasons. There is a need to take into account the significant evolution of the AMDAR network. Member states were not able to install the proposed EUCOS radiosonde network design with 4 ascents per day at most of the sites. The results from the S-T study are available with recommendations for the network design. Data assimilation of NWP models has improved significantly with advanced capability to make use of high time resolution data. The guidelines for the redesign of the EUCOS upper-air network will be derived from a study which is currently organised by EUCOS and conducted by ECMWF and several national Met. services. They contribute by running OSEs for different observation network setups with their model suites. The S-T study has shown that despite of all the additional new satellite observations, the degrading of the current terrestrial observing system to a basic (GUAN+GSN) network would have a significant negative impact on the forecast skill. The expected result from the envisaged OSEs is to find an optimum setting of upper-air measurements in space and time which maintains forecast skill. Throughout the second phase of the programme (2009-2011) the revised EUCOS design will be implemented. In the field of observation targeting EUCOS supported the PREVIEW Data Targeting (DTS) project. The main goal of this project was to develop and to assess the feasibility of an operational adaptive control of the operational observing system. The DTS project was lead by Met Office and co-funded by EUCOS and the European Commission (within the PREVIEW project). The main software, an interactive web-based tool, was developed by ECMWF and ran on their computer system during the trial phase which lasted from February until December 2008. During the trial the focus was on improving short range (1-3 days) forecasts of potentially high-impact and/or high-uncertainty weather events in Europe. Forecasters from all EUMETNET members had had the chance to submit sensitive area prediction requests on a daily basis. Afterwards the DTS displayed the sensitive areas calculated by ECMWF, Météo-France and Met Office and the lead user (an experienced forecaster) could then use the system to issue requests for additional, unscheduled observations. The trial has shown that a data targeting system can be routinely used. Targeted observations were successfully deployed from E-ASAP units, by the E-AMDAR programme and in 21 countries. 88% of the additionally requested radiosondes from land stations have been launched. Furthermore, the DTS was used to support research field campaigns like THORPEX-IPY, THORPEX-PARC and MEDEX. During the envisaged MEDEX Phase 2 campaign in autumn 2009, the DTS will be used as an operational tool to aid research. Further tasks for EUCOS will be the proposal and implementation of a new E-programme responsible for running a central data hub and centralised monitoring, setting of new objectives for the programme components E-ASAP, E-AMDAR, E-SURFMAR and E-WINPROF, and an extension of quality monitoring activities. An example for new programme objectives is the introduction of a humidity sensor on commercial aircraft within the E-AMDAR programme.

Dynamical Networks Characterization of Geomagnetic Substorms and Transient Response to the Solar Wind State.

NASA Astrophysics Data System (ADS)

Chapman, S. C.; Dods, J.; Gjerloev, J. W.

2017-12-01

Observations of how the solar wind interacts with earth's magnetosphere, and its dynamical response, are increasingly becoming a data analytics challenge. Constellations of satellites observe the solar corona, the upstream solar wind and throughout earth's magnetosphere. These data are multipoint in space and extended in time, so in principle are ideal for study using dynamical networks to characterize the full time evolving spatial pattern. We focus here on analysis of data from the full set of 100+ auroral ground based magnetometer stations that have been collated by SuperMAG. Spatio-temporal patterns of correlation between the magnetometer time series can be used to form a dynamical network [1]. The properties of the network can then be captured by (time dependent) network parameters. This offers the possibility of characterizing detailed spatio-temporal pattern by a few parameters, so that many events can then be compared [2] with each other. Whilst networks are in widespread use in the data analytics of societal and commercial data, there are additional challenges in their application to physical timeseries. Determining whether two nodes (here, ground based magnetometer stations) are connected in a network (seeing the same dynamics) requires normalization w.r.t. the detailed sensitivities and dynamical responses of specific observing stations and seasonal conductivity variations and we have developed methods to achieve this dynamical normalization. The detailed properties of the network capture time dependent spatial correlation in the magnetometer responses and we will show how this can be used to infer a transient current system response to magnetospheric activity. [l] Dods et al, J. Geophys. Res 120, doi:10.1002/2015JA02 (2015). [2] Dods et al, J. Geophys. Res. 122, doi:10.1002/2016JA02 (2017).

Observer-based output feedback control of networked control systems with non-uniform sampling and time-varying delay

NASA Astrophysics Data System (ADS)

Meng, Su; Chen, Jie; Sun, Jian

2017-10-01

This paper investigates the problem of observer-based output feedback control for networked control systems with non-uniform sampling and time-varying transmission delay. The sampling intervals are assumed to vary within a given interval. The transmission delay belongs to a known interval. A discrete-time model is first established, which contains time-varying delay and norm-bounded uncertainties coming from non-uniform sampling intervals. It is then converted to an interconnection of two subsystems in which the forward channel is delay-free. The scaled small gain theorem is used to derive the stability condition for the closed-loop system. Moreover, the observer-based output feedback controller design method is proposed by utilising a modified cone complementary linearisation algorithm. Finally, numerical examples illustrate the validity and superiority of the proposed method.

The GAW Aerosol Lidar Observation Network (GALION) as a source of near-real time aerosol profile data for model evaluation and assimilation

NASA Astrophysics Data System (ADS)

Hoff, R. M.; Pappalardo, G.

2010-12-01

In 2007, the WMO Global Atmospheric Watch’s Science Advisory Group on Aerosols described a global network of lidar networks called GAW Aerosol Lidar Observation Network (GALION). GALION has a purpose of providing expanded coverage of aerosol observations for climate and air quality use. Comprised of networks in Asia (AD-NET), Europe (EARLINET and CIS-LINET), North America (CREST and CORALNET), South America (ALINE) and with contribution from global networks such as MPLNET and NDACC, the collaboration provides a unique capability to define aerosol profiles in the vertical. GALION is designed to supplement existing ground-based and column profiling (AERONET, PHOTONS, SKYNET, GAWPFR) stations. In September 2010, GALION held its second workshop and one component of discussion focussed how the network would integrate into model needs. GALION partners have contributed to the Sand and Dust Storm Warning and Analysis System (SDS-WAS) and to assimilation in models such as DREAM. This paper will present the conclusions of those discussions and how these observations can fit into a global model analysis framework. Questions of availability, latency, and aerosol parameters that might be ingested into models will be discussed. An example of where EARLINET and GALION have contributed in near-real time observations was the suite of measurements during the Eyjafjallajokull eruption in Iceland and its impact on European air travel. Lessons learned from this experience will be discussed.

Risk assessment by dynamic representation of vulnerability, exploitation, and impact

NASA Astrophysics Data System (ADS)

Cam, Hasan

2015-05-01

Assessing and quantifying cyber risk accurately in real-time is essential to providing security and mission assurance in any system and network. This paper presents a modeling and dynamic analysis approach to assessing cyber risk of a network in real-time by representing dynamically its vulnerabilities, exploitations, and impact using integrated Bayesian network and Markov models. Given the set of vulnerabilities detected by a vulnerability scanner in a network, this paper addresses how its risk can be assessed by estimating in real-time the exploit likelihood and impact of vulnerability exploitation on the network, based on real-time observations and measurements over the network. The dynamic representation of the network in terms of its vulnerabilities, sensor measurements, and observations is constructed dynamically using the integrated Bayesian network and Markov models. The transition rates of outgoing and incoming links of states in hidden Markov models are used in determining exploit likelihood and impact of attacks, whereas emission rates help quantify the attack states of vulnerabilities. Simulation results show the quantification and evolving risk scores over time for individual and aggregated vulnerabilities of a network.

The GEOS-5 Neural Network Retrieval for AOD

NASA Astrophysics Data System (ADS)

Castellanos, P.; da Silva, A. M., Jr.

2017-12-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

The GEOS-5 Neural Network Retrieval (NNR) for AOD

NASA Technical Reports Server (NTRS)

Castellanos, Patricia; Da Silva, Arlindo

2017-01-01

One of the difficulties in data assimilation is the need for multi-sensor data merging that can account for temporal and spatial biases between satellite sensors. In the Goddard Earth Observing System Model Version 5 (GEOS-5) aerosol data assimilation system, a neural network retrieval (NNR) is used as a mapping between satellite observed top of the atmosphere (TOA) reflectance and AOD, which is the target variable that is assimilated in the model. By training observations of TOA reflectance from multiple sensors to map to a common AOD dataset (in this case AOD observed by the ground based Aerosol Robotic Network, AERONET), we are able to create a global, homogenous, satellite data record of AOD from MODIS observations on board the Terra and Aqua satellites. In this talk, I will present the implementation of and recent updates to the GEOS-5 NNR for MODIS collection 6 data.

IOOC Organizational Network (ION) Project

NASA Astrophysics Data System (ADS)

Dean, H.

2013-12-01

In order to meet the growing need for ocean information, research communities at the national and international levels have responded most recently by developing organizational frameworks that can help to integrate information across systems of existing networks and standardize methods of data gathering, management, and processing that facilitate integration. To address recommendations and identified challenges related to the need for a better understanding of ocean observing networks, members of the U.S. Interagency Ocean Observation Committee (IOOC) supported pursuing a project that came to be titled the IOOC Organizational Network (ION). The ION tool employs network mapping approaches which mirror approaches developed in academic literature aimed at understanding political networks. Researchers gathered data on the list of global ocean observing organizations included in the Framework for Ocean Observing (FOO), developed in 2012 by the international Task Team for an Integrated Framework for Sustained Ocean Observing. At the international scale, researchers reviewed organizational research plans and documents, websites, and formal international agreement documents. At the U.S. national scale, researchers analyzed legislation, formal inter-agency agreements, work plans, charters, and policy documents. Researchers based analysis of relationships among global organizations and national federal organizations on four broad relationship categories: Communications, Data, Infrastructure, and Human Resources. In addition to the four broad relationship categories, researchers also gathered data on relationship instrument types, strength of relationships, and (at the global level) ocean observing variables. Using network visualization software, researchers then developed a series of dynamic webpages. Researchers used the tool to address questions identified by the ocean observing community, including identifying gaps in global relationships and the types of tools used to develop networks at the U.S. national level. As the ION project goes through beta testing and is utilized to address specific questions posed by the ocean observing community, it will become more refined and more closely linked to user needs and interests.

Learning Bayesian Networks from Correlated Data

NASA Astrophysics Data System (ADS)

Bae, Harold; Monti, Stefano; Montano, Monty; Steinberg, Martin H.; Perls, Thomas T.; Sebastiani, Paola

2016-05-01

Bayesian networks are probabilistic models that represent complex distributions in a modular way and have become very popular in many fields. There are many methods to build Bayesian networks from a random sample of independent and identically distributed observations. However, many observational studies are designed using some form of clustered sampling that introduces correlations between observations within the same cluster and ignoring this correlation typically inflates the rate of false positive associations. We describe a novel parameterization of Bayesian networks that uses random effects to model the correlation within sample units and can be used for structure and parameter learning from correlated data without inflating the Type I error rate. We compare different learning metrics using simulations and illustrate the method in two real examples: an analysis of genetic and non-genetic factors associated with human longevity from a family-based study, and an example of risk factors for complications of sickle cell anemia from a longitudinal study with repeated measures.

The Energetic Transient Array ETA - A network of 'space buoys' in solar orbit for observations of gamma-ray bursts

NASA Technical Reports Server (NTRS)

Ricker, George R.

1990-01-01

The Energetic Transient Array (ETA) is a concept for a dedicated interplanetary network of about 40 microsatellites ('space buoys') deployed in an about 1 AU radius solar orbit for the observation of cosmic gamma ray bursts (GRBs). Such a network is essential for the determination of highly accurate (about 0.1 arcsec) error boxes for GRBs. For each of about 100 bursts which would be detectable per year of observation by such a network, high resolution spectra could be obtained through the use of passively-cooled Ge gamma-ray detectors. Stabilization of each microsatellite would be achieved by a novel technique based on the radiation pressure exerted on 'featherable' solar paddles. It should be possible to have a fully functional array of satellites in place before the end of the decade for a total cost of about $20M, exclusive of launcher fees.

Designing hydrologic monitoring networks to maximize predictability of hydrologic conditions in a data assimilation system: a case study from South Florida, U.S.A

NASA Astrophysics Data System (ADS)

Flores, A. N.; Pathak, C. S.; Senarath, S. U.; Bras, R. L.

2009-12-01

Robust hydrologic monitoring networks represent a critical element of decision support systems for effective water resource planning and management. Moreover, process representation within hydrologic simulation models is steadily improving, while at the same time computational costs are decreasing due to, for instance, readily available high performance computing resources. The ability to leverage these increasingly complex models together with the data from these monitoring networks to provide accurate and timely estimates of relevant hydrologic variables within a multiple-use, managed water resources system would substantially enhance the information available to resource decision makers. Numerical data assimilation techniques provide mathematical frameworks through which uncertain model predictions can be constrained to observational data to compensate for uncertainties in the model forcings and parameters. In ensemble-based data assimilation techniques such as the ensemble Kalman Filter (EnKF), information in observed variables such as canal, marsh and groundwater stages are propagated back to the model states in a manner related to: (1) the degree of certainty in the model state estimates and observations, and (2) the cross-correlation between the model states and the observable outputs of the model. However, the ultimate degree to which hydrologic conditions can be accurately predicted in an area of interest is controlled, in part, by the configuration of the monitoring network itself. In this proof-of-concept study we developed an approach by which the design of an existing hydrologic monitoring network is adapted to iteratively improve the predictions of hydrologic conditions within an area of the South Florida Water Management District (SFWMD). The objective of the network design is to minimize prediction errors of key hydrologic states and fluxes produced by the spatially distributed Regional Simulation Model (RSM), developed specifically to simulate the hydrologic conditions in several intensively managed and hydrologically complex watersheds within the SFWMD system. In a series of synthetic experiments RSM is used to generate the notionally true hydrologic state and the relevant observational data. The EnKF is then used as the mechanism to fuse RSM hydrologic estimates with data from the candidate network. The performance of the candidate network is measured by the prediction errors of the EnKF estimates of hydrologic states, relative to the notionally true scenario. The candidate network is then adapted by relocating existing observational sites to unobserved areas where predictions of local hydrologic conditions are most uncertain and the EnKF procedure repeated. Iteration of the monitoring network continues until further improvements in EnKF-based predictions of hydrologic conditions are negligible.

Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

NASA Astrophysics Data System (ADS)

Zhu, Xiaoyuan; Zhang, Hui; Yang, Bo; Zhang, Guichen

2018-01-01

In order to improve oscillation damping control performance as well as gear shift quality of electric vehicle equipped with integrated motor-transmission system, a cloud-based shaft torque estimation scheme is proposed in this paper by using measurable motor and wheel speed signals transmitted by wireless network. It can help reduce computational burden of onboard controllers and also relief network bandwidth requirement of individual vehicle. Considering possible delays during signal wireless transmission, delay-dependent full-order observer design is proposed to estimate the shaft torque in cloud server. With these random delays modeled by using homogenous Markov chain, robust H∞ performance is adopted to minimize the effect of wireless network-induced delays, signal measurement noise as well as system modeling uncertainties on shaft torque estimation error. Observer parameters are derived by solving linear matrix inequalities, and simulation results using acceleration test and tip-in, tip-out test demonstrate the effectiveness of proposed shaft torque observer design.

Remote sensing and the Mississippi high accuracy reference network

NASA Technical Reports Server (NTRS)

Mick, Mark; Alexander, Timothy M.; Woolley, Stan

1994-01-01

Since 1986, NASA's Commercial Remote Sensing Program (CRSP) at Stennis Space Center has supported commercial remote sensing partnerships with industry. CRSP's mission is to maximize U.S. market exploitation of remote sensing and related space-based technologies and to develop advanced technical solutions for spatial information requirements. Observation, geolocation, and communications technologies are converging and their integration is critical to realize the economic potential for spatial informational needs. Global positioning system (GPS) technology enables a virtual revolution in geopositionally accurate remote sensing of the earth. A majority of states are creating GPS-based reference networks, or high accuracy reference networks (HARN). A HARN can be defined for a variety of local applications and tied to aerial or satellite observations to provide an important contribution to geographic information systems (GIS). This paper details CRSP's experience in the design and implementation of a HARN in Mississippi and the design and support of future applications of integrated earth observations, geolocation, and communications technology.

The wireless networking system of Earthquake precursor mobile field observation

NASA Astrophysics Data System (ADS)

Wang, C.; Teng, Y.; Wang, X.; Fan, X.; Wang, X.

2012-12-01

The mobile field observation network could be real-time, reliably record and transmit large amounts of data, strengthen the physical signal observations in specific regions and specific period, it can improve the monitoring capacity and abnormal tracking capability. According to the features of scatter everywhere, a large number of current earthquake precursor observation measuring points, networking technology is based on wireless broadband accessing McWILL system, the communication system of earthquake precursor mobile field observation would real-time, reliably transmit large amounts of data to the monitoring center from measuring points through the connection about equipment and wireless accessing system, broadband wireless access system and precursor mobile observation management center system, thereby implementing remote instrument monitoring and data transmition. At present, the earthquake precursor field mobile observation network technology has been applied to fluxgate magnetometer array geomagnetic observations of Tianzhu, Xichang,and Xinjiang, it can be real-time monitoring the working status of the observational instruments of large area laid after the last two or three years, large scale field operation. Therefore, it can get geomagnetic field data of the local refinement regions and provide high-quality observational data for impending earthquake tracking forecast. Although, wireless networking technology is very suitable for mobile field observation with the features of simple, flexible networking etc, it also has the phenomenon of packet loss etc when transmitting a large number of observational data due to the wireless relatively weak signal and narrow bandwidth. In view of high sampling rate instruments, this project uses data compression and effectively solves the problem of data transmission packet loss; Control commands, status data and observational data transmission use different priorities and means, which control the packet loss rate within an acceptable range and do not affect real-time observation curve. After field running test and earthquake tracking project applications, the field mobile observation wireless networking system is operate normally, various function have good operability and show good performance, the quality of data transmission meet the system design requirements and play a significant role in practical applications.

Global network structure of dominance hierarchy of ant workers.

PubMed

Shimoji, Hiroyuki; Abe, Masato S; Tsuji, Kazuki; Masuda, Naoki

2014-10-06

Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks. © 2014 The Author(s) Published by the Royal Society. All rights reserved.

Global network structure of dominance hierarchy of ant workers

PubMed Central

Shimoji, Hiroyuki; Abe, Masato S.; Tsuji, Kazuki; Masuda, Naoki

2014-01-01

Dominance hierarchy among animals is widespread in various species and believed to serve to regulate resource allocation within an animal group. Unlike small groups, however, detection and quantification of linear hierarchy in large groups of animals are a difficult task. Here, we analyse aggression-based dominance hierarchies formed by worker ants in Diacamma sp. as large directed networks. We show that the observed dominance networks are perfect or approximate directed acyclic graphs, which are consistent with perfect linear hierarchy. The observed networks are also sparse and random but significantly different from networks generated through thinning of the perfect linear tournament (i.e. all individuals are linearly ranked and dominance relationship exists between every pair of individuals). These results pertain to global structure of the networks, which contrasts with the previous studies inspecting frequencies of different types of triads. In addition, the distribution of the out-degree (i.e. number of workers that the focal worker attacks), not in-degree (i.e. number of workers that attack the focal worker), of each observed network is right-skewed. Those having excessively large out-degrees are located near the top, but not the top, of the hierarchy. We also discuss evolutionary implications of the discovered properties of dominance networks. PMID:25100318

Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks

DOE PAGES

Shen, Yiwen; Hattink, Maarten; Samadi, Payman; ...

2018-04-13

Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. Here, we present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly networkmore » testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 microsecond control plane latency for data-center and high performance computing platforms.« less

Software-defined networking control plane for seamless integration of multiple silicon photonic switches in Datacom networks

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

Shen, Yiwen; Hattink, Maarten; Samadi, Payman

Silicon photonics based switches offer an effective option for the delivery of dynamic bandwidth for future large-scale Datacom systems while maintaining scalable energy efficiency. The integration of a silicon photonics-based optical switching fabric within electronic Datacom architectures requires novel network topologies and arbitration strategies to effectively manage the active elements in the network. Here, we present a scalable software-defined networking control plane to integrate silicon photonic based switches with conventional Ethernet or InfiniBand networks. Our software-defined control plane manages both electronic packet switches and multiple silicon photonic switches for simultaneous packet and circuit switching. We built an experimental Dragonfly networkmore » testbed with 16 electronic packet switches and 2 silicon photonic switches to evaluate our control plane. Observed latencies occupied by each step of the switching procedure demonstrate a total of 344 microsecond control plane latency for data-center and high performance computing platforms.« less

MIIC online: a web server to reconstruct causal or non-causal networks from non-perturbative data.

PubMed

Sella, Nadir; Verny, Louis; Uguzzoni, Guido; Affeldt, Séverine; Isambert, Hervé

2018-07-01

We present a web server running the MIIC algorithm, a network learning method combining constraint-based and information-theoretic frameworks to reconstruct causal, non-causal or mixed networks from non-perturbative data, without the need for an a priori choice on the class of reconstructed network. Starting from a fully connected network, the algorithm first removes dispensable edges by iteratively subtracting the most significant information contributions from indirect paths between each pair of variables. The remaining edges are then filtered based on their confidence assessment or oriented based on the signature of causality in observational data. MIIC online server can be used for a broad range of biological data, including possible unobserved (latent) variables, from single-cell gene expression data to protein sequence evolution and outperforms or matches state-of-the-art methods for either causal or non-causal network reconstruction. MIIC online can be freely accessed at https://miic.curie.fr. Supplementary data are available at Bioinformatics online.

Comparative analysis of two discretizations of Ricci curvature for complex networks.

PubMed

Samal, Areejit; Sreejith, R P; Gu, Jiao; Liu, Shiping; Saucan, Emil; Jost, Jürgen

2018-06-05

We have performed an empirical comparison of two distinct notions of discrete Ricci curvature for graphs or networks, namely, the Forman-Ricci curvature and Ollivier-Ricci curvature. Importantly, these two discretizations of the Ricci curvature were developed based on different properties of the classical smooth notion, and thus, the two notions shed light on different aspects of network structure and behavior. Nevertheless, our extensive computational analysis in a wide range of both model and real-world networks shows that the two discretizations of Ricci curvature are highly correlated in many networks. Moreover, we show that if one considers the augmented Forman-Ricci curvature which also accounts for the two-dimensional simplicial complexes arising in graphs, the observed correlation between the two discretizations is even higher, especially, in real networks. Besides the potential theoretical implications of these observations, the close relationship between the two discretizations has practical implications whereby Forman-Ricci curvature can be employed in place of Ollivier-Ricci curvature for faster computation in larger real-world networks whenever coarse analysis suffices.

Discontinuities in effective permeability due to fracture percolation

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

Hyman, Jeffrey De'Haven; Karra, Satish; Carey, James William

Motivated by a triaxial coreflood experiment with a sample of Utica shale where an abrupt jump in permeability was observed, possibly due to the creation of a percolating fracture network through the sample, we perform numerical simulations based on the experiment to characterize how the effective permeability of otherwise low-permeability porous media depends on fracture formation, connectivity, and the contrast between the fracture and matrix permeabilities. While a change in effective permeability due to fracture formation is expected, the dependence of its magnitude upon the contrast between the matrix permeability and fracture permeability and the fracture network structure is poorlymore » characterized. We use two different high-fidelity fracture network models to characterize how effective permeability changes as percolation occurs. The first is a dynamic two-dimensional fracture propagation model designed to mimic the laboratory settings of the experiment. The second is a static three-dimensional discrete fracture network (DFN) model, whose fracture and network statistics are based on the fractured sample of Utica shale. Once the network connects the inflow and outflow boundaries, the effective permeability increases non-linearly with network density. In most networks considered, a jump in the effective permeability was observed when the embedded fracture network percolated. We characterize how the magnitude of the jump, should it occur, depends on the contrast between the fracture and matrix permeabilities. For small contrasts between the matrix and fracture permeabilities the change is insignificant. However, for larger contrasts, there is a substantial jump whose magnitude depends non-linearly on the difference between matrix and fracture permeabilities. A power-law relationship between the size of the jump and the difference between the matrix and fracture permeabilities is observed. In conclusion, the presented results underscore the importance of fracture network topology on the upscaled properties of the porous medium in which it is embedded.« less

Discontinuities in effective permeability due to fracture percolation

DOE PAGES

Hyman, Jeffrey De'Haven; Karra, Satish; Carey, James William; ...

2018-01-31

Motivated by a triaxial coreflood experiment with a sample of Utica shale where an abrupt jump in permeability was observed, possibly due to the creation of a percolating fracture network through the sample, we perform numerical simulations based on the experiment to characterize how the effective permeability of otherwise low-permeability porous media depends on fracture formation, connectivity, and the contrast between the fracture and matrix permeabilities. While a change in effective permeability due to fracture formation is expected, the dependence of its magnitude upon the contrast between the matrix permeability and fracture permeability and the fracture network structure is poorlymore » characterized. We use two different high-fidelity fracture network models to characterize how effective permeability changes as percolation occurs. The first is a dynamic two-dimensional fracture propagation model designed to mimic the laboratory settings of the experiment. The second is a static three-dimensional discrete fracture network (DFN) model, whose fracture and network statistics are based on the fractured sample of Utica shale. Once the network connects the inflow and outflow boundaries, the effective permeability increases non-linearly with network density. In most networks considered, a jump in the effective permeability was observed when the embedded fracture network percolated. We characterize how the magnitude of the jump, should it occur, depends on the contrast between the fracture and matrix permeabilities. For small contrasts between the matrix and fracture permeabilities the change is insignificant. However, for larger contrasts, there is a substantial jump whose magnitude depends non-linearly on the difference between matrix and fracture permeabilities. A power-law relationship between the size of the jump and the difference between the matrix and fracture permeabilities is observed. In conclusion, the presented results underscore the importance of fracture network topology on the upscaled properties of the porous medium in which it is embedded.« less

The Orphan Disease Networks

PubMed Central

Zhang, Minlu; Zhu, Cheng; Jacomy, Alexis; Lu, Long J.; Jegga, Anil G.

2011-01-01

The low prevalence rate of orphan diseases (OD) requires special combined efforts to improve diagnosis, prevention, and discovery of novel therapeutic strategies. To identify and investigate relationships based on shared genes or shared functional features, we have conducted a bioinformatic-based global analysis of all orphan diseases with known disease-causing mutant genes. Starting with a bipartite network of known OD and OD-causing mutant genes and using the human protein interactome, we first construct and topologically analyze three networks: the orphan disease network, the orphan disease-causing mutant gene network, and the orphan disease-causing mutant gene interactome. Our results demonstrate that in contrast to the common disease-causing mutant genes that are predominantly nonessential, a majority of orphan disease-causing mutant genes are essential. In confirmation of this finding, we found that OD-causing mutant genes are topologically important in the protein interactome and are ubiquitously expressed. Additionally, functional enrichment analysis of those genes in which mutations cause ODs shows that a majority result in premature death or are lethal in the orthologous mouse gene knockout models. To address the limitations of traditional gene-based disease networks, we also construct and analyze OD networks on the basis of shared enriched features (biological processes, cellular components, pathways, phenotypes, and literature citations). Analyzing these functionally-linked OD networks, we identified several additional OD-OD relations that are both phenotypically similar and phenotypically diverse. Surprisingly, we observed that the wiring of the gene-based and other feature-based OD networks are largely different; this suggests that the relationship between ODs cannot be fully captured by the gene-based network alone. PMID:21664998

Improving the knowledge about dissolved oxygen and chlorophyll variability at ESTOC by using autonomous vehicles.

NASA Astrophysics Data System (ADS)

Cianca, A.; Caudet, E.; Vega, D.; Barrera, C.; Hernandez Brito, J.

2016-02-01

The European Station for Time Series in the Ocean, Canary Islands "ESTOC" is located in the Eastern Subtropical North Atlantic Gyre (29'10ºN, 15'30ºW). ESTOC started operations in 1994 based on a monthly ship-based sampling, in addition to hydrographic and sediment trap moorings. Since 2002, ESTOC is part of the European network for deep sea ocean observatories through several projects, among others ANIMATE (Atlantic Network of Interdisciplinary Moorings and Time-series for Europe), EuroSITES (European Ocean Observatory Network) and Fixed point Open Ocean Observatory network (FixO3). The main purpose of these projects was to improve the time-resolution of the biogeochemical measurements through moored biogeochemical sensors. Additionally, ESTOC is included in the Marine-Maritime observational network of the Macaronesian region, which is supported by the European overseas territories programs since 2009. This network aims to increase the quantity and quality of marine environmental observations. The goal is to understand phenomena which impact in the environment, and consequently at the socio-economy of the region to attempt their prediction. With this purpose, ESTOC has included the use of autonomous vehicles "glider" in order to increase the observational resolution and, by comparison with the parallel observational programs, to study the biogeochemical processes at different time scale resolutions. This study investigates the time variability of the dissolved oxygen and chlorophyll distributions in the water column focusing on the diel cycle, looking at the relevance of this variability in the already known seasonal distributions. Our interest is assessing net community production and remineralization rates through the use of oxygen variations, establishing the relationship between the DO anomalies values and those from the chlorophyll distribution in the water column.

English and Chinese languages as weighted complex networks

NASA Astrophysics Data System (ADS)

Sheng, Long; Li, Chunguang

2009-06-01

In this paper, we analyze statistical properties of English and Chinese written human language within the framework of weighted complex networks. The two language networks are based on an English novel and a Chinese biography, respectively, and both of the networks are constructed in the same way. By comparing the intensity and density of connections between the two networks, we find that high weight connections in Chinese language networks prevail more than those in English language networks. Furthermore, some of the topological and weighted quantities are compared. The results display some differences in the structural organizations between the two language networks. These observations indicate that the two languages may have different linguistic mechanisms and different combinatorial natures.

Distributed Telescope Networks in the Era of Network-Centric Astronomy

NASA Astrophysics Data System (ADS)

Solomos, N. H.

2010-07-01

In parallel with the world-wide demand for pushing our observational limits (increasingly larger telescope collecting power (ELTs) on the ground, most advanced technology satellites in space), we nowadays realize rapid rising of interest for the construction and deployment of a technologically advanced meta-network or Heterogeneous Telescope Network (hereafter HTN). The HTN is a Network of networks of telescopes and each node of it, consists of an inhomogeneous ensemble of different telescopes, sharing one common feature: the incorporation of a high degree of automation. The rationale behind this new tool, is that crucial astrophysical problems could be tackled very soon from the world-wide spread variety of well equipped autonomous telescopes working as a single instrument. In the full version of this paper, the research potential and future prospects of worldwide networked telescopic systems, is reviewed in the framework of current progress in Astrophysics. It is concluded that the research horizons of HTNs are very broad and the associated technology is currently in a maturity level that permits deployment. An extended interoperability-establishment initiative, involving telescopes of both hemispheres, based on accepted standards, appears a matter of priority. Observatories with infrastructure -of any size-, maintaining computerized telescope facilities, could respond to the challenge, devote part of their resources to the HTN and, in return, receive the rewards of shared resources, observing flexibility, optimized observing performance and the very high observing efficiency of a telescopic meta-network in facilitating competitive front line research.

Multi-phenomenology Observation Network Evaluation Tool'' (MONET)

NASA Astrophysics Data System (ADS)

Oltrogge, D.; North, P.; Vallado, D.

2014-09-01

Evaluating overall performance of an SSA "system-of-systems" observational network collecting against thousands of Resident Space Objects (RSO) is very difficult for typical tasking or scheduling-based analysis tools. This is further complicated by networks that have a wide variety of sensor types and phenomena, to include optical, radar and passive RF types, each having unique resource, ops tempo, competing customer and detectability constraints. We present details of the Multi-phenomenology Observation Network Evaluation Tool (MONET), which circumvents these difficulties by assessing the ideal performance of such a network via a digitized supply-vs-demand approach. Cells of each sensors supply time are distributed among RSO targets of interest to determine the average performance of the network against that set of RSO targets. Orbit Determination heuristics are invoked to represent observation quantity and geometry notionally required to obtain the desired orbit estimation quality. To feed this approach, we derive the detectability and collection rate performance of optical, radar and passive RF sensor physical and performance characteristics. We then prioritize the selected RSO targets according to object size, active/inactive status, orbit regime, and/or other considerations. Finally, the OD-derived tracking demands of each RSO of interest are levied against remaining sensor supply until either (a) all sensor time is exhausted; or (b) the list of RSO targets is exhausted. The outputs from MONET include overall network performance metrics delineated by sensor type, objects and orbits tracked, along with likely orbit accuracies which might result from the conglomerate network tracking.

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks

PubMed Central

Meyer-Bäse, Anke; Roberts, Rodney G.; Illan, Ignacio A.; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts. PMID:29051730

Dynamical Graph Theory Networks Methods for the Analysis of Sparse Functional Connectivity Networks and for Determining Pinning Observability in Brain Networks.

PubMed

Meyer-Bäse, Anke; Roberts, Rodney G; Illan, Ignacio A; Meyer-Bäse, Uwe; Lobbes, Marc; Stadlbauer, Andreas; Pinker-Domenig, Katja

2017-01-01

Neuroimaging in combination with graph theory has been successful in analyzing the functional connectome. However almost all analysis are performed based on static graph theory. The derived quantitative graph measures can only describe a snap shot of the disease over time. Neurodegenerative disease evolution is poorly understood and treatment strategies are consequently only of limited efficiency. Fusing modern dynamic graph network theory techniques and modeling strategies at different time scales with pinning observability of complex brain networks will lay the foundation for a transformational paradigm in neurodegnerative diseases research regarding disease evolution at the patient level, treatment response evaluation and revealing some central mechanism in a network that drives alterations in these diseases. We model and analyze brain networks as two-time scale sparse dynamic graph networks with hubs (clusters) representing the fast sub-system and the interconnections between hubs the slow sub-system. Alterations in brain function as seen in dementia can be dynamically modeled by determining the clusters in which disturbance inputs have entered and the impact they have on the large-scale dementia dynamic system. Observing a small fraction of specific nodes in dementia networks such that the others can be recovered is accomplished by the novel concept of pinning observability. In addition, how to control this complex network seems to be crucial in understanding the progressive abnormal neural circuits in many neurodegenerative diseases. Detecting the controlling regions in the networks, which serve as key nodes to control the aberrant dynamics of the networks to a desired state and thus influence the progressive abnormal behavior, will have a huge impact in understanding and developing therapeutic solutions and also will provide useful information about the trajectory of the disease. In this paper, we present the theoretical framework and derive the necessary conditions for (1) area aggregation and time-scale modeling in brain networks and for (2) pinning observability of nodes in dynamic graph networks. Simulation examples are given to illustrate the theoretical concepts.

Synergistic Modification Induced Specific Recognition between Histone and TRIM24 via Fluctuation Correlation Network Analysis

NASA Astrophysics Data System (ADS)

Zhang, Jinmai; Luo, Huajie; Liu, Hao; Ye, Wei; Luo, Ray; Chen, Hai-Feng

2016-04-01

Histone modification plays a key role in gene regulation and gene expression. TRIM24 as a histone reader can recognize histone modification. However the specific recognition mechanism between TRIM24 and histone modification is unsolved. Here, systems biology method of dynamics correlation network based on molecular dynamics simulation was used to answer the question. Our network analysis shows that the dynamics correlation network of H3K23ac is distinctly different from that of wild type and other modifications. A hypothesis of “synergistic modification induced recognition” is then proposed to link histone modification and TRIM24 binding. These observations were further confirmed from community analysis of networks with mutation and network perturbation. Finally, a possible recognition pathway is also identified based on the shortest path search for H3K23ac. Significant difference of recognition pathway was found among different systems due to methylation and acetylation modifications. The analysis presented here and other studies show that the dynamic network-based analysis might be a useful general strategy to study the biology of protein post-translational modification and associated recognition.

Optimization of deformation monitoring networks using finite element strain analysis

NASA Astrophysics Data System (ADS)

Alizadeh-Khameneh, M. Amin; Eshagh, Mehdi; Jensen, Anna B. O.

2018-04-01

An optimal design of a geodetic network can fulfill the requested precision and reliability of the network, and decrease the expenses of its execution by removing unnecessary observations. The role of an optimal design is highlighted in deformation monitoring network due to the repeatability of these networks. The core design problem is how to define precision and reliability criteria. This paper proposes a solution, where the precision criterion is defined based on the precision of deformation parameters, i. e. precision of strain and differential rotations. A strain analysis can be performed to obtain some information about the possible deformation of a deformable object. In this study, we split an area into a number of three-dimensional finite elements with the help of the Delaunay triangulation and performed the strain analysis on each element. According to the obtained precision of deformation parameters in each element, the precision criterion of displacement detection at each network point is then determined. The developed criterion is implemented to optimize the observations from the Global Positioning System (GPS) in Skåne monitoring network in Sweden. The network was established in 1989 and straddled the Tornquist zone, which is one of the most active faults in southern Sweden. The numerical results show that 17 out of all 21 possible GPS baseline observations are sufficient to detect minimum 3 mm displacement at each network point.

A novel multi-model neuro-fuzzy-based MPPT for three-phase grid-connected photovoltaic system

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

Chaouachi, Aymen; Kamel, Rashad M.; Nagasaka, Ken

This paper presents a novel methodology for Maximum Power Point Tracking (MPPT) of a grid-connected 20 kW photovoltaic (PV) system using neuro-fuzzy network. The proposed method predicts the reference PV voltage guarantying optimal power transfer between the PV generator and the main utility grid. The neuro-fuzzy network is composed of a fuzzy rule-based classifier and three multi-layered feed forwarded Artificial Neural Networks (ANN). Inputs of the network (irradiance and temperature) are classified before they are fed into the appropriated ANN for either training or estimation process while the output is the reference voltage. The main advantage of the proposed methodology,more » comparing to a conventional single neural network-based approach, is the distinct generalization ability regarding to the nonlinear and dynamic behavior of a PV generator. In fact, the neuro-fuzzy network is a neural network based multi-model machine learning that defines a set of local models emulating the complex and nonlinear behavior of a PV generator under a wide range of operating conditions. Simulation results under several rapid irradiance variations proved that the proposed MPPT method fulfilled the highest efficiency comparing to a conventional single neural network and the Perturb and Observe (P and O) algorithm dispositive. (author)« less

A Composite Model of Wound Segmentation Based on Traditional Methods and Deep Neural Networks

PubMed Central

Wang, Changjian; Liu, Xiaohui; Jin, Shiyao

2018-01-01

Wound segmentation plays an important supporting role in the wound observation and wound healing. Current methods of image segmentation include those based on traditional process of image and those based on deep neural networks. The traditional methods use the artificial image features to complete the task without large amounts of labeled data. Meanwhile, the methods based on deep neural networks can extract the image features effectively without the artificial design, but lots of training data are required. Combined with the advantages of them, this paper presents a composite model of wound segmentation. The model uses the skin with wound detection algorithm we designed in the paper to highlight image features. Then, the preprocessed images are segmented by deep neural networks. And semantic corrections are applied to the segmentation results at last. The model shows a good performance in our experiment. PMID:29955227

Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)-based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations.

Weighted networks as randomly reinforced urn processes

NASA Astrophysics Data System (ADS)

Caldarelli, Guido; Chessa, Alessandro; Crimaldi, Irene; Pammolli, Fabio

2013-02-01

We analyze weighted networks as randomly reinforced urn processes, in which the edge-total weights are determined by a reinforcement mechanism. We develop a statistical test and a procedure based on it to study the evolution of networks over time, detecting the “dominance” of some edges with respect to the others and then assessing if a given instance of the network is taken at its steady state or not. Distance from the steady state can be considered as a measure of the relevance of the observed properties of the network. Our results are quite general, in the sense that they are not based on a particular probability distribution or functional form of the random weights. Moreover, the proposed tool can be applied also to dense networks, which have received little attention by the network community so far, since they are often problematic. We apply our procedure in the context of the International Trade Network, determining a core of “dominant edges.”

Causal Inference and Explaining Away in a Spiking Network

PubMed Central

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-01-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification. PMID:26621426

Causal Inference and Explaining Away in a Spiking Network.

PubMed

Moreno-Bote, Rubén; Drugowitsch, Jan

2015-12-01

While the brain uses spiking neurons for communication, theoretical research on brain computations has mostly focused on non-spiking networks. The nature of spike-based algorithms that achieve complex computations, such as object probabilistic inference, is largely unknown. Here we demonstrate that a family of high-dimensional quadratic optimization problems with non-negativity constraints can be solved exactly and efficiently by a network of spiking neurons. The network naturally imposes the non-negativity of causal contributions that is fundamental to causal inference, and uses simple operations, such as linear synapses with realistic time constants, and neural spike generation and reset non-linearities. The network infers the set of most likely causes from an observation using explaining away, which is dynamically implemented by spike-based, tuned inhibition. The algorithm performs remarkably well even when the network intrinsically generates variable spike trains, the timing of spikes is scrambled by external sources of noise, or the network is mistuned. This type of network might underlie tasks such as odor identification and classification.

Mesoscopic model of actin-based propulsion.

PubMed

Zhu, Jie; Mogilner, Alex

2012-01-01

Two theoretical models dominate current understanding of actin-based propulsion: microscopic polymerization ratchet model predicts that growing and writhing actin filaments generate forces and movements, while macroscopic elastic propulsion model suggests that deformation and stress of growing actin gel are responsible for the propulsion. We examine both experimentally and computationally the 2D movement of ellipsoidal beads propelled by actin tails and show that neither of the two models can explain the observed bistability of the orientation of the beads. To explain the data, we develop a 2D hybrid mesoscopic model by reconciling these two models such that individual actin filaments undergoing nucleation, elongation, attachment, detachment and capping are embedded into the boundary of a node-spring viscoelastic network representing the macroscopic actin gel. Stochastic simulations of this 'in silico' actin network show that the combined effects of the macroscopic elastic deformation and microscopic ratchets can explain the observed bistable orientation of the actin-propelled ellipsoidal beads. To test the theory further, we analyze observed distribution of the curvatures of the trajectories and show that the hybrid model's predictions fit the data. Finally, we demonstrate that the model can explain both concave-up and concave-down force-velocity relations for growing actin networks depending on the characteristic time scale and network recoil. To summarize, we propose that both microscopic polymerization ratchets and macroscopic stresses of the deformable actin network are responsible for the force and movement generation.

Development of Integration Framework for Sensor Network and Satellite Image based on OGC Web Services

NASA Astrophysics Data System (ADS)

Ninsawat, Sarawut; Yamamoto, Hirokazu; Kamei, Akihide; Nakamura, Ryosuke; Tsuchida, Satoshi; Maeda, Takahisa

2010-05-01

With the availability of network enabled sensing devices, the volume of information being collected by networked sensors has increased dramatically in recent years. Over 100 physical, chemical and biological properties can be sensed using in-situ or remote sensing technology. A collection of these sensor nodes forms a sensor network, which is easily deployable to provide a high degree of visibility into real-world physical processes as events unfold. The sensor observation network could allow gathering of diverse types of data at greater spatial and temporal resolution, through the use of wired or wireless network infrastructure, thus real-time or near-real time data from sensor observation network allow researchers and decision-makers to respond speedily to events. However, in the case of environmental monitoring, only a capability to acquire in-situ data periodically is not sufficient but also the management and proper utilization of data also need to be careful consideration. It requires the implementation of database and IT solutions that are robust, scalable and able to interoperate between difference and distributed stakeholders to provide lucid, timely and accurate update to researchers, planners and citizens. The GEO (Global Earth Observation) Grid is primarily aiming at providing an e-Science infrastructure for the earth science community. The GEO Grid is designed to integrate various kinds of data related to the earth observation using the grid technology, which is developed for sharing data, storage, and computational powers of high performance computing, and is accessible as a set of services. A comprehensive web-based system for integrating field sensor and data satellite image based on various open standards of OGC (Open Geospatial Consortium) specifications has been developed. Web Processing Service (WPS), which is most likely the future direction of Web-GIS, performs the computation of spatial data from distributed data sources and returns the outcome in a standard format. The interoperability capabilities and Service Oriented Architecture (SOA) of web services allow incorporating between sensor network measurement available from Sensor Observation Service (SOS) and satellite remote sensing data from Web Mapping Service (WMS) as distributed data sources for WPS. Various applications have been developed to demonstrate the efficacy of integrating heterogeneous data source. For example, the validation of the MODIS aerosol products (MOD08_D3, the Level-3 MODIS Atmosphere Daily Global Product) by ground-based measurements using the sunphotometer (skyradiometer, Prede POM-02) installed at Phenological Eyes Network (PEN) sites in Japan. Furthermore, the web-based framework system for studying a relationship between calculated Vegetation Index from MODIS satellite image surface reflectance (MOD09GA, the Surface Reflectance Daily L2G Global 1km and 500m Product) and Gross Primary Production (GPP) field measurement at flux tower site in Thailand and Japan has been also developed. The success of both applications will contribute to maximize data utilization and improve accuracy of information by validate MODIS satellite products using high degree of accuracy and temporal measurement of field measurement data.

Multi-Temporal Land Cover Classification with Long Short-Term Memory Neural Networks

NASA Astrophysics Data System (ADS)

Rußwurm, M.; Körner, M.

2017-05-01

Land cover classification (LCC) is a central and wide field of research in earth observation and has already put forth a variety of classification techniques. Many approaches are based on classification techniques considering observation at certain points in time. However, some land cover classes, such as crops, change their spectral characteristics due to environmental influences and can thus not be monitored effectively with classical mono-temporal approaches. Nevertheless, these temporal observations should be utilized to benefit the classification process. After extensive research has been conducted on modeling temporal dynamics by spectro-temporal profiles using vegetation indices, we propose a deep learning approach to utilize these temporal characteristics for classification tasks. In this work, we show how long short-term memory (LSTM) neural networks can be employed for crop identification purposes with SENTINEL 2A observations from large study areas and label information provided by local authorities. We compare these temporal neural network models, i.e., LSTM and recurrent neural network (RNN), with a classical non-temporal convolutional neural network (CNN) model and an additional support vector machine (SVM) baseline. With our rather straightforward LSTM variant, we exceeded state-of-the-art classification performance, thus opening promising potential for further research.

OBSERVATIONAL DATA PROCESSING AT NCEP

Science.gov Websites

operations, but also for research and study. 2. The various NCEP networks access the observational data base Statistics Observational Data Processing Data Assimilation Monsoon Desk Model Transition Seminars Seminar / VISION | About EMC Observational Data Processing at NCEP Dennis Keyser - NOAA/NWS/NCEP/EMC (Last Revised

Leveraging disjoint communities for detecting overlapping community structure

NASA Astrophysics Data System (ADS)

Chakraborty, Tanmoy

2015-05-01

Network communities represent mesoscopic structure for understanding the organization of real-world networks, where nodes often belong to multiple communities and form overlapping community structure in the network. Due to non-triviality in finding the exact boundary of such overlapping communities, this problem has become challenging, and therefore huge effort has been devoted to detect overlapping communities from the network. In this paper, we present PVOC (Permanence based Vertex-replication algorithm for Overlapping Community detection), a two-stage framework to detect overlapping community structure. We build on a novel observation that non-overlapping community structure detected by a standard disjoint community detection algorithm from a network has high resemblance with its actual overlapping community structure, except the overlapping part. Based on this observation, we posit that there is perhaps no need of building yet another overlapping community finding algorithm; but one can efficiently manipulate the output of any existing disjoint community finding algorithm to obtain the required overlapping structure. We propose a new post-processing technique that by combining with any existing disjoint community detection algorithm, can suitably process each vertex using a new vertex-based metric, called permanence, and thereby finds out overlapping candidates with their community memberships. Experimental results on both synthetic and large real-world networks show that PVOC significantly outperforms six state-of-the-art overlapping community detection algorithms in terms of high similarity of the output with the ground-truth structure. Thus our framework not only finds meaningful overlapping communities from the network, but also allows us to put an end to the constant effort of building yet another overlapping community detection algorithm.

High-resolution urban observation network for user-specific meteorological information service in the Seoul Metropolitan Area, South Korea

NASA Astrophysics Data System (ADS)

Park, Moon-Soo; Park, Sung-Hwa; Chae, Jung-Hoon; Choi, Min-Hyeok; Song, Yunyoung; Kang, Minsoo; Roh, Joon-Woo

2017-04-01

To improve our knowledge of urban meteorology, including those processes applicable to high-resolution meteorological models in the Seoul Metropolitan Area (SMA), the Weather Information Service Engine (WISE) Urban Meteorological Observation System (UMS-Seoul) has been designed and installed. The UMS-Seoul incorporates 14 surface energy balance (EB) systems, 7 surface-based three-dimensional (3-D) meteorological observation systems and applied meteorological (AP) observation systems, and the existing surface-based meteorological observation network. The EB system consists of a radiation balance system, sonic anemometers, infrared CO2/H2O gas analyzers, and many sensors measuring the wind speed and direction, temperature and humidity, precipitation, and air pressure. The EB-produced radiation, meteorological, and turbulence data will be used to quantify the surface EB according to land use and to improve the boundary-layer and surface processes in meteorological models. The 3-D system, composed of a wind lidar, microwave radiometer, aerosol lidar, or ceilometer, produces the cloud height, vertical profiles of backscatter by aerosols, wind speed and direction, temperature, humidity, and liquid water content. It will be used for high-resolution reanalysis data based on observations and for the improvement of the boundary-layer, radiation, and microphysics processes in meteorological models. The AP system includes road weather information, mosquito activity, water quality, and agrometeorological observation instruments. The standardized metadata for networks and stations are documented and renewed periodically to provide a detailed observation environment. The UMS-Seoul data are designed to support real-time acquisition and display and automatically quality check within 10 min from observation. After the quality check, data can be distributed to relevant potential users such as researchers and policy makers. Finally, two case studies demonstrate that the observed data have a great potential to help to understand the boundary-layer structures more deeply, improve the performance of high-resolution meteorological models, and provide useful information customized based on the user demands in the SMA.

Neural network disturbance observer-based distributed finite-time formation tracking control for multiple unmanned helicopters.

PubMed

Wang, Dandan; Zong, Qun; Tian, Bailing; Shao, Shikai; Zhang, Xiuyun; Zhao, Xinyi

2018-02-01

The distributed finite-time formation tracking control problem for multiple unmanned helicopters is investigated in this paper. The control object is to maintain the positions of follower helicopters in formation with external interferences. The helicopter model is divided into a second order outer-loop subsystem and a second order inner-loop subsystem based on multiple-time scale features. Using radial basis function neural network (RBFNN) technique, we first propose a novel finite-time multivariable neural network disturbance observer (FMNNDO) to estimate the external disturbance and model uncertainty, where the neural network (NN) approximation errors can be dynamically compensated by adaptive law. Next, based on FMNNDO, a distributed finite-time formation tracking controller and a finite-time attitude tracking controller are designed using the nonsingular fast terminal sliding mode (NFTSM) method. In order to estimate the second derivative of the virtual desired attitude signal, a novel finite-time sliding mode integral filter is designed. Finally, Lyapunov analysis and multiple-time scale principle ensure the realization of control goal in finite-time. The effectiveness of the proposed FMNNDO and controllers are then verified by numerical simulations. Copyright © 2017 ISA. Published by Elsevier Ltd. All rights reserved.

Learning-based computing techniques in geoid modeling for precise height transformation

NASA Astrophysics Data System (ADS)

Erol, B.; Erol, S.

2013-03-01

Precise determination of local geoid is of particular importance for establishing height control in geodetic GNSS applications, since the classical leveling technique is too laborious. A geoid model can be accurately obtained employing properly distributed benchmarks having GNSS and leveling observations using an appropriate computing algorithm. Besides the classical multivariable polynomial regression equations (MPRE), this study attempts an evaluation of learning based computing algorithms: artificial neural networks (ANNs), adaptive network-based fuzzy inference system (ANFIS) and especially the wavelet neural networks (WNNs) approach in geoid surface approximation. These algorithms were developed parallel to advances in computer technologies and recently have been used for solving complex nonlinear problems of many applications. However, they are rather new in dealing with precise modeling problem of the Earth gravity field. In the scope of the study, these methods were applied to Istanbul GPS Triangulation Network data. The performances of the methods were assessed considering the validation results of the geoid models at the observation points. In conclusion the ANFIS and WNN revealed higher prediction accuracies compared to ANN and MPRE methods. Beside the prediction capabilities, these methods were also compared and discussed from the practical point of view in conclusions.

Delineation of a Re-establishing Drainage Network Using SPOT and Landsat Images

NASA Astrophysics Data System (ADS)

Bailey, J. E.; Self, S.; Mouginis-Mark, P. J.

2008-12-01

The 1991 eruption of Mt. Pinatubo, The Philippines, provided a unique opportunity to study the effects on the landscape of a large eruption in part because it took place after the advent of regular satellite-based observations. The eruption formed one large (>100km2) ignimbrite sheet, with over 70% of the total deposit deposited in three primary drainage basins to the west of the volcano. High-resolution (20 m/pixel) satellite images, showing the western drainage basins and surrounding region both before and after the eruption were used to observe the re-establishment and evolution of drainage networks on the newly emplaced ignimbrite sheet. Changes in the drainage networks were delineated from a time series of SPOT (Satellite Pour l'Observation de la Terre) and Landsat multi-spectral satellite images. The analysis of which was supplemented by ground- based observations. The satellite images showed that the blue prints for the new drainage systems were established early (within days of the eruption) and at a large-scale followed the pre-eruption pattern. However, the images also illustrated the ephemeral nature of many channels due to the influence of secondary pyroclastic flows, lahar- dammed lake breakouts, stream piracy and shifts due to erosion. Characteristics of the defined drainage networks were used to infer the relative influence on the lahar hazard within each drainage basin.

Micropore analysis of polymer networks by gas sorption and 129Xe NMR spectroscopy: toward a better understanding of intrinsic microporosity.

PubMed

Weber, Jens; Schmidt, Johannes; Thomas, Arne; Böhlmann, Winfried

2010-10-05

The microporosity of two microporous polymer networks is investigated in detail. Both networks are based on a central spirobifluorene motif but have different linker groups, namely, imide and thiophene units. The microporosity of the networks is based on the "polymers of intrinsic microporosity (PIM)" design strategy. Nitrogen, argon, and carbon dioxide were used as sorbates in order to analyze the microporosity in greater detail. The gas sorption data was analyzed with respect to important parameters such as specific surface area, pore volume, and pore size (distribution). It is shown that the results can be strongly model dependent and swelling effects have to be regarded. (129)Xe NMR was used as an independent technique for the estimation of the average pore size of the polymer networks. The results indicate that both networks are mainly ultramicroporous (pore sizes < 0.8 nm) in the dry state, which was not expected based on the molecular design. Phase separation and network defects might influence the overall network morphology strongly. Finally, the observed swelling indicates that this "soft" microporous matter might have a different micropore size in the solvent swollen/filled state that in the dry state.

Simulating Network Retrieval of Arithmetic Facts.

ERIC Educational Resources Information Center

Ashcraft, Mark H.

This report describes a simulation of adults' retrieval of arithmetic facts from a network-based memory representation. The goals of the simulation project are to: demonstrate in specific form the nature of a spreading activation model of mental arithmetic; account for three important reaction time effects observed in laboratory investigations;…

Dynamic Bayesian Network Modeling of Game Based Diagnostic Assessments. CRESST Report 837

ERIC Educational Resources Information Center

Levy, Roy

2014-01-01

Digital games offer an appealing environment for assessing student proficiencies, including skills and misconceptions in a diagnostic setting. This paper proposes a dynamic Bayesian network modeling approach for observations of student performance from an educational video game. A Bayesian approach to model construction, calibration, and use in…

Probabilistic diffusion tractography reveals improvement of structural network in musicians.

PubMed

Li, Jianfu; Luo, Cheng; Peng, Yueheng; Xie, Qiankun; Gong, Jinnan; Dong, Li; Lai, Yongxiu; Li, Hong; Yao, Dezhong

2014-01-01

Musicians experience a large amount of information transfer and integration of complex sensory, motor, and auditory processes when training and playing musical instruments. Therefore, musicians are a useful model in which to investigate neural adaptations in the brain. Here, based on diffusion-weighted imaging, probabilistic tractography was used to determine the architecture of white matter anatomical networks in musicians and non-musicians. Furthermore, the features of the white matter networks were analyzed using graph theory. Small-world properties of the white matter network were observed in both groups. Compared with non-musicians, the musicians exhibited significantly increased connectivity strength in the left and right supplementary motor areas, the left calcarine fissure and surrounding cortex and the right caudate nucleus, as well as a significantly larger weighted clustering coefficient in the right olfactory cortex, the left medial superior frontal gyrus, the right gyrus rectus, the left lingual gyrus, the left supramarginal gyrus, and the right pallidum. Furthermore, there were differences in the node betweenness centrality in several regions. However, no significant differences in topological properties were observed at a global level. We illustrated preliminary findings to extend the network level understanding of white matter plasticity in musicians who have had long-term musical training. These structural, network-based findings may indicate that musicians have enhanced information transmission efficiencies in local white matter networks that are related to musical training.

An iterative network partition algorithm for accurate identification of dense network modules

PubMed Central

Sun, Siqi; Dong, Xinran; Fu, Yao; Tian, Weidong

2012-01-01

A key step in network analysis is to partition a complex network into dense modules. Currently, modularity is one of the most popular benefit functions used to partition network modules. However, recent studies suggested that it has an inherent limitation in detecting dense network modules. In this study, we observed that despite the limitation, modularity has the advantage of preserving the primary network structure of the undetected modules. Thus, we have developed a simple iterative Network Partition (iNP) algorithm to partition a network. The iNP algorithm provides a general framework in which any modularity-based algorithm can be implemented in the network partition step. Here, we tested iNP with three modularity-based algorithms: multi-step greedy (MSG), spectral clustering and Qcut. Compared with the original three methods, iNP achieved a significant improvement in the quality of network partition in a benchmark study with simulated networks, identified more modules with significantly better enrichment of functionally related genes in both yeast protein complex network and breast cancer gene co-expression network, and discovered more cancer-specific modules in the cancer gene co-expression network. As such, iNP should have a broad application as a general method to assist in the analysis of biological networks. PMID:22121225

An observer-based compensator for distributed delays

NASA Technical Reports Server (NTRS)

Luck, Rogelio; Ray, Asok

1990-01-01

This paper presents an algorithm for compensating delays that are distributed between the sensor(s), controller and actuator(s) within a control loop. This observer-based algorithm is specially suited to compensation of network-induced delays in integrated communication and control systems. The robustness of the algorithm relative to plant model uncertainties has been examined.

A coevolving model based on preferential triadic closure for social media networks

PubMed Central

Li, Menghui; Zou, Hailin; Guan, Shuguang; Gong, Xiaofeng; Li, Kun; Di, Zengru; Lai, Choy-Heng

2013-01-01

The dynamical origin of complex networks, i.e., the underlying principles governing network evolution, is a crucial issue in network study. In this paper, by carrying out analysis to the temporal data of Flickr and Epinions–two typical social media networks, we found that the dynamical pattern in neighborhood, especially the formation of triadic links, plays a dominant role in the evolution of networks. We thus proposed a coevolving dynamical model for such networks, in which the evolution is only driven by the local dynamics–the preferential triadic closure. Numerical experiments verified that the model can reproduce global properties which are qualitatively consistent with the empirical observations. PMID:23979061

Effects of substrate network topologies on competition dynamics

NASA Astrophysics Data System (ADS)

Lee, Sang Hoon; Jeong, Hawoong

2006-08-01

We study a competition dynamics, based on the minority game, endowed with various substrate network structures. We observe the effects of the network topologies by investigating the volatility of the system and the structure of follower networks. The topology of substrate structures significantly influences the system efficiency represented by the volatility and such substrate networks are shown to amplify the herding effect and cause inefficiency in most cases. The follower networks emerging from the leadership structure show a power-law incoming degree distribution. This study shows the emergence of scale-free structures of leadership in the minority game and the effects of the interaction among players on the networked version of the game.

Gray matter alterations in chronic pain: A network-oriented meta-analytic approach

PubMed Central

Cauda, Franco; Palermo, Sara; Costa, Tommaso; Torta, Riccardo; Duca, Sergio; Vercelli, Ugo; Geminiani, Giuliano; Torta, Diana M.E.

2014-01-01

Several studies have attempted to characterize morphological brain changes due to chronic pain. Although it has repeatedly been suggested that longstanding pain induces gray matter modifications, there is still some controversy surrounding the direction of the change (increase or decrease in gray matter) and the role of psychological and psychiatric comorbidities. In this study, we propose a novel, network-oriented, meta-analytic approach to characterize morphological changes in chronic pain. We used network decomposition to investigate whether different kinds of chronic pain are associated with a common or specific set of altered networks. Representational similarity techniques, network decomposition and model-based clustering were employed: i) to verify the presence of a core set of brain areas commonly modified by chronic pain; ii) to investigate the involvement of these areas in a large-scale network perspective; iii) to study the relationship between altered networks and; iv) to find out whether chronic pain targets clusters of areas. Our results showed that chronic pain causes both core and pathology-specific gray matter alterations in large-scale networks. Common alterations were observed in the prefrontal regions, in the anterior insula, cingulate cortex, basal ganglia, thalamus, periaqueductal gray, post- and pre-central gyri and inferior parietal lobule. We observed that the salience and attentional networks were targeted in a very similar way by different chronic pain pathologies. Conversely, alterations in the sensorimotor and attention circuits were differentially targeted by chronic pain pathologies. Moreover, model-based clustering revealed that chronic pain, in line with some neurodegenerative diseases, selectively targets some large-scale brain networks. Altogether these findings indicate that chronic pain can be better conceived and studied in a network perspective. PMID:24936419

An analysis of USSPACECOM's space surveillance network sensor tasking methodology

NASA Astrophysics Data System (ADS)

Berger, Jeff M.; Moles, Joseph B.; Wilsey, David G.

1992-12-01

This study provides the basis for the development of a cost/benefit assessment model to determine the effects of alterations to the Space Surveillance Network (SSN) on orbital element (OE) set accuracy. It provides a review of current methods used by NORAD and the SSN to gather and process observations, an alternative to the current Gabbard classification method, and the development of a model to determine the effects of observation rate and correction interval on OE set accuracy. The proposed classification scheme is based on satellite J2 perturbations. Specifically, classes were established based on mean motion, eccentricity, and inclination since J2 perturbation effects are functions of only these elements. Model development began by creating representative sensor observations using a highly accurate orbital propagation model. These observations were compared to predicted observations generated using the NORAD Simplified General Perturbation (SGP4) model and differentially corrected using a Bayes, sequential estimation, algorithm. A 10-run Monte Carlo analysis was performed using this model on 12 satellites using 16 different observation rate/correction interval combinations. An ANOVA and confidence interval analysis of the results show that this model does demonstrate the differences in steady state position error based on varying observation rate and correction interval.

Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks

PubMed Central

Moon, Hankyu; Lu, Tsai-Ching

2015-01-01

Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of—how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description — of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible. PMID:25822423

Network Catastrophe: Self-Organized Patterns Reveal both the Instability and the Structure of Complex Networks

NASA Astrophysics Data System (ADS)

Moon, Hankyu; Lu, Tsai-Ching

2015-03-01

Critical events in society or biological systems can be understood as large-scale self-emergent phenomena due to deteriorating stability. We often observe peculiar patterns preceding these events, posing a question of--how to interpret the self-organized patterns to know more about the imminent crisis. We start with a very general description -- of interacting population giving rise to large-scale emergent behaviors that constitute critical events. Then we pose a key question: is there a quantifiable relation between the network of interactions and the emergent patterns? Our investigation leads to a fundamental understanding to: 1. Detect the system's transition based on the principal mode of the pattern dynamics; 2. Identify its evolving structure based on the observed patterns. The main finding of this study is that while the pattern is distorted by the network of interactions, its principal mode is invariant to the distortion even when the network constantly evolves. Our analysis on real-world markets show common self-organized behavior near the critical transitions, such as housing market collapse and stock market crashes, thus detection of critical events before they are in full effect is possible.

Coupled Gravity and Elevation Measurements of Ice Sheet Mass Change

NASA Technical Reports Server (NTRS)

Jezek, K. C.

2005-01-01

We measured surface gravity and position at ten locations about two glaciological measurement networks located on the South-central Greenland Ice during June 2004. Six of the individual sites of the first network were occupied the previous year. At the repeat sites we were able to measure annual accumulation rate and surface displacement by referencing measurements to aluminum poles left in the firn the previous year. We occupied 4 additional sites at a second measurement network for the first time since initial observations were last made at the network in 1981. At each individual site, we operated a GPS unit for 90 minutes - the unit was operated simultaneously with a base station unit in Sondrestrom Fjord so as to enable differential, post-processing of the data. We installed an aluminum, accumulation-rate-pole at each site. The base section of the pole also served as the mount for the GPS antenna. A new, Scintrex gravimeter was used at each site and relative gravity measurements were tied to the network of absolute gravity stations in Sondrestrom. We measured snow physical properties in two shallow pits. This report summarizes our observations and data analysis.

Controls on valley spacing in landscapes subject to rapid base-level fall

USGS Publications Warehouse

McGuire, Luke; Pelletier, John D.

2015-01-01

What controls the architecture of drainage networks is a fundamental question in geomorphology. Recent work has elucidated the mechanisms of drainage network development in steadily uplifting landscapes, but the controls on drainage-network morphology in transient landscapes are relatively unknown. In this paper we exploit natural experiments in drainage network development in incised Plio-Quaternary alluvial fan surfaces in order to understand and quantify drainage network development in highly transient landscapes, i.e. initially unincised low-relief surfaces that experience a pulse of rapid base-level drop followed by relative base-level stasis. Parallel drainage networks formed on incised alluvial-fan surfaces tend to have a drainage spacing that is approximately proportional to the magnitude of the base-level drop. Numerical experiments suggest that this observed relationship between the magnitude of base-level drop and mean drainage spacing is the result of feedbacks among the depth of valley incision, mass wasting and nonlinear increases in the rate of colluvial sediment transport with slope gradient on steep valley side slopes that lead to increasingly wide valleys in cases of larger base-level drop. We identify a threshold magnitude of base-level drop above which side slopes lengthen sufficiently to promote increases in contributing area and fluvial incision rates that lead to branching and encourage drainage networks to transition from systems of first-order valleys to systems of higher-order, branching valleys. The headward growth of these branching tributaries prevents the development of adjacent, ephemeral drainages and promotes a higher mean valley spacing relative to cases in which tributaries do not form. Model results offer additional insights into the response of initially unincised landscapes to rapid base-level drop and provide a preliminary basis for understanding how varying amounts of base-level change influence valley network morphology.

Intra-Urban Movement Flow Estimation Using Location Based Social Networking Data

NASA Astrophysics Data System (ADS)

Kheiri, A.; Karimipour, F.; Forghani, M.

2015-12-01

In recent years, there has been a rapid growth of location-based social networking services, such as Foursquare and Facebook, which have attracted an increasing number of users and greatly enriched their urban experience. Location-based social network data, as a new travel demand data source, seems to be an alternative or complement to survey data in the study of mobility behavior and activity analysis because of its relatively high access and low cost. In this paper, three OD estimation models have been utilized in order to investigate their relative performance when using Location-Based Social Networking (LBSN) data. For this, the Foursquare LBSN data was used to analyze the intra-urban movement behavioral patterns for the study area, Manhattan, the most densely populated of the five boroughs of New York city. The outputs of models are evaluated using real observations based on different criterions including distance distribution, destination travel constraints. The results demonstrate the promising potential of using LBSN data for urban travel demand analysis and monitoring.

Preparing for LSST with the LCOGT NEO Follow-up Network

NASA Astrophysics Data System (ADS)

Greenstreet, Sarah; Lister, Tim; Gomez, Edward

2016-10-01

The Las Cumbres Observatory Global Telescope Network (LCOGT) provides an ideal platform for follow-up and characterization of Solar System objects (e.g. asteroids, Kuiper Belt Objects, comets, Near-Earth Objects (NEOs)) and ultimately for the discovery of new objects. The LCOGT NEO Follow-up Network is using the LCOGT telescope network in addition to a web-based system developed to perform prioritized target selection, scheduling, and data reduction to confirm NEO candidates and characterize radar-targeted known NEOs.In order to determine how to maximize our NEO follow-up efforts, we must first define our goals for the LCOGT NEO Follow-up Network. This means answering the following questions. Should we follow-up all objects brighter than some magnitude limit? Should we only focus on the brightest objects or push to the limits of our capabilities by observing the faintest objects we think we can see and risk not finding the objects in our data? Do we (and how do we) prioritize objects somewhere in the middle of our observable magnitude range? If we want to push to faint objects, how do we minimize the amount of data in which the signal-to-noise ratio is too low to see the object? And how do we find a balance between performing follow-up and characterization observations?To help answer these questions, we have developed a LCOGT NEO Follow-up Network simulator that allows us to test our prioritization algorithms for target selection, confirm signal-to-noise predictions, and determine ideal block lengths and exposure times for observing NEO candidates. We will present our results from the simulator and progress on our NEO follow-up efforts.In the era of LSST, developing/utilizing infrastructure, such as the LCOGT NEO Follow-up Network and our web-based platform for selecting, scheduling, and reducing NEO observations, capable of handling the large number of detections expected to be produced on a daily basis by LSST will be critical to follow-up efforts. We hope our work can act as an example and tool for the community as together we prepare for the age of LSST.

Emergent explosive synchronization in adaptive complex networks

NASA Astrophysics Data System (ADS)

Avalos-Gaytán, Vanesa; Almendral, Juan A.; Leyva, I.; Battiston, F.; Nicosia, V.; Latora, V.; Boccaletti, S.

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

Emergent explosive synchronization in adaptive complex networks.

PubMed

Avalos-Gaytán, Vanesa; Almendral, Juan A; Leyva, I; Battiston, F; Nicosia, V; Latora, V; Boccaletti, S

2018-04-01

Adaptation plays a fundamental role in shaping the structure of a complex network and improving its functional fitting. Even when increasing the level of synchronization in a biological system is considered as the main driving force for adaptation, there is evidence of negative effects induced by excessive synchronization. This indicates that coherence alone cannot be enough to explain all the structural features observed in many real-world networks. In this work, we propose an adaptive network model where the dynamical evolution of the node states toward synchronization is coupled with an evolution of the link weights based on an anti-Hebbian adaptive rule, which accounts for the presence of inhibitory effects in the system. We found that the emergent networks spontaneously develop the structural conditions to sustain explosive synchronization. Our results can enlighten the shaping mechanisms at the heart of the structural and dynamical organization of some relevant biological systems, namely, brain networks, for which the emergence of explosive synchronization has been observed.

Spectral Entropies as Information-Theoretic Tools for Complex Network Comparison

NASA Astrophysics Data System (ADS)

De Domenico, Manlio; Biamonte, Jacob

2016-10-01

Any physical system can be viewed from the perspective that information is implicitly represented in its state. However, the quantification of this information when it comes to complex networks has remained largely elusive. In this work, we use techniques inspired by quantum statistical mechanics to define an entropy measure for complex networks and to develop a set of information-theoretic tools, based on network spectral properties, such as Rényi q entropy, generalized Kullback-Leibler and Jensen-Shannon divergences, the latter allowing us to define a natural distance measure between complex networks. First, we show that by minimizing the Kullback-Leibler divergence between an observed network and a parametric network model, inference of model parameter(s) by means of maximum-likelihood estimation can be achieved and model selection can be performed with appropriate information criteria. Second, we show that the information-theoretic metric quantifies the distance between pairs of networks and we can use it, for instance, to cluster the layers of a multilayer system. By applying this framework to networks corresponding to sites of the human microbiome, we perform hierarchical cluster analysis and recover with high accuracy existing community-based associations. Our results imply that spectral-based statistical inference in complex networks results in demonstrably superior performance as well as a conceptual backbone, filling a gap towards a network information theory.

Percolation on shopping and cashback electronic commerce networks

NASA Astrophysics Data System (ADS)

Fu, Tao; Chen, Yini; Qin, Zhen; Guo, Liping

2013-06-01

Many realistic networks live in the form of multiple networks, including interacting networks and interdependent networks. Here we study percolation properties of a special kind of interacting networks, namely Shopping and Cashback Electronic Commerce Networks (SCECNs). We investigate two actual SCECNs to extract their structural properties, and develop a mathematical framework based on generating functions for analyzing directed interacting networks. Then we derive the necessary and sufficient condition for the absence of the system-wide giant in- and out- component, and propose arithmetic to calculate the corresponding structural measures in the sub-critical and supercritical regimes. We apply our mathematical framework and arithmetic to those two actual SCECNs to observe its accuracy, and give some explanations on the discrepancies. We show those structural measures based on our mathematical framework and arithmetic are useful to appraise the status of SCECNs. We also find that the supercritical regime of the whole network is maintained mainly by hyperlinks between different kinds of websites, while those hyperlinks between the same kinds of websites can only enlarge the sizes of in-components and out-components.

Building AN International Polar Data Coordination Network

NASA Astrophysics Data System (ADS)

Pulsifer, P. L.; Yarmey, L.; Manley, W. F.; Gaylord, A. G.; Tweedie, C. E.

2013-12-01

In the spirit of the World Data Center system developed to manage data resulting from the International Geophysical Year of 1957-58, the International Polar Year 2007-2009 (IPY) resulted in significant progress towards establishing an international polar data management network. However, a sustained international network is still evolving. In this paper we argue that the fundamental building blocks for such a network exist and that the time is right to move forward. We focus on the Arctic component of such a network with linkages to Antarctic network building activities. A review of an important set of Network building blocks is presented: i) the legacy of the IPY data and information service; ii) global data management services with a polar component (e.g. World Data System); iii) regional systems (e.g. Arctic Observing Viewer; iv) nationally focused programs (e.g. Arctic Observing Viewer, Advanced Cooperative Arctic Data and Information Service, Polar Data Catalogue, Inuit Knowledge Centre); v) programs focused on the local (e.g. Exchange for Local Observations and Knowledge of the Arctic, Geomatics and Cartographic Research Centre). We discuss current activities and results with respect to three priority areas needed to establish a strong and effective Network. First, a summary of network building activities reports on a series of productive meetings, including the Arctic Observing Summit and the Polar Data Forum, that have resulted in a core set of Network nodes and participants and a refined vision for the Network. Second, we recognize that interoperability for information sharing fundamentally relies on the creation and adoption of community-based data description standards and data delivery mechanisms. There is a broad range of interoperability frameworks and specifications available; however, these need to be adapted for polar community needs. Progress towards Network interoperability is reviewed, and a prototype distributed data systems is demonstrated. We discuss remaining challenges. Lastly, to establish a sustainable Arctic Data Coordination Network (ADCN) as part of a broader polar Network will require adequate continued resources. We conclude by outlining proposed business models for the emerging Arctic Data Coordination Network and a broader polar Network.

GPS baseline configuration design based on robustness analysis

NASA Astrophysics Data System (ADS)

Yetkin, M.; Berber, M.

2012-11-01

The robustness analysis results obtained from a Global Positioning System (GPS) network are dramatically influenced by the configurationof the observed baselines. The selection of optimal GPS baselines may allow for a cost effective survey campaign and a sufficiently robustnetwork. Furthermore, using the approach described in this paper, the required number of sessions, the baselines to be observed, and thesignificance levels for statistical testing and robustness analysis can be determined even before the GPS campaign starts. In this study, wepropose a robustness criterion for the optimal design of geodetic networks, and present a very simple and efficient algorithm based on thiscriterion for the selection of optimal GPS baselines. We also show the relationship between the number of sessions and the non-centralityparameter. Finally, a numerical example is given to verify the efficacy of the proposed approach.

Anomalous Signal Detection in ELF Band Electromagnetic Wave using Multi-layer Neural Network with Wavelet Decomposition

NASA Astrophysics Data System (ADS)

Itai, Akitoshi; Yasukawa, Hiroshi; Takumi, Ichi; Hata, Masayasu

It is well known that electromagnetic waves radiated from the earth's crust are useful for predicting earthquakes. We analyze the electromagnetic waves received at the extremely low frequency band of 223Hz. These observed signals contain the seismic radiation from the earth's crust, but also include several undesired signals. Our research focuses on the signal detection technique to identify an anomalous signal corresponding to the seismic radiation in the observed signal. Conventional anomalous signal detections lack a wide applicability due to their assumptions, e.g. the digital data have to be observed at the same time or the same sensor. In order to overcome the limitation related to the observed signal, we proposed the anomalous signals detection based on a multi-layer neural network which is trained by digital data observed during a span of a day. In the neural network approach, training data do not need to be recorded at the same place or the same time. However, some noises, which have a large amplitude, are detected as the anomalous signal. This paper develops a multi-layer neural network to decrease the false detection of the anomalous signal from the electromagnetic wave. The training data for the proposed network is the decomposed signal of the observed signal during several days, since the seismic radiations are often recorded from several days to a couple of weeks. Results show that the proposed neural network is useful to achieve the accurate detection of the anomalous signal that indicates seismic activity.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization.

PubMed

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y; Alouini, Mohamed-Slim

2017-12-26

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique.

Energy Harvesting Hybrid Acoustic-Optical Underwater Wireless Sensor Networks Localization

PubMed Central

Saeed, Nasir; Celik, Abdulkadir; Al-Naffouri, Tareq Y.; Alouini, Mohamed-Slim

2017-01-01

Underwater wireless technologies demand to transmit at higher data rate for ocean exploration. Currently, large coverage is achieved by acoustic sensor networks with low data rate, high cost, high latency, high power consumption, and negative impact on marine mammals. Meanwhile, optical communication for underwater networks has the advantage of the higher data rate albeit for limited communication distances. Moreover, energy consumption is another major problem for underwater sensor networks, due to limited battery power and difficulty in replacing or recharging the battery of a sensor node. The ultimate solution to this problem is to add energy harvesting capability to the acoustic-optical sensor nodes. Localization of underwater sensor networks is of utmost importance because the data collected from underwater sensor nodes is useful only if the location of the nodes is known. Therefore, a novel localization technique for energy harvesting hybrid acoustic-optical underwater wireless sensor networks (AO-UWSNs) is proposed. AO-UWSN employs optical communication for higher data rate at a short transmission distance and employs acoustic communication for low data rate and long transmission distance. A hybrid received signal strength (RSS) based localization technique is proposed to localize the nodes in AO-UWSNs. The proposed technique combines the noisy RSS based measurements from acoustic communication and optical communication and estimates the final locations of acoustic-optical sensor nodes. A weighted multiple observations paradigm is proposed for hybrid estimated distances to suppress the noisy observations and give more importance to the accurate observations. Furthermore, the closed form solution for Cramer-Rao lower bound (CRLB) is derived for localization accuracy of the proposed technique. PMID:29278405

First Ground-Based Observation of Sprites Over Southern Africa and Estimation of Their Physical and Optical Characteristics

NASA Astrophysics Data System (ADS)

Nnadih, O.; Martinez, P.; Kosch, M.; Lotz, S.; Fullekrug, M.

2016-12-01

We present the first ground-based observations of sprites over convective thunderstorms in southern Africa. The observations, acquired during the austral summer of 2015/16. show sprites with dendritic, carrot, angel and jellyfish-like shapes. The sprite locations are compared with lightning locations and peak amplitudes determined from the lightning detection network operated by the South African Weather Service, and also with the lightning locations reported by the World Wide Lightning Location Network (WLLN) and Low Frequency radio waveforms of the electric field strength recorded in the conjugate hemisphere in South-West England. The charge moment of the lightning discharges causing sprites is inferred from Extremely Low Frequency magnetic field measurements recorded at remote distances. These measurements reveal that a number of the sprites that we observed were triggered below and above the charge moment threshold for sprite production.

Information transmission on hybrid networks

NASA Astrophysics Data System (ADS)

Chen, Rongbin; Cui, Wei; Pu, Cunlai; Li, Jie; Ji, Bo; Gakis, Konstantinos; Pardalos, Panos M.

2018-01-01

Many real-world communication networks often have hybrid nature with both fixed nodes and moving modes, such as the mobile phone networks mainly composed of fixed base stations and mobile phones. In this paper, we discuss the information transmission process on the hybrid networks with both fixed and mobile nodes. The fixed nodes (base stations) are connected as a spatial lattice on the plane forming the information-carrying backbone, while the mobile nodes (users), which are the sources and destinations of information packets, connect to their current nearest fixed nodes respectively to deliver and receive information packets. We observe the phase transition of traffic load in the hybrid network when the packet generation rate goes from below and then above a critical value, which measures the network capacity of packets delivery. We obtain the optimal speed of moving nodes leading to the maximum network capacity. We further improve the network capacity by rewiring the fixed nodes and by considering the current load of fixed nodes during packets transmission. Our purpose is to optimize the network capacity of hybrid networks from the perspective of network science, and provide some insights for the construction of future communication infrastructures.

A neural network to retrieve the mesoscale instantaneous latent heat flux over oceans from SSM/I observations

NASA Technical Reports Server (NTRS)

Bourras, D.; Eymard, L.; Liu, W. T.

2000-01-01

The turbulent latent and sensible heat fluxes are necessary to study heat budget of the upper ocean or initialize ocean general circulation models. In order to retrieve the latent heat flux from satellite observations authors mostly use a bulk approximation of the flux whose parameters are derived from different instrument. In this paper, an approach based on artificial neural networks is proposed and compared to the bulk method on a global data set and 3 local data sets.

Statistical inference approach to structural reconstruction of complex networks from binary time series

NASA Astrophysics Data System (ADS)

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

Statistical inference approach to structural reconstruction of complex networks from binary time series.

PubMed

Ma, Chuang; Chen, Han-Shuang; Lai, Ying-Cheng; Zhang, Hai-Feng

2018-02-01

Complex networks hosting binary-state dynamics arise in a variety of contexts. In spite of previous works, to fully reconstruct the network structure from observed binary data remains challenging. We articulate a statistical inference based approach to this problem. In particular, exploiting the expectation-maximization (EM) algorithm, we develop a method to ascertain the neighbors of any node in the network based solely on binary data, thereby recovering the full topology of the network. A key ingredient of our method is the maximum-likelihood estimation of the probabilities associated with actual or nonexistent links, and we show that the EM algorithm can distinguish the two kinds of probability values without any ambiguity, insofar as the length of the available binary time series is reasonably long. Our method does not require any a priori knowledge of the detailed dynamical processes, is parameter-free, and is capable of accurate reconstruction even in the presence of noise. We demonstrate the method using combinations of distinct types of binary dynamical processes and network topologies, and provide a physical understanding of the underlying reconstruction mechanism. Our statistical inference based reconstruction method contributes an additional piece to the rapidly expanding "toolbox" of data based reverse engineering of complex networked systems.

SurvNet: a web server for identifying network-based biomarkers that most correlate with patient survival data.

PubMed

Li, Jun; Roebuck, Paul; Grünewald, Stefan; Liang, Han

2012-07-01

An important task in biomedical research is identifying biomarkers that correlate with patient clinical data, and these biomarkers then provide a critical foundation for the diagnosis and treatment of disease. Conventionally, such an analysis is based on individual genes, but the results are often noisy and difficult to interpret. Using a biological network as the searching platform, network-based biomarkers are expected to be more robust and provide deep insights into the molecular mechanisms of disease. We have developed a novel bioinformatics web server for identifying network-based biomarkers that most correlate with patient survival data, SurvNet. The web server takes three input files: one biological network file, representing a gene regulatory or protein interaction network; one molecular profiling file, containing any type of gene- or protein-centred high-throughput biological data (e.g. microarray expression data or DNA methylation data); and one patient survival data file (e.g. patients' progression-free survival data). Given user-defined parameters, SurvNet will automatically search for subnetworks that most correlate with the observed patient survival data. As the output, SurvNet will generate a list of network biomarkers and display them through a user-friendly interface. SurvNet can be accessed at http://bioinformatics.mdanderson.org/main/SurvNet.

Real-time flood forecasts & risk assessment using a possibility-theory based fuzzy neural network

NASA Astrophysics Data System (ADS)

Khan, U. T.

2016-12-01

Globally floods are one of the most devastating natural disasters and improved flood forecasting methods are essential for better flood protection in urban areas. Given the availability of high resolution real-time datasets for flood variables (e.g. streamflow and precipitation) in many urban areas, data-driven models have been effectively used to predict peak flow rates in river; however, the selection of input parameters for these types of models is often subjective. Additionally, the inherit uncertainty associated with data models along with errors in extreme event observations means that uncertainty quantification is essential. Addressing these concerns will enable improved flood forecasting methods and provide more accurate flood risk assessments. In this research, a new type of data-driven model, a quasi-real-time updating fuzzy neural network is developed to predict peak flow rates in urban riverine watersheds. A possibility-to-probability transformation is first used to convert observed data into fuzzy numbers. A possibility theory based training regime is them used to construct the fuzzy parameters and the outputs. A new entropy-based optimisation criterion is used to train the network. Two existing methods to select the optimum input parameters are modified to account for fuzzy number inputs, and compared. These methods are: Entropy-Wavelet-based Artificial Neural Network (EWANN) and Combined Neural Pathway Strength Analysis (CNPSA). Finally, an automated algorithm design to select the optimum structure of the neural network is implemented. The overall impact of each component of training this network is to replace the traditional ad hoc network configuration methods, with one based on objective criteria. Ten years of data from the Bow River in Calgary, Canada (including two major floods in 2005 and 2013) are used to calibrate and test the network. The EWANN method selected lagged peak flow as a candidate input, whereas the CNPSA method selected lagged precipitation and lagged mean daily flow as candidate inputs. Model performance metric show that the CNPSA method had higher performance (with an efficiency of 0.76). Model output was used to assess the risk of extreme peak flows for a given day using an inverse possibility-to-probability transformation.

Monitoring the Environmental Impact of TiO2 Nanoparticles Using a Plant-Based Sensor Network

PubMed Central

Lenaghan, Scott C.; Li, Yuanyuan; Zhang, Hao; Burris, Jason N.; Stewart, C. Neal; Parker, Lynne E.; Zhang, Mingjun

2016-01-01

The increased manufacturing of nanoparticles for use in cosmetics, foods, and clothing necessitates the need for an effective system to monitor and evaluate the potential environmental impact of these nanoparticles. The goal of this research was to develop a plant-based sensor network for characterizing, monitoring, and understanding the environmental impact of TiO2 nanoparticles. The network consisted of potted Arabidopsis thaliana with a surrounding water supply, which was monitored by cameras attached to a laptop computer running a machine learning algorithm. Using the proposed plant sensor network, we were able to examine the toxicity of TiO2 nanoparticles in two systems: algae and terrestrial plants. Increased terrestrial plant growth was observed upon introduction of the nanoparticles, whereas algal growth decreased significantly. The proposed system can be further automated for high-throughput screening of nanoparticle toxicity in the environment at multiple trophic levels. The proposed plant-based sensor network could be used for more accurate characterization of the environmental impact of nanomaterials. PMID:28458617

Riemannian multi-manifold modeling and clustering in brain networks

NASA Astrophysics Data System (ADS)

Slavakis, Konstantinos; Salsabilian, Shiva; Wack, David S.; Muldoon, Sarah F.; Baidoo-Williams, Henry E.; Vettel, Jean M.; Cieslak, Matthew; Grafton, Scott T.

2017-08-01

This paper introduces Riemannian multi-manifold modeling in the context of brain-network analytics: Brainnetwork time-series yield features which are modeled as points lying in or close to a union of a finite number of submanifolds within a known Riemannian manifold. Distinguishing disparate time series amounts thus to clustering multiple Riemannian submanifolds. To this end, two feature-generation schemes for brain-network time series are put forth. The first one is motivated by Granger-causality arguments and uses an auto-regressive moving average model to map low-rank linear vector subspaces, spanned by column vectors of appropriately defined observability matrices, to points into the Grassmann manifold. The second one utilizes (non-linear) dependencies among network nodes by introducing kernel-based partial correlations to generate points in the manifold of positivedefinite matrices. Based on recently developed research on clustering Riemannian submanifolds, an algorithm is provided for distinguishing time series based on their Riemannian-geometry properties. Numerical tests on time series, synthetically generated from real brain-network structural connectivity matrices, reveal that the proposed scheme outperforms classical and state-of-the-art techniques in clustering brain-network states/structures.

Physarum solver: A biologically inspired method of road-network navigation

NASA Astrophysics Data System (ADS)

Tero, Atsushi; Kobayashi, Ryo; Nakagaki, Toshiyuki

2006-04-01

We have proposed a mathematical model for the adaptive dynamics of the transport network in an amoeba-like organism, the true slime mold Physarum polycephalum. The model is based on physiological observations of this species, but can also be used for path-finding in the complicated networks of mazes and road maps. In this paper, we describe the physiological basis and the formulation of the model, as well as the results of simulations of some complicated networks. The path-finding method used by Physarum is a good example of cellular computation.

Brain network dynamics characterization in epileptic seizures. Joint directed graph and pairwise synchronization measures

NASA Astrophysics Data System (ADS)

Rodrigues, A. C.; Machado, B. S.; Florence, G.; Hamad, A. P.; Sakamoto, A. C.; Fujita, A.; Baccalá, L. A.; Amaro, E.; Sameshima, K.

2014-12-01

Here we propose and evaluate a new approach to analyse multichannel mesial temporal lobe epilepsy EEG data from eight patients through complex network and synchronization theories. The method employs a Granger causality test to infer the directed connectivity graphs and a wavelet transform based phase synchronization measure whose characteristics allow studying dynamical transitions during epileptic seizures. We present a new combined graph measure that quantifies the level of network hub formation, called network hub out-degree, which closely reflects the level of synchronization observed during the ictus.

Distributed micro-releases of bioterror pathogens : threat characterizations and epidemiology from uncertain patient observables.

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

Wolf, Michael M.; Marzouk, Youssef M.; Adams, Brian M.

2008-10-01

Terrorist attacks using an aerosolized pathogen preparation have gained credibility as a national security concern since the anthrax attacks of 2001. The ability to characterize the parameters of such attacks, i.e., to estimate the number of people infected, the time of infection, the average dose received, and the rate of disease spread in contemporary American society (for contagious diseases), is important when planning a medical response. For non-contagious diseases, we address the characterization problem by formulating a Bayesian inverse problem predicated on a short time-series of diagnosed patients exhibiting symptoms. To keep the approach relevant for response planning, we limitmore » ourselves to 3.5 days of data. In computational tests performed for anthrax, we usually find these observation windows sufficient, especially if the outbreak model employed in the inverse problem is accurate. For contagious diseases, we formulated a Bayesian inversion technique to infer both pathogenic transmissibility and the social network from outbreak observations, ensuring that the two determinants of spreading are identified separately. We tested this technique on data collected from a 1967 smallpox epidemic in Abakaliki, Nigeria. We inferred, probabilistically, different transmissibilities in the structured Abakaliki population, the social network, and the chain of transmission. Finally, we developed an individual-based epidemic model to realistically simulate the spread of a rare (or eradicated) disease in a modern society. This model incorporates the mixing patterns observed in an (American) urban setting and accepts, as model input, pathogenic transmissibilities estimated from historical outbreaks that may have occurred in socio-economic environments with little resemblance to contemporary society. Techniques were also developed to simulate disease spread on static and sampled network reductions of the dynamic social networks originally in the individual-based model, yielding faster, though approximate, network-based epidemic models. These reduced-order models are useful in scenario analysis for medical response planning, as well as in computationally intensive inverse problems.« less

Approaching mathematical model of the immune network based DNA Strand Displacement system.

PubMed

Mardian, Rizki; Sekiyama, Kosuke; Fukuda, Toshio

2013-12-01

One biggest obstacle in molecular programming is that there is still no direct method to compile any existed mathematical model into biochemical reaction in order to solve a computational problem. In this paper, the implementation of DNA Strand Displacement system based on nature-inspired computation is observed. By using the Immune Network Theory and Chemical Reaction Network, the compilation of DNA-based operation is defined and the formulation of its mathematical model is derived. Furthermore, the implementation on this system is compared with the conventional implementation by using silicon-based programming. From the obtained results, we can see a positive correlation between both. One possible application from this DNA-based model is for a decision making scheme of intelligent computer or molecular robot. Copyright © 2013 Elsevier Ireland Ltd. All rights reserved.

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks

PubMed Central

Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-01-01

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid “particle degeneracy” problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network. PMID:29267252

A Support Vector Learning-Based Particle Filter Scheme for Target Localization in Communication-Constrained Underwater Acoustic Sensor Networks.

PubMed

Li, Xinbin; Zhang, Chenglin; Yan, Lei; Han, Song; Guan, Xinping

2017-12-21

Target localization, which aims to estimate the location of an unknown target, is one of the key issues in applications of underwater acoustic sensor networks (UASNs). However, the constrained property of an underwater environment, such as restricted communication capacity of sensor nodes and sensing noises, makes target localization a challenging problem. This paper relies on fractional sensor nodes to formulate a support vector learning-based particle filter algorithm for the localization problem in communication-constrained underwater acoustic sensor networks. A node-selection strategy is exploited to pick fractional sensor nodes with short-distance pattern to participate in the sensing process at each time frame. Subsequently, we propose a least-square support vector regression (LSSVR)-based observation function, through which an iterative regression strategy is used to deal with the distorted data caused by sensing noises, to improve the observation accuracy. At the same time, we integrate the observation to formulate the likelihood function, which effectively update the weights of particles. Thus, the particle effectiveness is enhanced to avoid "particle degeneracy" problem and improve localization accuracy. In order to validate the performance of the proposed localization algorithm, two different noise scenarios are investigated. The simulation results show that the proposed localization algorithm can efficiently improve the localization accuracy. In addition, the node-selection strategy can effectively select the subset of sensor nodes to improve the communication efficiency of the sensor network.

Changes of intranetwork and internetwork functional connectivity in Alzheimer’s disease and mild cognitive impairment

NASA Astrophysics Data System (ADS)

Zhu, Haoze; Zhou, Peng; Alcauter, Sarael; Chen, Yuanyuan; Cao, Hongbao; Tian, Miao; Ming, Dong; Qi, Hongzhi; Wang, Xuemin; Zhao, Xin; He, Feng; Ni, Hongyan; Gao, Wei

2016-08-01

Objective. Alzheimer’s disease (AD) is a serious neurodegenerative disorder characterized by deficits of working memory, attention, language and many other cognitive functions. Although different stages of the disease are relatively well characterized by clinical criteria, stage-specific pathological changes in the brain remain relatively poorly understood, especially at the level of large-scale functional networks. In this study, we aimed to characterize the potential disruptions of large-scale functional brain networks based on a sample including amnestic mild cognition impairment (aMCI) and AD patients to help delineate the underlying stage-dependent AD pathology. Approach. We sought to identify the neural connectivity mechanisms of aMCI and AD through examination of both intranetwork and internetwork interactions among four of the brain’s key networks, namely dorsal attention network (DAN), default mode network (DMN), executive control network (ECN) and salience network (SAL). We analyzed functional connectivity based on resting-state functional magnetic resonance imaging (rs-fMRI) data from 25 Alzheimer’s disease patients, 20 aMCI patients and 35 elderly normal controls (NC). Main results. Intranetwork functional disruptions within the DAN and ECN were detected in both aMCI and AD patients. Disrupted intranetwork connectivity of DMN and anti-correlation between DAN and DMN were observed in AD patients. Moreover, aMCI-specific alterations in the internetwork functional connectivity of SAL were observed. Significance. Our results confirmed previous findings that AD pathology was related to dysconnectivity both within and between resting-state networks but revealed more spatial details. Moreover, the SAL network, reportedly flexibly coupling either with the DAN or DMN networks during different brain states, demonstrated interesting alterations specifically in the early stage of the disease.

Observer-Based Non-PDC Control for Networked T-S Fuzzy Systems With an Event-Triggered Communication.

PubMed

Peng, Chen; Ma, Shaodong; Xie, Xiangpeng

2017-02-07

This paper addresses the problem of an event-triggered non-parallel distribution compensation (PDC) control for networked Takagi-Sugeno (T-S) fuzzy systems, under consideration of the limited data transmission bandwidth and the imperfect premise matching membership functions. First, a unified event-triggered T-S fuzzy model is provided, in which: 1) a fuzzy observer with the imperfect premise matching is constructed to estimate the unmeasurable states of the studied system; 2) a fuzzy controller is designed following the same premise as the observer; and 3) an output-based event-triggering transmission scheme is designed to economize the restricted network resources. Different from the traditional PDC method, the synchronous premise between the fuzzy observer and the T-S fuzzy system are no longer needed in this paper. Second, by use of Lyapunov theory, a stability criterion and a stabilization condition are obtained for ensuring asymptotically stable of the studied system. On account of the imperfect premise matching conditions are well considered in the derivation of the above criteria, less conservation can be expected to enhance the design flexibility. Compared with some existing emulation-based methods, the controller gains are no longer required to be known a priori. Finally, the availability of proposed non-PDC design scheme is illustrated by the backing-up control of a truck-trailer system.

RegNetwork: an integrated database of transcriptional and post-transcriptional regulatory networks in human and mouse

PubMed Central

Liu, Zhi-Ping; Wu, Canglin; Miao, Hongyu; Wu, Hulin

2015-01-01

Transcriptional and post-transcriptional regulation of gene expression is of fundamental importance to numerous biological processes. Nowadays, an increasing amount of gene regulatory relationships have been documented in various databases and literature. However, to more efficiently exploit such knowledge for biomedical research and applications, it is necessary to construct a genome-wide regulatory network database to integrate the information on gene regulatory relationships that are widely scattered in many different places. Therefore, in this work, we build a knowledge-based database, named ‘RegNetwork’, of gene regulatory networks for human and mouse by collecting and integrating the documented regulatory interactions among transcription factors (TFs), microRNAs (miRNAs) and target genes from 25 selected databases. Moreover, we also inferred and incorporated potential regulatory relationships based on transcription factor binding site (TFBS) motifs into RegNetwork. As a result, RegNetwork contains a comprehensive set of experimentally observed or predicted transcriptional and post-transcriptional regulatory relationships, and the database framework is flexibly designed for potential extensions to include gene regulatory networks for other organisms in the future. Based on RegNetwork, we characterized the statistical and topological properties of genome-wide regulatory networks for human and mouse, we also extracted and interpreted simple yet important network motifs that involve the interplays between TF-miRNA and their targets. In summary, RegNetwork provides an integrated resource on the prior information for gene regulatory relationships, and it enables us to further investigate context-specific transcriptional and post-transcriptional regulatory interactions based on domain-specific experimental data. Database URL: http://www.regnetworkweb.org PMID:26424082

An Optimal Algorithm towards Successive Location Privacy in Sensor Networks with Dynamic Programming

NASA Astrophysics Data System (ADS)

Zhao, Baokang; Wang, Dan; Shao, Zili; Cao, Jiannong; Chan, Keith C. C.; Su, Jinshu

In wireless sensor networks, preserving location privacy under successive inference attacks is extremely critical. Although this problem is NP-complete in general cases, we propose a dynamic programming based algorithm and prove it is optimal in special cases where the correlation only exists between p immediate adjacent observations.

Learning through Social Networking Sites--The Critical Role of the Teacher

ERIC Educational Resources Information Center

Callaghan, Noelene; Bower, Matt

2012-01-01

This comparative case study examined factors affecting behaviour and learning in social networking sites (SNS). The behaviour and learning of two classes completing identical SNS based modules of work was observed and compared. All student contributions to the SNS were analysed, with the cognitive process dimension of the Revised Bloom's Taxonomy…

Long-term monitoring of blazars - the DWARF network

NASA Astrophysics Data System (ADS)

Backes, Michael; Biland, Adrian; Boller, Andrea; Braun, Isabel; Bretz, Thomas; Commichau, Sebastian; Commichau, Volker; Dorner, Daniela; von Gunten, Hanspeter; Gendotti, Adamo; Grimm, Oliver; Hildebrand, Dorothée; Horisberger, Urs; Krähenbühl, Thomas; Kranich, Daniel; Lustermann, Werner; Mannheim, Karl; Neise, Dominik; Pauss, Felicitas; Renker, Dieter; Rhode, Wolfgang; Rissi, Michael; Rollke, Sebastian; Röser, Ulf; Stark, Luisa Sabrina; Stucki, Jean-Pierre; Viertel, Gert; Vogler, Patrick; Weitzel, Quirin

The variability of the very high energy (VHE) emission from blazars seems to be connected with the feeding and propagation of relativistic jets and with their origin in supermassive black hole binaries. The key to understanding their properties is measuring well-sampled gamma-ray lightcurves, revealing the typical source behavior unbiased by prior knowledge from other wavebands. Using ground-based gamma-ray observatories with exposures limited by dark-time, a global network of several telescopes is needed to carry out fulltime measurements. Obviously, such observations are time-consuming and, therefore, cannot be carried out with the present state of the art instruments. The DWARF telescope on the Canary Island of La Palma is dedicated to monitoring observations. It is currently being set up, employing a costefficient and robotic design. Part of this project is the future construction of a distributed network of small telescopes. The physical motivation of VHE long-term monitoring will be outlined in detail and the perspective for a network for 24/7 observations will be presented.

On the estimability of parameters in undifferenced, uncombined GNSS network and PPP-RTK user models by means of $mathcal {S}$ S -system theory

NASA Astrophysics Data System (ADS)

Odijk, Dennis; Zhang, Baocheng; Khodabandeh, Amir; Odolinski, Robert; Teunissen, Peter J. G.

2016-01-01

The concept of integer ambiguity resolution-enabled Precise Point Positioning (PPP-RTK) relies on appropriate network information for the parameters that are common between the single-receiver user that applies and the network that provides this information. Most of the current methods for PPP-RTK are based on forming the ionosphere-free combination using dual-frequency Global Navigation Satellite System (GNSS) observations. These methods are therefore restrictive in the light of the development of new multi-frequency GNSS constellations, as well as from the point of view that the PPP-RTK user requires ionospheric corrections to obtain integer ambiguity resolution results based on short observation time spans. The method for PPP-RTK that is presented in this article does not have above limitations as it is based on the undifferenced, uncombined GNSS observation equations, thereby keeping all parameters in the model. Working with the undifferenced observation equations implies that the models are rank-deficient; not all parameters are unbiasedly estimable, but only combinations of them. By application of S-system theory the model is made of full rank by constraining a minimum set of parameters, or S-basis. The choice of this S-basis determines the estimability and the interpretation of the parameters that are transmitted to the PPP-RTK users. As this choice is not unique, one has to be very careful when comparing network solutions in different S-systems; in that case the S-transformation, which is provided by the S-system method, should be used to make the comparison. Knowing the estimability and interpretation of the parameters estimated by the network is shown to be crucial for a correct interpretation of the estimable PPP-RTK user parameters, among others the essential ambiguity parameters, which have the integer property which is clearly following from the interpretation of satellite phase biases from the network. The flexibility of the S-system method is furthermore demonstrated by the fact that all models in this article are derived in multi-epoch mode, allowing to incorporate dynamic model constraints on all or subsets of parameters.

A novel wavelet sequence based on deep bidirectional LSTM network model for ECG signal classification.

PubMed

Yildirim, Özal

2018-05-01

Long-short term memory networks (LSTMs), which have recently emerged in sequential data analysis, are the most widely used type of recurrent neural networks (RNNs) architecture. Progress on the topic of deep learning includes successful adaptations of deep versions of these architectures. In this study, a new model for deep bidirectional LSTM network-based wavelet sequences called DBLSTM-WS was proposed for classifying electrocardiogram (ECG) signals. For this purpose, a new wavelet-based layer is implemented to generate ECG signal sequences. The ECG signals were decomposed into frequency sub-bands at different scales in this layer. These sub-bands are used as sequences for the input of LSTM networks. New network models that include unidirectional (ULSTM) and bidirectional (BLSTM) structures are designed for performance comparisons. Experimental studies have been performed for five different types of heartbeats obtained from the MIT-BIH arrhythmia database. These five types are Normal Sinus Rhythm (NSR), Ventricular Premature Contraction (VPC), Paced Beat (PB), Left Bundle Branch Block (LBBB), and Right Bundle Branch Block (RBBB). The results show that the DBLSTM-WS model gives a high recognition performance of 99.39%. It has been observed that the wavelet-based layer proposed in the study significantly improves the recognition performance of conventional networks. This proposed network structure is an important approach that can be applied to similar signal processing problems. Copyright © 2018 Elsevier Ltd. All rights reserved.

Network-based H.264/AVC whole frame loss visibility model and frame dropping methods.

PubMed

Chang, Yueh-Lun; Lin, Ting-Lan; Cosman, Pamela C

2012-08-01

We examine the visual effect of whole frame loss by different decoders. Whole frame losses are introduced in H.264/AVC compressed videos which are then decoded by two different decoders with different common concealment effects: frame copy and frame interpolation. The videos are seen by human observers who respond to each glitch they spot. We found that about 39% of whole frame losses of B frames are not observed by any of the subjects, and over 58% of the B frame losses are observed by 20% or fewer of the subjects. Using simple predictive features which can be calculated inside a network node with no access to the original video and no pixel level reconstruction of the frame, we developed models which can predict the visibility of whole B frame losses. The models are then used in a router to predict the visual impact of a frame loss and perform intelligent frame dropping to relieve network congestion. Dropping frames based on their visual scores proves superior to random dropping of B frames.

Ground- and Space-based Observations of Horizontally-extensive Lightning Flashes

NASA Astrophysics Data System (ADS)

Zhang, D.; Cummins, K. L.; Bitzer, P. M.

2017-12-01

Horizontally-extensive lightning flashes occur frequently in association with mature and late phases of multicellular thunderstorms, both in trailing stratiform regions and horizontally-extensive anvils. The spatial relationship between these flashes and the parent cloud volume is of importance for space launch operational decision making, and is of broader scientific interest. Before this question can be accurately addressed, there is a need to understand the degree to which current lightning observation systems can depict the spatial extent of these long flashes. In this ongoing work, we will intercompare the depiction of horizontally-extensive flashes using several ground-based lightning locating systems (LLSs) located at Kennedy Space Center (KSC) with space-based observations observed by the recently-launched Geostationary Lightning Mapper (GLM) onboard the GOES-16 satellite. Ground-based datasets include the KSC Lightning Mapping Array (KSCLMA), the operational narrowband digital interferometer network MERLIN, and the combined cloud-to-ground and cloud lightning dataset produced by the U.S. National Lightning Detection Network (NLDN). The KSCLMA system is a network of VHF time-of-arrival sensors that preferentially report breakdown processes, and MERLIN is a network of VHF interferometers that point to the discharges in the horizontal plane. Observations to date indicate that MERLIN and the KSCSLMA provide similar overall descriptions of the spatial and temporal extent of these flashes, while the NLDN does not provide adequate spatial mapping of these flashes. The KSC LMA system has much better location accuracy, and provides excellent 3-dimensional representation within 100 km of KSC. It also has sufficient sensitivity to provide 2-dimensional flash mapping within 250 km of KSC. The MERLIN system provides a more-detailed representation of fast leader propagation (in 2 dimensions) with 100 km of KSC. Earlier work during the CHUVA campaign in Brazil with similar systems and the (orbital) Lightning Imaging System (LIS) has shown that the interferometric data correlated much better in space and time with the LIS optical observations. We are currently investigating this relationship at KSC, where both the LMA and interferometer perform much better than the systems used during CHUVA.

The Global Geodetic Observing System: Space Geodesy Networks for the Future

NASA Technical Reports Server (NTRS)

Pearlman, Michael; Pavlis, Erricos; Ma, Chopo; Altamini, Zuheir; Noll, Carey; Stowers, David

2011-01-01

Ground-based networks of co-located space geodetic techniques (VLBI, SLR, GNSS. and DORIS) are the basis for the development and maintenance of the International Terrestrial Reference frame (ITRF), which is our metric of reference for measurements of global change, The Global Geodetic Observing System (GGOS) of the International Association of Geodesy (IAG) has established a task to develop a strategy to design, integrate and maintain the fundamental geodetic network and supporting infrastructure in a sustainable way to satisfy the long-term requirements for the reference frame. The GGOS goal is an origin definition at 1 mm or better and a temporal stability on the order of 0.1 mm/y, with similar numbers for the scale and orientation components. These goals are based on scientific requirements to address sea level rise with confidence, but other applications are not far behind. Recent studies including one by the US National Research Council has strongly stated the need and the urgency for the fundamental space geodesy network. Simulations are underway to examining accuracies for origin, scale and orientation of the resulting ITRF based on various network designs and system performance to determine the optimal global network to achieve this goal. To date these simulations indicate that 24 - 32 co-located stations are adequate to define the reference frame and a more dense GNSS and DORIS network will be required to distribute the reference frame to users anywhere on Earth. Stations in the new global network will require geologically stable sites with good weather, established infrastructure, and local support and personnel. GGOS wil seek groups that are interested in participation. GGOS intends to issues a Call for Participation of groups that would like to contribute in the network implementation and operation. Some examples of integrated stations currently in operation or under development will be presented. We will examine necessary conditions and challenges in designing a co-location station.

Quantifying the spatio-temporal pattern of the ground impact of space weather events using dynamical networks formed from the SuperMAG database of ground based magnetometer stations.

NASA Astrophysics Data System (ADS)

Dods, Joe; Chapman, Sandra; Gjerloev, Jesper

2016-04-01

Quantitative understanding of the full spatial-temporal pattern of space weather is important in order to estimate the ground impact. Geomagnetic indices such as AE track the peak of a geomagnetic storm or substorm, but cannot capture the full spatial-temporal pattern. Observations by the ~100 ground based magnetometers in the northern hemisphere have the potential to capture the detailed evolution of a given space weather event. We present the first analysis of the full available set of ground based magnetometer observations of substorms using dynamical networks. SuperMAG offers a database containing ground station magnetometer data at a cadence of 1min from 100s stations situated across the globe. We use this data to form dynamic networks which capture spatial dynamics on timescales from the fast reconfiguration seen in the aurora, to that of the substorm cycle. Windowed linear cross-correlation between pairs of magnetometer time series along with a threshold is used to determine which stations are correlated and hence connected in the network. Variations in ground conductivity and differences in the response functions of magnetometers at individual stations are overcome by normalizing to long term averages of the cross-correlation. These results are tested against surrogate data in which phases have been randomised. The network is then a collection of connected points (ground stations); the structure of the network and its variation as a function of time quantify the detailed dynamical processes of the substorm. The network properties can be captured quantitatively in time dependent dimensionless network parameters and we will discuss their behaviour for examples of 'typical' substorms and storms. The network parameters provide a detailed benchmark to compare data with models of substorm dynamics, and can provide new insights on the similarities and differences between substorms and how they correlate with external driving and the internal state of the magnetosphere. We can also investigate the solar wind control of the magnetospheric-ionospheric convection system using dynamical networks. The dynamical networks are first interpolated onto a regular grid. Statistically averaged network responses are then formed for a variety of solar wind conditions, including investigating the network response to southward turnings. [1] Dods, J., S. C. Chapman, and J. W. Gjerloev (2015), Network analysis of geomagnetic substorms using the SuperMAG database of ground-based magnetometer stations, J. Geophys. Res. Space Physics, 120, 7774-7784, doi:10.1002/2015JA021456

An FPGA-Based Silicon Neuronal Network with Selectable Excitability Silicon Neurons

PubMed Central

Li, Jing; Katori, Yuichi; Kohno, Takashi

2012-01-01

This paper presents a digital silicon neuronal network which simulates the nerve system in creatures and has the ability to execute intelligent tasks, such as associative memory. Two essential elements, the mathematical-structure-based digital spiking silicon neuron (DSSN) and the transmitter release based silicon synapse, allow us to tune the excitability of silicon neurons and are computationally efficient for hardware implementation. We adopt mixed pipeline and parallel structure and shift operations to design a sufficient large and complex network without excessive hardware resource cost. The network with 256 full-connected neurons is built on a Digilent Atlys board equipped with a Xilinx Spartan-6 LX45 FPGA. Besides, a memory control block and USB control block are designed to accomplish the task of data communication between the network and the host PC. This paper also describes the mechanism of associative memory performed in the silicon neuronal network. The network is capable of retrieving stored patterns if the inputs contain enough information of them. The retrieving probability increases with the similarity between the input and the stored pattern increasing. Synchronization of neurons is observed when the successful stored pattern retrieval occurs. PMID:23269911

Cognitive radio based optimal channel sensing and resources allocation

NASA Astrophysics Data System (ADS)

Vijayasarveswari, V.; Khatun, S.; Fakir, M. M.; Nayeem, M. N.; Kamarudin, L. M.; Jakaria, A.

2017-03-01

Cognitive radio (CR) is the latest type of wireless technoloy that is proposed to mitigate spectrum saturation problem. İn cognitve radio, secondary user will use primary user's spectrum during primary user's absence without interupting primary user's transmission. This paper focuses on practical cognitive radio network development process using Android based smart phone for the data transmission. Energy detector based sensing method was proposed and used here because it doesnot require primary user's information. Bluetooth and Wi-fi are the two available types of spectrum that was sensed for CR detection. Simulation showed cognitive radio network can be developed using Android based smart phones. So, a complete application was developed using Java based Android Eclipse program. Finally, the application was uploaded and run on Android based smart phone to form and verify CR network for channel sensing and resource allocation. The observed efficiency of the application was around 81%.

Virtual colleagues, virtually colleagues—physicians’ use of Twitter: a population-based observational study

PubMed Central

Brynolf, Anne; Johansson, Stefan; Appelgren, Ester; Lynoe, Niels; Edstedt Bonamy, Anna-Karin

2013-01-01

Objective To investigate potential violations of patient confidentiality or other breaches of medical ethics committed by physicians and medical students active on the social networking site Twitter. Design Population-based cross-sectional observational study. Setting The social networking site Twitter (Swedish-speaking users, n=298819). Population Physicians and medical students (Swedish-speaking users, n=237) active on the social networking site Twitter between July 2007 and March 2012. Main outcome measure Postings that reflect unprofessional behaviour and ethical breaches among physicians and medical students. Results In all, 237 Twitter accounts were established as held by physicians and medical students and a total of 13 780 tweets were analysed by content. In all, 276 (1.9%) tweets were labelled as ‘unprofessional’. Among these, 26 (0.2%) tweets written by 15 (6.3%) physicians and medical students included information that could violate patient privacy. No information on the personal ID number or names was disclosed, but parts of the patient documentation or otherwise specific indicatory information on patients were found. Unprofessional tweets were more common among users writing under a pseudonym and among medical students. Conclusions In this study of physicians and medical students on Twitter, we observed potential violations of patient privacy and other breaches of medical ethics. Our findings underline that every physician and medical student has to consider his or her presence on social networking sites. It remains to be investigated if the introduction of social networking site guidelines for medical professionals will improve awareness. PMID:23883885

Virtual colleagues, virtually colleagues--physicians' use of Twitter: a population-based observational study.

PubMed

Brynolf, Anne; Johansson, Stefan; Appelgren, Ester; Lynoe, Niels; Edstedt Bonamy, Anna-Karin

2013-01-01

To investigate potential violations of patient confidentiality or other breaches of medical ethics committed by physicians and medical students active on the social networking site Twitter. Population-based cross-sectional observational study. The social networking site Twitter (Swedish-speaking users, n=298819). Physicians and medical students (Swedish-speaking users, n=237) active on the social networking site Twitter between July 2007 and March 2012. Postings that reflect unprofessional behaviour and ethical breaches among physicians and medical students. In all, 237 Twitter accounts were established as held by physicians and medical students and a total of 13 780 tweets were analysed by content. In all, 276 (1.9%) tweets were labelled as 'unprofessional'. Among these, 26 (0.2%) tweets written by 15 (6.3%) physicians and medical students included information that could violate patient privacy. No information on the personal ID number or names was disclosed, but parts of the patient documentation or otherwise specific indicatory information on patients were found. Unprofessional tweets were more common among users writing under a pseudonym and among medical students. In this study of physicians and medical students on Twitter, we observed potential violations of patient privacy and other breaches of medical ethics. Our findings underline that every physician and medical student has to consider his or her presence on social networking sites. It remains to be investigated if the introduction of social networking site guidelines for medical professionals will improve awareness.

Validation and quantification of uncertainty in coupled climate models using network analysis

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

Bracco, Annalisa

We developed a fast, robust and scalable methodology to examine, quantify, and visualize climate patterns and their relationships. It is based on a set of notions, algorithms and metrics used in the study of graphs, referred to as complex network analysis. This approach can be applied to explain known climate phenomena in terms of an underlying network structure and to uncover regional and global linkages in the climate system, while comparing general circulation models outputs with observations. The proposed method is based on a two-layer network representation, and is substantially new within the available network methodologies developed for climate studies.more » At the first layer, gridded climate data are used to identify ‘‘areas’’, i.e., geographical regions that are highly homogeneous in terms of the given climate variable. At the second layer, the identified areas are interconnected with links of varying strength, forming a global climate network. The robustness of the method (i.e. the ability to separate between topological distinct fields, while identifying correctly similarities) has been extensively tested. It has been proved that it provides a reliable, fast framework for comparing and ranking the ability of climate models of reproducing observed climate patterns and their connectivity. We further developed the methodology to account for lags in the connectivity between climate patterns and refined our area identification algorithm to account for autocorrelation in the data. The new methodology based on complex network analysis has been applied to state-of-the-art climate model simulations that participated to the last IPCC (International Panel for Climate Change) assessment to verify their performances, quantify uncertainties, and uncover changes in global linkages between past and future projections. Network properties of modeled sea surface temperature and rainfall over 1956–2005 have been constrained towards observations or reanalysis data sets, and their differences quantified using two metrics. Projected changes from 2051 to 2300 under the scenario with the highest representative and extended concentration pathways (RCP8.5 and ECP8.5) have then been determined. The network of models capable of reproducing well major climate modes in the recent past, changes little during this century. In contrast, among those models the uncertainties in the projections after 2100 remain substantial, and primarily associated with divergences in the representation of the modes of variability, particularly of the El Niño Southern Oscillation (ENSO), and their connectivity, and therefore with their intrinsic predictability, more so than with differences in the mean state evolution. Additionally, we evaluated the relation between the size and the ‘strength’ of the area identified by the network analysis as corresponding to ENSO noting that only a small subset of models can reproduce realistically the observations.« less

Harmonising and semantically linking key variables from in-situ observing networks of an Integrated Atlantic Ocean Observing System, AtlantOS

NASA Astrophysics Data System (ADS)

Darroch, Louise; Buck, Justin

2017-04-01

Atlantic Ocean observation is currently undertaken through loosely-coordinated, in-situ observing networks, satellite observations and data management arrangements at regional, national and international scales. The EU Horizon 2020 AtlantOS project aims to deliver an advanced framework for the development of an Integrated Atlantic Ocean Observing System that strengthens the Global Ocean Observing System (GOOS) and contributes to the aims of the Galway Statement on Atlantic Ocean Cooperation. One goal is to ensure that data from different and diverse in-situ observing networks are readily accessible and useable to a wider community, including the international ocean science community and other stakeholders in this field. To help achieve this goal, the British Oceanographic Data Centre (BODC) produced a parameter matrix to harmonise data exchange, data flow and data integration for the key variables acquired by multiple in-situ AtlantOS observing networks such as ARGO, Seafloor Mapping and OceanSITES. Our solution used semantic linking of controlled vocabularies and metadata for parameters that were "mappable" to existing EU and international standard vocabularies. An AtlantOS Essential Variables list of terms (aggregated level) based on Global Climate Observing System (GCOS) Essential Climate Variables (ECV), GOOS Essential Ocean Variables (EOV) and other key network variables was defined and published on the Natural Environment Research Council (NERC) Vocabulary Server (version 2.0) as collection A05 (http://vocab.nerc.ac.uk/collection/A05/current/). This new vocabulary was semantically linked to standardised metadata for observed properties and units that had been validated by the AtlantOS community: SeaDataNet parameters (P01), Climate and Forecast (CF) Standard Names (P07) and SeaDataNet units (P06). Observed properties were mapped to biological entities from the internationally assured AphiaID from the WOrld Register of Marine Species (WoRMS), http://www.marinespecies.org/aphia.php?p=webservice. The AtlantOS parameter matrix offers a way to harmonise the globally important variables (such as ECVs and EOVs) from in-situ observing networks that use different flavours of exchange formats based on SeaDataNet and CF parameter metadata. It also offers a way to standardise data in the wider Integrated Ocean Observing System. It uses sustainable and trusted standardised vocabularies that are governed by internationally renowned and long-standing organisations and is interoperable through the use of persistent resource identifiers, such as URNs and PURLs. It is the first step to integrating and serving data in a variety of international exchange formats using Application programming interfaces (API) improving both data discoverability and utility for users.

ISED: Constructing a high-resolution elevation road dataset from massive, low-quality in-situ observations derived from geosocial fitness tracking data.

PubMed

McKenzie, Grant; Janowicz, Krzysztof

2017-01-01

Gaining access to inexpensive, high-resolution, up-to-date, three-dimensional road network data is a top priority beyond research, as such data would fuel applications in industry, governments, and the broader public alike. Road network data are openly available via user-generated content such as OpenStreetMap (OSM) but lack the resolution required for many tasks, e.g., emergency management. More importantly, however, few publicly available data offer information on elevation and slope. For most parts of the world, up-to-date digital elevation products with a resolution of less than 10 meters are a distant dream and, if available, those datasets have to be matched to the road network through an error-prone process. In this paper we present a radically different approach by deriving road network elevation data from massive amounts of in-situ observations extracted from user-contributed data from an online social fitness tracking application. While each individual observation may be of low-quality in terms of resolution and accuracy, taken together they form an accurate, high-resolution, up-to-date, three-dimensional road network that excels where other technologies such as LiDAR fail, e.g., in case of overpasses, overhangs, and so forth. In fact, the 1m spatial resolution dataset created in this research based on 350 million individual 3D location fixes has an RMSE of approximately 3.11m compared to a LiDAR-based ground-truth and can be used to enhance existing road network datasets where individual elevation fixes differ by up to 60m. In contrast, using interpolated data from the National Elevation Dataset (NED) results in 4.75m RMSE compared to the base line. We utilize Linked Data technologies to integrate the proposed high-resolution dataset with OpenStreetMap road geometries without requiring any changes to the OSM data model.

CBEO:N, Chesapeake Bay Environmental Observatory as a Cyberinfrastructure Node

NASA Astrophysics Data System (ADS)

Zaslavsky, I.; Piasecki, M.; Whitenack, T.; Ball, W. P.; Murphy, R.

2008-12-01

Chesapeake Bay Environmental Observatory (CBEO) is an NSF-supported project focused on studying hypoxia in Chesapeake Bay using advanced cyberinfrastructure (CI) technologies. The project is organized around four concurrent and interacting activities: 1) CBEO:S provides science and management context for the use of CI technologies, focusing on hypoxia and its non-linear dynamics as affected by management and climate; 2) CBEO:T constructs a locally-accessible CBEO test bed prototype centered on spatio-temporal interpolation and advanced querying of model runs; 3) CBEO:N incorporates the test bed CI into national environmental observation networks, and 4) CBEO:E develops education and outreach components of the project that translate observational science for public consumption. CBEO:N activities, which are the focus of this paper, are four-fold: - constructing an online project portal to enable researchers to publish, discover, query, visualize and integrate project-related datasets of different types. The portal is based on the technologies developed within the GEON (the Geosciences Network) project, and has established the CBEO project data server as part of the GEON network of servers; * developing a CBEO node within the WATERS network, taking advantage of the CUAHSI Hydrologic Information System (HIS) Server technology that supports online publication of observation data as web services, and ontology-assisted data discovery; *developing new data structures and metadata in order to describe water quality observational data, and model run output, obtained for the Chesapeake Bay area, using data structures adopted and modified from the Observations Data Model of CUAHSI HIS; * prototyping CBEO tools that can be re-used through the portal, in particular implementing a portal version of R-based spatial interpolation tools. The paper describes recent accomplishments in these four development areas, and demonstrates how CI approaches transform research and data sharing in environmental observing systems.

Highly dynamic animal contact network and implications on disease transmission

PubMed Central

Chen, Shi; White, Brad J.; Sanderson, Michael W.; Amrine, David E.; Ilany, Amiyaal; Lanzas, Cristina

2014-01-01

Contact patterns among hosts are considered as one of the most critical factors contributing to unequal pathogen transmission. Consequently, networks have been widely applied in infectious disease modeling. However most studies assume static network structure due to lack of accurate observation and appropriate analytic tools. In this study we used high temporal and spatial resolution animal position data to construct a high-resolution contact network relevant to infectious disease transmission. The animal contact network aggregated at hourly level was highly variable and dynamic within and between days, for both network structure (network degree distribution) and individual rank of degree distribution in the network (degree order). We integrated network degree distribution and degree order heterogeneities with a commonly used contact-based, directly transmitted disease model to quantify the effect of these two sources of heterogeneity on the infectious disease dynamics. Four conditions were simulated based on the combination of these two heterogeneities. Simulation results indicated that disease dynamics and individual contribution to new infections varied substantially among these four conditions under both parameter settings. Changes in the contact network had a greater effect on disease dynamics for pathogens with smaller basic reproduction number (i.e. R0 < 2). PMID:24667241

Dynamic pricing of network goods with boundedly rational consumers.

PubMed

Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

2014-01-07

We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller's optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product's user base evolving over time and consumers basing their choices on a mixture of a myopic and a "stubborn" expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice.

Dynamic pricing of network goods with boundedly rational consumers

PubMed Central

Radner, Roy; Radunskaya, Ami; Sundararajan, Arun

2014-01-01

We present a model of dynamic monopoly pricing for a good that displays network effects. In contrast with the standard notion of a rational-expectations equilibrium, we model consumers as boundedly rational and unable either to pay immediate attention to each price change or to make accurate forecasts of the adoption of the network good. Our analysis shows that the seller’s optimal price trajectory has the following structure: The price is low when the user base is below a target level, is high when the user base is above the target, and is set to keep the user base stationary once the target level has been attained. We show that this pricing policy is robust to a number of extensions, which include the product’s user base evolving over time and consumers basing their choices on a mixture of a myopic and a “stubborn” expectation of adoption. Our results differ significantly from those that would be predicted by a model based on rational-expectations equilibrium and are more consistent with the pricing of network goods observed in practice. PMID:24367101

Controlling nosocomial infection based on structure of hospital social networks.

PubMed

Ueno, Taro; Masuda, Naoki

2008-10-07

Nosocomial infection (i.e. infection in healthcare facilities) raises a serious public health problem, as implied by the existence of pathogens characteristic to healthcare facilities such as methicillin-resistant Staphylococcus aureus and hospital-mediated outbreaks of influenza and severe acute respiratory syndrome. For general communities, epidemic modeling based on social networks is being recognized as a useful tool. However, disease propagation may occur in a healthcare facility in a manner different from that in a urban community setting due to different network architecture. We simulate stochastic susceptible-infected-recovered dynamics on social networks, which are based on observations in a hospital in Tokyo, to explore effective containment strategies against nosocomial infection. The observed social networks in the hospital have hierarchical and modular structure in which dense substructure such as departments, wards, and rooms, are globally but only loosely connected, and do not reveal extremely right-skewed distributions of the number of contacts per individual. We show that healthcare workers, particularly medical doctors, are main vectors (i.e. transmitters) of diseases on these networks. Intervention methods that restrict interaction between medical doctors and their visits to different wards shrink the final epidemic size more than intervention methods that directly protect patients, such as isolating patients in single rooms. By the same token, vaccinating doctors with priority rather than patients or nurses is more effective. Finally, vaccinating individuals with large betweenness centrality (frequency of mediating connection between pairs of individuals along the shortest paths) is superior to vaccinating ones with large connectedness to others or randomly chosen individuals, which was suggested by previous model studies.

'can i get your Email': Gender, Networking and Social Capital in AN Undergraduate Bioengineering Classroom

NASA Astrophysics Data System (ADS)

Erickson, Shelley K.

Based on observations and interviews, this article explores how female and male biomedical engineering students network and generate social capital (who one knows) in an undergraduate classroom. Stark differences were observed between female and male students and their interactions with a series of guest lecturers. Although women engineering students may be differentially affected by how they raise their social capital, this study does not suggest that women engineering students are wholly incapable of raising their social capital. Rather, a disconnect occurs between the student population receiving information about networking and women students acting on informal and spontaneous opportunities as they arise. Institutional and departmental support (i.e., internship programs and discussion in the classroom and at orientation) appears to favor those who rely on more formal means of networking.

Regional and local variations in atmospheric aerosols using ground-based sun photometry during Distributed Regional Aerosol Gridded Observation Networks (DRAGON) in 2012

NASA Astrophysics Data System (ADS)

Sano, Itaru; Mukai, Sonoyo; Nakata, Makiko; Holben, Brent N.

2016-11-01

Aerosol mass concentrations are affected by local emissions as well as long-range transboundary (LRT) aerosols. This work investigates regional and local variations of aerosols based on Distributed Regional Aerosol Gridded Observation Networks (DRAGON). We constructed DRAGON-Japan and DRAGON-Osaka in spring of 2012. The former network covers almost all of Japan in order to obtain aerosol information in regional scale over Japanese islands. It was determined from the DRAGON-Japan campaign that the values of aerosol optical thickness (AOT) decrease from west to east during an aerosol episode. In fact, the highest AOT was recorded at Fukue Island at the western end of the network, and the value was much higher than that of urban areas. The latter network (DRAGON-Osaka) was set as a dense instrument network in the megalopolis of Osaka, with a population of 12 million, to better understand local aerosol dynamics in urban areas. AOT was further measured with a mobile sun photometer attached to a car. This transect information showed that aerosol concentrations rapidly changed in time and space together when most of the Osaka area was covered with moderate LRT aerosols. The combined use of the dense instrument network (DRAGON-Osaka) and high-frequency measurements provides the motion of aerosol advection, which coincides with the wind vector around the layer between 700 and 850 hPa as provided by the reanalysis data of the National Centers for Environmental Prediction (NCEP).

Regional and Local Variations in Atmospheric Aerosols Using Ground-Based Sun Photometry During Distributed Regional Aerosol Gridded Observation Networks (DRAGON) in 2012

NASA Technical Reports Server (NTRS)

Sano, Itaru; Mukai, Sonoyo; Nakata, Makiko; Holben, Brent N.

2016-01-01

Aerosol mass concentrations are affected by local emissions as well as long-range transboundary (LRT) aerosols. This work investigates regional and local variations of aerosols based on Distributed Regional Aerosol Gridded Observation Networks (DRAGON).We constructed DRAGON-Japan and DRAGON-Osaka in spring of 2012. The former network covers almost all of Japan in order to obtain aerosol information in regional scale over Japanese islands. It was determined from the DRAGON-Japan campaign that the values of aerosol optical thickness (AOT) decrease from west to east during an aerosol episode. In fact, the highest AOT was recorded at Fukue Island at the western end of the network, and the value was much higher than that of urban areas. The latter network (DRAGON-Osaka) was set as a dense instrument network in the megalopolis of Osaka, with a population of 12 million, to better understand local aerosol dynamics in urban areas. AOT was further measured with a mobile sun photometer attached to a car. This transect information showed that aerosol concentrations rapidly changed in time and space together when most of the Osaka area was covered with moderate LRT aerosols. The combined use of the dense instrument network (DRAGON-Osaka) and high-frequency measurements provides the motion of aerosol advection, which coincides with the wind vector around the layer between 700 and 850 hPa as provided by the reanalysis data of the National Centers for Environmental Prediction (NCEP).

A global gridded dataset of daily precipitation going back to 1950, ideal for analysing precipitation extremes

NASA Astrophysics Data System (ADS)

Contractor, S.; Donat, M.; Alexander, L. V.

2017-12-01

Reliable observations of precipitation are necessary to determine past changes in precipitation and validate models, allowing for reliable future projections. Existing gauge based gridded datasets of daily precipitation and satellite based observations contain artefacts and have a short length of record, making them unsuitable to analyse precipitation extremes. The largest limiting factor for the gauge based datasets is a dense and reliable station network. Currently, there are two major data archives of global in situ daily rainfall data, first is Global Historical Station Network (GHCN-Daily) hosted by National Oceanic and Atmospheric Administration (NOAA) and the other by Global Precipitation Climatology Centre (GPCC) part of the Deutsche Wetterdienst (DWD). We combine the two data archives and use automated quality control techniques to create a reliable long term network of raw station data, which we then interpolate using block kriging to create a global gridded dataset of daily precipitation going back to 1950. We compare our interpolated dataset with existing global gridded data of daily precipitation: NOAA Climate Prediction Centre (CPC) Global V1.0 and GPCC Full Data Daily Version 1.0, as well as various regional datasets. We find that our raw station density is much higher than other datasets. To avoid artefacts due to station network variability, we provide multiple versions of our dataset based on various completeness criteria, as well as provide the standard deviation, kriging error and number of stations for each grid cell and timestep to encourage responsible use of our dataset. Despite our efforts to increase the raw data density, the in situ station network remains sparse in India after the 1960s and in Africa throughout the timespan of the dataset. Our dataset would allow for more reliable global analyses of rainfall including its extremes and pave the way for better global precipitation observations with lower and more transparent uncertainties.

The advanced qualtiy control techniques planned for the Internation Soil Moisture Network

NASA Astrophysics Data System (ADS)

Xaver, A.; Gruber, A.; Hegiova, A.; Sanchis-Dufau, A. D.; Dorigo, W. A.

2012-04-01

In situ soil moisture observations are essential to evaluate and calibrate modeled and remotely sensed soil moisture products. Although a number of meteorological networks and field campaigns measuring soil moisture exist on a global and long-term scale, their observations are not easily accessible and lack standardization of both technique and protocol. Thus, handling and especially comparing these datasets with satellite products or land surface models is a demanding issue. To overcome these limitations the International Soil Moisture Network (ISMN; http://www.ipf.tuwien.ac.at/insitu/) has been initiated to act as a centralized data hosting facility. One advantage of the ISMN is that users are able to access the harmonized datasets easily through a web portal. Another advantage is the fully automated processing chain including the data harmonization in terms of units and sampling interval, but even more important is the advanced quality control system each measurement has to run through. The quality of in situ soil moisture measurements is crucial for the validation of satellite- and model-based soil moisture retrievals; therefore a sophisticated quality control system was developed. After a check for plausibility and geophysical limits a quality flag is added to each measurement. An enhanced flagging mechanism was recently defined using a spectrum based approach to detect spurious spikes, jumps and plateaus. The International Soil Moisture Network has already evolved to one of the most important distribution platforms for in situ soil moisture observations and is still growing. Currently, data from 27 networks in total covering more than 800 stations in Europe, North America, Australia, Asia and Africa is hosted by the ISMN. Available datasets also include historical datasets as well as near real-time measurements. The improved quality control system will provide important information for satellite-based as well as land surface model-based validation studies.

Cloud-based Communications Planning Collaboration and Interoperability

DTIC Science & Technology

2012-06-01

battle concept is derived from the observation that all actions in the battle space have the ability to affect other areas or functions in the battle... space . This is equally true for tactical networks, which grow and transform dynamically as an operation evolves. Changes in one aspect of the network...availability of any updated network plans not only to the local SYSCON and TECHCON, but to all other units operating in the battle space (keeping in mind

An efficient annealing in Boltzmann machine in Hopfield neural network

NASA Astrophysics Data System (ADS)

Kin, Teoh Yeong; Hasan, Suzanawati Abu; Bulot, Norhisam; Ismail, Mohammad Hafiz

2012-09-01

This paper proposes and implements Boltzmann machine in Hopfield neural network doing logic programming based on the energy minimization system. The temperature scheduling in Boltzmann machine enhancing the performance of doing logic programming in Hopfield neural network. The finest temperature is determined by observing the ratio of global solution and final hamming distance using computer simulations. The study shows that Boltzmann Machine model is more stable and competent in term of representing and solving difficult combinatory problems.

Synchronization of heteroclinic circuits through learning in coupled neural networks

NASA Astrophysics Data System (ADS)

Selskii, Anton; Makarov, Valeri A.

2016-01-01

The synchronization of oscillatory activity in neural networks is usually implemented by coupling the state variables describing neuronal dynamics. Here we study another, but complementary mechanism based on a learning process with memory. A driver network, acting as a teacher, exhibits winner-less competition (WLC) dynamics, while a driven network, a learner, tunes its internal couplings according to the oscillations observed in the teacher. We show that under appropriate training the learner can "copy" the coupling structure and thus synchronize oscillations with the teacher. The replication of the WLC dynamics occurs for intermediate memory lengths only, consequently, the learner network exhibits a phenomenon of learning resonance.

Using Neural Networks for Sensor Validation

NASA Technical Reports Server (NTRS)

Mattern, Duane L.; Jaw, Link C.; Guo, Ten-Huei; Graham, Ronald; McCoy, William

1998-01-01

This paper presents the results of applying two different types of neural networks in two different approaches to the sensor validation problem. The first approach uses a functional approximation neural network as part of a nonlinear observer in a model-based approach to analytical redundancy. The second approach uses an auto-associative neural network to perform nonlinear principal component analysis on a set of redundant sensors to provide an estimate for a single failed sensor. The approaches are demonstrated using a nonlinear simulation of a turbofan engine. The fault detection and sensor estimation results are presented and the training of the auto-associative neural network to provide sensor estimates is discussed.

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks

PubMed Central

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-01-01

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability. PMID:28937632

Design and Analysis of a Low Latency Deterministic Network MAC for Wireless Sensor Networks.

PubMed

Sahoo, Prasan Kumar; Pattanaik, Sudhir Ranjan; Wu, Shih-Lin

2017-09-22

The IEEE 802.15.4e standard has four different superframe structures for different applications. Use of a low latency deterministic network (LLDN) superframe for the wireless sensor network is one of them, which can operate in a star topology. In this paper, a new channel access mechanism for IEEE 802.15.4e-based LLDN shared slots is proposed, and analytical models are designed based on this channel access mechanism. A prediction model is designed to estimate the possible number of retransmission slots based on the number of failed transmissions. Performance analysis in terms of data transmission reliability, delay, throughput and energy consumption are provided based on our proposed designs. Our designs are validated for simulation and analytical results, and it is observed that the simulation results well match with the analytical ones. Besides, our designs are compared with the IEEE 802.15.4 MAC mechanism, and it is shown that ours outperforms in terms of throughput, energy consumption, delay and reliability.

Satellite to Ground-based LIDAR Comparisons using MPLNET Data Products

NASA Technical Reports Server (NTRS)

Berkoff, T.A.; Belcher, L.; Campbell, J.; Spinhirne, J.; Welton, E. J.

2007-01-01

The Micro-Pulse Lidar Network (MPLNET) is a network of ground-based lidar systems that provide continuous long-term observations of aerosol and cloud properties at approximately 10 different locations around the globe. Each site in the network uses an elastic scattering lidar co-located with a sunphotometer to provide data products of aerosol optical physical properties. Data products from sites are available on a next-day basis from the MPLNET website. Expansion of the network is based on partnering with research groups interested in joining MPLNET. Results have contributed to a variety of studies including aerosol transport studies and satellite calibration and validation efforts. One of the key motivations for MPLNET is to contribute towards the calibration and validation of satellite-based lidars such as GLAS/ICESAT and CALIPSO. MPLNET is able to provide comparison to several of the key aerosol and cloud CALIPSO data products including: layer height and thickness, optical depth, backscatter and extinction profiles, and the extinction-to-backscatter ratio.

In Silico Reconstitution of Actin-Based Symmetry Breaking and Motility

PubMed Central

Dayel, Mark J.; Akin, Orkun; Landeryou, Mark; Risca, Viviana; Mogilner, Alex; Mullins, R. Dyche

2009-01-01

Eukaryotic cells assemble viscoelastic networks of crosslinked actin filaments to control their shape, mechanical properties, and motility. One important class of actin network is nucleated by the Arp2/3 complex and drives both membrane protrusion at the leading edge of motile cells and intracellular motility of pathogens such as Listeria monocytogenes. These networks can be reconstituted in vitro from purified components to drive the motility of spherical micron-sized beads. An Elastic Gel model has been successful in explaining how these networks break symmetry, but how they produce directed motile force has been less clear. We have combined numerical simulations with in vitro experiments to reconstitute the behavior of these motile actin networks in silico using an Accumulative Particle-Spring (APS) model that builds on the Elastic Gel model, and demonstrates simple intuitive mechanisms for both symmetry breaking and sustained motility. The APS model explains observed transitions between smooth and pulsatile motion as well as subtle variations in network architecture caused by differences in geometry and conditions. Our findings also explain sideways symmetry breaking and motility of elongated beads, and show that elastic recoil, though important for symmetry breaking and pulsatile motion, is not necessary for smooth directional motility. The APS model demonstrates how a small number of viscoelastic network parameters and construction rules suffice to recapture the complex behavior of motile actin networks. The fact that the model not only mirrors our in vitro observations, but also makes novel predictions that we confirm by experiment, suggests that the model captures much of the essence of actin-based motility in this system. PMID:19771152

Detection performance of three different lightning location networks in Beijing area based on accurate fast antenna records

NASA Astrophysics Data System (ADS)

Srivastava, A.; Tian, Y.; Wang, D.; Yuan, S.; Chen, Z.; Sun, Z.; Qie, X.

2016-12-01

Scientists have developed the regional and worldwide lightning location network to study the lightning physics and locating the lightning stroke. One of the key issue in all the networks; to recognize the performance of the network. The performance of each network would be different based on the regional geographic conditions and the instrumental limitation. To improve the performance of the network. it is necessary to know the ground truth of the network and to discuss about the detection efficiency (DE) and location accuracy (LA). A comparative study has been discussed among World Wide Lightning Location Network (WWLLN), ADvanced TOA and Direction system (ADTD) and Beijing Lightning NETwork (BLNET) lightning detection network in Beijing area. WWLLN locate the cloud to ground (CG) and strong inter cloud (IC) globally without demonstrating any differences. ADTD locate the CG strokes in the entire China as regional. Both these networks are long range detection system that does not provide the focused details of a thunderstorm. BLNET can locate the CG and IC and is focused on thunderstorm detection. The waveform of fast antenna checked manually and the relative DE among the three networks has been obtained based on the CG strokes. The relative LA has been obtained using the matched flashes among these networks as well as LA obtained using the strike on the tower. The relative DE of BLNET is much higher than the ADTD and WWLLN as these networks has approximately similar relative DE. The relative LA of WWLLN and ADTD location is eastward and northward respectively from the BLNET. The LA based on tower observation is relatively high-quality in favor of BLNET. The ground truth of WWLLN, ADTD and BLNET has been obtained and found the performance of BLNET network is much better. This study is helpful to improve the performance of the networks and to provide a belief of LA that can follow the thunderstorm path with the prediction and forecasting of thunderstorm and lightning.

Equity venture capital platform model based on complex network

NASA Astrophysics Data System (ADS)

Guo, Dongwei; Zhang, Lanshu; Liu, Miao

2018-05-01

This paper uses the small-world network and the random-network to simulate the relationship among the investors, construct the network model of the equity venture capital platform to explore the impact of the fraud rate and the bankruptcy rate on the robustness of the network model while observing the impact of the average path length and the average agglomeration coefficient of the investor relationship network on the income of the network model. The study found that the fraud rate and bankruptcy rate exceeded a certain threshold will lead to network collapse; The bankruptcy rate has a great influence on the income of the platform; The risk premium exists, and the average return is better under a certain range of bankruptcy risk; The structure of the investor relationship network has no effect on the income of the investment model.

Making better sense of the mosaic of environmental measurement networks: a system-of-systems approach and quantitative assessment

NASA Astrophysics Data System (ADS)

Thorne, Peter W.; Madonna, Fabio; Schulz, Joerg; Oakley, Tim; Ingleby, Bruce; Rosoldi, Marco; Tramutola, Emanuele; Arola, Antti; Buschmann, Matthias; Mikalsen, Anna C.; Davy, Richard; Voces, Corinne; Kreher, Karin; De Maziere, Martine; Pappalardo, Gelsomina

2017-11-01

There are numerous networks and initiatives concerned with the non-satellite-observing segment of Earth observation. These are owned and operated by various entities and organisations often with different practices, norms, data policies, etc. The Horizon 2020 project GAIA-CLIM is working to improve our collective ability to use an appropriate subset of these observations to rigorously characterise satellite observations. The first fundamental question is which observations from the mosaic of non-satellite observational capabilities are appropriate for such an application. This requires an assessment of the relevant, quantifiable aspects of the measurement series which are available. While fundamentally poor or incorrect measurements can be relatively easily identified, it is metrologically impossible to be sure that a measurement series is correct. Certain assessable aspects of the measurement series can, however, build confidence in their scientific maturity and appropriateness for given applications. These are aspects such as that it is well documented, well understood, representative, updated, publicly available and maintains rich metadata. Entities such as the Global Climate Observing System have suggested a hierarchy of networks whereby different subsets of the observational capabilities are assigned to different layers based on such assessable aspects. Herein, we make a first attempt to formalise both such a system-of-systems networks concept and a means by which to, as objectively as possible, assess where in this framework different networks may reside. In this study, we concentrate on networks measuring primarily a subset of the atmospheric Essential Climate Variables of interest to GAIA-CLIM activities. We show assessment results from our application of the guidance and how we plan to use this in downstream example applications of the GAIA-CLIM project. However, the approach laid out should be more widely applicable across a broad range of application areas. If broadly adopted, the system-of-systems approach will have potential benefits in guiding users to the most appropriate set of observations for their needs and in highlighting to network owners and operators areas for potential improvement.

Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

Abstract The influence of the global average signal (GAS) on functional-magnetic resonance imaging (fMRI)–based resting-state functional connectivity is a matter of ongoing debate. The global average fluctuations increase the correlation between functional systems beyond the correlation that reflects their specific functional connectivity. Hence, removal of the GAS is a common practice for facilitating the observation of network-specific functional connectivity. This strategy relies on the implicit assumption of a linear-additive model according to which global fluctuations, irrespective of their origin, and network-specific fluctuations are super-positioned. However, removal of the GAS introduces spurious negative correlations between functional systems, bringing into question the validity of previous findings of negative correlations between fluctuations in the default-mode and the task-positive networks. Here we present an alternative method for estimating global fluctuations, immune to the complications associated with the GAS. Principal components analysis was applied to resting-state fMRI time-series. A global-signal effect estimator was defined as the principal component (PC) that correlated best with the GAS. The mean correlation coefficient between our proposed PC-based global effect estimator and the GAS was 0.97±0.05, demonstrating that our estimator successfully approximated the GAS. In 66 out of 68 runs, the PC that showed the highest correlation with the GAS was the first PC. Since PCs are orthogonal, our method provides an estimator of the global fluctuations, which is uncorrelated to the remaining, network-specific fluctuations. Moreover, unlike the regression of the GAS, the regression of the PC-based global effect estimator does not introduce spurious anti-correlations beyond the decrease in seed-based correlation values allowed by the assumed additive model. After regressing this PC-based estimator out of the original time-series, we observed robust anti-correlations between resting-state fluctuations in the default-mode and the task-positive networks. We conclude that resting-state global fluctuations and network-specific fluctuations are uncorrelated, supporting a Resting-State Linear-Additive Model. In addition, we conclude that the network-specific resting-state fluctuations of the default-mode and task-positive networks show artifact-free anti-correlations. PMID:22444074

Simulating Social Networks of Online Communities: Simulation as a Method for Sociability Design

NASA Astrophysics Data System (ADS)

Ang, Chee Siang; Zaphiris, Panayiotis

We propose the use of social simulations to study and support the design of online communities. In this paper, we developed an Agent-Based Model (ABM) to simulate and study the formation of social networks in a Massively Multiplayer Online Role Playing Game (MMORPG) guild community. We first analyzed the activities and the social network (who-interacts-with-whom) of an existing guild community to identify its interaction patterns and characteristics. Then, based on the empirical results, we derived and formalized the interaction rules, which were implemented in our simulation. Using the simulation, we reproduced the observed social network of the guild community as a means of validation. The simulation was then used to examine how various parameters of the community (e.g. the level of activity, the number of neighbors of each agent, etc) could potentially influence the characteristic of the social networks.

Effect of network topology on the evolutionary ultimatum game based on the net-profit decision

NASA Astrophysics Data System (ADS)

Ye, Shun-Qiang; Wang, Lu; Jones, Michael C.; Ye, Ye; Wang, Meng; Xie, Neng-Gang

2016-04-01

The ubiquity of altruist behavior amongst humans has long been a significant puzzle in the social sciences. Ultimatum game has proved to be a useful tool for explaining altruistic behavior among selfish individuals. In an ultimatum game where alternating roles exist, we suppose that players make their decisions based on the net profit of their own. In this paper, we specify a player's strategy with two parameters: offer level α ∈ [ 0,1) and net profit acceptance level β ∈ [ - 1,1). By Monte Carlo simulation, we analyze separately the effect of the size of the neighborhood, the small-world property and the heterogeneity of the degree distributions of the networks. Results show that compared with results observed for homogeneous networks, heterogeneous networks lead to more rational outcomes. Moreover, network structure has no effect on the evolution of kindness level, so moderate kindness is adaptable to any social groups and organizations.

Discovering urban mobility patterns with PageRank based traffic modeling and prediction

NASA Astrophysics Data System (ADS)

Wang, Minjie; Yang, Su; Sun, Yi; Gao, Jun

2017-11-01

Urban transportation system can be viewed as complex network with time-varying traffic flows as links to connect adjacent regions as networked nodes. By computing urban traffic evolution on such temporal complex network with PageRank, it is found that for most regions, there exists a linear relation between the traffic congestion measure at present time and the PageRank value of the last time. Since the PageRank measure of a region does result from the mutual interactions of the whole network, it implies that the traffic state of a local region does not evolve independently but is affected by the evolution of the whole network. As a result, the PageRank values can act as signatures in predicting upcoming traffic congestions. We observe the aforementioned laws experimentally based on the trajectory data of 12000 taxies in Beijing city for one month.

Scoring sensor observations to facilitate the exchange of space surveillance data

NASA Astrophysics Data System (ADS)

Weigel, M.; Fiedler, H.; Schildknecht, T.

2017-08-01

In this paper, a scoring metric for space surveillance sensor observations is introduced. A scoring metric allows for direct comparison of data quantity and data quality, and makes transparent the effort made by different sensor operators. The concept might be applied to various sensor types like tracking and surveillance radar, active optical laser tracking, or passive optical telescopes as well as combinations of different measurement types. For each measurement type, a polynomial least squares fit is performed on the measurement values contained in the track. The track score is the average sum over the polynomial coefficients uncertainties and scaled by reference measurement accuracy. Based on the newly developed scoring metric, an accounting model and a rating model are introduced. Both models facilitate the exchange of observation data within a network of space surveillance sensors operators. In this paper, optical observations are taken as an example for analysis purposes, but both models can also be utilized for any other type of observations. The rating model has the capability to distinguish between network participants with major and minor data contribution to the network. The level of sanction on data reception is defined by the participants themselves enabling a high flexibility. The more elaborated accounting model translates the track score to credit points earned for data provision and spend for data reception. In this model, data reception is automatically limited for participants with low contribution to the network. The introduced method for observation scoring is first applied for transparent data exchange within the Small Aperture Robotic Telescope Network (SMARTnet). Therefore a detailed mathematical description is presented for line of sight measurements from optical telescopes, as well as numerical simulations for different network setups.

Link Power Budget and Traffict QoS Performance Analysis of Gygabit Passive Optical Network

NASA Astrophysics Data System (ADS)

Ubaidillah, A.; Alfita, R.; Toyyibah

2018-01-01

Data service of telecommunication network is needed widely in the world; therefore extra wide bandwidth must be provided. For this case, PT. Telekomunikasi Tbk. applies GPON (Gigabit Passive Optical Network) as optical fibre based on telecommunication network system. GPON is a point to a multipoint technology of FTTx (Fiber to The x) that transmits information signals to the subscriber over optical fibre. In GPON trunking system, from OLT (Optical Line Terminal), the network is split to many ONT (Optical Network Terminal) of the subscribers, so it causes path loss and attenuation. In this research, the GPON performance is measured from the link power budget system and the Quality of Service (QoS) of the traffic. And the observation result shows that the link power budget system of this GPON is in good condition. The link power budget values from the mathematical calculation and direct measurement are satisfy the ITU-T G984 Class B standard, that the power level must be between -8 dBm to -27 dBm. While from the traffic performance, the observation result shows that the network resource utility of the subscribers of the observed area is not optimum. The mean of subscriber utility rate is 27.985 bps for upstream and 79.687 bps for downstream. While maximally, It should be 60.800 bps for upstream and 486.400 bps for downstream.

Confidant Network Types and Well-Being Among Older Europeans

PubMed Central

Litwin, Howard; Stoeckel, Kimberly J.

2014-01-01

Purpose of the Study: To derive a typology of confidant networks among older adults in Europe and to examine them in relation to country differences and well-being (CASP-12). Design and Methods: The study population was composed of persons aged 65 and older in 16 countries from the 4th wave of the Survey of Health, Ageing and Retirement in Europe (N = 28,697). K-means cluster analysis was applied to data from a newly implemented name-generating network inventory. CASP-12 scores were regressed on network type controlling for country and potential sociodemographic and health confounders. Results: Six prototypical confidant network types were discerned, including proximal and distal family-based networks of varying configurations, as well as friend-based and other-based network types. Regional country differences in network type constellations were observed. Better well-being was found to be associated with network types with greater social capital. Respondents with no named confidants had the lowest CASP-12 scores, and those embedded in “other” network types also exhibited a negative association with well-being. Implications: The study demonstrates the utility of name-generating network inventories in understanding the social capital of older persons. It also shows that accessible family ties are strong correlates of well-being in this population. Finally, it documents the importance of improving the means to detect the small but significant subgroup of isolated older people—those who have no confidants on whom they may rely. PMID:23749390

Vigie-Ciel : a french citizen network to study meteors and meteorites

NASA Astrophysics Data System (ADS)

Bouley, S.; Zanda, B.; Colas, F.; Vaubaillon, J.; Marmo, C.; Vernazza, P.; Gattacceca, J.

2013-12-01

Vigie Ciel is a french citizen network supported by the Muséum National d'Histoire Naturelle (MNHN) and the Université Paris-Sud (UPsud). It is based on the scientific FRIPON program developed by Paris Observatory (Fireball Recovery and Planetary Inter Observation Network) which has for main goal to (i) determine the source region(s) of the various meteorite classes, (ii) collect both fresh and rare meteorite types and (iii) perform scientific outreach. This will be achieved by building the densest camera network in the world, based on state of the art technologies and associated with a participative network for meteorite recovery. We propose to install a network of 100 digital cameras covering the entire French territory to compute impact locations with accuracy of the order of one kilometer. Considering that there are 5 to 25 falls over France per year (~15 on average), during the same time, we will observe ~50 falls out of which we realistically expect to find 10 meteorites. Our project is original in several ways. (i) It is inter-disciplinary, involving experts in meteoritics, asteroidal science as well as fireball observation and dynamics. It will thus create new synergies between prominent institutions and/or laboratories, namely between MNHN, Paris Observatory and Université Paris-Sud in the Parisian region; and between CEREGE and LAM in the Provence region. Overall, scientists from over 25 laboratories will be involved, covering a mix of scientific disciplines and all the regions of France. (ii) It will generate a large body of data, feeding databases of interest to several disciplines (e.g. bird migration, variations of the luminosity of the brightest stars, observation of space debris, meteorology...). (iii) It will for the first time involve the general public (including schools) in the search for the meteorite falls, thus boosting the interest in meteorite and asteroid related science.

Norwegian Ocean Observatory Network (NOON)

NASA Astrophysics Data System (ADS)

Ferré, Bénédicte; Mienert, Jürgen; Winther, Svein; Hageberg, Anne; Rune Godoe, Olav; Partners, Noon

2010-05-01

The Norwegian Ocean Observatory Network (NOON) is led by the University of Tromsø and collaborates with the Universities of Oslo and Bergen, UniResearch, Institute of Marine Research, Christian Michelsen Research and SINTEF. It is supported by the Research Council of Norway and oil and gas (O&G) industries like Statoil to develop science, technology and new educational programs. Main topics relate to ocean climate and environment as well as marine resources offshore Norway from the northern North Atlantic to the Arctic Ocean. NOON's vision is to bring Norway to the international forefront in using cable based ocean observatory technology for marine science and management, by establishing an infrastructure that enables real-time and long term monitoring of processes and interactions between hydrosphere, geosphere and biosphere. This activity is in concert with the EU funded European Strategy Forum on Research Infrastructures (ESFRI) roadmap and European Multidisciplinary Seafloor Observation (EMSO) project to attract international leading research developments. NOON envisions developing towards a European Research Infrastructure Consortium (ERIC). Beside, the research community in Norway already possesses a considerable marine infrastructure that can expand towards an international focus for real-time multidisciplinary observations in times of rapid climate change. PIC The presently established cable-based fjord observatory, followed by the establishment of a cable-based ocean observatory network towards the Arctic from an O&G installation, will provide invaluable knowledge and experience necessary to make a successful larger cable-based observatory network at the Norwegian and Arctic margin (figure 1). Access to large quantities of real-time observation from the deep sea, including high definition video, could be used to provide the public and future recruits to science a fascinating insight into an almost unexplored part of the Earth beyond the Arctic Circle. More information about NOON is available at NOON's web site www.oceanobservatory.com. PIC

Towards a quantification of ocean wave heights off the west coast of Ireland using land based seismic data

NASA Astrophysics Data System (ADS)

Donne, S.; Bean, C. J.; Lokmer, I.; Lambkin, K.; Creamer, C.

2012-12-01

Ocean gravity waves are driven by atmospheric pressure systems. Their interactions with one another and reflection off coastlines generate pressure changes at the sea floor. These pressure fluctuations are the cause of continuous background seismic noise known as microseisms. The levels of microseism activity vary as a function of the sea state and increase during periods of intensive ocean wave activity. In 2011 a seismic network was deployed along the west coast of Ireland to continuously record microseisms generated in the Atlantic Ocean, as part of the Wave Observation (WaveObs) project based in University College Dublin. This project aims to determine the characteristics of the causative ocean gravity waves through calibration of the microseism data with ocean buoy data. In initial tests we are using a Backpropagation Feed-forward Artificial Neural Network (BP ANN) to establish the underlying relationships between microseisms and ocean waves. ANNs were originally inspired by studies of the mammalian brain and nervous system and are designed to learn by example. If successful these tools could then be used to estimate ocean wave heights and wave periods using a land-based seismic network and complement current wave observations being made offshore by marine buoys. Preliminary ANN results are promising with the network successfully able to reconstruct trends in ocean wave heights and periods. Microseisms can provide significant information about oceanic processes. With a deeper understanding of how these processes work there is potential for 1) locating and tracking the evolution of the largest waves in the Atlantic and 2) reconstructing the wave climate off the west coast of Ireland using legacy seismic data on a longer time scale than is currently available using marine based observations.

Demonstrating the value of community-based ('citizen science') observations for catchment modelling and characterisation

NASA Astrophysics Data System (ADS)

Starkey, Eleanor; Parkin, Geoff; Birkinshaw, Stephen; Large, Andy; Quinn, Paul; Gibson, Ceri

2017-05-01

Despite there being well-established meteorological and hydrometric monitoring networks in the UK, many smaller catchments remain ungauged. This leaves a challenge for characterisation, modelling, forecasting and management activities. Here we demonstrate the value of community-based ('citizen science') observations for modelling and understanding catchment response as a contribution to catchment science. The scheme implemented within the 42 km2 Haltwhistle Burn catchment, a tributary of the River Tyne in northeast England, has harvested and used quantitative and qualitative observations from the public in a novel way to effectively capture spatial and temporal river response. Community-based rainfall, river level and flood observations have been successfully collected and quality-checked, and used to build and run a physically-based, spatially-distributed catchment model, SHETRAN. Model performance using different combinations of observations is tested against traditionally-derived hydrographs. Our results show how the local network of community-based observations alongside traditional sources of hydro-information supports characterisation of catchment response more accurately than using traditional observations alone over both spatial and temporal scales. We demonstrate that these community-derived datasets are most valuable during local flash flood events, particularly towards peak discharge. This information is often missed or poorly represented by ground-based gauges, or significantly underestimated by rainfall radar, as this study clearly demonstrates. While community-based observations are less valuable during prolonged and widespread floods, or over longer hydrological periods of interest, they can still ground-truth existing traditional sources of catchment data to increase confidence during characterisation and management activities. Involvement of the public in data collection activities also encourages wider community engagement, and provides important information for catchment management.

A robust observer based on H∞ filtering with parameter uncertainties combined with Neural Networks for estimation of vehicle roll angle

NASA Astrophysics Data System (ADS)

Boada, Beatriz L.; Boada, Maria Jesus L.; Vargas-Melendez, Leandro; Diaz, Vicente

2018-01-01

Nowadays, one of the main objectives in road transport is to decrease the number of accident victims. Rollover accidents caused nearly 33% of all deaths from passenger vehicle crashes. Roll Stability Control (RSC) systems prevent vehicles from untripped rollover accidents. The lateral load transfer is the main parameter which is taken into account in the RSC systems. This parameter is related to the roll angle, which can be directly measured from a dual-antenna GPS. Nevertheless, this is a costly technique. For this reason, roll angle has to be estimated. In this paper, a novel observer based on H∞ filtering in combination with a neural network (NN) for the vehicle roll angle estimation is proposed. The design of this observer is based on four main criteria: to use a simplified vehicle model, to use signals of sensors which are installed onboard in current vehicles, to consider the inaccuracy in the system model and to attenuate the effect of the external disturbances. Experimental results show the effectiveness of the proposed observer.

An observer-based compensator for distributed delays in integrated control systems

NASA Technical Reports Server (NTRS)

Luck, Rogelio; Ray, Asok

1989-01-01

This paper presents an algorithm for compensation of delays that are distributed within a control loop. The observer-based algorithm is especially suitable for compensating network-induced delays that are likely to occur in integrated control systems of the future generation aircraft. The robustness of the algorithm relative to uncertainties in the plant model have been examined.

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres

PubMed Central

Gronau, Greta; Jacobsen, Matthew M.; Huang, Wenwen; Rizzo, Daniel J.; Li, David; Staii, Cristian; Pugno, Nicola M.; Wong, Joyce Y.; Kaplan, David L.; Buehler, Markus J.

2016-01-01

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified. PMID:26017575

Predictive modelling-based design and experiments for synthesis and spinning of bioinspired silk fibres.

PubMed

Lin, Shangchao; Ryu, Seunghwa; Tokareva, Olena; Gronau, Greta; Jacobsen, Matthew M; Huang, Wenwen; Rizzo, Daniel J; Li, David; Staii, Cristian; Pugno, Nicola M; Wong, Joyce Y; Kaplan, David L; Buehler, Markus J

2015-05-28

Scalable computational modelling tools are required to guide the rational design of complex hierarchical materials with predictable functions. Here, we utilize mesoscopic modelling, integrated with genetic block copolymer synthesis and bioinspired spinning process, to demonstrate de novo materials design that incorporates chemistry, processing and material characterization. We find that intermediate hydrophobic/hydrophilic block ratios observed in natural spider silks and longer chain lengths lead to outstanding silk fibre formation. This design by nature is based on the optimal combination of protein solubility, self-assembled aggregate size and polymer network topology. The original homogeneous network structure becomes heterogeneous after spinning, enhancing the anisotropic network connectivity along the shear flow direction. Extending beyond the classical polymer theory, with insights from the percolation network model, we illustrate the direct proportionality between network conductance and fibre Young's modulus. This integrated approach provides a general path towards de novo functional network materials with enhanced mechanical properties and beyond (optical, electrical or thermal) as we have experimentally verified.

Complex networks as a unified framework for descriptive analysis and predictive modeling in climate

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

Steinhaeuser, Karsten J K; Chawla, Nitesh; Ganguly, Auroop R

The analysis of climate data has relied heavily on hypothesis-driven statistical methods, while projections of future climate are based primarily on physics-based computational models. However, in recent years a wealth of new datasets has become available. Therefore, we take a more data-centric approach and propose a unified framework for studying climate, with an aim towards characterizing observed phenomena as well as discovering new knowledge in the climate domain. Specifically, we posit that complex networks are well-suited for both descriptive analysis and predictive modeling tasks. We show that the structural properties of climate networks have useful interpretation within the domain. Further,more » we extract clusters from these networks and demonstrate their predictive power as climate indices. Our experimental results establish that the network clusters are statistically significantly better predictors than clusters derived using a more traditional clustering approach. Using complex networks as data representation thus enables the unique opportunity for descriptive and predictive modeling to inform each other.« less

Measuring the value of accurate link prediction for network seeding.

PubMed

Wei, Yijin; Spencer, Gwen

2017-01-01

The influence-maximization literature seeks small sets of individuals whose structural placement in the social network can drive large cascades of behavior. Optimization efforts to find the best seed set often assume perfect knowledge of the network topology. Unfortunately, social network links are rarely known in an exact way. When do seeding strategies based on less-than-accurate link prediction provide valuable insight? We introduce optimized-against-a-sample ([Formula: see text]) performance to measure the value of optimizing seeding based on a noisy observation of a network. Our computational study investigates [Formula: see text] under several threshold-spread models in synthetic and real-world networks. Our focus is on measuring the value of imprecise link information. The level of investment in link prediction that is strategic appears to depend closely on spread model: in some parameter ranges investments in improving link prediction can pay substantial premiums in cascade size. For other ranges, such investments would be wasted. Several trends were remarkably consistent across topologies.

Identifying influential user communities on the social network

NASA Astrophysics Data System (ADS)

Hu, Weishu; Gong, Zhiguo; Hou U, Leong; Guo, Jingzhi

2015-10-01

Nowadays social network services have been popularly used in electronic commerce systems. Users on the social network can develop different relationships based on their common interests and activities. In order to promote the business, it is interesting to explore hidden relationships among users developed on the social network. Such knowledge can be used to locate target users for different advertisements and to provide effective product recommendations. In this paper, we define and study a novel community detection problem that is to discover the hidden community structure in large social networks based on their common interests. We observe that the users typically pay more attention to those users who share similar interests, which enable a way to partition the users into different communities according to their common interests. We propose two algorithms to detect influential communities using common interests in large social networks efficiently and effectively. We conduct our experimental evaluation using a data set from Epinions, which demonstrates that our method achieves 4-11.8% accuracy improvement over the state-of-the-art method.

The Global Oscillation Network Group site survey, 2: Results

NASA Technical Reports Server (NTRS)

Hill, Frank; Fischer, George; Forgach, Suzanne; Grier, Jennifer; Leibacher, John W.; Jones, Harrison P.; Jones, Patricia B.; Kupke, Renate; Stebbins, Robin T.; Clay, Donald W.

1994-01-01

The Global Oscillation Network Group (GONG) Project will place a network of instruments around the world to observe solar oscillations as continuously as possible for three years. The Project has now chosen the six network sites based on analysis of survey data from fifteen sites around the world. The chosen sites are: Big Bear Solar Observatory, California; Mauna Loa Solar Observatory, Hawaii; Learmonth Solar Observatory, Australia; Udaipur Solar Observatory, India; Observatorio del Teide, Tenerife; and Cerro Tololo Interamerican Observatory, Chile. Total solar intensity at each site yields information on local cloud cover, extinction coefficient, and transparency fluctuations. In addition, the performance of 192 reasonable networks assembled from the individual site records is compared using a statistical principal components analysis. An accompanying paper descibes the analysis methods in detail; here we present the results of both the network and individual site analyses. The selected network has a duty cycle of 93.3%, in good agreement with numerical simulations. The power spectrum of the network observing window shows a first diurnal sidelobe height of 3 x 10(exp -4) with respect to the central component, an improvement of a factor of 1300 over a single site. The background level of the network spectrum is lower by a factor of 50 compared to a single-site spectrum.

Coupling effects on turning points of infectious diseases epidemics in scale-free networks.

PubMed

Kim, Kiseong; Lee, Sangyeon; Lee, Doheon; Lee, Kwang Hyung

2017-05-31

Pandemic is a typical spreading phenomenon that can be observed in the human society and is dependent on the structure of the social network. The Susceptible-Infective-Recovered (SIR) model describes spreading phenomena using two spreading factors; contagiousness (β) and recovery rate (γ). Some network models are trying to reflect the social network, but the real structure is difficult to uncover. We have developed a spreading phenomenon simulator that can input the epidemic parameters and network parameters and performed the experiment of disease propagation. The simulation result was analyzed to construct a new marker VRTP distribution. We also induced the VRTP formula for three of the network mathematical models. We suggest new marker VRTP (value of recovered on turning point) to describe the coupling between the SIR spreading and the Scale-free (SF) network and observe the aspects of the coupling effects with the various of spreading and network parameters. We also derive the analytic formulation of VRTP in the fully mixed model, the configuration model, and the degree-based model respectively in the mathematical function form for the insights on the relationship between experimental simulation and theoretical consideration. We discover the coupling effect between SIR spreading and SF network through devising novel marker VRTP which reflects the shifting effect and relates to entropy.

A Comparative Analysis of Community Detection Algorithms on Artificial Networks

PubMed Central

Yang, Zhao; Algesheimer, René; Tessone, Claudio J.

2016-01-01

Many community detection algorithms have been developed to uncover the mesoscopic properties of complex networks. However how good an algorithm is, in terms of accuracy and computing time, remains still open. Testing algorithms on real-world network has certain restrictions which made their insights potentially biased: the networks are usually small, and the underlying communities are not defined objectively. In this study, we employ the Lancichinetti-Fortunato-Radicchi benchmark graph to test eight state-of-the-art algorithms. We quantify the accuracy using complementary measures and algorithms’ computing time. Based on simple network properties and the aforementioned results, we provide guidelines that help to choose the most adequate community detection algorithm for a given network. Moreover, these rules allow uncovering limitations in the use of specific algorithms given macroscopic network properties. Our contribution is threefold: firstly, we provide actual techniques to determine which is the most suited algorithm in most circumstances based on observable properties of the network under consideration. Secondly, we use the mixing parameter as an easily measurable indicator of finding the ranges of reliability of the different algorithms. Finally, we study the dependency with network size focusing on both the algorithm’s predicting power and the effective computing time. PMID:27476470

Peeking Network States with Clustered Patterns

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

Kim, Jinoh; Sim, Alex

2015-10-20

Network traffic monitoring has long been a core element for effec- tive network management and security. However, it is still a chal- lenging task with a high degree of complexity for comprehensive analysis when considering multiple variables and ever-increasing traffic volumes to monitor. For example, one of the widely con- sidered approaches is to scrutinize probabilistic distributions, but it poses a scalability concern and multivariate analysis is not gen- erally supported due to the exponential increase of the complexity. In this work, we propose a novel method for network traffic moni- toring based on clustering, one of the powerful deep-learningmore » tech- niques. We show that the new approach enables us to recognize clustered results as patterns representing the network states, which can then be utilized to evaluate “similarity” of network states over time. In addition, we define a new quantitative measure for the similarity between two compared network states observed in dif- ferent time windows, as a supportive means for intuitive analysis. Finally, we demonstrate the clustering-based network monitoring with public traffic traces, and show that the proposed approach us- ing the clustering method has a great opportunity for feasible, cost- effective network monitoring.« less

Dynamic social community detection and its applications.

PubMed

Nguyen, Nam P; Dinh, Thang N; Shen, Yilin; Thai, My T

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods.

Dynamic Social Community Detection and Its Applications

PubMed Central

Nguyen, Nam P.; Dinh, Thang N.; Shen, Yilin; Thai, My T.

2014-01-01

Community structure is one of the most commonly observed features of Online Social Networks (OSNs) in reality. The knowledge of this feature is of great advantage: it not only provides helpful insights into developing more efficient social-aware solutions but also promises a wide range of applications enabled by social and mobile networking, such as routing strategies in Mobile Ad Hoc Networks (MANETs) and worm containment in OSNs. Unfortunately, understanding this structure is very challenging, especially in dynamic social networks where social interactions are evolving rapidly. Our work focuses on the following questions: How can we efficiently identify communities in dynamic social networks? How can we adaptively update the network community structure based on its history instead of recomputing from scratch? To this end, we present Quick Community Adaptation (QCA), an adaptive modularity-based framework for not only discovering but also tracing the evolution of network communities in dynamic OSNs. QCA is very fast and efficient in the sense that it adaptively updates and discovers the new community structure based on its history together with the network changes only. This flexible approach makes QCA an ideal framework applicable for analyzing large-scale dynamic social networks due to its lightweight computing-resource requirement. To illustrate the effectiveness of our framework, we extensively test QCA on both synthesized and real-world social networks including Enron, arXiv e-print citation, and Facebook networks. Finally, we demonstrate the applicability of QCA in real applications: (1) A social-aware message forwarding strategy in MANETs, and (2) worm propagation containment in OSNs. Competitive results in comparison with other methods reveal that social-based techniques employing QCA as a community detection core outperform current available methods. PMID:24722164

Role of adjacency-matrix degeneracy in maximum-entropy-weighted network models

NASA Astrophysics Data System (ADS)

Sagarra, O.; Pérez Vicente, C. J.; Díaz-Guilera, A.

2015-11-01

Complex network null models based on entropy maximization are becoming a powerful tool to characterize and analyze data from real systems. However, it is not easy to extract good and unbiased information from these models: A proper understanding of the nature of the underlying events represented in them is crucial. In this paper we emphasize this fact stressing how an accurate counting of configurations compatible with given constraints is fundamental to build good null models for the case of networks with integer-valued adjacency matrices constructed from an aggregation of one or multiple layers. We show how different assumptions about the elements from which the networks are built give rise to distinctively different statistics, even when considering the same observables to match those of real data. We illustrate our findings by applying the formalism to three data sets using an open-source software package accompanying the present work and demonstrate how such differences are clearly seen when measuring network observables.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation.

PubMed

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages.

Reconstruction of a Real World Social Network using the Potts Model and Loopy Belief Propagation

PubMed Central

Bisconti, Cristian; Corallo, Angelo; Fortunato, Laura; Gentile, Antonio A.; Massafra, Andrea; Pellè, Piergiuseppe

2015-01-01

The scope of this paper is to test the adoption of a statistical model derived from Condensed Matter Physics, for the reconstruction of the structure of a social network. The inverse Potts model, traditionally applied to recursive observations of quantum states in an ensemble of particles, is here addressed to observations of the members' states in an organization and their (anti)correlations, thus inferring interactions as links among the members. Adopting proper (Bethe) approximations, such an inverse problem is showed to be tractable. Within an operational framework, this network-reconstruction method is tested for a small real-world social network, the Italian parliament. In this study case, it is easy to track statuses of the parliament members, using (co)sponsorships of law proposals as the initial dataset. In previous studies of similar activity-based networks, the graph structure was inferred directly from activity co-occurrences: here we compare our statistical reconstruction with such standard methods, outlining discrepancies and advantages. PMID:26617539

Isolation and evolution of labile sulfur allotropes via kinetic encapsulation in interactive porous networks

PubMed Central

Kitagawa, Hakuba; Ohtsu, Hiroyoshi; Cruz-Cabeza, Aurora J.; Kawano, Masaki

2016-01-01

The isolation and characterization of small sulfur allotropes have long remained unachievable because of their extreme lability. This study reports the first direct observation of disulfur (S2) with X-ray crystallography. Sulfur gas was kinetically trapped and frozen into the pores of two Cu-based porous coordination networks containing interactive iodide sites. Stabilization of S2 was achieved either through physisorption or chemisorption on iodide anions. One of the networks displayed shape selectivity for linear molecules only, therefore S2 was trapped and remained stable within the material at room temperature and higher. In the second network, however, the S2 molecules reacted further to produce bent-S3 species as the temperature was increased. Following the thermal evolution of the S2 species in this network using X-ray diffraction and Raman spectroscopy unveiled the generation of a new reaction intermediate never observed before, the cyclo-tri­sulfur dication (cyclo-S3 2+). It is envisaged that kinetic guest trapping in interactive crystalline porous networks will be a promising method to investigate transient chemical species. PMID:27437110

Tail-scope: Using friends to estimate heavy tails of degree distributions in large-scale complex networks

NASA Astrophysics Data System (ADS)

Eom, Young-Ho; Jo, Hang-Hyun

2015-05-01

Many complex networks in natural and social phenomena have often been characterized by heavy-tailed degree distributions. However, due to rapidly growing size of network data and concerns on privacy issues about using these data, it becomes more difficult to analyze complete data sets. Thus, it is crucial to devise effective and efficient estimation methods for heavy tails of degree distributions in large-scale networks only using local information of a small fraction of sampled nodes. Here we propose a tail-scope method based on local observational bias of the friendship paradox. We show that the tail-scope method outperforms the uniform node sampling for estimating heavy tails of degree distributions, while the opposite tendency is observed in the range of small degrees. In order to take advantages of both sampling methods, we devise the hybrid method that successfully recovers the whole range of degree distributions. Our tail-scope method shows how structural heterogeneities of large-scale complex networks can be used to effectively reveal the network structure only with limited local information.

Synchronization invariance under network structural transformations

NASA Astrophysics Data System (ADS)

Arola-Fernández, Lluís; Díaz-Guilera, Albert; Arenas, Alex

2018-06-01

Synchronization processes are ubiquitous despite the many connectivity patterns that complex systems can show. Usually, the emergence of synchrony is a macroscopic observable; however, the microscopic details of the system, as, e.g., the underlying network of interactions, is many times partially or totally unknown. We already know that different interaction structures can give rise to a common functionality, understood as a common macroscopic observable. Building upon this fact, here we propose network transformations that keep the collective behavior of a large system of Kuramoto oscillators invariant. We derive a method based on information theory principles, that allows us to adjust the weights of the structural interactions to map random homogeneous in-degree networks into random heterogeneous networks and vice versa, keeping synchronization values invariant. The results of the proposed transformations reveal an interesting principle; heterogeneous networks can be mapped to homogeneous ones with local information, but the reverse process needs to exploit higher-order information. The formalism provides analytical insight to tackle real complex scenarios when dealing with uncertainty in the measurements of the underlying connectivity structure.

Strategy of thunderstorm measurement with super dense ground-based observation network

NASA Astrophysics Data System (ADS)

Takahashi, Y.; Sato, M.

2014-12-01

It's not easy to understand the inside structure and developing process of thunderstorm only with existing meteorological instruments since its horizontal extent of the storm cell is sometimes smaller than an order of 10 km while one of the densest ground network in Japan, AMEDAS, consists of sites located every 17 km in average and the resolution of meteorological radar is 1-2 km in general. Even the X-band radar realizes the resolution of 250 m or larger. Here we suggest a new super dense observation network with simple and low cost sensors that can be used for measurement both of raindrop and vertical electric field change caused by cloud-to-ground lightning discharge. This sensor consists of two aluminum plates with a diameter of 10-20 cm. We carried out an observation campaign in summer of 2013 in the foothills of Mt. Yastugatake, Yamanashi and Nagano prefectures in Japan, installing 6 plate-type sensors at a distance of about 4 km. Horizontal location, height and charge amount of each lightning discharge are estimated successfully based on the information of electric field changes at several observing sites. Moreover, it was found that the thunderstorm has a very narrow structure well smaller than 300 m that cannot be measured by any other ways, counting the positive and negative pulses caused by attachment of raindrop to the sensor plate, respectively. We plan to construct a new super dense observation network in the north Kanto region, Japan, where the lightning activity is most prominent in summer Japan, distributing more than several tens of sensors at every 4 km or shorter, such as an order of 100 m at minimum. This kind of new type network will reveal the unknown fine structures of thunderstorms and open the door for constructing real time alert system of torrential rainfall and lightning stroke especially in the city area.

An Adaptive Failure Detector Based on Quality of Service in Peer-to-Peer Networks

PubMed Central

Dong, Jian; Ren, Xiao; Zuo, Decheng; Liu, Hongwei

2014-01-01

The failure detector is one of the fundamental components that maintain high availability of Peer-to-Peer (P2P) networks. Under different network conditions, the adaptive failure detector based on quality of service (QoS) can achieve the detection time and accuracy required by upper applications with lower detection overhead. In P2P systems, complexity of network and high churn lead to high message loss rate. To reduce the impact on detection accuracy, baseline detection strategy based on retransmission mechanism has been employed widely in many P2P applications; however, Chen's classic adaptive model cannot describe this kind of detection strategy. In order to provide an efficient service of failure detection in P2P systems, this paper establishes a novel QoS evaluation model for the baseline detection strategy. The relationship between the detection period and the QoS is discussed and on this basis, an adaptive failure detector (B-AFD) is proposed, which can meet the quantitative QoS metrics under changing network environment. Meanwhile, it is observed from the experimental analysis that B-AFD achieves better detection accuracy and time with lower detection overhead compared to the traditional baseline strategy and the adaptive detectors based on Chen's model. Moreover, B-AFD has better adaptability to P2P network. PMID:25198005

Video Games, Internet and Social Networks: A Study among French School students

PubMed

Dany, Lionel; Moreau, Laure; Guillet, Clémentine; Franchina, Carmelo

2016-11-25

Aim : Screen-based media use is gradually becoming a public health issue, especially among young people.Method : A local descriptive observational study was conducted in 11 colleges of the Bouches-du-Rhône department. All middle high school students were asked to fill in a questionnaire comprising questions about their demographic characteristics, their screen-based media use (Internet, video games, social networks), any problematic use (video games and social networks), self-esteem and quality of life.Results : A total of 950 college students (mean age : 12.96 years) participated in the research. The results show a high level and a very diverse screen-based media use. Boys more frequently played video games and girls go more frequently used social networks. The levels of problematic use were relatively low for all middle high school students. The level of problematic video game use was significantly higher in boys, and the level of problematic social network use was higher in girls.Conclusion : Differences in the use of video games or social networks raise the general issue of gender differences in society. This study indicates the need for more specific preventive interventions for screen-based media use. The addictive “nature” of certain practices needs to be studied in more detail.

Optimal topologies for maximizing network transmission capacity

NASA Astrophysics Data System (ADS)

Chen, Zhenhao; Wu, Jiajing; Rong, Zhihai; Tse, Chi K.

2018-04-01

It has been widely demonstrated that the structure of a network is a major factor that affects its traffic dynamics. In this work, we try to identify the optimal topologies for maximizing the network transmission capacity, as well as to build a clear relationship between structural features of a network and the transmission performance in terms of traffic delivery. We propose an approach for designing optimal network topologies against traffic congestion by link rewiring and apply them on the Barabási-Albert scale-free, static scale-free and Internet Autonomous System-level networks. Furthermore, we analyze the optimized networks using complex network parameters that characterize the structure of networks, and our simulation results suggest that an optimal network for traffic transmission is more likely to have a core-periphery structure. However, assortative mixing and the rich-club phenomenon may have negative impacts on network performance. Based on the observations of the optimized networks, we propose an efficient method to improve the transmission capacity of large-scale networks.

A Novel Characterization of Amalgamated Networks in Natural Systems

PubMed Central

Barranca, Victor J.; Zhou, Douglas; Cai, David

2015-01-01

Densely-connected networks are prominent among natural systems, exhibiting structural characteristics often optimized for biological function. To reveal such features in highly-connected networks, we introduce a new network characterization determined by a decomposition of network-connectivity into low-rank and sparse components. Based on these components, we discover a new class of networks we define as amalgamated networks, which exhibit large functional groups and dense connectivity. Analyzing recent experimental findings on cerebral cortex, food-web, and gene regulatory networks, we establish the unique importance of amalgamated networks in fostering biologically advantageous properties, including rapid communication among nodes, structural stability under attacks, and separation of network activity into distinct functional modules. We further observe that our network characterization is scalable with network size and connectivity, thereby identifying robust features significant to diverse physical systems, which are typically undetectable by conventional characterizations of connectivity. We expect that studying the amalgamation properties of biological networks may offer new insights into understanding their structure-function relationships. PMID:26035066

A study of EMR-based medical knowledge network and its applications.

PubMed

Zhao, Chao; Jiang, Jingchi; Xu, Zhiming; Guan, Yi

2017-05-01

Electronic medical records (EMRs) contain an amount of medical knowledge which can be used for clinical decision support. We attempt to integrate this medical knowledge into a complex network, and then implement a diagnosis model based on this network. The dataset of our study contains 992 records which are uniformly sampled from different departments of the hospital. In order to integrate the knowledge of these records, an EMR-based medical knowledge network (EMKN) is constructed. This network takes medical entities as nodes, and co-occurrence relationships between the two entities as edges. Selected properties of this network are analyzed. To make use of this network, a basic diagnosis model is implemented. Seven hundred records are randomly selected to re-construct the network, and the remaining 292 records are used as test records. The vector space model is applied to illustrate the relationships between diseases and symptoms. Because there may exist more than one actual disease in a record, the recall rate of the first ten results, and the average precision are adopted as evaluation measures. Compared with a random network of the same size, this network has a similar average length but a much higher clustering coefficient. Additionally, it can be observed that there are direct correlations between the community structure and the real department classes in the hospital. For the diagnosis model, the vector space model using disease as a base obtains the best result. At least one accurate disease can be obtained in 73.27% of the records in the first ten results. We constructed an EMR-based medical knowledge network by extracting the medical entities. This network has the small-world and scale-free properties. Moreover, the community structure showed that entities in the same department have a tendency to be self-aggregated. Based on this network, a diagnosis model was proposed. This model uses only the symptoms as inputs and is not restricted to a specific disease. The experiments conducted demonstrated that EMKN is a simple and universal technique to integrate different medical knowledge from EMRs, and can be used for clinical decision support. Copyright © 2017 Elsevier B.V. All rights reserved.

Geocenter Coordinates from a Combined Processing of LEO and Ground-based GPS Observations

NASA Astrophysics Data System (ADS)

Männel, Benjamin; Rothacher, Markus

2017-04-01

The GPS observations provided by the global IGS (International GNSS Service) tracking network play an important role for the realization of a unique terrestrial reference frame that is accurate enough to allow the monitoring of the Earth's system. Combining these ground-based data with GPS observations tracked by high-quality dual-frequency receivers on-board Low Earth Orbiters (LEO) might help to further improve the realization of the terrestrial reference frame and the estimation of the geocenter coordinates, GPS satellite orbits and Earth rotation parameters (ERP). To assess the scope of improvement, we processed a network of 50 globally distributed and stable IGS-stations together with four LEOs (GRACE-A, GRACE-B, OSTM/Jason-2 and GOCE) over a time interval of three years (2010-2012). To ensure fully consistent solutions the zero-difference phase observations of the ground stations and LEOs were processed in a common least-square adjustment, estimating GPS orbits, LEO orbits, station coordinates, ERPs, site-specific tropospheric delays, satellite and receiver clocks and ambiguities. We present the significant impact of the individual LEOs and a combination of all four LEOs on geocenter coordinates derived by using a translational approach (also called network shift approach). In addition, we present geocenter coordinates derived from the same set of GPS observations by using a unified approach. This approach combines the translational and the degree-one approach by estimating translations and surface deformations simultaneously. Based on comparisons against each other and against geocenter time series derived by other techniques the effect of the selected approach is assessed.

Results from Joint Observations of Jupiter's Atmosphere by Juno and a Network of Earth-Based Observing Stations

NASA Astrophysics Data System (ADS)

Orton, G. S.; Bolton, S. J.; Levin, S.; Hansen, C. J.; Janssen, M. A.; Adriani, A.; Gladstone, R.; Bagenal, F.; Ingersoll, A. P.; Momary, T.; Payne, A.

2016-12-01

The Juno mission has promoted and coordinated a network of Earth-based observations, including both space- and ground-based facilities, to extend and enhance observations made by the Juno mission. The spectral region and timeline of all of these observations are summarized in the web site: https://www.missionjuno.swri.edu/planned-observations. Among the earliest of these were observation of Jovian auroral phenomena at X-ray, ultraviolet and infrared wavelengths and measurements of Jovian synchrotron radiation from the Earth simultaneously with the measurement of properties of the upstream solar wind described elsewhere in this meeting. Other observations of significance to the magnetosphere measured the mass loading from Io by tracking its observed volcanic activity and the opacity of its torus. Observations of Jupiter's neutral atmosphere included observations of reflected sunlight from the near-ultraviolet through the near-infrared and thermal emission from 5 microns through the radio region. The point of these measurements is to relate properties of the deep atmosphere that are the focus of Juno's mission to the state of the "weather layer" at much higher atmospheric levels. These observations cover spectral regions not included in Juno's instrumentation, provide spatial context for Juno's often spatially limited coverage of Jupiter, and they describe the evolution of atmospheric features in time that are measured only once by Juno. We will summarize the results of measurements during the approach phase of the mission that characterized the state of the atmosphere, as well as observations made by Juno and the supporting campaign during Juno's perijoves 1 (August 27), 2 (October 19), 3 (November 2), 4 (November 15), and 5 (November 30). The Juno mission also benefited from the enlistment of a network of dedicated amateur astronomers who, besides providing input needed for public operation of the JunoCam visible camera, tracked the evolution of features in Jupiter's atmosphere on a variety of time scales. The amateur contributions also aided professional astronomical observations by providing a quasi-continuous picture of the evolution of features observed by Juno's instruments.

Estimation of the proteomic cancer co-expression sub networks by using association estimators.

PubMed

Erdoğan, Cihat; Kurt, Zeyneb; Diri, Banu

2017-01-01

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators' performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists.

Estimation of the proteomic cancer co-expression sub networks by using association estimators

PubMed Central

Kurt, Zeyneb; Diri, Banu

2017-01-01

In this study, the association estimators, which have significant influences on the gene network inference methods and used for determining the molecular interactions, were examined within the co-expression network inference concept. By using the proteomic data from five different cancer types, the hub genes/proteins within the disease-associated gene-gene/protein-protein interaction sub networks were identified. Proteomic data from various cancer types is collected from The Cancer Proteome Atlas (TCPA). Correlation and mutual information (MI) based nine association estimators that are commonly used in the literature, were compared in this study. As the gold standard to measure the association estimators’ performance, a multi-layer data integration platform on gene-disease associations (DisGeNET) and the Molecular Signatures Database (MSigDB) was used. Fisher's exact test was used to evaluate the performance of the association estimators by comparing the created co-expression networks with the disease-associated pathways. It was observed that the MI based estimators provided more successful results than the Pearson and Spearman correlation approaches, which are used in the estimation of biological networks in the weighted correlation network analysis (WGCNA) package. In correlation-based methods, the best average success rate for five cancer types was 60%, while in MI-based methods the average success ratio was 71% for James-Stein Shrinkage (Shrink) and 64% for Schurmann-Grassberger (SG) association estimator, respectively. Moreover, the hub genes and the inferred sub networks are presented for the consideration of researchers and experimentalists. PMID:29145449

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity.

PubMed

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-09-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. © 2015 The Authors Protein Science published by Wiley Periodicals, Inc. on behalf of The Protein Society.

Comparison of topological clustering within protein networks using edge metrics that evaluate full sequence, full structure, and active site microenvironment similarity

PubMed Central

Leuthaeuser, Janelle B; Knutson, Stacy T; Kumar, Kiran; Babbitt, Patricia C; Fetrow, Jacquelyn S

2015-01-01

The development of accurate protein function annotation methods has emerged as a major unsolved biological problem. Protein similarity networks, one approach to function annotation via annotation transfer, group proteins into similarity-based clusters. An underlying assumption is that the edge metric used to identify such clusters correlates with functional information. In this contribution, this assumption is evaluated by observing topologies in similarity networks using three different edge metrics: sequence (BLAST), structure (TM-Align), and active site similarity (active site profiling, implemented in DASP). Network topologies for four well-studied protein superfamilies (enolase, peroxiredoxin (Prx), glutathione transferase (GST), and crotonase) were compared with curated functional hierarchies and structure. As expected, network topology differs, depending on edge metric; comparison of topologies provides valuable information on structure/function relationships. Subnetworks based on active site similarity correlate with known functional hierarchies at a single edge threshold more often than sequence- or structure-based networks. Sequence- and structure-based networks are useful for identifying sequence and domain similarities and differences; therefore, it is important to consider the clustering goal before deciding appropriate edge metric. Further, conserved active site residues identified in enolase and GST active site subnetworks correspond with published functionally important residues. Extension of this analysis yields predictions of functionally determinant residues for GST subgroups. These results support the hypothesis that active site similarity-based networks reveal clusters that share functional details and lay the foundation for capturing functionally relevant hierarchies using an approach that is both automatable and can deliver greater precision in function annotation than current similarity-based methods. PMID:26073648

Delay/Disruption Tolerant Networking for the International Space Station (ISS)

NASA Technical Reports Server (NTRS)

Schlesinger, Adam; Willman, Brett M.; Pitts, Lee; Davidson, Suzanne R.; Pohlchuck, William A.

2017-01-01

Disruption Tolerant Networking (DTN) is an emerging data networking technology designed to abstract the hardware communication layer from the spacecraft/payload computing resources. DTN is specifically designed to operate in environments where link delays and disruptions are common (e.g., space-based networks). The National Aeronautics and Space Administration (NASA) has demonstrated DTN on several missions, such as the Deep Impact Networking (DINET) experiment, the Earth Observing Mission 1 (EO-1) and the Lunar Laser Communication Demonstration (LLCD). To further the maturation of DTN, NASA is implementing DTN protocols on the International Space Station (ISS). This paper explains the architecture of the ISS DTN network, the operational support for the system, the results from integrated ground testing, and the future work for DTN expansion.

Analysis of stationary availability factor of two-level backbone computer networks with arbitrary topology

NASA Astrophysics Data System (ADS)

Rahman, P. A.

2018-05-01

This scientific paper deals with the two-level backbone computer networks with arbitrary topology. A specialized method, offered by the author for calculation of the stationary availability factor of the two-level backbone computer networks, based on the Markov reliability models for the set of the independent repairable elements with the given failure and repair rates and the methods of the discrete mathematics, is also discussed. A specialized algorithm, offered by the author for analysis of the network connectivity, taking into account different kinds of the network equipment failures, is also observed. Finally, this paper presents an example of calculation of the stationary availability factor for the backbone computer network with the given topology.

High Performance Implementation of 3D Convolutional Neural Networks on a GPU.

PubMed

Lan, Qiang; Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version.

High Performance Implementation of 3D Convolutional Neural Networks on a GPU

PubMed Central

Wang, Zelong; Wen, Mei; Zhang, Chunyuan; Wang, Yijie

2017-01-01

Convolutional neural networks have proven to be highly successful in applications such as image classification, object tracking, and many other tasks based on 2D inputs. Recently, researchers have started to apply convolutional neural networks to video classification, which constitutes a 3D input and requires far larger amounts of memory and much more computation. FFT based methods can reduce the amount of computation, but this generally comes at the cost of an increased memory requirement. On the other hand, the Winograd Minimal Filtering Algorithm (WMFA) can reduce the number of operations required and thus can speed up the computation, without increasing the required memory. This strategy was shown to be successful for 2D neural networks. We implement the algorithm for 3D convolutional neural networks and apply it to a popular 3D convolutional neural network which is used to classify videos and compare it to cuDNN. For our highly optimized implementation of the algorithm, we observe a twofold speedup for most of the 3D convolution layers of our test network compared to the cuDNN version. PMID:29250109

Cortical Thinning and Altered Cortico-Cortical Structural Covariance of the Default Mode Network in Patients with Persistent Insomnia Symptoms.

PubMed

Suh, Sooyeon; Kim, Hosung; Dang-Vu, Thien Thanh; Joo, Eunyeon; Shin, Chol

2016-01-01

Recent studies have suggested that structural abnormalities in insomnia may be linked with alterations in the default-mode network (DMN). This study compared cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia (PI) and good sleepers (GS). The current study used a clinical subsample from the longitudinal community-based Korean Genome and Epidemiology Study (KoGES). Cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia symptoms (PIS; n = 57) were compared to good sleepers (GS; n = 40). All participants underwent MRI acquisition. Based on literature review, we selected cortical regions corresponding to the DMN. A seed-based structural covariance analysis measured cortical thickness correlation between each seed region of the DMN and other cortical areas. Association of cortical thickness and covariance with sleep quality and neuropsychological assessments were further assessed. Compared to GS, cortical thinning was found in PIS in the anterior cingulate cortex, precentral cortex, and lateral prefrontal cortex. Decreased structural connectivity between anterior and posterior regions of the DMN was observed in the PIS group. Decreased structural covariance within the DMN was associated with higher PSQI scores. Cortical thinning in the lateral frontal lobe was related to poor performance in executive function in PIS. Disrupted structural covariance network in PIS might reflect malfunctioning of antero-posterior disconnection of the DMN during the wake to sleep transition that is commonly found during normal sleep. The observed structural network alteration may further implicate commonly observed sustained sleep difficulties and cognitive impairment in insomnia. © 2016 Associated Professional Sleep Societies, LLC.

Successful integration efforts in water quality from the integrated Ocean Observing System Regional Associations and the National Water Quality Monitoring Network

USGS Publications Warehouse

Ragsdale, R.; Vowinkel, E.; Porter, D.; Hamilton, P.; Morrison, R.; Kohut, J.; Connell, B.; Kelsey, H.; Trowbridge, P.

2011-01-01

The Integrated Ocean Observing System (IOOS??) Regional Associations and Interagency Partners hosted a water quality workshop in January 2010 to discuss issues of nutrient enrichment and dissolved oxygen depletion (hypoxia), harmful algal blooms (HABs), and beach water quality. In 2007, the National Water Quality Monitoring Council piloted demonstration projects as part of the National Water Quality Monitoring Network (Network) for U.S. Coastal Waters and their Tributaries in three IOOS Regional Associations, and these projects are ongoing. Examples of integrated science-based solutions to water quality issues of major concern from the IOOS regions and Network demonstration projects are explored in this article. These examples illustrate instances where management decisions have benefited from decision-support tools that make use of interoperable data. Gaps, challenges, and outcomes are identified, and a proposal is made for future work toward a multiregional water quality project for beach water quality.

Development of a Deep Learning Algorithm for Automatic Diagnosis of Diabetic Retinopathy.

PubMed

Raju, Manoj; Pagidimarri, Venkatesh; Barreto, Ryan; Kadam, Amrit; Kasivajjala, Vamsichandra; Aswath, Arun

2017-01-01

This paper mainly focuses on the deep learning application in classifying the stage of diabetic retinopathy and detecting the laterality of the eye using funduscopic images. Diabetic retinopathy is a chronic, progressive, sight-threatening disease of the retinal blood vessels. Ophthalmologists diagnose diabetic retinopathy through early funduscopic screening. Normally, there is a time delay in reporting and intervention, apart from the financial cost and risk of blindness associated with it. Using a convolutional neural network based approach for automatic diagnosis of diabetic retinopathy, we trained the prediction network on the publicly available Kaggle dataset. Approximately 35,000 images were used to train the network, which observed a sensitivity of 80.28% and a specificity of 92.29% on the validation dataset of ~53,000 images. Using 8,810 images, the network was trained for detecting the laterality of the eye and observed an accuracy of 93.28% on the validation set of 8,816 images.

The European Marine Observing Network and the development of an Integrated European Ocean Observing System. An EuroGOOS perspective

NASA Astrophysics Data System (ADS)

Fernandez, Vicente; Gorringe, Patrick; Nolan, Glenn

2016-04-01

The ocean benefits many sectors of society, being the biggest reservoir of heat, water, carbon and oxygen and playing a fundamental role regulating the earth's climate. We rely on the oceans for food, transport, energy and recreation. Therefore, a sustained marine observation network is crucial to further our understanding of the oceanic environment and to supply scientific data to meet society's need. Marine data and observations in Europe, collected primarily by state governmental agencies, is offered via five Regional Operational Oceanographic Systems (ROOS) within the context of EuroGOOS (http://www.eurogos.eu), an International Non-Profit Association of national governmental agencies and research organizations (40 members from 19 member states) committed to European-scale operational oceanography within the context of the Intergovernmental Global Ocean Observing System (GOOS). Strong cooperation within these regions, enabling the involvement of additional partners and countries, forms the basis of EuroGOOS work. Ocean data collected from different type of sensors (e.g. moored buoys, tide gauges, Ferrybox systems, High Frequency radars, gliders and profiling floats) is accessible to scientist and other end users through data portals and initiatives such as the European Marine Observations and Data Network (EMODnet) (www.emodnet.eu) and the Copernicus Marine Service Copernicus (www.copernicus.eu). Although a relatively mature European ocean observing capability already exists and its well-coordinated at European level, some gaps have been identified, for example the demand for ecosystem products and services, or the case that biogeochemical observations are still relatively sparse particularly in coastal and shelf seas. Assessing gaps based on the capacity of the observing system to answer key societal challenges e.g. site suitability for aquaculture and ocean energy, oil spill response and contextual oceanographic products for fisheries and ecosystems is still required. In this respect, an important effort is being carried out at European level aiming to stablish and consolidate an Integrated and sustained European Ocean Observing System (EOOS). In this paper we present the emerging vision for EOOS with a review of the existing observing networks on which it will be based.

The Global GNSS, SLR, VLBI, and DORIS Networks and their Support of GGOS: IGS+ILRS+IVS+IDS

NASA Technical Reports Server (NTRS)

Noll, Carey

2008-01-01

The global network of the International GNSS Service (IGS), the International Laser Ranging Service (ILRS), the International VLBI Service for Geodesy and Astrometry (IVS), and the International DORIS Service (IDS) are part of the ground-based infrastructure for GGOS. The observations obtained from these global networks provide for the determination and maintenance of the International Terrestrial Reference Frame (ITRF), an accurate set of positions and velocities that provides a stable coordinate system allowing scientists ts to link measurements over space and time. Many of these sites offer co-location of two or more techniques. Co-location provides integration of technique-specific networks into the ITRF as well as an assessment/validation of the quality and accuracy of the resulting measurements. As of fall 2008, these networks consisted of 410 GNSS sites, 42 laser ranging sites, 45 VLBI sites, and 58 DORIS sites. This poster will illustrate the global coverage of these networks, highlighting inter-technique co-locations, and show the importance of these networks 60 the underlying goals of GGOS including providing the observational basis to maintain a stable, accurate, global reference frame.

Modeling infection transmission in primate networks to predict centrality-based risk.

PubMed

Romano, Valéria; Duboscq, Julie; Sarabian, Cécile; Thomas, Elodie; Sueur, Cédric; MacIntosh, Andrew J J

2016-07-01

Social structure can theoretically regulate disease risk by mediating exposure to pathogens via social proximity and contact. Investigating the role of central individuals within a network may help predict infectious agent transmission as well as implement disease control strategies, but little is known about such dynamics in real primate networks. We combined social network analysis and a modeling approach to better understand transmission of a theoretical infectious agent in wild Japanese macaques, highly social animals which form extended but highly differentiated social networks. We collected focal data from adult females living on the islands of Koshima and Yakushima, Japan. Individual identities as well as grooming networks were included in a Markov graph-based simulation. In this model, the probability that an individual will transmit an infectious agent depends on the strength of its relationships with other group members. Similarly, its probability of being infected depends on its relationships with already infected group members. We correlated: (i) the percentage of subjects infected during a latency-constrained epidemic; (ii) the mean latency to complete transmission; (iii) the probability that an individual is infected first among all group members; and (iv) each individual's mean rank in the chain of transmission with different individual network centralities (eigenvector, strength, betweenness). Our results support the hypothesis that more central individuals transmit infections in a shorter amount of time and to more subjects but also become infected more quickly than less central individuals. However, we also observed that the spread of infectious agents on the Yakushima network did not always differ from expectations of spread on random networks. Generalizations about the importance of observed social networks in pathogen flow should thus be made with caution, since individual characteristics in some real world networks appear less relevant than they are in others in predicting disease spread. Am. J. Primatol. 78:767-779, 2016. © 2016 Wiley Periodicals, Inc. © 2016 Wiley Periodicals, Inc.

Studying the evolutionary relationships and phylogenetic trees of 21 groups of tRNA sequences based on complex networks.

PubMed

Wei, Fangping; Chen, Bowen

2012-03-01

To find out the evolutionary relationships among different tRNA sequences of 21 amino acids, 22 networks are constructed. One is constructed from whole tRNAs, and the other 21 networks are constructed from the tRNAs which carry the same amino acids. A new method is proposed such that the alignment scores of any two amino acids groups are determined by the average degree and the average clustering coefficient of their networks. The anticodon feature of isolated tRNA and the phylogenetic trees of 21 group networks are discussed. We find that some isolated tRNA sequences in 21 networks still connect with other tRNAs outside their group, which reflects the fact that those tRNAs might evolve by intercrossing among these 21 groups. We also find that most anticodons among the same cluster are only one base different in the same sites when S ≥ 70, and they stay in the same rank in the ladder of evolutionary relationships. Those observations seem to agree on that some tRNAs might mutate from the same ancestor sequences based on point mutation mechanisms.

Is there any connection between the network morphology and the fluctuations of the stock market index?

NASA Astrophysics Data System (ADS)

Stefan, F. M.; Atman, A. P. F.

2015-02-01

Models which consider behavioral aspects of the investors have attracted increasing interest in the Finance and Econophysics literature in the last years. Different behavioral profiles (imitation, anti-imitation, indifference) were proposed for the investors, which take their decision based on their trust network (neighborhood). Results from agent-based models have shown that most of the features observed in actual stock market indices can be replicated in simulations. Here, we present a deeper investigation of an agent based model considering different network morphologies (regular, random, small-world) for the investors' trust network, in an attempt to answer the question raised in the title. We study the model by considering four scenarios for the investors and different initial conditions to analyze their influence in the stock market fluctuations. We have characterized the stationary limit for each scenario tested, focusing on the changes introduced when complex networks were used, and calculated the Hurst exponent in some cases. Simulations showed interesting results suggesting that the fluctuations of the stock market index are strongly affected by the network morphology, a remarkable result which we believe was never reported or predicted before.

Hebbian based learning with winner-take-all for spiking neural networks

NASA Astrophysics Data System (ADS)

Gupta, Ankur; Long, Lyle

2009-03-01

Learning methods for spiking neural networks are not as well developed as the traditional neural networks that widely use back-propagation training. We propose and implement a Hebbian based learning method with winner-take-all competition for spiking neural networks. This approach is spike time dependent which makes it naturally well suited for a network of spiking neurons. Homeostasis with Hebbian learning is implemented which ensures stability and quicker learning. Homeostasis implies that the net sum of incoming weights associated with a neuron remains the same. Winner-take-all is also implemented for competitive learning between output neurons. We implemented this learning rule on a biologically based vision processing system that we are developing, and use layers of leaky integrate and fire neurons. The network when presented with 4 bars (or Gabor filters) of different orientation learns to recognize the bar orientations (or Gabor filters). After training, each output neuron learns to recognize a bar at specific orientation and responds by firing more vigorously to that bar and less vigorously to others. These neurons are found to have bell shaped tuning curves and are similar to the simple cells experimentally observed by Hubel and Wiesel in the striate cortex of cat and monkey.

Community detection for networks with unipartite and bipartite structure

NASA Astrophysics Data System (ADS)

Chang, Chang; Tang, Chao

2014-09-01

Finding community structures in networks is important in network science, technology, and applications. To date, most algorithms that aim to find community structures only focus either on unipartite or bipartite networks. A unipartite network consists of one set of nodes and a bipartite network consists of two nonoverlapping sets of nodes with only links joining the nodes in different sets. However, a third type of network exists, defined here as the mixture network. Just like a bipartite network, a mixture network also consists of two sets of nodes, but some nodes may simultaneously belong to two sets, which breaks the nonoverlapping restriction of a bipartite network. The mixture network can be considered as a general case, with unipartite and bipartite networks viewed as its limiting cases. A mixture network can represent not only all the unipartite and bipartite networks, but also a wide range of real-world networks that cannot be properly represented as either unipartite or bipartite networks in fields such as biology and social science. Based on this observation, we first propose a probabilistic model that can find modules in unipartite, bipartite, and mixture networks in a unified framework based on the link community model for a unipartite undirected network [B Ball et al (2011 Phys. Rev. E 84 036103)]. We test our algorithm on synthetic networks (both overlapping and nonoverlapping communities) and apply it to two real-world networks: a southern women bipartite network and a human transcriptional regulatory mixture network. The results suggest that our model performs well for all three types of networks, is competitive with other algorithms for unipartite or bipartite networks, and is applicable to real-world networks.

Network-induced oscillatory behavior in material flow networks and irregular business cycles

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefen; Witt, Ulrich; Brenner, Thomas

2004-11-01

Network theory is rapidly changing our understanding of complex systems, but the relevance of topological features for the dynamic behavior of metabolic networks, food webs, production systems, information networks, or cascade failures of power grids remains to be explored. Based on a simple model of supply networks, we offer an interpretation of instabilities and oscillations observed in biological, ecological, economic, and engineering systems. We find that most supply networks display damped oscillations, even when their units—and linear chains of these units—behave in a nonoscillatory way. Moreover, networks of damped oscillators tend to produce growing oscillations. This surprising behavior offers, for example, a different interpretation of business cycles and of oscillating or pulsating processes. The network structure of material flows itself turns out to be a source of instability, and cyclical variations are an inherent feature of decentralized adjustments.

Neural Network-Based Retrieval of Surface and Root Zone Soil Moisture using Multi-Frequency Remotely-Sensed Observations

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Kolassa, Jana; Prigent, Catherine; Aires, Filipe; Gentine, Pierre

2017-04-01

Knowledge of root zone soil moisture is essential in studying plant's response to different stress conditions since plant photosynthetic activity and transpiration rate are constrained by the water available through their roots. Current global root zone soil moisture estimates are based on either outputs from physical models constrained by observations, or assimilation of remotely-sensed microwave-based surface soil moisture estimates with physical model outputs. However, quality of these estimates are limited by the accuracy of the model representations of physical processes (such as radiative transfer, infiltration, percolation, and evapotranspiration) as well as errors in the estimates of the surface parameters. Additionally, statistical approaches provide an alternative efficient platform to develop root zone soil moisture retrieval algorithms from remotely-sensed observations. In this study, we present a new neural network based retrieval algorithm to estimate surface and root zone soil moisture from passive microwave observations of SMAP satellite (L-band) and AMSR2 instrument (X-band). SMAP early morning observations are ideal for surface soil moisture retrieval. AMSR2 mid-night observations are used here as an indicator of plant hydraulic properties that are related to root zone soil moisture. The combined observations from SMAP and AMSR2 together with other ancillary observations including the Solar-Induced Fluorescence (SIF) estimates from GOME-2 instrument provide necessary information to estimate surface and root zone soil moisture. The algorithm is applied to observations from the first 18 months of SMAP mission and retrievals are validated against in-situ observations and other global datasets.

The study and realization of BDS un-differenced network-RTK based on raw observations

NASA Astrophysics Data System (ADS)

Tu, Rui; Zhang, Pengfei; Zhang, Rui; Lu, Cuixian; Liu, Jinhai; Lu, Xiaochun

2017-06-01

A BeiDou Navigation Satellite System (BDS) Un-Differenced (UD) Network Real Time Kinematic (URTK) positioning algorithm, which is based on raw observations, is developed in this study. Given an integer ambiguity datum, the UD integer ambiguity can be recovered from Double-Differenced (DD) integer ambiguities, thus the UD observation corrections can be calculated and interpolated for the rover station to achieve the fast positioning. As this URTK model uses raw observations instead of the ionospheric-free combinations, it is applicable for both dual- and single-frequency users to realize the URTK service. The algorithm was validated with the experimental BDS data collected at four regional stations from day of year 080 to 083 in 2016. The achieved results confirmed the high efficiency of the proposed URTK for providing the rover users a rapid and precise positioning service compared to the standard NRTK. In our test, the BDS URTK can provide a positioning service with cm level accuracy, i.e., 1 cm in the horizontal components, and 2-3 cm in the vertical component. Within the regional network, the mean convergence time for the users to fix the UD ambiguities is 2.7 s for the dual-frequency observations and of 6.3 s for the single-frequency observations after the DD ambiguity resolution. Furthermore, due to the feature of realizing URTK technology under the UD processing mode, it is possible to integrate the global Precise Point Positioning (PPP) and the local NRTK into a seamless positioning service.

Multiplication free neural network for cancer stem cell detection in H-and-E stained liver images

NASA Astrophysics Data System (ADS)

Badawi, Diaa; Akhan, Ece; Mallah, Ma'en; Üner, Ayşegül; ćetin-Atalay, Rengül; ćetin, A. Enis

2017-05-01

Markers such as CD13 and CD133 have been used to identify Cancer Stem Cells (CSC) in various tissue images. It is highly likely that CSC nuclei appear as brown in CD13 stained liver tissue images. We observe that there is a high correlation between the ratio of brown to blue colored nuclei in CD13 images and the ratio between the dark blue to blue colored nuclei in H&E stained liver images. Therefore, we recommend that a pathologist observing many dark blue nuclei in an H&E stained tissue image may also order CD13 staining to estimate the CSC ratio. In this paper, we describe a computer vision method based on a neural network estimating the ratio of dark blue to blue colored nuclei in an H&E stained liver tissue image. The neural network structure is based on a multiplication free operator using only additions and sign operations. Experimental results are presented.

Grand canonical validation of the bipartite international trade network.

PubMed

Straka, Mika J; Caldarelli, Guido; Saracco, Fabio

2017-08-01

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

Grand canonical validation of the bipartite international trade network

NASA Astrophysics Data System (ADS)

Straka, Mika J.; Caldarelli, Guido; Saracco, Fabio

2017-08-01

Devising strategies for economic development in a globally competitive landscape requires a solid and unbiased understanding of countries' technological advancements and similarities among export products. Both can be addressed through the bipartite representation of the International Trade Network. In this paper, we apply the recently proposed grand canonical projection algorithm to uncover country and product communities. Contrary to past endeavors, our methodology, based on information theory, creates monopartite projections in an unbiased and analytically tractable way. Single links between countries or products represent statistically significant signals, which are not accounted for by null models such as the bipartite configuration model. We find stable country communities reflecting the socioeconomic distinction in developed, newly industrialized, and developing countries. Furthermore, we observe product clusters based on the aforementioned country groups. Our analysis reveals the existence of a complicated structure in the bipartite International Trade Network: apart from the diversification of export baskets from the most basic to the most exclusive products, we observe a statistically significant signal of an export specialization mechanism towards more sophisticated products.

Fully automatic detection and segmentation of abdominal aortic thrombus in post-operative CTA images using Deep Convolutional Neural Networks.

PubMed

López-Linares, Karen; Aranjuelo, Nerea; Kabongo, Luis; Maclair, Gregory; Lete, Nerea; Ceresa, Mario; García-Familiar, Ainhoa; Macía, Iván; González Ballester, Miguel A

2018-05-01

Computerized Tomography Angiography (CTA) based follow-up of Abdominal Aortic Aneurysms (AAA) treated with Endovascular Aneurysm Repair (EVAR) is essential to evaluate the progress of the patient and detect complications. In this context, accurate quantification of post-operative thrombus volume is required. However, a proper evaluation is hindered by the lack of automatic, robust and reproducible thrombus segmentation algorithms. We propose a new fully automatic approach based on Deep Convolutional Neural Networks (DCNN) for robust and reproducible thrombus region of interest detection and subsequent fine thrombus segmentation. The DetecNet detection network is adapted to perform region of interest extraction from a complete CTA and a new segmentation network architecture, based on Fully Convolutional Networks and a Holistically-Nested Edge Detection Network, is presented. These networks are trained, validated and tested in 13 post-operative CTA volumes of different patients using a 4-fold cross-validation approach to provide more robustness to the results. Our pipeline achieves a Dice score of more than 82% for post-operative thrombus segmentation and provides a mean relative volume difference between ground truth and automatic segmentation that lays within the experienced human observer variance without the need of human intervention in most common cases. Copyright © 2018 Elsevier B.V. All rights reserved.

Efficient discovery of overlapping communities in massive networks

PubMed Central

Gopalan, Prem K.; Blei, David M.

2013-01-01

Detecting overlapping communities is essential to analyzing and exploring natural networks such as social networks, biological networks, and citation networks. However, most existing approaches do not scale to the size of networks that we regularly observe in the real world. In this paper, we develop a scalable approach to community detection that discovers overlapping communities in massive real-world networks. Our approach is based on a Bayesian model of networks that allows nodes to participate in multiple communities, and a corresponding algorithm that naturally interleaves subsampling from the network and updating an estimate of its communities. We demonstrate how we can discover the hidden community structure of several real-world networks, including 3.7 million US patents, 575,000 physics articles from the arXiv preprint server, and 875,000 connected Web pages from the Internet. Furthermore, we demonstrate on large simulated networks that our algorithm accurately discovers the true community structure. This paper opens the door to using sophisticated statistical models to analyze massive networks. PMID:23950224

Analytical theory of polymer-network-mediated interaction between colloidal particles

PubMed Central

Di Michele, Lorenzo; Zaccone, Alessio; Eiser, Erika

2012-01-01

Nanostructured materials based on colloidal particles embedded in a polymer network are used in a variety of applications ranging from nanocomposite rubbers to organic-inorganic hybrid solar cells. Further, polymer-network-mediated colloidal interactions are highly relevant to biological studies whereby polymer hydrogels are commonly employed to probe the mechanical response of living cells, which can determine their biological function in physiological environments. The performance of nanomaterials crucially relies upon the spatial organization of the colloidal particles within the polymer network that depends, in turn, on the effective interactions between the particles in the medium. Existing models based on nonlocal equilibrium thermodynamics fail to clarify the nature of these interactions, precluding the way toward the rational design of polymer-composite materials. In this article, we present a predictive analytical theory of these interactions based on a coarse-grained model for polymer networks. We apply the theory to the case of colloids partially embedded in cross-linked polymer substrates and clarify the origin of attractive interactions recently observed experimentally. Monte Carlo simulation results that quantitatively confirm the theoretical predictions are also presented. PMID:22679289

Data assimilation to extract soil moisture information from SMAP observations

USDA-ARS?s Scientific Manuscript database

This study compares different methods to extract soil moisture information through the assimilation of Soil Moisture Active Passive (SMAP) observations. Neural Network(NN) and physically-based SMAP soil moisture retrievals were assimilated into the NASA Catchment model over the contiguous United Sta...

Calibrating Bayesian Network Representations of Social-Behavioral Models

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

Whitney, Paul D.; Walsh, Stephen J.

2010-04-08

While human behavior has long been studied, recent and ongoing advances in computational modeling present opportunities for recasting research outcomes in human behavior. In this paper we describe how Bayesian networks can represent outcomes of human behavior research. We demonstrate a Bayesian network that represents political radicalization research – and show a corresponding visual representation of aspects of this research outcome. Since Bayesian networks can be quantitatively compared with external observations, the representation can also be used for empirical assessments of the research which the network summarizes. For a political radicalization model based on published research, we show this empiricalmore » comparison with data taken from the Minorities at Risk Organizational Behaviors database.« less

Patent Citation Networks

NASA Astrophysics Data System (ADS)

Strandburg, Katherine; Tobochnik, Jan; Csardi, Gabor

2005-03-01

Patent applications contain citations which are similar to but different from those found in published scientific papers. In particular, patent citations are governed by legal rules. Moreover, a large fraction of citations are made not by the patent inventor, but by a patent examiner during the application procedure. Using a patent database, which contains the patent citations, assignees and inventors, we have applied network analysis and built network models. Our work includes determining the structure of the patent citation network and comparing it to existing results for scientific citation networks; identifying differences between various technological fields and comparing the observed differences to expectations based on anecdotal evidence about patenting practice; and developing models to explain the results.

How to become a superhero

NASA Astrophysics Data System (ADS)

Gleiser, Pablo M.

2007-09-01

We analyze a collaboration network based on the Marvel Universe comic books. First, we consider the system as a binary network, where two characters are connected if they appear in the same publication. The analysis of degree correlations reveals that, in contrast to most real social networks, the Marvel Universe presents a disassortative mixing on the degree. Then, we use a weight measure to study the system as a weighted network. This allows us to find and characterize well defined communities. Through the analysis of the community structure and the clustering as a function of the degree we show that the network presents a hierarchical structure. Finally, we comment on possible mechanisms responsible for the particular motifs observed.

EARLINET: towards an advanced sustainable European aerosol lidar network

NASA Astrophysics Data System (ADS)

Pappalardo, G.; Amodeo, A.; Apituley, A.; Comeron, A.; Freudenthaler, V.; Linné, H.; Ansmann, A.; Bösenberg, J.; D'Amico, G.; Mattis, I.; Mona, L.; Wandinger, U.; Amiridis, V.; Alados-Arboledas, L.; Nicolae, D.; Wiegner, M.

2014-08-01

The European Aerosol Research Lidar Network, EARLINET, was founded in 2000 as a research project for establishing a quantitative, comprehensive, and statistically significant database for the horizontal, vertical, and temporal distribution of aerosols on a continental scale. Since then EARLINET has continued to provide the most extensive collection of ground-based data for the aerosol vertical distribution over Europe. This paper gives an overview of the network's main developments since 2000 and introduces the dedicated EARLINET special issue, which reports on the present innovative and comprehensive technical solutions and scientific results related to the use of advanced lidar remote sensing techniques for the study of aerosol properties as developed within the network in the last 13 years. Since 2000, EARLINET has developed greatly in terms of number of stations and spatial distribution: from 17 stations in 10 countries in 2000 to 27 stations in 16 countries in 2013. EARLINET has developed greatly also in terms of technological advances with the spread of advanced multiwavelength Raman lidar stations in Europe. The developments for the quality assurance strategy, the optimization of instruments and data processing, and the dissemination of data have contributed to a significant improvement of the network towards a more sustainable observing system, with an increase in the observing capability and a reduction of operational costs. Consequently, EARLINET data have already been extensively used for many climatological studies, long-range transport events, Saharan dust outbreaks, plumes from volcanic eruptions, and for model evaluation and satellite data validation and integration. Future plans are aimed at continuous measurements and near-real-time data delivery in close cooperation with other ground-based networks, such as in the ACTRIS (Aerosols, Clouds, and Trace gases Research InfraStructure Network) www.actris.net, and with the modeling and satellite community, linking the research community with the operational world, with the aim of establishing of the atmospheric part of the European component of the integrated global observing system.

A Sparse Representation-Based Deployment Method for Optimizing the Observation Quality of Camera Networks

PubMed Central

Wang, Chang; Qi, Fei; Shi, Guangming; Wang, Xiaotian

2013-01-01

Deployment is a critical issue affecting the quality of service of camera networks. The deployment aims at adopting the least number of cameras to cover the whole scene, which may have obstacles to occlude the line of sight, with expected observation quality. This is generally formulated as a non-convex optimization problem, which is hard to solve in polynomial time. In this paper, we propose an efficient convex solution for deployment optimizing the observation quality based on a novel anisotropic sensing model of cameras, which provides a reliable measurement of the observation quality. The deployment is formulated as the selection of a subset of nodes from a redundant initial deployment with numerous cameras, which is an ℓ0 minimization problem. Then, we relax this non-convex optimization to a convex ℓ1 minimization employing the sparse representation. Therefore, the high quality deployment is efficiently obtained via convex optimization. Simulation results confirm the effectiveness of the proposed camera deployment algorithms. PMID:23989826

Extending Resolution of Fault Slip With Geodetic Networks Through Optimal Network Design

NASA Astrophysics Data System (ADS)

Sathiakumar, Sharadha; Barbot, Sylvain Denis; Agram, Piyush

2017-12-01

Geodetic networks consisting of high precision and high rate Global Navigation Satellite Systems (GNSS) stations continuously monitor seismically active regions of the world. These networks measure surface displacements and the amount of geodetic strain accumulated in the region and give insight into the seismic potential. SuGar (Sumatra GPS Array) in Sumatra, GEONET (GNSS Earth Observation Network System) in Japan, and PBO (Plate Boundary Observatory) in California are some examples of established networks around the world that are constantly expanding with the addition of new stations to improve the quality of measurements. However, installing new stations to existing networks is tedious and expensive. Therefore, it is important to choose suitable locations for new stations to increase the precision obtained in measuring the geophysical parameters of interest. Here we describe a methodology to design optimal geodetic networks that augment the existing system and use it to investigate seismo-tectonics at convergent and transform boundaries considering land-based and seafloor geodesy. The proposed network design optimization would be pivotal to better understand seismic and tsunami hazards around the world. Land-based and seafloor networks can monitor fault slip around subduction zones with significant resolution, but transform faults are more challenging to monitor due to their near-vertical geometry.

A virus spreading model for cognitive radio networks

NASA Astrophysics Data System (ADS)

Hou, L.; Yeung, K. H.; Wong, K. Y.

2012-12-01

Since cognitive radio (CR) networks could solve the spectrum scarcity problem, they have drawn much research in recent years. Artificial intelligence(AI) is introduced into CRs to learn from and adapt to their environment. Nonetheless, AI brings in a new kind of attacks specific to CR networks. The most powerful one is a self-propagating AI virus. And no spreading properties specific to this virus have been reported in the literature. To fill this research gap, we propose a virus spreading model of an AI virus by considering the characteristics of CR networks and the behavior of CR users. Several important observations are made from the simulation results based on the model. Firstly, the time taken to infect the whole network increases exponentially with the network size. Based on this result, CR network designers could calculate the optimal network size to slow down AI virus propagation rate. Secondly, the anti-virus performance of static networks to an AI virus is better than dynamic networks. Thirdly, if the CR devices with the highest degree are initially infected, the AI virus propagation rate will be increased substantially. Finally, it is also found that in the area with abundant spectrum resource, the AI virus propagation speed increases notably but the variability of the spectrum does not affect the propagation speed much.

Metsahovi Radio Observatory - IVS Network Station

NASA Technical Reports Server (NTRS)

Uunila, Minttu; Zubko, Nataliya; Poutanen, Markku; Kallunki, Juha; Kallio, Ulla

2013-01-01

In 2012, Metsahovi Radio Observatory together with Finnish Geodetic Institute officially became an IVS Network Station. Eight IVS sessions were observed during the year. Two spacecraft tracking and one EVN X-band experiment were also performed. In 2012, the Metsahovi VLBI equipment was upgraded with a Digital Base Band Converter, a Mark 5B+, a FILA10G, and a FlexBuff.

Results of the IMO Video Meteor Network - June 2015

NASA Astrophysics Data System (ADS)

Molau, Sirko; Kac, Javor; Crivello, Stefano; Stomeo, Enrico; Barentsen, Geert; Goncalves, Rui; Saraiva, Carlos; Maciejewski, Maciej; Maslov, Mikhail

2015-10-01

Observations of the IMO Video Meteor Network are presented for 2015 June. Activity profile is presented for the Daytime Arietids, based on 28 shower meteors. The meteor rate of the Daytime Arietids between June 5 and 11, normalized for the limiting magnitude and angular velocity, is found to be about one quarter of that of the eta-Aquariids during their maximum.

EUPOS and SLR Contribution to GOCE Mission

NASA Astrophysics Data System (ADS)

Balodis, J.; Caunite, M.; Janpaule, I.; Kenyeres, A.; Rubans, A.; Silabriedis, G.; Rosenthal, G.; Zarinsjh, A.; Zvirgzds, J.; Abel, M.

2010-12-01

After the interest of geodesists from several East European countries on successful use of SAPOS in Germany the European Position Determination System EUPOS® project has been established at 2002 under the leadership of Gerd Rosenthal, Berlin State Department of Urban Development. Currently the ground based GNSS augmentation system EUPOS® sub-networks has been developed successfully in 17 countries and the wish to join has been expressed by several other countries. EUPOS® is widely used in many practical applications. Two proposals - "EUPOS® Contribution to GOCE Mission" (Id 4307), "GOCE Observations using SLR for LEO satellites" (Id 4333), were submitted to ESA when ESA in autumn 2006 invited research people to submit proposals for GOCE mission applications. The report is presented in this article on the work which has been done in EUPOS® community and at the University of Latvia. During last 3 years the EUPOS® sub- networks has been completed (Poland, Lithuania, Slovakia, Bulgaria, they tied to the National levelling networks, detailed system behaviour has been depicted on the bases of EUPOS®-Riga network. The development of the SLR for LEO satellites is presented. Initially it was developed for GOCE spacecraft positioning. However, SLR till now was able to observe satellites at night.

Evidence of magma intrusion at Fourpeaked volcano, Alaska in 2006-2007 from a rapid-response seismic network and volcanic gases

USGS Publications Warehouse

Gardine, M.; West, M.; Werner, C.; Doukas, M.

2011-01-01

On September 17th, 2006, Fourpeaked volcano had a widely-observed phreatic eruption. At the time, Fourpeaked was an unmonitored volcano with no known Holocene activity, based on limited field work. Airborne gas sampling began within days of the eruption and a modest seismic network was installed in stages. Vigorous steaming continued for months; however, there were no further eruptions similar in scale to the September 17 event. This eruption was followed by several months of sustained seismicity punctuated by vigorous swarms, and SO2 emissions exceeding a thousand tons/day. Based on observations during and after the phreatic eruption, and assuming no recent pre-historical eruptive activity at Fourpeaked, we propose that the activity was caused by a minor injection of new magma at or near 5km depth beneath Fourpeaked, which remained active over several months as this magma equilibrated into the crust. By early 2007 declining seismicity and SO2 emission signaled the end of unrest. Because the Fourpeaked seismic network was installed in stages and the seismicity was punctuated by discrete swarms, we use Fourpeaked to illustrate quantitatively the efficacy and shortcomings of rapid response seismic networks for tracking volcanic earthquakes.

Comparisons of COSMIC and C/NOFS GPS Occultation Ionospheric Scintillation Measurements with Ground-based Radar and VHF Measurements

NASA Astrophysics Data System (ADS)

Ruggiero, F. H.; Groves, K. M.; Straus, P. R.; Caton, R. G.; Starks, M. J.; Tanyi, K. L.; Verlinden, M.

2009-12-01

Ionospheric irregularities are known to cause scintillation of trans-ionospheric radio signals and can affect space-based UHF/VHF communications, causing outages, and degrading GPS accuracy and precision. Current capability for characterizing and predicting ionospheric scintillation utilizes a network of ground-based receivers to detect scintillation and then extrapolate for short-term forecasts. Practical limits on deploying the ground receivers limits the accuracy and spatial coverage one can achieve with this approach. A more global approach is to use a set of space-based satellites equipped with GPS receivers, such as the COSMIC satellite constellation, to measure scintillations observed during so-called occultations with GPS satellites. In this paper the signal-to-noise values of GPS L1 signals received on the COSMIC and C/NOFS satellites for the portions of the occultations that are not affected by the terrestrial atmosphere are examined to help identify areas of ionospheric scintillation. Three years of S4 scintillation index values from COSMIC occultations are compared with near-zenith ground-based VHF S4 scintillation measurements from the AFRL SCIntillation Network Decision Aid (SCINDA) network stations. The data are correlated to ascertain the viability of using space-based scintillation measurements to characterize and predict scintillation to ground-based receivers. Several days of COSMIC and C/NOFS data are compared with each other and the ALTAIR radar located on Kwajalein Atoll, Marshall Islands to examine how occultation geometry affects observed scintillation and also to verify techniques that provide an upper bound on the spatial location of the ionospheric irregularities contributing to scintillations observed in the occultations.

Data Access System for Hydrology

NASA Astrophysics Data System (ADS)

Whitenack, T.; Zaslavsky, I.; Valentine, D.; Djokic, D.

2007-12-01

As part of the CUAHSI HIS (Consortium of Universities for the Advancement of Hydrologic Science, Inc., Hydrologic Information System), the CUAHSI HIS team has developed Data Access System for Hydrology or DASH. DASH is based on commercial off the shelf technology, which has been developed in conjunction with a commercial partner, ESRI. DASH is a web-based user interface, developed in ASP.NET developed using ESRI ArcGIS Server 9.2 that represents a mapping, querying and data retrieval interface over observation and GIS databases, and web services. This is the front end application for the CUAHSI Hydrologic Information System Server. The HIS Server is a software stack that organizes observation databases, geographic data layers, data importing and management tools, and online user interfaces such as the DASH application, into a flexible multi- tier application for serving both national-level and locally-maintained observation data. The user interface of the DASH web application allows online users to query observation networks by location and attributes, selecting stations in a user-specified area where a particular variable was measured during a given time interval. Once one or more stations and variables are selected, the user can retrieve and download the observation data for further off-line analysis. The DASH application is highly configurable. The mapping interface can be configured to display map services from multiple sources in multiple formats, including ArcGIS Server, ArcIMS, and WMS. The observation network data is configured in an XML file where you specify the network's web service location and its corresponding map layer. Upon initial deployment, two national level observation networks (USGS NWIS daily values and USGS NWIS Instantaneous values) are already pre-configured. There is also an optional login page which can be used to restrict access as well as providing a alternative to immediate downloads. For large request, users would be notified via email with a link to their data when it is ready.

Modality-Spanning Deficits in Attention-Deficit/Hyperactivity Disorder in Functional Networks, Gray Matter, and White Matter

PubMed Central

Kessler, Daniel; Angstadt, Michael; Welsh, Robert C.

2014-01-01

Previous neuroimaging investigations in attention-deficit/hyperactivity disorder (ADHD) have separately identified distributed structural and functional deficits, but interconnections between these deficits have not been explored. To unite these modalities in a common model, we used joint independent component analysis, a multivariate, multimodal method that identifies cohesive components that span modalities. Based on recent network models of ADHD, we hypothesized that altered relationships between large-scale networks, in particular, default mode network (DMN) and task-positive networks (TPNs), would co-occur with structural abnormalities in cognitive regulation regions. For 756 human participants in the ADHD-200 sample, we produced gray and white matter volume maps with voxel-based morphometry, as well as whole-brain functional connectomes. Joint independent component analysis was performed, and the resulting transmodal components were tested for differential expression in ADHD versus healthy controls. Four components showed greater expression in ADHD. Consistent with our a priori hypothesis, we observed reduced DMN-TPN segregation co-occurring with structural abnormalities in dorsolateral prefrontal cortex and anterior cingulate cortex, two important cognitive control regions. We also observed altered intranetwork connectivity in DMN, dorsal attention network, and visual network, with co-occurring distributed structural deficits. There was strong evidence of spatial correspondence across modalities: For all four components, the impact of the respective component on gray matter at a region strongly predicted the impact on functional connectivity at that region. Overall, our results demonstrate that ADHD involves multiple, cohesive modality spanning deficits, each one of which exhibits strong spatial overlap in the pattern of structural and functional alterations. PMID:25505309

A Hydrologic Routing Model Based on Geomorphological Characteristics of the River Network

NASA Astrophysics Data System (ADS)

Krajewski, W. F.; Quintero, F.; Ghimire, G.; Rojas, M.

2017-12-01

The Iowa Flood Center (IFC) provides streamflow forecasts for about 2000 locations in Iowa using a real-time distributed hydrologic model, forced with radar and raingage rainfall information. The model structure is based on ordinary differential equations that represent the flow of water from the hillslopes to the channels of the river network. The formulation of the routing of water across the rivers constitutes a fundamental aspect of the model, because this component is mostly responsible for providing estimates of the time-to-peak and peak magnitude. The routing model structure of the system is based on the scaling properties of river velocity with the discharge and drainage area of the channel, which can be written in terms of a power-law function. This study examines how this scaling relation is connected to the Horton-Strahler order of the channel network. This evaluation represents a step forward towards formulating model structures that are based on characteristics that are invariant across spatial scales. We proposed a routing model for every different Horton orders of the network, by adjusting a power-law function to available observations of velocity and discharge provided by USGS. The models were implemented into the Hillslope-Link Model (HLM) of the IFC for offline evaluation. Model simulations were compared to discharge observations to assess their performance, and compared to simulations obtained with other hydrologic routing schemes, to determine if the new formulation improves performance of the model.

Optimal Design for Placements of Tsunami Observing Systems to Accurately Characterize the Inducing Earthquake

NASA Astrophysics Data System (ADS)

Mulia, Iyan E.; Gusman, Aditya Riadi; Satake, Kenji

2017-12-01

Recently, there are numerous tsunami observation networks deployed in several major tsunamigenic regions. However, guidance on where to optimally place the measurement devices is limited. This study presents a methodological approach to select strategic observation locations for the purpose of tsunami source characterizations, particularly in terms of the fault slip distribution. Initially, we identify favorable locations and determine the initial number of observations. These locations are selected based on extrema of empirical orthogonal function (EOF) spatial modes. To further improve the accuracy, we apply an optimization algorithm called a mesh adaptive direct search to remove redundant measurement locations from the EOF-generated points. We test the proposed approach using multiple hypothetical tsunami sources around the Nankai Trough, Japan. The results suggest that the optimized observation points can produce more accurate fault slip estimates with considerably less number of observations compared to the existing tsunami observation networks.

Artificial synapse network on inorganic proton conductor for neuromorphic systems.

PubMed

Zhu, Li Qiang; Wan, Chang Jin; Guo, Li Qiang; Shi, Yi; Wan, Qing

2014-01-01

The basic units in our brain are neurons, and each neuron has more than 1,000 synapse connections. Synapse is the basic structure for information transfer in an ever-changing manner, and short-term plasticity allows synapses to perform critical computational functions in neural circuits. Therefore, the major challenge for the hardware implementation of neuromorphic computation is to develop artificial synapse network. Here in-plane lateral-coupled oxide-based artificial synapse network coupled by proton neurotransmitters are self-assembled on glass substrates at room-temperature. A strong lateral modulation is observed due to the proton-related electrical-double-layer effect. Short-term plasticity behaviours, including paired-pulse facilitation, dynamic filtering and spatiotemporally correlated signal processing are mimicked. Such laterally coupled oxide-based protonic/electronic hybrid artificial synapse network proposed here is interesting for building future neuromorphic systems.

Extended target recognition in cognitive radar networks.

PubMed

Wei, Yimin; Meng, Huadong; Liu, Yimin; Wang, Xiqin

2010-01-01

We address the problem of adaptive waveform design for extended target recognition in cognitive radar networks. A closed-loop active target recognition radar system is extended to the case of a centralized cognitive radar network, in which a generalized likelihood ratio (GLR) based sequential hypothesis testing (SHT) framework is employed. Using Doppler velocities measured by multiple radars, the target aspect angle for each radar is calculated. The joint probability of each target hypothesis is then updated using observations from different radar line of sights (LOS). Based on these probabilities, a minimum correlation algorithm is proposed to adaptively design the transmit waveform for each radar in an amplitude fluctuation situation. Simulation results demonstrate performance improvements due to the cognitive radar network and adaptive waveform design. Our minimum correlation algorithm outperforms the eigen-waveform solution and other non-cognitive waveform design approaches.

Network integrity of the parental brain in infancy supports the development of children's social competencies.

PubMed

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna; Feldman, Ruth

2016-11-01

The cross-generational transmission of mammalian sociality, initiated by the parent's postpartum brain plasticity and species-typical behavior that buttress offspring's socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant's stimuli, observed parent-infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent's network integrity in infancy predicted preschoolers' social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent-infant synchrony mediated the links between connectivity of the parent's embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent's inter-network core limbic-embodied simulation connectivity predicted children's OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent-offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. © The Author (2016). Published by Oxford University Press.

Spontaneous eyelid closures link vigilance fluctuation with fMRI dynamic connectivity states

PubMed Central

Wang, Chenhao; Ong, Ju Lynn; Patanaik, Amiya; Chee, Michael W. L.

2016-01-01

Fluctuations in resting-state functional connectivity occur but their behavioral significance remains unclear, largely because correlating behavioral state with dynamic functional connectivity states (DCS) engages probes that disrupt the very behavioral state we seek to observe. Observing spontaneous eyelid closures following sleep deprivation permits nonintrusive arousal monitoring. During periods of low arousal dominated by eyelid closures, sliding-window correlation analysis uncovered a DCS associated with reduced within-network functional connectivity of default mode and dorsal/ventral attention networks, as well as reduced anticorrelation between these networks. Conversely, during periods when participants’ eyelids were wide open, a second DCS was associated with less decoupling between the visual network and higher-order cognitive networks that included dorsal/ventral attention and default mode networks. In subcortical structures, eyelid closures were associated with increased connectivity between the striatum and thalamus with the ventral attention network, and greater anticorrelation with the dorsal attention network. When applied to task-based fMRI data, these two DCS predicted interindividual differences in frequency of behavioral lapsing and intraindividual temporal fluctuations in response speed. These findings with participants who underwent a night of total sleep deprivation were replicated in an independent dataset involving partially sleep-deprived participants. Fluctuations in functional connectivity thus appear to be clearly associated with changes in arousal. PMID:27512040

Network integrity of the parental brain in infancy supports the development of children’s social competencies

PubMed Central

Abraham, Eyal; Hendler, Talma; Zagoory-Sharon, Orna

2016-01-01

The cross-generational transmission of mammalian sociality, initiated by the parent’s postpartum brain plasticity and species-typical behavior that buttress offspring’s socialization, has not been studied in humans. In this longitudinal study, we measured brain response of 45 primary-caregiving parents to their infant’s stimuli, observed parent–infant interactions, and assayed parental oxytocin (OT). Intra- and inter-network connectivity were computed in three main networks of the human parental brain: core limbic, embodied simulation and mentalizing. During preschool, two key child social competencies were observed: emotion regulation and socialization. Parent’s network integrity in infancy predicted preschoolers’ social outcomes, with subcortical and cortical network integrity foreshadowing simple evolutionary-based regulatory tactics vs complex self-regulatory strategies and advanced socialization. Parent–infant synchrony mediated the links between connectivity of the parent’s embodied simulation network and preschoolers' ability to use cognitive/executive emotion regulation strategies, highlighting the inherently dyadic nature of this network and its long-term effects on tuning young to social life. Parent’s inter-network core limbic-embodied simulation connectivity predicted children’s OT as moderated by parental OT. Findings challenge solipsistic neuroscience perspectives by demonstrating how the parent–offspring interface enables the brain of one human to profoundly impact long-term adaptation of another. PMID:27369068

SONG China project - participating in the global network

NASA Astrophysics Data System (ADS)

Deng, Licai; Xin, Yu; Zhang, Xiaobin; Li, Yan; Jiang, Xiaojun; Wang, Guomin; Wang, Kun; Zhou, Jilin; Yan, Zhengzhou; Luo, Zhiquan

2013-01-01

SONG (Stellar Observations Network Goup) is a low-cost ground based international collaboration aimed at two cutting edge problems in contemporary astrophysics in the time-domain: 1) Direct diagnostics of the internal structure of stars and 2) looking for and studying extra solar planets, possibly in the habitable zone. The general plan is to set up a network of 1m telescopes uniformly distributed in geographic latitude (in both hemispheres). China jointed the collaboration (initiated by Danish astronomers) at the very beginning. In addition to SONG's original plan (http://song.phys.au.dk), the Chinese team proposed a parallel photometry subnet work in the northern hemisphere, namely 50BiN (50cm Binocular Network, previously known as mini-SONG), to enable a large field photometric capability for the network, therefore maximising the potential of the network platform. The network will be able to produce nearly continuous time series observations of a number of selected objects with high resolution spectroscopy (SONG) and accurate photometry (50BiN), and to produce ultra-high accuracy photometry in dense field to look for micro-lensing events caused by planetary systems. This project has great synergy with Chinese Astronomical activities in Antarctica (Dome A), and other similar networks (e.g. LCOGT). The plan and current status of the project are overviewed in this poster.

Network Coded Cooperative Communication in a Real-Time Wireless Hospital Sensor Network.

PubMed

Prakash, R; Balaji Ganesh, A; Sivabalan, Somu

2017-05-01

The paper presents a network coded cooperative communication (NC-CC) enabled wireless hospital sensor network architecture for monitoring health as well as postural activities of a patient. A wearable device, referred as a smartband is interfaced with pulse rate, body temperature sensors and an accelerometer along with wireless protocol services, such as Bluetooth and Radio-Frequency transceiver and Wi-Fi. The energy efficiency of wearable device is improved by embedding a linear acceleration based transmission duty cycling algorithm (NC-DRDC). The real-time demonstration is carried-out in a hospital environment to evaluate the performance characteristics, such as power spectral density, energy consumption, signal to noise ratio, packet delivery ratio and transmission offset. The resource sharing and energy efficiency features of network coding technique are improved by proposing an algorithm referred as network coding based dynamic retransmit/rebroadcast decision control (LA-TDC). From the experimental results, it is observed that the proposed LA-TDC algorithm reduces network traffic and end-to-end delay by an average of 27.8% and 21.6%, respectively than traditional network coded wireless transmission. The wireless architecture is deployed in a hospital environment and results are then successfully validated.

Geostatistics-based groundwater-level monitoring network design and its application to the Upper Floridan aquifer, USA.

PubMed

Bhat, Shirish; Motz, Louis H; Pathak, Chandra; Kuebler, Laura

2015-01-01

A geostatistical method was applied to optimize an existing groundwater-level monitoring network in the Upper Floridan aquifer for the South Florida Water Management District in the southeastern United States. Analyses were performed to determine suitable numbers and locations of monitoring wells that will provide equivalent or better quality groundwater-level data compared to an existing monitoring network. Ambient, unadjusted groundwater heads were expressed as salinity-adjusted heads based on the density of freshwater, well screen elevations, and temperature-dependent saline groundwater density. The optimization of the numbers and locations of monitoring wells is based on a pre-defined groundwater-level prediction error. The newly developed network combines an existing network with the addition of new wells that will result in a spatial distribution of groundwater monitoring wells that better defines the regional potentiometric surface of the Upper Floridan aquifer in the study area. The network yields groundwater-level predictions that differ significantly from those produced using the existing network. The newly designed network will reduce the mean prediction standard error by 43% compared to the existing network. The adoption of a hexagonal grid network for the South Florida Water Management District is recommended to achieve both a uniform level of information about groundwater levels and the minimum required accuracy. It is customary to install more monitoring wells for observing groundwater levels and groundwater quality as groundwater development progresses. However, budget constraints often force water managers to implement cost-effective monitoring networks. In this regard, this study provides guidelines to water managers concerned with groundwater planning and monitoring.

Measuring the impact of practice-based research networks on member dentists in the Collaboration on Networked Dental and Oral Health Research, CONDOR.

PubMed

McBride, Ruth; Leroux, Brian; Lindblad, Anne; Williams, O Dale; Lehmann, Maryann; Rindal, D Brad; Botello-Harbaum, Maria; Gilbert, Gregg H; Gillette, Jane; Demko, Catherine

2013-05-01

The National Institute of Dental and Craniofacial Research funded three practice-based research networks (PBRNs), NW-PRECEDENT, PEARL and DPBRN to conduct studies relevant to practicing general dentists. These PBRNs collaborated to develop a questionnaire to assess the impact of network participation on changes in practice patterns. This report presents results from the initial administration of the questionnaire. Questionnaires were administered to network dentists and a non-network reference group. Practice patterns including caries diagnosis and treatment, pulp cap materials, third molar extraction, dentine hypersensitivity treatments and endodontic treatment and restoration were assessed by network, years in practice, and level of network participation. Test-retest reliability of the questionnaire was evaluated. 950 practitioners completed the questionnaire. Test-retest reliability was good-excellent (kappa>0.4) for most questions. Significant differences in responses by network were not observed. The use of caries risk assessment forms differed by both network participation (p<0.001) and years since dental degree (p=0.026). Recent dental graduates are more likely to recommend third molar removal for preventive reasons (p=0.003). Practitioners in the CONDOR research networks are similar to their US colleagues. As a group, however, these practitioners show a more evidence-based approach to their practice. Dental PBRNs have the potential to improve the translation of evidence into daily practice. Designing methods to assess practice change and the associated factors is essential to addressing this important issue. Copyright © 2013 Elsevier Ltd. All rights reserved.

Development of gridded solar radiation data over Belgium based on Meteosat and in-situ observations

NASA Astrophysics Data System (ADS)

Journée, Michel; Vanderveken, Gilles; Bertrand, Cédric

2013-04-01

Knowledge on solar resources is highly important for all forms of solar energy applications. With the recent development in solar-based technologies national meteorological services are faced with increasing demands for high-quality and reliable site-time specific solar resource information. Traditionally, solar radiation is observed by means of networks of meteorological stations. Costs for installation and maintenance of such networks are very high and national networks comprise only few stations. Consequently the availability of ground-based solar radiation measurements has proven to be spatially and temporally inadequate for many applications. To overcome such a limitation, a major effort has been undertaken at the Royal Meteorological Institute of Belgium (RMI) to provide the solar energy industry, the electricity sector, governments, and renewable energy organizations and institutions with the most suitable and accurate information on the solar radiation resources at the Earth's surface over the Belgian territory. Only space-based observations can deliver a global coverage of the solar irradiation impinging on horizontal surface at the ground level. Because only geostationary data allow to capture the diurnal cycle of the solar irradiance at the Earth's surface, a method that combines information from Meteosat Second Generation satellites and ground-measurement has been implemented at RMI to generate high resolution solar products over Belgium on an operational basis. Besides these new products, the annual and seasonal variability of solar energy resource was evaluated, solar radiation climate zones were defined and the recent trend in solar radiation was characterized.

Update on the activities of the GGOS Bureau of Networks and Observations

NASA Technical Reports Server (NTRS)

Pearlman, Michael R.; Pavlis, Erricos C.; Ma, Chopo; Noll, Carey; Thaller, Daniela; Richter, Bernd; Gross, Richard; Neilan, Ruth; Mueller, Juergen; Barzaghi, Ricardo;

Toward an integrated Volcanic Ash Observing System in Europe

NASA Astrophysics Data System (ADS)

Lee, Deborah; Lisk, Ian

2014-05-01

Volcanic ash from the Icelandic eruption of Eyjafjallajökull in April and May of 2010 resulted in the decision by many northern European countries to impose significant restrictions on the use of their airspace. The eruption, extent and persistence of the ash revealed how reliant society now is on a safe and efficient air transport system and the fragility of that system when affected by the impact of complex natural hazards. As part of an EC framework programme, the 2011-2013 WEZARD (WEather HaZARD for aeronautics) consortium conducted a cross-industry volcanic ash capability and gap analyses, with the EUMETNET (network of 29 National Meteorological Services) led Work Package 3 focussing on a review of observational and monitoring capabilities, atmospheric dispersion modelling and data exchange. The review has revealed a patchwork of independent observing capabilities for volcanic ash, with some countries investing and others not at all, and most existing networks focus on space-based products. Existing capabilities do not provide the necessary detail on the geographical and vertical extent of volcanic ash and associated levels of contamination, which decision makers in the aviation industry require in order to decide where it is safe to fly. A resultant high priority was identified by WEZARD Work Package 3 for an enhanced observational network of complementary monitoring systems needed to initialise, validate and verify volcanic ash dispersion model output and forecasts. Thus a key recommendation is to invest in a major pre-operational demonstrator "European volcanic ash observing network", focussing on distal monitoring, and aiming to a) fill R&D gaps identified in instrumentation and algorithms and b) integrate data, where possible in near-real-time, from a range of ground-based, airborne and space-based techniques. Here we present a key WEZARD recommendation toward an integrated volcanic ash observing system in Europe, in context with other related projects and initiatives. We will also look to highlight the work underway by VAACs (Volcanic Ash Advisory Centres) and aviation regulatory authorities within the IAVWOPSG (International Airways Volcano Watch Operations Group) to develop the 'agreed in situ and/or remote sensing techniques' that underpin the newly approved definition of 'Discernible ash'.

Extremely preterm children exhibit increased interhemispheric connectivity for language: findings from fMRI-constrained MEG analysis.

PubMed

Barnes-Davis, Maria E; Merhar, Stephanie L; Holland, Scott K; Kadis, Darren S

2018-04-16

Children born extremely preterm are at significant risk for cognitive impairment, including language deficits. The relationship between preterm birth and neurological changes that underlie cognitive deficits is poorly understood. We use a stories-listening task in fMRI and MEG to characterize language network representation and connectivity in children born extremely preterm (n = 15, <28 weeks gestation, ages 4-6 years), and in a group of typically developing control participants (n = 15, term birth, 4-6 years). Participants completed a brief neuropsychological assessment. Conventional fMRI analyses revealed no significant differences in language network representation across groups (p > .05, corrected). The whole-group fMRI activation map was parcellated to define the language network as a set of discrete nodes, and the timecourse of neuronal activity at each position was estimated using linearly constrained minimum variance beamformer in MEG. Virtual timecourses were subjected to connectivity and network-based analyses. We observed significantly increased beta-band functional connectivity in extremely preterm compared to controls (p < .05). Specifically, we observed an increase in connectivity between left and right perisylvian cortex. Subsequent effective connectivity analyses revealed that hyperconnectivity in preterms was due to significantly increased information flux originating from the right hemisphere (p < 0.05). The total strength and density of the language network were not related to language or nonverbal performance, suggesting that the observed hyperconnectivity is a "pure" effect of prematurity. Although our extremely preterm children exhibited typical language network architecture, we observed significantly altered network dynamics, indicating reliance on an alternative neural strategy for the language task. © 2018 The Authors. Developmental Science Published by John Wiley & Sons Ltd.

The Community Collaborative Rain, Hail and Snow Network (CoCoRaHS): Timely Volunteer Precipitation Measurements to Supplement Existing Hydrometeorological Networks

NASA Astrophysics Data System (ADS)

Reges, H. W.; Doesken, N. J.; Cifelli, R. C.; Turner, J. S.

2005-12-01

The Community Collaborative Rain, Hail and Snow Network (CoCoRaHS) is a community-based, education-focused high density network of individual and family volunteers of all ages and backgrounds, who take daily measurements of rain, hail and snow at their homes, schools and businesses. Precipitation is measured using low-cost high capacity 4" diameter plastic rain gauges and Styrofoam wrapped in aluminum foil "hail pads". Thanks to the "low-tech/low-cost" approach, thousands of volunteers can afford to participate, giving the end user a large collection of data points that fill in gaps in many existing networks and data sets. Where feasible, CoCoRaHS is striving to achieve a station density approaching one observation per km-squared providing exceptional detail on cumulative storm precipitation over populated areas. These observations are collected and made available on the CoCoRaHS website: www.cocorahs.org in map and table format. The data are already being used daily by federal, state and community organizations and businesses for many resource management and hydrologic monitoring and predication applications. CoCoRaHS "Intense Rain Reports" and "Hail Reports" are used in "real time" by the National Weather Service in the issuing of flash flood warnings and severe thunderstorm warnings. While only providing once-daily and occasional event reports, CoCoRaHS does provide excellent observational consistency and accuracy including snowfall, depth and water content measurements, as well as the only comprehensive hail data currently being gathered in the U.S. The CoCoRaHS network currently engages over 2,000 volunteer observers in communities across six states, and the network continues to grow.

Cloud-based serviced-orientated data systems for ocean observational data - an example from the coral reef community

NASA Astrophysics Data System (ADS)

Bainbridge, S.

2012-04-01

The advent of new observing systems, such as sensor networks, have dramatically increased our ability to collect marine data; the issue now is not data drought but data deluge. The challenge now is to extract data representing events of interest from the background data, that is how to deliver information and potentially knowledge from an increasing large store of base observations. Given that each potential user will have differing definitions of 'interesting' and that this is often defined by other events and data, systems need to deliver information or knowledge in a form and context defined by the user. This paper reports on a series of coral reef sensor networks set up under the Coral Reef Environmental Observation Network (CREON). CREON is a community of interest group deploying coral reef sensor networks with the goal of increasing capacity in coral reef observation, especially into developing areas. Issues such as coral bleaching, terrestrial runoff, human impacts and climate change are impacting reefs with one assessment indicating a quarter of the worlds reefs being severely degraded with another quarter under immediate threat. Increasing our ability to collect scientifically valid observations is fundamental to understanding these systems and ultimately in preserving and sustaining them. A cloud based data management system was used to store the base sensor data from each agency involved using service based agents to push the data from individual field sensors to the cloud. The system supports a range of service based outputs such as on-line graphs, a smart-phone application and simple event detection. A more complex event detection system was written that takes input from the cloud services and outputs natural language 'tweets' to Twitter as events occur. It therefore becomes possible to distil the entire data set down to a series of Twitter entries that interested parties can subscribe to. The next step is to allow users to define their own events and to deliver results, in context, to their preferred medium. The paper contrasts what has been achieved within a small community with well defined issues with what it would take to build equivalent systems to hold a wide range of cross community observational data addressing a wider range of potential issues. The role of discoverability, quality control, uncertainly, conformity and metadata are investigated along with a brief discussion of existing and emerging standards in this area. The elements of such as system are described along with the role of modelling and scenario tools in delivering a higher level of outputs linking what may have already occurred (event detection) with what may potentially occur (scenarios). The development of service based cloud computing open data systems coupled with complex event detection systems delivering through social media and other channels linked into model and scenario systems represents one vision for delivering value from the increasing store of ocean observations, most of which lie unknown, unused and unloved.

A proposal of optimal sampling design using a modularity strategy

NASA Astrophysics Data System (ADS)

Simone, A.; Giustolisi, O.; Laucelli, D. B.

2016-08-01

In real water distribution networks (WDNs) are present thousands nodes and optimal placement of pressure and flow observations is a relevant issue for different management tasks. The planning of pressure observations in terms of spatial distribution and number is named sampling design and it was faced considering model calibration. Nowadays, the design of system monitoring is a relevant issue for water utilities e.g., in order to manage background leakages, to detect anomalies and bursts, to guarantee service quality, etc. In recent years, the optimal location of flow observations related to design of optimal district metering areas (DMAs) and leakage management purposes has been faced considering optimal network segmentation and the modularity index using a multiobjective strategy. Optimal network segmentation is the basis to identify network modules by means of optimal conceptual cuts, which are the candidate locations of closed gates or flow meters creating the DMAs. Starting from the WDN-oriented modularity index, as a metric for WDN segmentation, this paper proposes a new way to perform the sampling design, i.e., the optimal location of pressure meters, using newly developed sampling-oriented modularity index. The strategy optimizes the pressure monitoring system mainly based on network topology and weights assigned to pipes according to the specific technical tasks. A multiobjective optimization minimizes the cost of pressure meters while maximizing the sampling-oriented modularity index. The methodology is presented and discussed using the Apulian and Exnet networks.

Retrieval and Validation of Zenith and Slant Path Delays From the Irish GPS Network

NASA Astrophysics Data System (ADS)

Hanafin, Jennifer; Jennings, S. Gerard; O'Dowd, Colin; McGrath, Ray; Whelan, Eoin

2010-05-01

Retrieval of atmospheric integrated water vapour (IWV) from ground-based GPS receivers and provision of this data product for meteorological applications has been the focus of a number of Europe-wide networks and projects, most recently the EUMETNET GPS water vapour programme. The results presented here are from a project to provide such information about the state of the atmosphere around Ireland for climate monitoring and improved numerical weather prediction. Two geodetic reference GPS receivers have been deployed at Valentia Observatory in Co. Kerry and Mace Head Atmospheric Research Station in Co. Galway, Ireland. These two receivers supplement the existing Ordnance Survey Ireland active network of 17 permanent ground-based receivers. A system to retrieve column-integrated atmospheric water vapour from the data provided by this network has been developed, based on the GPS Analysis at MIT (GAMIT) software package. The data quality of the zenith retrievals has been assessed using co-located radiosondes at the Valentia site and observations from a microwave profiling radiometer at the Mace Head site. Validation of the slant path retrievals requires a numerical weather prediction model and HIRLAM (High-Resolution Limited Area Model) version 7.2, the current operational forecast model in use at Met Éireann for the region, has been used for this validation work. Results from the data processing and comparisons with the independent observations and model will be presented.

Extracting Association Patterns in Network Communications

PubMed Central

Portela, Javier; Villalba, Luis Javier García; Trujillo, Alejandra Guadalupe Silva; Orozco, Ana Lucila Sandoval; Kim, Tai-hoon

2015-01-01

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense. PMID:25679311

Extracting association patterns in network communications.

PubMed

Portela, Javier; Villalba, Luis Javier García; Trujillo, Alejandra Guadalupe Silva; Orozco, Ana Lucila Sandoval; Kim, Tai-hoon

2015-02-11

In network communications, mixes provide protection against observers hiding the appearance of messages, patterns, length and links between senders and receivers. Statistical disclosure attacks aim to reveal the identity of senders and receivers in a communication network setting when it is protected by standard techniques based on mixes. This work aims to develop a global statistical disclosure attack to detect relationships between users. The only information used by the attacker is the number of messages sent and received by each user for each round, the batch of messages grouped by the anonymity system. A new modeling framework based on contingency tables is used. The assumptions are more flexible than those used in the literature, allowing to apply the method to multiple situations automatically, such as email data or social networks data. A classification scheme based on combinatoric solutions of the space of rounds retrieved is developed. Solutions about relationships between users are provided for all pairs of users simultaneously, since the dependence of the data retrieved needs to be addressed in a global sense.

Structural network efficiency is associated with cognitive impairment in small-vessel disease.

PubMed

Lawrence, Andrew J; Chung, Ai Wern; Morris, Robin G; Markus, Hugh S; Barrick, Thomas R

2014-07-22

To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. © 2014 American Academy of Neurology.

Structural network efficiency is associated with cognitive impairment in small-vessel disease

PubMed Central

Chung, Ai Wern; Morris, Robin G.; Markus, Hugh S.; Barrick, Thomas R.

2014-01-01

Objective: To characterize brain network connectivity impairment in cerebral small-vessel disease (SVD) and its relationship with MRI disease markers and cognitive impairment. Methods: A cross-sectional design applied graph-based efficiency analysis to deterministic diffusion tensor tractography data from 115 patients with lacunar infarction and leukoaraiosis and 50 healthy individuals. Structural connectivity was estimated between 90 cortical and subcortical brain regions and efficiency measures of resulting graphs were analyzed. Networks were compared between SVD and control groups, and associations between efficiency measures, conventional MRI disease markers, and cognitive function were tested. Results: Brain diffusion tensor tractography network connectivity was significantly reduced in SVD: networks were less dense, connection weights were lower, and measures of network efficiency were significantly disrupted. The degree of brain network disruption was associated with MRI measures of disease severity and cognitive function. In multiple regression models controlling for confounding variables, associations with cognition were stronger for network measures than other MRI measures including conventional diffusion tensor imaging measures. A total mediation effect was observed for the association between fractional anisotropy and mean diffusivity measures and executive function and processing speed. Conclusions: Brain network connectivity in SVD is disturbed, this disturbance is related to disease severity, and within a mediation framework fully or partly explains previously observed associations between MRI measures and SVD-related cognitive dysfunction. These cross-sectional results highlight the importance of network disruption in SVD and provide support for network measures as a disease marker in treatment studies. PMID:24951477

CdSe/ZnS quantum dot fluorescence spectra shape-based thermometry via neural network reconstruction

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

Munro, Troy; Laboratory of Soft Matter and Biophysics, Department of Physics and Astronomy, KU Leuven, Celestijnenlaan 200D, B-3001 Heverlee; Liu, Liwang

As a system of interest gets small, due to the influence of the sensor mass and heat leaks through the sensor contacts, thermal characterization by means of contact temperature measurements becomes cumbersome. Non-contact temperature measurement offers a suitable alternative, provided a reliable relationship between the temperature and the detected signal is available. In this work, exploiting the temperature dependence of their fluorescence spectrum, the use of quantum dots as thermomarkers on the surface of a fiber of interest is demonstrated. The performance is assessed of a series of neural networks that use different spectral shape characteristics as inputs (peak-based—peak intensity,more » peak wavelength; shape-based—integrated intensity, their ratio, full-width half maximum, peak normalized intensity at certain wavelengths, and summation of intensity over several spectral bands) and that yield at their output the fiber temperature in the optically probed area on a spider silk fiber. Starting from neural networks trained on fluorescence spectra acquired in steady state temperature conditions, numerical simulations are performed to assess the quality of the reconstruction of dynamical temperature changes that are photothermally induced by illuminating the fiber with periodically intensity-modulated light. Comparison of the five neural networks investigated to multiple types of curve fits showed that using neural networks trained on a combination of the spectral characteristics improves the accuracy over use of a single independent input, with the greatest accuracy observed for inputs that included both intensity-based measurements (peak intensity) and shape-based measurements (normalized intensity at multiple wavelengths), with an ultimate accuracy of 0.29 K via numerical simulation based on experimental observations. The implications are that quantum dots can be used as a more stable and accurate fluorescence thermometer for solid materials and that use of neural networks for temperature reconstruction improves the accuracy of the measurement.« less

Cooperate to Validate: OBSERVAL-NET Experts' Report on Validation of Non-Formal and Informal Learning (VNIL) 2013

ERIC Educational Resources Information Center

Weber Guisan, Saskia; Voit, Janine; Lengauer, Sonja; Proinger, Eva; Duvekot, Ruud; Aagaard, Kirsten

2014-01-01

The present publication is one of the outcomes of the OBSERVAL-NET project (follow-up of the OBSERVAL project). The main aim of OBSERVAL-NET was to set up a stakeholder-centric network of organisations supporting the validation of non-formal and informal learning in Europe based on the formation of national working groups in the 8 participating…

Cooperate to Validate. Observal-Net Experts' Report on Validation of Non-Formal and Informal Learning (VNIL) 2013

ERIC Educational Resources Information Center

Weber Guisan, Saskia; Voit, Janine; Lengauer, Sonja; Proinger, Eva; Duvekot, Ruud; Aagaard, Kirsten

2014-01-01

The present publication is one of the outcomes of the OBSERVAL-NET project (followup of the OBSERVAL project). The main aim of OBSERVAL-NET was to set up a stakeholder centric network of organisations supporting the validation of non-formal and informal learning in Europe based on the formation of national working groups in the 8 participating…

Physics, stability, and dynamics of supply networks

NASA Astrophysics Data System (ADS)

Helbing, Dirk; Lämmer, Stefan; Seidel, Thomas; Šeba, Pétr; Płatkowski, Tadeusz

2004-12-01

We show how to treat supply networks as physical transport problems governed by balance equations and equations for the adaptation of production speeds. Although the nonlinear behavior is different, the linearized set of coupled differential equations is formally related to those of mechanical or electrical oscillator networks. Supply networks possess interesting features due to their complex topology and directed links. We derive analytical conditions for absolute and convective instabilities. The empirically observed “bullwhip effect” in supply chains is explained as a form of convective instability based on resonance effects. Moreover, it is generalized to arbitrary supply networks. Their related eigenvalues are usually complex, depending on the network structure (even without loops). Therefore, their generic behavior is characterized by damped or growing oscillations. We also show that regular distribution networks possess two negative eigenvalues only, but perturbations generate a spectrum of complex eigenvalues.

Co-Inheritance Analysis within the Domains of Life Substantially Improves Network Inference by Phylogenetic Profiling

PubMed Central

Shin, Junha; Lee, Insuk

2015-01-01

Phylogenetic profiling, a network inference method based on gene inheritance profiles, has been widely used to construct functional gene networks in microbes. However, its utility for network inference in higher eukaryotes has been limited. An improved algorithm with an in-depth understanding of pathway evolution may overcome this limitation. In this study, we investigated the effects of taxonomic structures on co-inheritance analysis using 2,144 reference species in four query species: Escherichia coli, Saccharomyces cerevisiae, Arabidopsis thaliana, and Homo sapiens. We observed three clusters of reference species based on a principal component analysis of the phylogenetic profiles, which correspond to the three domains of life—Archaea, Bacteria, and Eukaryota—suggesting that pathways inherit primarily within specific domains or lower-ranked taxonomic groups during speciation. Hence, the co-inheritance pattern within a taxonomic group may be eroded by confounding inheritance patterns from irrelevant taxonomic groups. We demonstrated that co-inheritance analysis within domains substantially improved network inference not only in microbe species but also in the higher eukaryotes, including humans. Although we observed two sub-domain clusters of reference species within Eukaryota, co-inheritance analysis within these sub-domain taxonomic groups only marginally improved network inference. Therefore, we conclude that co-inheritance analysis within domains is the optimal approach to network inference with the given reference species. The construction of a series of human gene networks with increasing sample sizes of the reference species for each domain revealed that the size of the high-accuracy networks increased as additional reference species genomes were included, suggesting that within-domain co-inheritance analysis will continue to expand human gene networks as genomes of additional species are sequenced. Taken together, we propose that co-inheritance analysis within the domains of life will greatly potentiate the use of the expected onslaught of sequenced genomes in the study of molecular pathways in higher eukaryotes. PMID:26394049

GEO Carbon and GHG Initiative Task 3: Optimizing in-situ measurements of essential carbon cycle variables across observational networks

NASA Astrophysics Data System (ADS)

Durden, D.; Muraoka, H.; Scholes, R. J.; Kim, D. G.; Loescher, H. W.; Bombelli, A.

2017-12-01

The development of an integrated global carbon cycle observation system to monitor changes in the carbon cycle, and ultimately the climate system, across the globe is of crucial importance in the 21stcentury. This system should be comprised of space and ground-based observations, in concert with modelling and analysis, to produce more robust budgets of carbon and other greenhouse gases (GHGs). A global initiative, the GEO Carbon and GHG Initiative, is working within the framework of Group on Earth Observations (GEO) to promote interoperability and provide integration across different parts of the system, particularly at domain interfaces. Thus, optimizing the efforts of existing networks and initiatives to reduce uncertainties in budgets of carbon and other GHGs. This is a very ambitious undertaking; therefore, the initiative is separated into tasks to provide actionable objectives. Task 3 focuses on the optimization of in-situ observational networks. The main objective of Task 3 is to develop and implement a procedure for enhancing and refining the observation system for identified essential carbon cycle variables (ECVs) that meets user-defined specifications at minimum total cost. This work focuses on the outline of the implementation plan, which includes a review of essential carbon cycle variables and observation technologies, mapping the ECVs performance, and analyzing gaps and opportunities in order to design an improved observing system. A description of the gap analysis of in-situ observations that will begin in the terrestrial domain to address issues of missing coordination and large spatial gaps, then extend to ocean and atmospheric observations in the future, will be outlined as the subsequent step to landscape mapping of existing observational networks.

The role of interpersonal communication in the process of knowledge mobilization within a community-based organization: a network analysis.

PubMed

Gainforth, Heather L; Latimer-Cheung, Amy E; Athanasopoulos, Peter; Moore, Spencer; Ginis, Kathleen A Martin

2014-05-22

Diffusion of innovations theory has been widely used to explain knowledge mobilization of research findings. This theory posits that individuals who are more interpersonally connected within an organization may be more likely to adopt an innovation (e.g., research evidence) than individuals who are less interconnected. Research examining this tenet of diffusion of innovations theory in the knowledge mobilization literature is limited. The purpose of the present study was to use network analysis to examine the role of interpersonal communication in the adoption and mobilization of the physical activity guidelines for people with spinal cord injury (SCI) among staff in a community-based organization (CBO). The study used a cross-sectional, whole-network design. In total, 56 staff completed the network survey. Adoption of the guidelines was assessed using Rogers' innovation-decision process and interpersonal communication was assessed using an online network instrument. The patterns of densities observed within the network were indicative of a core-periphery structure revealing that interpersonal communication was greater within the core than between the core and periphery and within the periphery. Membership in the core, as opposed to membership in the periphery, was associated with greater knowledge of the evidence-based physical activity resources available and engagement in physical activity promotion behaviours (ps < 0.05). Greater in-degree centrality was associated with adoption of evidence-based behaviours (p < 0.05). Findings suggest that interpersonal communication is associated with knowledge mobilization and highlight how the network structure could be improved for further dissemination efforts. diffusion of innovations; network analysis; community-based organization; knowledge mobilization; knowledge translation, interpersonal communication.

Update on Plans to Establish a National Phenology Network in the U.S.A.

NASA Astrophysics Data System (ADS)

Betancourt, J.; Schwartz, M.; Breshears, D.; Cayan, D.; Dettinger, M.; Inouye, D.; Post, E.; Reed, B.; Gray, S.

2005-12-01

The passing of the seasons is the most pervasive source of climatic and biological variability on Earth, yet phenological monitoring has been spotty worldwide. Formal phenological networks were recently established in Europe and Canada, and we are now following their lead in organizing a National Phenology Network (NPN) for the U.S.A. With support from federal agencies (NSF, USGS, NPS, USDA-FS, EPA, NOAA, NASA), on Aug. 22-26 we organized a workshop in Tucson, Arizona to begin planning a national-scale, multi-tiered phenological network. A prototype for a web-based NPN and preliminary workshop results are available at http://www.npn.uwm.edu. The main goals of NPN will be to: (1) facilitate thorough understanding of phenological phenomena, including causes and effects; (2) provide ground truthing to make the most of heavy public investment in remote sensing data; (3) allow detection and prediction of environmental change for a wide of variety of applications; (4) harness the power of mass participation and engage tens of thousands of "citizen scientists" in meeting national needs in Education, Health, Commerce, Natural Resources and Agriculture; (5) develop a model system for substantive collaboration across different levels of government, academia and the private sector. Just as the national networks of weather stations and stream gauges are critical for providing weather, climate and water-related information, NPN will help safeguard and procure goods and services that ecosystems provide. We expect that NPN will consist of a four-tiered, expandable structure: 1) a backbone network linked to existing weather stations, run by recruited public observers; 2) A smaller, second tier of intensive observations, run by scientists at established research sites; 3) a much larger network of observations made by citizen scientists; and 4) remote sensing observations that can be validated with surface observations, thereby providing wall-to-wall coverage for the U.S.A. Key to the success of NPN will be formal linkages with other ecological networks (e.g., LTER, AmeriFlux, NEON, USDA-FS Inventory and Analysis, NPS Inventory and Monitoring) and strategic co-location of phenological measurements with weather stations (e.g., NOAA's Real-Time Observation Network and state mesonets). Establishment and operation of NPN will require partnerships among multiple federal and state agencies, universities, and NGO's. Interagency agreements will facilitate data sharing, staff commitments, and the transfer of funds, while demonstrating policy-level support for NPN and smoothing the path for use of phenological data in decision-making. A formal implementation report will be completed and circulated for review by Dec. 1, 2005. As soon as the network can start assimilating observations from the public at large (tier 3), NPN will start recruiting observers through NGO's, as well as regional and national media. Every effort will be made to start making observations and expanding the monitoring network by Spring 2006.

Compact localized states and flat-band generators in one dimension

NASA Astrophysics Data System (ADS)

Maimaiti, Wulayimu; Andreanov, Alexei; Park, Hee Chul; Gendelman, Oleg; Flach, Sergej

2017-03-01

Flat bands (FB) are strictly dispersionless bands in the Bloch spectrum of a periodic lattice Hamiltonian, recently observed in a variety of photonic and dissipative condensate networks. FB Hamiltonians are fine-tuned networks, still lacking a comprehensive generating principle. We introduce a FB generator based on local network properties. We classify FB networks through the properties of compact localized states (CLS) which are exact FB eigenstates and occupy U unit cells. We obtain the complete two-parameter FB family of two-band d =1 networks with nearest unit cell interaction and U =2 . We discover a novel high symmetry sawtooth chain with identical hoppings in a transverse dc field, easily accessible in experiments. Our results pave the way towards a complete description of FBs in networks with more bands and in higher dimensions.

Modeling the average shortest-path length in growth of word-adjacency networks

NASA Astrophysics Data System (ADS)

Kulig, Andrzej; DroŻdŻ, Stanisław; Kwapień, Jarosław; OświÈ©cimka, Paweł

2015-03-01

We investigate properties of evolving linguistic networks defined by the word-adjacency relation. Such networks belong to the category of networks with accelerated growth but their shortest-path length appears to reveal the network size dependence of different functional form than the ones known so far. We thus compare the networks created from literary texts with their artificial substitutes based on different variants of the Dorogovtsev-Mendes model and observe that none of them is able to properly simulate the novel asymptotics of the shortest-path length. Then, we identify the local chainlike linear growth induced by grammar and style as a missing element in this model and extend it by incorporating such effects. It is in this way that a satisfactory agreement with the empirical result is obtained.

Time-frequency dynamics of resting-state brain connectivity measured with fMRI.

PubMed

Chang, Catie; Glover, Gary H

2010-03-01

Most studies of resting-state functional connectivity using fMRI employ methods that assume temporal stationarity, such as correlation and data-driven decompositions computed across the duration of the scan. However, evidence from both task-based fMRI studies and animal electrophysiology suggests that functional connectivity may exhibit dynamic changes within time scales of seconds to minutes. In the present study, we investigated the dynamic behavior of resting-state connectivity across the course of a single scan, performing a time-frequency coherence analysis based on the wavelet transform. We focused on the connectivity of the posterior cingulate cortex (PCC), a primary node of the default-mode network, examining its relationship with both the "anticorrelated" ("task-positive") network as well as other nodes of the default-mode network. It was observed that coherence and phase between the PCC and the anticorrelated network was variable in time and frequency, and statistical testing based on Monte Carlo simulations revealed the presence of significant scale-dependent temporal variability. In addition, a sliding-window correlation procedure identified other regions across the brain that exhibited variable connectivity with the PCC across the scan, which included areas previously implicated in attention and salience processing. Although it is unclear whether the observed coherence and phase variability can be attributed to residual noise or modulation of cognitive state, the present results illustrate that resting-state functional connectivity is not static, and it may therefore prove valuable to consider measures of variability, in addition to average quantities, when characterizing resting-state networks. Copyright (c) 2009 Elsevier Inc. All rights reserved.

Reconstruction of magnetic configurations in W7-X using artificial neural networks

NASA Astrophysics Data System (ADS)

Böckenhoff, Daniel; Blatzheim, Marko; Hölbe, Hauke; Niemann, Holger; Pisano, Fabio; Labahn, Roger; Pedersen, Thomas Sunn; The W7-X Team

2018-05-01

It is demonstrated that artificial neural networks can be used to accurately and efficiently predict details of the magnetic topology at the plasma edge of the Wendelstein 7-X stellarator, based on simulated as well as measured heat load patterns onto plasma-facing components observed with infrared cameras. The connection between heat load patterns and the magnetic topology is a challenging regression problem, but one that suits artificial neural networks well. The use of a neural network makes it feasible to analyze and control the plasma exhaust in real-time, an important goal for Wendelstein 7-X, and for magnetic confinement fusion research in general.

Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273

Stability of an ERP-based measure of brain network activation (BNA) in athletes: A new electrophysiological assessment tool for concussion.

PubMed

Eckner, James T; Rettmann, Ashley; Narisetty, Naveen; Greer, Jacob; Moore, Brandon; Brimacombe, Susan; He, Xuming; Broglio, Steven P

2016-01-01

To determine test-re-test reliabilities of novel Evoked Response Potential (ERP)-based Brain Network Activation (BNA) scores in healthy athletes. Observational, repeated-measures study. Forty-two healthy male and female high school and collegiate athletes completed auditory oddball and go/no-go ERP assessments at baseline, 1 week, 6 weeks and 1 year. The BNA algorithm was applied to the ERP data, considering electrode location, frequency band, peak latency and normalized amplitude to generate seven unique BNA scores for each testing session. Mean BNA scores, intra-class correlation coefficient (ICC) values and reliable change (RC) values were calculated for each of the seven BNA networks. BNA scores ranged from 46.3 ± 34.9 to 69.9 ± 22.8, ICC values ranged from 0.46-0.65 and 95% RC values ranged from 38.3-68.1 across the seven networks. The wide range of BNA scores observed in this population of healthy athletes suggests that a single BNA score or set of BNA scores from a single after-injury test session may be difficult to interpret in isolation without knowledge of the athlete's own baseline BNA score(s) and/or the results of serial tests performed at additional time points. The stability of each BNA network should be considered when interpreting test-re-test BNA score changes.

Cortical Thinning and Altered Cortico-Cortical Structural Covariance of the Default Mode Network in Patients with Persistent Insomnia Symptoms

PubMed Central

Suh, Sooyeon; Kim, Hosung; Dang-Vu, Thien Thanh; Joo, Eunyeon; Shin, Chol

2016-01-01

Study Objectives: Recent studies have suggested that structural abnormalities in insomnia may be linked with alterations in the default-mode network (DMN). This study compared cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia (PI) and good sleepers (GS). Methods: The current study used a clinical subsample from the longitudinal community-based Korean Genome and Epidemiology Study (KoGES). Cortical thickness and structural connectivity linked to the DMN in patients with persistent insomnia symptoms (PIS; n = 57) were compared to good sleepers (GS; n = 40). All participants underwent MRI acquisition. Based on literature review, we selected cortical regions corresponding to the DMN. A seed-based structural covariance analysis measured cortical thickness correlation between each seed region of the DMN and other cortical areas. Association of cortical thickness and covariance with sleep quality and neuropsychological assessments were further assessed. Results: Compared to GS, cortical thinning was found in PIS in the anterior cingulate cortex, precentral cortex, and lateral prefrontal cortex. Decreased structural connectivity between anterior and posterior regions of the DMN was observed in the PIS group. Decreased structural covariance within the DMN was associated with higher PSQI scores. Cortical thinning in the lateral frontal lobe was related to poor performance in executive function in PIS. Conclusion: Disrupted structural covariance network in PIS might reflect malfunctioning of antero-posterior disconnection of the DMN during the wake to sleep transition that is commonly found during normal sleep. The observed structural network alteration may further implicate commonly observed sustained sleep difficulties and cognitive impairment in insomnia. Citation: Suh S, Kim H, Dang-Vu TT, Joo E, Shin C. Cortical thinning and altered cortico-cortical structural covariance of the default mode network in patients with persistent insomnia symptoms. SLEEP 2016;39(1):161–171. PMID:26414892

Suppressing epidemics on networks by exploiting observer nodes.

PubMed

Takaguchi, Taro; Hasegawa, Takehisa; Yoshida, Yuichi

2014-07-01

To control infection spreading on networks, we investigate the effect of observer nodes that recognize infection in a neighboring node and make the rest of the neighbor nodes immune. We numerically show that random placement of observer nodes works better on networks with clustering than on locally treelike networks, implying that our model is promising for realistic social networks. The efficiency of several heuristic schemes for observer placement is also examined for synthetic and empirical networks. In parallel with numerical simulations of epidemic dynamics, we also show that the effect of observer placement can be assessed by the size of the largest connected component of networks remaining after removing observer nodes and links between their neighboring nodes.

Suppressing epidemics on networks by exploiting observer nodes

NASA Astrophysics Data System (ADS)

Takaguchi, Taro; Hasegawa, Takehisa; Yoshida, Yuichi

2014-07-01

To control infection spreading on networks, we investigate the effect of observer nodes that recognize infection in a neighboring node and make the rest of the neighbor nodes immune. We numerically show that random placement of observer nodes works better on networks with clustering than on locally treelike networks, implying that our model is promising for realistic social networks. The efficiency of several heuristic schemes for observer placement is also examined for synthetic and empirical networks. In parallel with numerical simulations of epidemic dynamics, we also show that the effect of observer placement can be assessed by the size of the largest connected component of networks remaining after removing observer nodes and links between their neighboring nodes.

Dynamics of hate based Internet user networks

NASA Astrophysics Data System (ADS)

Sobkowicz, P.; Sobkowicz, A.

2010-02-01

We present a study of the properties of network of political discussions on one of the most popular Polish Internet forums. This provides the opportunity to study the computer mediated human interactions in strongly bipolar environment. The comments of the participants are found to be mostly disagreements, with strong percentage of invective and provocative ones. Binary exchanges (quarrels) play significant role in the network growth and topology. Statistical analysis shows that the growth of the discussions depends on the degree of controversy of the subject and the intensity of personal conflict between the participants. This is in contrast to most previously studied social networks, for example networks of scientific citations, where the nature of the links is much more positive and based on similarity and collaboration rather than opposition and abuse. The work discusses also the implications of the findings for more general studies of consensus formation, where our observations of increased conflict contradict the usual assumptions that interactions between people lead to averaging of opinions and agreement.

Hop Optimization and Relay Node Selection in Multi-hop Wireless Ad-Hoc Networks

NASA Astrophysics Data System (ADS)

Li, Xiaohua(Edward)

In this paper we propose an efficient approach to determine the optimal hops for multi-hop ad hoc wireless networks. Based on the assumption that nodes use successive interference cancellation (SIC) and maximal ratio combining (MRC) to deal with mutual interference and to utilize all the received signal energy, we show that the signal-to-interference-plus-noise ratio (SINR) of a node is determined only by the nodes before it, not the nodes after it, along a packet forwarding path. Based on this observation, we propose an iterative procedure to select the relay nodes and to calculate the path SINR as well as capacity of an arbitrary multi-hop packet forwarding path. The complexity of the algorithm is extremely low, and scaling well with network size. The algorithm is applicable in arbitrarily large networks. Its performance is demonstrated as desirable by simulations. The algorithm can be helpful in analyzing the performance of multi-hop wireless networks.

Computational models of location-invariant orthographic processing

NASA Astrophysics Data System (ADS)

Dandurand, Frédéric; Hannagan, Thomas; Grainger, Jonathan

2013-03-01

We trained three topologies of backpropagation neural networks to discriminate 2000 words (lexical representations) presented at different positions of a horizontal letter array. The first topology (zero-deck) contains no hidden layer, the second (one-deck) has a single hidden layer, and for the last topology (two-deck), the task is divided in two subtasks implemented as two stacked neural networks, with explicit word-centred letters as intermediate representations. All topologies successfully simulated two key benchmark phenomena observed in skilled human reading: transposed-letter priming and relative-position priming. However, the two-deck topology most accurately simulated the ability to discriminate words from nonwords, while containing the fewest connection weights. We analysed the internal representations after training. Zero-deck networks implement a letter-based scheme with a position bias to differentiate anagrams. One-deck networks implement a holographic overlap coding in which representations are essentially letter-based and words are linear combinations of letters. Two-deck networks also implement holographic-coding.

Network meta-analysis of disconnected networks: How dangerous are random baseline treatment effects?

PubMed

Béliveau, Audrey; Goring, Sarah; Platt, Robert W; Gustafson, Paul

2017-12-01

In network meta-analysis, the use of fixed baseline treatment effects (a priori independent) in a contrast-based approach is regularly preferred to the use of random baseline treatment effects (a priori dependent). That is because, often, there is not a need to model baseline treatment effects, which carry the risk of model misspecification. However, in disconnected networks, fixed baseline treatment effects do not work (unless extra assumptions are made), as there is not enough information in the data to update the prior distribution on the contrasts between disconnected treatments. In this paper, we investigate to what extent the use of random baseline treatment effects is dangerous in disconnected networks. We take 2 publicly available datasets of connected networks and disconnect them in multiple ways. We then compare the results of treatment comparisons obtained from a Bayesian contrast-based analysis of each disconnected network using random normally distributed and exchangeable baseline treatment effects to those obtained from a Bayesian contrast-based analysis of their initial connected network using fixed baseline treatment effects. For the 2 datasets considered, we found that the use of random baseline treatment effects in disconnected networks was appropriate. Because those datasets were not cherry-picked, there should be other disconnected networks that would benefit from being analyzed using random baseline treatment effects. However, there is also a risk for the normality and exchangeability assumption to be inappropriate in other datasets even though we have not observed this situation in our case study. We provide code, so other datasets can be investigated. Copyright © 2017 John Wiley & Sons, Ltd.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes.

PubMed

Huang, Justin K; Carlin, Daniel E; Yu, Michael Ku; Zhang, Wei; Kreisberg, Jason F; Tamayo, Pablo; Ideker, Trey

2018-04-25

Gene networks are rapidly growing in size and number, raising the question of which networks are most appropriate for particular applications. Here, we evaluate 21 human genome-wide interaction networks for their ability to recover 446 disease gene sets identified through literature curation, gene expression profiling, or genome-wide association studies. While all networks have some ability to recover disease genes, we observe a wide range of performance with STRING, ConsensusPathDB, and GIANT networks having the best performance overall. A general tendency is that performance scales with network size, suggesting that new interaction discovery currently outweighs the detrimental effects of false positives. Correcting for size, we find that the DIP network provides the highest efficiency (value per interaction). Based on these results, we create a parsimonious composite network with both high efficiency and performance. This work provides a benchmark for selection of molecular networks in human disease research. Copyright © 2018 Elsevier Inc. All rights reserved.

Statistical Evaluation of Combined Daily Gauge Observations and Rainfall Satellite Estimations over Continental South America

NASA Technical Reports Server (NTRS)

Vila, Daniel; deGoncalves, Luis Gustavo; Toll, David L.; Rozante, Jose Roberto

2008-01-01

This paper describes a comprehensive assessment of a new high-resolution, high-quality gauge-satellite based analysis of daily precipitation over continental South America during 2004. This methodology is based on a combination of additive and multiplicative bias correction schemes in order to get the lowest bias when compared with the observed values. Inter-comparisons and cross-validations tests have been carried out for the control algorithm (TMPA real-time algorithm) and different merging schemes: additive bias correction (ADD), ratio bias correction (RAT) and TMPA research version, for different months belonging to different seasons and for different network densities. All compared merging schemes produce better results than the control algorithm, but when finer temporal (daily) and spatial scale (regional networks) gauge datasets is included in the analysis, the improvement is remarkable. The Combined Scheme (CoSch) presents consistently the best performance among the five techniques. This is also true when a degraded daily gauge network is used instead of full dataset. This technique appears a suitable tool to produce real-time, high-resolution, high-quality gauge-satellite based analyses of daily precipitation over land in regional domains.

From Ecology to Finance (and Back?): A Review on Entropy-Based Null Models for the Analysis of Bipartite Networks

NASA Astrophysics Data System (ADS)

Straka, Mika J.; Caldarelli, Guido; Squartini, Tiziano; Saracco, Fabio

2018-04-01

Bipartite networks provide an insightful representation of many systems, ranging from mutualistic networks of species interactions to investment networks in finance. The analyses of their topological structures have revealed the ubiquitous presence of properties which seem to characterize many—apparently different—systems. Nestedness, for example, has been observed in biological plant-pollinator as well as in country-product exportation networks. Due to the interdisciplinary character of complex networks, tools developed in one field, for example ecology, can greatly enrich other areas of research, such as economy and finance, and vice versa. With this in mind, we briefly review several entropy-based bipartite null models that have been recently proposed and discuss their application to real-world systems. The focus on these models is motivated by the fact that they show three very desirable features: analytical character, general applicability, and versatility. In this respect, entropy-based methods have been proven to perform satisfactorily both in providing benchmarks for testing evidence-based null hypotheses and in reconstructing unknown network configurations from partial information. Furthermore, entropy-based models have been successfully employed to analyze ecological as well as economic systems. As an example, the application of entropy-based null models has detected early-warning signals, both in economic and financial systems, of the 2007-2008 world crisis. Moreover, they have revealed a statistically-significant export specialization phenomenon of country export baskets in international trade, a result that seems to reconcile Ricardo's hypothesis in classical economics with recent findings on the (empirical) diversification industrial production at the national level. Finally, these null models have shown that the information contained in the nestedness is already accounted for by the degree sequence of the corresponding graphs.

The energetic transient array-ETA-A network of ``space buoys'' in solar orbit for observations of gamma-ray bursts

NASA Astrophysics Data System (ADS)

Ricker, George R.

1990-08-01

The Energetic Transient Array (ETA) is a concept for a dedicated interplanetary network of ~40 microsatellites (``space buoys'') deployed in an ~1 AU radius solar orbit for the observation of cosmic gamma ray bursts (GRBs). Such a network is essential for the determination of highly accurate (~0.1 arc sec) error boxes for GRBs. For each of ~100 bursts which would be detectable per year of observation by such a network, high resolution (ΔE/E ~0.2% at 1 MeV) spectra could be obtained through the use of passively-cooled Ge gamma-ray detectors. Stabilization of each microsatellite would be achieved by a novel technique based on the radiation pressure exerted on ``featherable'' solar paddles. Because of the simplicity of the microsats, as well as the economics of mass production and the failure tolerance of such a network of independent satellites, a unit cost of ~$250 K per microsat can be anticipated. Should such a project be undertaken in the mid 1990's, possibly as an International mission, it should be possible to have a fully functional array of satellites in place before the end of the decade for a total cost of ~$20M, exclusive of launcher fees.

Three-dimensional mosaicking of the South Korean radar network

NASA Astrophysics Data System (ADS)

Berenguer, Marc; Sempere-Torres, Daniel; Lee, GyuWon

2016-04-01

Dense radar networks offer the possibility of improved Quantitative Precipitation Estimation thanks to the additional information collected in the overlapping areas, which allows mitigating errors associated with the Vertical Profile of Reflectivity or path attenuation by intense rain. With this aim, Roca-Sancho et al. (2014) proposed a technique to generate 3-D reflectivity mosaics from the multiple radars of a network. The technique is based on an inverse method that simulates the radar sampling of the atmosphere considering the characteristics (location, frequency and scanning protocol) of each individual radar. This technique has been applied to mosaic the observations of the radar network of South Korea (composed of 14 S-band radars), and integrate the observations of the small X-band network which to be installed near Seoul in the framework of a project funded by the Korea Agency for Infrastructure Technology Advancement (KAIA). The evaluation of the generated 3-D mosaics has been done by comparison with point measurements (i.e. rain gauges and disdrometers) and with the observations of independent radars. Reference: Roca-Sancho, J., M. Berenguer, and D. Sempere-Torres (2014), An inverse method to retrieve 3D radar reflectivity composites, Journal of Hydrology, 519, 947-965, doi: 10.1016/j.jhydrol.2014.07.039.

Perspectives on Social Network Analysis for Observational Scientific Data

NASA Astrophysics Data System (ADS)

Singh, Lisa; Bienenstock, Elisa Jayne; Mann, Janet

This chapter is a conceptual look at data quality issues that arise during scientific observations and their impact on social network analysis. We provide examples of the many types of incompleteness, bias and uncertainty that impact the quality of social network data. Our approach is to leverage the insights and experience of observational behavioral scientists familiar with the challenges of making inference when data are not complete, and suggest avenues for extending these to relational data questions. The focus of our discussion is on network data collection using observational methods because they contain high dimensionality, incomplete data, varying degrees of observational certainty, and potential observer bias. However, the problems and recommendations identified here exist in many other domains, including online social networks, cell phone networks, covert networks, and disease transmission networks.

Dynamic Tasking of Networked Sensors Using Covariance Information

DTIC Science & Technology

2010-09-01

has been created under an effort called TASMAN (Tasking Autonomous Sensors in a Multiple Application Network). One of the first studies utilizing this...environment was focused on a novel resource management approach, namely covariance-based tasking. Under this scheme, the state error covariance of...resident space objects (RSO), sensor characteristics, and sensor- target geometry were used to determine the effectiveness of future observations in

Wiretapping the Internet

NASA Astrophysics Data System (ADS)

Antonelli, Charles J.; Honeyman, Peter

2001-02-01

This paper describes the Advanced Packet Vault, a technology for creating such a record by collecting and securely storing all packets observed on a network, with a scalable architecture intended to support network speeds in excess of 100 Mbps. Encryption is used to preserve users' security and privacy, permitting selected traffic to be made available without revealing other traffic. The Vault implementation, based on Linux and OpenBSD, is open-source.

A P2P Botnet detection scheme based on decision tree and adaptive multilayer neural networks.

PubMed

Alauthaman, Mohammad; Aslam, Nauman; Zhang, Li; Alasem, Rafe; Hossain, M A

2018-01-01

In recent years, Botnets have been adopted as a popular method to carry and spread many malicious codes on the Internet. These malicious codes pave the way to execute many fraudulent activities including spam mail, distributed denial-of-service attacks and click fraud. While many Botnets are set up using centralized communication architecture, the peer-to-peer (P2P) Botnets can adopt a decentralized architecture using an overlay network for exchanging command and control data making their detection even more difficult. This work presents a method of P2P Bot detection based on an adaptive multilayer feed-forward neural network in cooperation with decision trees. A classification and regression tree is applied as a feature selection technique to select relevant features. With these features, a multilayer feed-forward neural network training model is created using a resilient back-propagation learning algorithm. A comparison of feature set selection based on the decision tree, principal component analysis and the ReliefF algorithm indicated that the neural network model with features selection based on decision tree has a better identification accuracy along with lower rates of false positives. The usefulness of the proposed approach is demonstrated by conducting experiments on real network traffic datasets. In these experiments, an average detection rate of 99.08 % with false positive rate of 0.75 % was observed.

Distinctive laterality of neural networks supporting action understanding in left- and right-handed individuals: An EEG coherence study.

PubMed

Kelly, Rachel; Mizelle, J C; Wheaton, Lewis A

2015-08-01

Prior work has demonstrated that perspective and handedness of observed actions can affect action understanding differently in right and left-handed persons, suggesting potential differences in the neural networks underlying action understanding between right and left-handed individuals. We sought to evaluate potential differences in these neural networks using electroencephalography (EEG). Right- and left-handed participants observed images of tool-use actions from egocentric and allocentric perspectives, with right- and left-handed actors performing the actions. Participants judged the outcome of the observed actions, and response accuracy and latency were recorded. Behaviorally, the highest accuracy and shortest latency was found in the egocentric perspective for right- and left-handed observers. Handedness of subject showed an effect on accuracy and latency also, where right-handed observers were faster to respond than left-handed observers, but on average were less accurate. Mu band (8-10 Hz) cortico-cortical coherence analysis indicated that right-handed observers have coherence in the motor dominant left parietal-premotor networks when looking at an egocentric right or allocentric left hands. When looking in an egocentric perspective at a left hand or allocentric right hand, coherence was lateralized to right parietal-premotor areas. In left-handed observers, bilateral parietal-premotor coherence patterns were observed regardless of actor handedness. These findings suggest that the cortical networks involved in understanding action outcomes are dependent on hand dominance, and notably right handed participants seem to utilize motor systems based on the limb seen performing the action. The decreased accuracy for right-handed participants on allocentric images could be due to asymmetrical lateralization of encoding action and motoric dominance, which may interfere with translating allocentric limb action outcomes. Further neurophysiological studies will determine the specific processes of how left- and right-handed participants understand actions. Copyright © 2015 Elsevier Ltd. All rights reserved.

Urban MEMS based seismic network for post-earthquakes rapid disaster assessment

NASA Astrophysics Data System (ADS)

D'Alessandro, A.; Luzio, D.; D'Anna, G.

2014-09-01

In this paper, we introduce a project for the realization of the first European real-time urban seismic network based on Micro Electro-Mechanical Systems (MEMS) technology. MEMS accelerometers are a highly enabling technology, and nowadays, the sensitivity and the dynamic range of these sensors are such as to allow the recording of earthquakes of moderate magnitude even at a distance of several tens of kilometers. Moreover, thanks to their low cost and smaller size, MEMS accelerometers can be easily installed in urban areas in order to achieve an urban seismic network constituted by high density of observation points. The network is being implemented in the Acireale Municipality (Sicily, Italy), an area among those with the highest hazard, vulnerability and exposure to the earthquake of the Italian territory. The main objective of the implemented urban network will be to achieve an effective system for post-earthquake rapid disaster assessment. The earthquake recorded, also that with moderate magnitude will be used for the effective seismic microzonation of the area covered by the network. The implemented system will be also used to realize a site-specific earthquakes early warning system.

Network structures between strategies in iterated prisoners' dilemma games

NASA Astrophysics Data System (ADS)

Kim, Young Jin; Roh, Myungkyoon; Son, Seung-Woo

2014-02-01

We use replicator dynamics to study an iterated prisoners' dilemma game with memory. In this study, we investigate the characteristics of all 32 possible strategies with a single-step memory by observing the results when each strategy encounters another one. Based on these results, we define similarity measures between the 32 strategies and perform a network analysis of the relationship between the strategies by constructing a strategies network. Interestingly, we find that a win-lose circulation, like rock-paper-scissors, exists between strategies and that the circulation results from one unusual strategy.

From sparse to dense and from assortative to disassortative in online social networks

PubMed Central

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-01-01

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks. PMID:24798703

From sparse to dense and from assortative to disassortative in online social networks.

PubMed

Li, Menghui; Guan, Shuguang; Wu, Chensheng; Gong, Xiaofeng; Li, Kun; Wu, Jinshan; Di, Zengru; Lai, Choy-Heng

2014-05-06

Inspired by the analysis of several empirical online social networks, we propose a simple reaction-diffusion-like coevolving model, in which individuals are activated to create links based on their states, influenced by local dynamics and their own intention. It is shown that the model can reproduce the remarkable properties observed in empirical online social networks; in particular, the assortative coefficients are neutral or negative, and the power law exponents γ are smaller than 2. Moreover, we demonstrate that, under appropriate conditions, the model network naturally makes transition(s) from assortative to disassortative, and from sparse to dense in their characteristics. The model is useful in understanding the formation and evolution of online social networks.

EARLINET observations of the Eyjafjallajökull ash plume over Europe

NASA Astrophysics Data System (ADS)

Pappalardo, Gelsomina; Amodeo, Aldo; Ansmann, Albert; Apituley, Arnoud; Alados Arboledas, Lucas; Balis, Dimitris; Böckmann, Christine; Chaikovsky, Anatoli; Comeron, Adolfo; D'Amico, Giuseppe; De Tomasi, Ferdinando; Freudenthaler, Volker; Giannakaki, Elina; Giunta, Aldo; Grigorov, Ivan; Gustafsson, Ove; Gross, Silke; Haeffelin, Martial; Iarlori, Marco; Kinne, Stefan; Linné, Holger; Madonna, Fabio; Mamouri, Rodanthi; Mattis, Ina; McAuliffe, Michael; Molero, Francisco; Mona, Lucia; Müller, Detlef; Mitev, Valentin; Nicolae, Doina; Papayannis, Alexandros; Perrone, Maria Rita; Pietruczuk, Aleksander; Pujadas, Manuel; Putaud, Jean-Philippe; Ravetta, Francois; Rizi, Vincenzo; Serikov, Ilya; Sicard, Michael; Simeonov, Valentin; Spinelli, Nicola; Stebel, Kerstin; Trickl, Thomas; Wandinger, Ulla; Wang, Xuan; Wagner, Frank; Wiegner, Matthias

2010-10-01

EARLINET, the European Aerosol Research Lidar NETwork, established in 2000, is the first coordinated lidar network for tropospheric aerosol study on the continental scale. The network activity is based on scheduled measurements, a rigorous quality assurance program addressing both instruments and evaluation algorithms, and a standardised data exchange format. At present, the network includes 27 lidar stations distributed over Europe. EARLINET performed almost continuous measurements since 15 April 2010 in order to follow the evolution of the volcanic plume generated from the eruption of the Eyjafjallajökull volcano, providing the 4-dimensional distribution of the volcanic ash plume over Europe. During the 15-30 April period, volcanic particles were detected over Central Europe over a wide range of altitudes, from 10 km down to the local planetary boundary layer (PBL). Until 19 April, the volcanic plume transport toward South Europe was nearly completely blocked by the Alps. After 19 April volcanic particles were transported to the south and the southeast of Europe. Descending aerosol layers were typically observed all over Europe and intrusion of particles into the PBL was observed at almost each lidar site that was affected by the volcanic plume. A second event was observed over Portugal and Spain (6 May) and then over Italy on 9 May 2010. The volcanic plume was then observed again over Southern Germany on 11 May 2010.

Chemical control of the viscoelastic properties of vinylogous urethane vitrimers

PubMed Central

Denissen, Wim; Droesbeke, Martijn; Nicolaÿ, Renaud; Leibler, Ludwik; Winne, Johan M.; Du Prez, Filip E.

2017-01-01

Vinylogous urethane based vitrimers are polymer networks that have the intrinsic property to undergo network rearrangements, stress relaxation and viscoelastic flow, mediated by rapid addition/elimination reactions of free chain end amines. Here we show that the covalent exchange kinetics significantly can be influenced by combination with various simple additives. As anticipated, the exchange reactions on network level can be further accelerated using either Brønsted or Lewis acid additives. Remarkably, however, a strong inhibitory effect is observed when a base is added to the polymer matrix. These effects have been mechanistically rationalized, guided by low-molecular weight kinetic model experiments. Thus, vitrimer elastomer materials can be rationally designed to display a wide range of viscoelastic properties. PMID:28317893

A site evaluation campaign for a ground based atmospheric Cherenkov telescope in Romania

NASA Astrophysics Data System (ADS)

Radu, Aurelian Andrei; Angelescu, Tatiana; Curtef, Valentin; Delia, Florin; Felea, Daniel; Goia, Ioana; Haşegan, Dumitru; Lucaschi, Bogdan; Manea, Ancuta; Popa, Vlad; Raliţă, Ioan; Văcăreanu, Radu

2012-07-01

Around the world, several scientific projects share the interest of a global network of small Cherenkov telescopes for monitoring observations of the brightest blazars—the DWARF network. A small, ground based, imaging atmospheric Cherenkov telescope of last generation is intended to be installed and operated in Romania as a component of the DWARF network. To prepare the construction of the observatory, two support projects have been initiated. Within the framework of these projects, we have assessed a number of possible sites where to settle the observatory. In this paper we submit a brief report on the general characteristics of the best four sites selected after the local infrastructure, the nearby facilities and the social impact criteria have been applied.

Establishment of Stereo Multi-sensor Network for Giant Landslide Monitoring and its Deploy in Xishan landslide, Sichuan, China.

NASA Astrophysics Data System (ADS)

Liu, C.; Lu, P.; WU, H.

2015-12-01

Landslide is one of the most destructive natural disasters, which severely affects human lives as well as the safety of personal properties and public infrastructures. Monitoring and predicting landslide movements can keep an adequate safety level for human beings in those situations. This paper indicated a newly developed Stereo Multi-sensor Landslide Monitoring Network (SMSLMN) based on a uniform temporal geo-reference. Actually, early in 2003, SAMOA (Surveillance et Auscultation des Mouvements de Terrain Alpins, French) project was put forwarded as a plan for landslide movements monitoring. However, SAMOA project did not establish a stereo observation network to fully cover the surface and internal part of landslide. SMSLMN integrated various sensors, including space-borne, airborne, in-situ and underground sensors, which can quantitatively monitor the slide-body and obtain portent information of movement in high frequency with high resolution. The whole network has been deployed at the Xishan landslide, Sichuan, P.R.China. According to various characteristic of stereo monitoring sensors, observation capabilities indicators for different sensors were proposed in order to obtain the optimal sensors combination groups and observation strategy. Meanwhile, adaptive networking and reliable data communication methods were developed to apply intelligent observation and sensor data transmission. Some key technologies, such as signal amplification and intelligence extraction technology, data access frequency adaptive adjustment technology, different sensor synchronization control technology were developed to overcome the problems in complex observation environment. The collaboratively observation data have been transferred to the remote data center where is thousands miles away from the giant landslide spot. These data were introduced into the landslide stability analysis model, and some primary conclusion will be achieved at the end of paper.

Food-web structure and network theory: The role of connectance and size

PubMed Central

Dunne, Jennifer A.; Williams, Richard J.; Martinez, Neo D.

2002-01-01

Networks from a wide range of physical, biological, and social systems have been recently described as “small-world” and “scale-free.” However, studies disagree whether ecological networks called food webs possess the characteristic path lengths, clustering coefficients, and degree distributions required for membership in these classes of networks. Our analysis suggests that the disagreements are based on selective use of relatively few food webs, as well as analytical decisions that obscure important variability in the data. We analyze a broad range of 16 high-quality food webs, with 25–172 nodes, from a variety of aquatic and terrestrial ecosystems. Food webs generally have much higher complexity, measured as connectance (the fraction of all possible links that are realized in a network), and much smaller size than other networks studied, which have important implications for network topology. Our results resolve prior conflicts by demonstrating that although some food webs have small-world and scale-free structure, most do not if they exceed a relatively low level of connectance. Although food-web degree distributions do not display a universal functional form, observed distributions are systematically related to network connectance and size. Also, although food webs often lack small-world structure because of low clustering, we identify a continuum of real-world networks including food webs whose ratios of observed to random clustering coefficients increase as a power–law function of network size over 7 orders of magnitude. Although food webs are generally not small-world, scale-free networks, food-web topology is consistent with patterns found within those classes of networks. PMID:12235364

Perspectives for Distributed Observations of Near-Earth Space Using a Russian-Cuban Observatory

NASA Astrophysics Data System (ADS)

Bisikalo, D. V.; Savanov, I. S.; Naroenkov, S. A.; Nalivkin, M. A.; Shugarov, A. S.; Bakhtigaraev, N. S.; Levkina, P. A.; Ibragimov, M. A.; Kil'pio, E. Yu.; Sachkov, M. E.; Kartashova, A. P.; Fateeva, A. M.; Uratsuka, Marta R. Rodriguez; Estrada, Ramses Zaldivar; Diaz, Antonio Alonsa; Rodríguez, Omar Pons; Figuera, Fidel Hernandes; Garcia, Maritza Garcia

2018-06-01

The creation of a specialized network of large, wide-angle telescopes for distributed observations of near-Earth space using a Russian-Cuban Observatory is considered. An extremely important goal of routine monitoring of near-Earth and near-Sun space is warding off threats with both natural and technogenic origins. Natural threats are associated with asteroids or comets, and technogenic threats with man-made debris in near-Earth space. A modern network of ground-based optical instruments designed to ward off such threats must: (a) have a global and, if possible, uniform geographic distribution, (b) be suitable for wide-angle, high-accuracy precision survey observations, and (c) be created and operated within a single network-oriented framework. Experience at the Institute of Astronomy on the development of one-meter-class wide-angle telescopes and elements of a super-wide-angle telescope cluster is applied to determine preferences for the composition of each node of such a network. The efficiency of distributed observations in attaining maximally accurate predictions of the motions of potentially dangerous celestial bodies as they approach the Earth and in observations of space debris and man-made satellites is estimated. The first estimates of astroclimatic conditions at the proposed site of the future Russian-Cuban Observatory in the mountains of the Sierra del Rosario Biosphere Reserve are obtained. Special attention is given to the possible use of the network to carry out a wide range of astrophysical studies, including optical support for the localization of gravitational waves and other transient events.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks.

PubMed

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Evolutionary dynamics of the traveler's dilemma and minimum-effort coordination games on complex networks

NASA Astrophysics Data System (ADS)

Iyer, Swami; Killingback, Timothy

2014-10-01

The traveler's dilemma game and the minimum-effort coordination game are social dilemmas that have received significant attention resulting from the fact that the predictions of classical game theory are inconsistent with the results found when the games are studied experimentally. Moreover, both the traveler's dilemma and the minimum-effort coordination games have potentially important applications in evolutionary biology. Interestingly, standard deterministic evolutionary game theory, as represented by the replicator dynamics in a well-mixed population, is also inadequate to account for the behavior observed in these games. Here we study the evolutionary dynamics of both these games in populations with interaction patterns described by a variety of complex network topologies. We investigate the evolutionary dynamics of these games through agent-based simulations on both model and empirical networks. In particular, we study the effects of network clustering and assortativity on the evolutionary dynamics of both games. In general, we show that the evolutionary behavior of the traveler's dilemma and minimum-effort coordination games on complex networks is in good agreement with that observed experimentally. Thus, formulating the traveler's dilemma and the minimum-effort coordination games on complex networks neatly resolves the paradoxical aspects of these games.

Reverse-engineering of gene networks for regulating early blood development from single-cell measurements.

PubMed

Wei, Jiangyong; Hu, Xiaohua; Zou, Xiufen; Tian, Tianhai

2017-12-28

Recent advances in omics technologies have raised great opportunities to study large-scale regulatory networks inside the cell. In addition, single-cell experiments have measured the gene and protein activities in a large number of cells under the same experimental conditions. However, a significant challenge in computational biology and bioinformatics is how to derive quantitative information from the single-cell observations and how to develop sophisticated mathematical models to describe the dynamic properties of regulatory networks using the derived quantitative information. This work designs an integrated approach to reverse-engineer gene networks for regulating early blood development based on singel-cell experimental observations. The wanderlust algorithm is initially used to develop the pseudo-trajectory for the activities of a number of genes. Since the gene expression data in the developed pseudo-trajectory show large fluctuations, we then use Gaussian process regression methods to smooth the gene express data in order to obtain pseudo-trajectories with much less fluctuations. The proposed integrated framework consists of both bioinformatics algorithms to reconstruct the regulatory network and mathematical models using differential equations to describe the dynamics of gene expression. The developed approach is applied to study the network regulating early blood cell development. A graphic model is constructed for a regulatory network with forty genes and a dynamic model using differential equations is developed for a network of nine genes. Numerical results suggests that the proposed model is able to match experimental data very well. We also examine the networks with more regulatory relations and numerical results show that more regulations may exist. We test the possibility of auto-regulation but numerical simulations do not support the positive auto-regulation. In addition, robustness is used as an importantly additional criterion to select candidate networks. The research results in this work shows that the developed approach is an efficient and effective method to reverse-engineer gene networks using single-cell experimental observations.

The adaptive safety analysis and monitoring system

NASA Astrophysics Data System (ADS)

Tu, Haiying; Allanach, Jeffrey; Singh, Satnam; Pattipati, Krishna R.; Willett, Peter

2004-09-01

The Adaptive Safety Analysis and Monitoring (ASAM) system is a hybrid model-based software tool for assisting intelligence analysts to identify terrorist threats, to predict possible evolution of the terrorist activities, and to suggest strategies for countering terrorism. The ASAM system provides a distributed processing structure for gathering, sharing, understanding, and using information to assess and predict terrorist network states. In combination with counter-terrorist network models, it can also suggest feasible actions to inhibit potential terrorist threats. In this paper, we will introduce the architecture of the ASAM system, and discuss the hybrid modeling approach embedded in it, viz., Hidden Markov Models (HMMs) to detect and provide soft evidence on the states of terrorist network nodes based on partial and imperfect observations, and Bayesian networks (BNs) to integrate soft evidence from multiple HMMs. The functionality of the ASAM system is illustrated by way of application to the Indian Airlines Hijacking, as modeled from open sources.

Identifying Flow Networks in a Karstified Aquifer by Application of the Cellular Automata-Based Deterministic Inversion Method (Lez Aquifer, France)

NASA Astrophysics Data System (ADS)

Fischer, P.; Jardani, A.; Wang, X.; Jourde, H.; Lecoq, N.

2017-12-01

The distributed modeling of flow paths within karstic and fractured fields remains a complex task because of the high dependence of the hydraulic responses to the relative locations between observational boreholes and interconnected fractures and karstic conduits that control the main flow of the hydrosystem. The inverse problem in a distributed model is one alternative approach to interpret the hydraulic test data by mapping the karstic networks and fractured areas. In this work, we developed a Bayesian inversion approach, the Cellular Automata-based Deterministic Inversion (CADI) algorithm to infer the spatial distribution of hydraulic properties in a structurally constrained model. This method distributes hydraulic properties along linear structures (i.e., flow conduits) and iteratively modifies the structural geometry of this conduit network to progressively match the observed hydraulic data to the modeled ones. As a result, this method produces a conductivity model that is composed of a discrete conduit network embedded in the background matrix, capable of producing the same flow behavior as the investigated hydrologic system. The method is applied to invert a set of multiborehole hydraulic tests collected from a hydraulic tomography experiment conducted at the Terrieu field site in the Lez aquifer, Southern France. The emergent model shows a high consistency to field observation of hydraulic connections between boreholes. Furthermore, it provides a geologically realistic pattern of flow conduits. This method is therefore of considerable value toward an enhanced distributed modeling of the fractured and karstified aquifers.

Backbone of complex networks of corporations: the flow of control.

PubMed

Glattfelder, J B; Battiston, S

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

The South Fork Experimental Watershed: Soil moisture and precipitation network for satellite validation

NASA Astrophysics Data System (ADS)

Cosh, M. H.; Prueger, J. H.; McKee, L.; Bindlish, R.

2013-12-01

A recently deployed long term network for the study of soil moisture and precipitation was deployed in north central iowa, in cooperation between USDA and NASA. This site will be a joint calibration/validation network for the Soil Moisture Active Passive (SMAP) and Global Precipitation Measurement (GPM) missions. At total of 20 dual gauge precipitation gages were established across a watershed landscape with an area of approximately 600 km2. In addition, four soil moisture probes were installed in profile at 5, 10, 20, and 50 cm. The network was installed in April of 2013, at the initiation of the Iowa Flood Study (IFloodS) which was a six week intensive ground based radar observation period, conducted in coordination with NASA and the University of Iowa. This site is a member watershed of the Group on Earth Observations Joint Experiments on Crop Assessment and Monitoring (GEO-JECAM) program. A variety of quality control procedures are examined and spatial and temporal stability aspects of the network are examined. Initial comparisons of the watershed to soil moisture estimates from satellites are also conducted.

Backbone of complex networks of corporations: The flow of control

NASA Astrophysics Data System (ADS)

Glattfelder, J. B.; Battiston, S.

2009-09-01

We present a methodology to extract the backbone of complex networks based on the weight and direction of links, as well as on nontopological properties of nodes. We show how the methodology can be applied in general to networks in which mass or energy is flowing along the links. In particular, the procedure enables us to address important questions in economics, namely, how control and wealth are structured and concentrated across national markets. We report on the first cross-country investigation of ownership networks, focusing on the stock markets of 48 countries around the world. On the one hand, our analysis confirms results expected on the basis of the literature on corporate control, namely, that in Anglo-Saxon countries control tends to be dispersed among numerous shareholders. On the other hand, it also reveals that in the same countries, control is found to be highly concentrated at the global level, namely, lying in the hands of very few important shareholders. Interestingly, the exact opposite is observed for European countries. These results have previously not been reported as they are not observable without the kind of network analysis developed here.

Using metagenomic and metatranscriptomic observations to test a thermodynamic-based model of community metabolic expression over time and space

NASA Astrophysics Data System (ADS)

Vallino, J. J.; Huber, J. A.

2016-02-01

Marine biogeochemistry is orchestrated by a complex and dynamic community of microorganisms that attempt to maximize their own fecundity through a combination of competition and cooperation. At a systems level, the community can be described as a distributed metabolic network, where different species contribute their own unique set of metabolic capabilities. Our current project attempts to understand the governing principles that describe amplification or attenuation of metabolic pathways within the network through a combination of modeling and metagenomic, metatranscriptomic and biogeochemical observations. We will describe and present results from our thermodynamic-based model that determines optimal pathway expression from available resources based on the principle of maximum entropy production (MEP); that is, based on the hypothesis that non-equilibrium systems organize to maximize energy dissipation. The MEP model currently predicts metabolic pathway expression over time, and one spatial dimension. Model predictions will be compared to biogeochemical observations and gene presence and expression from samples collected over time and space from a costal meromictic basin (Siders Pond) located in Falmouth MA, US. Siders Pond permanent stratification, caused by occasional seawater intrusion, results in steep chemoclines and redox gradients, which supports both aerobic and anaerobic phototrophs as well as sulfur, Fe and Mn redox cycles. The diversity of metabolic capability and expression we have observed over depth makes it an ideal system to test our thermodynamic-based model.

Design and implementation of streaming media server cluster based on FFMpeg.

PubMed

Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

2015-01-01

Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system.

Design and Implementation of Streaming Media Server Cluster Based on FFMpeg

PubMed Central

Zhao, Hong; Zhou, Chun-long; Jin, Bao-zhao

2015-01-01

Poor performance and network congestion are commonly observed in the streaming media single server system. This paper proposes a scheme to construct a streaming media server cluster system based on FFMpeg. In this scheme, different users are distributed to different servers according to their locations and the balance among servers is maintained by the dynamic load-balancing algorithm based on active feedback. Furthermore, a service redirection algorithm is proposed to improve the transmission efficiency of streaming media data. The experiment results show that the server cluster system has significantly alleviated the network congestion and improved the performance in comparison with the single server system. PMID:25734187

Information filtering based on corrected redundancy-eliminating mass diffusion.

PubMed

Zhu, Xuzhen; Yang, Yujie; Chen, Guilin; Medo, Matus; Tian, Hui; Cai, Shi-Min

2017-01-01

Methods used in information filtering and recommendation often rely on quantifying the similarity between objects or users. The used similarity metrics often suffer from similarity redundancies arising from correlations between objects' attributes. Based on an unweighted undirected object-user bipartite network, we propose a Corrected Redundancy-Eliminating similarity index (CRE) which is based on a spreading process on the network. Extensive experiments on three benchmark data sets-Movilens, Netflix and Amazon-show that when used in recommendation, the CRE yields significant improvements in terms of recommendation accuracy and diversity. A detailed analysis is presented to unveil the origins of the observed differences between the CRE and mainstream similarity indices.

The NASA Micro-Pulse Lidar Network (MPLNET): Co-location of Lidars with AERONET

NASA Technical Reports Server (NTRS)

Welton, Ellsworth J.; Campbell, James R.; Berkoff, Timothy A.; Spinhirne, James D.; Holben, Brent; Tsay, Si-Chee

2004-01-01

We present the formation of a global-ground based eye-safe lidar network, the NASA Micro-Pulse Lidar Network (MPLNET). The aim of MPLNET is to acquire long-term observations of aerosol and cloud vertical profiles at unique geographic sites within the NASA Aerosol Robotic Network (AERONET). Network growth follows a federated approach, pioneered by AERONET, wherein independent research groups may join MPLNET with their own instrument and site. MPLNET utilizes standard instrumentation and data processing algorithms for efficient network operations and direct comparison of data between each site. The micro-pulse lidar is eye-safe, compact, and commercially available, and most easily allows growth of the network without sacrificing standardized instrumentation gods. Red-time data products (next-day) are available, and include Level 1 daily lidar signal images from the surface to -2Okm, and Level 1.5 aerosol extinction provides at times co-incident with AERONET observations. Testing of our quality assured aerosol extinction products, Level 2, is near completion and data will soon be available. Level 3 products, continuous daylight aerosol extinction profiles, are under development and testing has begun. An overview of h4PL" will be presented. Successful methods of merging standardized lidar operations with AERONET will also be discussed, with the first 4 years of MPLNET results serving as an example.

The Crucial Role of Amateur-Professional Networks in the Golden Age of Large Surveys (Abstract)

NASA Astrophysics Data System (ADS)

Rodriguez, J. E.

2017-06-01

(Abstract only) With ongoing projects such as HATNet, SuperWASP, KELT, MEarth, and the CoRoT and Kepler/K2 mission, we are in a golden era of large photometric surveys. In addition, LSST and TESS will be coming online in the next three to five years. The combination of all these projects will increased the number of photometrically monitored stars by orders of magnitude. It is expected that these surveys will enhance our knowledge of circumstellar architecture and the early stages of stellar and planetary formation, while providing a better understanding of exoplanet demographics. However, the success of these surveys will be dependent on simultaneous and continued follow up by large networks. With federal scientific funding reduced over the past few years, the availability of astronomical observations has been directly affected. Fortunately, ground based amateur-professional networks like the AAVSO and the KELT Follow-up Network (KELT-FUN) are already providing access to an international, independent resource for professional grade astronomical observations. These networks have both multi-band photometric and spectroscopic capabilities. I provide an overview of the ongoing and future surveys, highlight past and current contributions by amateur-professional networks to scientific discovery, and discuss the role of these networks in upcoming projects.

Microclimate Exposures of Surface-Based Weather Stations: Implications For The Assessment of Long-Term Temperature Trends.

NASA Astrophysics Data System (ADS)

Davey, Christopher A.; Pielke, Roger A., Sr.

2005-04-01

The U.S. Historical Climate Network is a subset of surface weather observation stations selected from the National Weather Service cooperative station network. The criteria used to select these stations do not sufficiently address station exposure characteristics. In addition, the current metadata available for cooperative network stations generally do not describe site exposure characteristics in sufficient detail. This paper focuses on site exposures with respect to air temperature measurements. A total of 57 stations were photographically surveyed in eastern Colorado, comparing existing exposures to the standards endorsed by the World Meteorological Organization. The exposures of most sites surveyed, including U.S. Historical Climate Network sites, were observed to fall short of these standards. This raises a critical question about the use of many Historical Climate Network sites in the development of long-term climate records and the detection of climate trends. Some of these sites clearly have poor exposures and therefore should be considered for removal from the Historical Climate Network. Candidate replacement sites do exist and should be considered for addition into the network to replace the removed sites. Documentation as performed for this study should be conducted worldwide in order to determine the extent of spatially nonrepresentative exposures and possible temperature biases.

Brain Network Theory Can Predict Whether Neuropsychological Outcomes Will Differ from Clinical Expectations.

PubMed

Warren, David E; Denburg, Natalie L; Power, Jonathan D; Bruss, Joel; Waldron, Eric J; Sun, Haoxin; Petersen, Steve E; Tranel, Daniel

2017-02-01

Theories of brain-network organization based on neuroimaging data have burgeoned in recent years, but the predictive power of such theories for cognition and behavior has only rarely been examined. Here, predictions from clinical neuropsychologists about the cognitive profiles of patients with focal brain lesions were used to evaluate a brain-network theory (Warren et al., 2014). Neuropsychologists made predictions regarding the neuropsychological profiles of a neurological patient sample (N = 30) based on lesion location. The neuropsychologists then rated the congruence of their predictions with observed neuropsychological outcomes, in regard to the "severity" of neuropsychological deficits and the "focality" of neuropsychological deficits. Based on the network theory, two types of lesion locations were identified: "target" locations (putative hubs in a brain-wide network) and "control" locations (hypothesized to play limited roles in network function). We found that patients with lesions of target locations (N = 19) had deficits of greater than expected severity that were more widespread than expected, whereas patients with lesions of control locations (N = 11) showed milder, circumscribed deficits that were more congruent with expectations. The findings for the target brain locations suggest that prevailing views of brain-behavior relationships may be sharpened and refined by integrating recently proposed network-oriented perspectives. © The Author 2016. Published by Oxford University Press. All rights reserved. For permissions, please e-mail: journals.permissions@oup.com.

Decreased Functional Brain Connectivity in Adolescents with Internet Addiction

PubMed Central

Hong, Soon-Beom; Zalesky, Andrew; Cocchi, Luca; Fornito, Alex; Choi, Eun-Jung; Kim, Ho-Hyun; Suh, Jeong-Eun; Kim, Chang-Dai; Kim, Jae-Won; Yi, Soon-Hyung

2013-01-01

Background Internet addiction has become increasingly recognized as a mental disorder, though its neurobiological basis is unknown. This study used functional neuroimaging to investigate whole-brain functional connectivity in adolescents diagnosed with internet addiction. Based on neurobiological changes seen in other addiction related disorders, it was predicted that connectivity disruptions in adolescents with internet addiction would be most prominent in cortico-striatal circuitry. Methods Participants were 12 adolescents diagnosed with internet addiction and 11 healthy comparison subjects. Resting-state functional magnetic resonance images were acquired, and group differences in brain functional connectivity were analyzed using the network-based statistic. We also analyzed network topology, testing for between-group differences in key graph-based network measures. Results Adolescents with internet addiction showed reduced functional connectivity spanning a distributed network. The majority of impaired connections involved cortico-subcortical circuits (∼24% with prefrontal and ∼27% with parietal cortex). Bilateral putamen was the most extensively involved subcortical brain region. No between-group difference was observed in network topological measures, including the clustering coefficient, characteristic path length, or the small-worldness ratio. Conclusions Internet addiction is associated with a widespread and significant decrease of functional connectivity in cortico-striatal circuits, in the absence of global changes in brain functional network topology. PMID:23451272

Secure and lightweight network admission and transmission protocol for body sensor networks.

PubMed

He, Daojing; Chen, Chun; Chan, Sammy; Bu, Jiajun; Zhang, Pingxin

2013-05-01

A body sensor network (BSN) is a wireless network of biosensors and a local processing unit, which is commonly referred to as the personal wireless hub (PWH). Personal health information (PHI) is collected by biosensors and delivered to the PWH before it is forwarded to the remote healthcare center for further processing. In a BSN, it is critical to only admit eligible biosensors and PWH into the network. Also, securing the transmission from each biosensor to PWH is essential not only for ensuring safety of PHI delivery, but also for preserving the privacy of PHI. In this paper, we present the design, implementation, and evaluation of a secure network admission and transmission subsystem based on a polynomial-based authentication scheme. The procedures in this subsystem to establish keys for each biosensor are communication efficient and energy efficient. Moreover, based on the observation that an adversary eavesdropping in a BSN faces inevitable channel errors, we propose to exploit the adversary's uncertainty regarding the PHI transmission to update the individual key dynamically and improve key secrecy. In addition to the theoretical analysis that demonstrates the security properties of our system, this paper also reports the experimental results of the proposed protocol on resource-limited sensor platforms, which show the efficiency of our system in practice.

A Workflow-based Intelligent Network Data Movement Advisor with End-to-end Performance Optimization

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

Zhu, Michelle M.; Wu, Chase Q.

2013-11-07

Next-generation eScience applications often generate large amounts of simulation, experimental, or observational data that must be shared and managed by collaborative organizations. Advanced networking technologies and services have been rapidly developed and deployed to facilitate such massive data transfer. However, these technologies and services have not been fully utilized mainly because their use typically requires significant domain knowledge and in many cases application users are even not aware of their existence. By leveraging the functionalities of an existing Network-Aware Data Movement Advisor (NADMA) utility, we propose a new Workflow-based Intelligent Network Data Movement Advisor (WINDMA) with end-to-end performance optimization formore » this DOE funded project. This WINDMA system integrates three major components: resource discovery, data movement, and status monitoring, and supports the sharing of common data movement workflows through account and database management. This system provides a web interface and interacts with existing data/space management and discovery services such as Storage Resource Management, transport methods such as GridFTP and GlobusOnline, and network resource provisioning brokers such as ION and OSCARS. We demonstrate the efficacy of the proposed transport-support workflow system in several use cases based on its implementation and deployment in DOE wide-area networks.« less

Phenological observations since the Linnean time in Finland

NASA Astrophysics Data System (ADS)

Kubin, E.; Poikolainen, J.; Karhu, J.; Terhivuo, J.

2012-04-01

The Finnish National Phenological Network was established in 1996 by the Finnish Forest Research Institute in collaboration with other research institutes and universities. The Network investigates the timing of phenological phases of forest plants in relation to climate factors, develops real time information to the internet and studies digital techniques as tools for monitoring. Monitoring is done troughout the growth period, focusing on nine forest tree species and two dwarf shrubs. The results can be followed in real time at: http://www.metla.fi/metinfo/fenologia/index-en.htm. The results indicate that spring phenophases usually advanced with respect to climatic conditions, but there were also differences between the years. The research period started in 1995 is relatively short and the results indicates that long-term monitoring is needed in order to detect true trends in the impacts of climate on plant phenology. The Finnish National Phenological Network has therefore collaborated with the Finnish Museum of Natural History and analysed historical phenological data based on voluntary monitoring. The oldest phenological observation series based on voluntary observers started in Finland in 1752. The long-term data shows an advancement in the timing of bud burst by five days per 100 years in Prunus padus. The onset of flowering in the rowan (Sorbus aucuparia) has become correspondingly earlier in Finland at the rate of three days per century. In the conference the focus is on a historical long-term dataset as well as on the newer Finnish National Phenological Network established for monitoring annual phenological events taking place in the same individual plants. The latest results of the network will be updated with the earlier presented historical data. Phenological monitoring is nowadays more important than ever especially in boreal regions, where spring temperatures are elevated. Compilation and documentation of observations on plant phenophases play a key role in working out the rate of global dimate change. The timing of spring phenolgy will be discussed in the conference.

Early grey matter changes in structural covariance networks in Huntington's disease.

PubMed

Coppen, Emma M; van der Grond, Jeroen; Hafkemeijer, Anne; Rombouts, Serge A R B; Roos, Raymund A C

2016-01-01

Progressive subcortical changes are known to occur in Huntington's disease (HD), a hereditary neurodegenerative disorder. Less is known about the occurrence and cohesion of whole brain grey matter changes in HD. We aimed to detect network integrity changes in grey matter structural covariance networks and examined relationships with clinical assessments. Structural magnetic resonance imaging data of premanifest HD ( n  = 30), HD patients (n = 30) and controls (n = 30) was used to identify ten structural covariance networks based on a novel technique using the co-variation of grey matter with independent component analysis in FSL. Group differences were studied controlling for age and gender. To explore whether our approach is effective in examining grey matter changes, regional voxel-based analysis was additionally performed. Premanifest HD and HD patients showed decreased network integrity in two networks compared to controls. One network included the caudate nucleus, precuneous and anterior cingulate cortex (in HD p  < 0.001, in pre-HD p  = 0.003). One other network contained the hippocampus, premotor, sensorimotor, and insular cortices (in HD p  < 0.001, in pre-HD p  = 0.023). Additionally, in HD patients only, decreased network integrity was observed in a network including the lingual gyrus, intracalcarine, cuneal, and lateral occipital cortices ( p  = 0.032). Changes in network integrity were significantly associated with scores of motor and neuropsychological assessments. In premanifest HD, voxel-based analyses showed pronounced volume loss in the basal ganglia, but less prominent in cortical regions. Our results suggest that structural covariance might be a sensitive approach to reveal early grey matter changes, especially for premanifest HD.

The contagious nature of imprisonment: an agent-based model to explain racial disparities in incarceration rates.

PubMed

Lum, Kristian; Swarup, Samarth; Eubank, Stephen; Hawdon, James

2014-09-06

We build an agent-based model of incarceration based on the susceptible-infected-suspectible (SIS) model of infectious disease propagation. Our central hypothesis is that the observed racial disparities in incarceration rates between Black and White Americans can be explained as the result of differential sentencing between the two demographic groups. We demonstrate that if incarceration can be spread through a social influence network, then even relatively small differences in sentencing can result in large disparities in incarceration rates. Controlling for effects of transmissibility, susceptibility and influence network structure, our model reproduces the observed large disparities in incarceration rates given the differences in sentence lengths for White and Black drug offenders in the USA without extensive parameter tuning. We further establish the suitability of the SIS model as applied to incarceration by demonstrating that the observed structural patterns of recidivism are an emergent property of the model. In fact, our model shows a remarkably close correspondence with California incarceration data. This work advances efforts to combine the theories and methods of epidemiology and criminology.

Cluster-based adaptive power control protocol using Hidden Markov Model for Wireless Sensor Networks

NASA Astrophysics Data System (ADS)

Vinutha, C. B.; Nalini, N.; Nagaraja, M.

2017-06-01

This paper presents strategies for an efficient and dynamic transmission power control technique, in order to reduce packet drop and hence energy consumption of power-hungry sensor nodes operated in highly non-linear channel conditions of Wireless Sensor Networks. Besides, we also focus to prolong network lifetime and scalability by designing cluster-based network structure. Specifically we consider weight-based clustering approach wherein, minimum significant node is chosen as Cluster Head (CH) which is computed stemmed from the factors distance, remaining residual battery power and received signal strength (RSS). Further, transmission power control schemes to fit into dynamic channel conditions are meticulously implemented using Hidden Markov Model (HMM) where probability transition matrix is formulated based on the observed RSS measurements. Typically, CH estimates initial transmission power of its cluster members (CMs) from RSS using HMM and broadcast this value to its CMs for initialising their power value. Further, if CH finds that there are variations in link quality and RSS of the CMs, it again re-computes and optimises the transmission power level of the nodes using HMM to avoid packet loss due noise interference. We have demonstrated our simulation results to prove that our technique efficiently controls the power levels of sensing nodes to save significant quantity of energy for different sized network.

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-Comparison of Ground-Based Lightning Reference Networks

NASA Technical Reports Server (NTRS)

Cummins, Kenneth L.; Carey, Lawrence D.; Schultz, Christopher J.; Bateman, Monte G.; Cecil, Daniel J.; Rudlosky, Scott D.; Petersen, Walter Arthur; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala s Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

GLM Proxy Data Generation: Methods for Stroke/Pulse Level Inter-comparison of Ground-based Lightning Reference Networks

NASA Astrophysics Data System (ADS)

Cummins, K. L.; Carey, L. D.; Schultz, C. J.; Bateman, M. G.; Cecil, D. J.; Rudlosky, S. D.; Petersen, W. A.; Blakeslee, R. J.; Goodman, S. J.

2011-12-01

In order to produce useful proxy data for the GOES-R Geostationary Lightning Mapper (GLM) in regions not covered by VLF lightning mapping systems, we intend to employ data produced by ground-based (regional or global) VLF/LF lightning detection networks. Before using these data in GLM Risk Reduction tasks, it is necessary to have a quantitative understanding of the performance of these networks, in terms of CG flash/stroke DE, cloud flash/pulse DE, location accuracy, and CLD/CG classification error. This information is being obtained through inter-comparison with LMAs and well-quantified VLF/LF lightning networks. One of our approaches is to compare "bulk" counting statistics on the spatial scale of convective cells, in order to both quantify relative performance and observe variations in cell-based temporal trends provided by each network. In addition, we are using microsecond-level stroke/pulse time correlation to facilitate detailed inter-comparisons at a more-fundamental level. The current development status of our ground-based inter-comparison and evaluation tools will be presented, and performance metrics will be discussed through a comparison of Vaisala's Global Lightning Dataset (GLD360) with the NLDN at locations within and outside the U.S.

Framework based on communicability and flow to analyze complex network dynamics

NASA Astrophysics Data System (ADS)

Gilson, M.; Kouvaris, N. E.; Deco, G.; Zamora-López, G.

2018-05-01

Graph theory constitutes a widely used and established field providing powerful tools for the characterization of complex networks. The intricate topology of networks can also be investigated by means of the collective dynamics observed in the interactions of self-sustained oscillations (synchronization patterns) or propagationlike processes such as random walks. However, networks are often inferred from real-data-forming dynamic systems, which are different from those employed to reveal their topological characteristics. This stresses the necessity for a theoretical framework dedicated to the mutual relationship between the structure and dynamics in complex networks, as the two sides of the same coin. Here we propose a rigorous framework based on the network response over time (i.e., Green function) to study interactions between nodes across time. For this purpose we define the flow that describes the interplay between the network connectivity and external inputs. This multivariate measure relates to the concepts of graph communicability and the map equation. We illustrate our theory using the multivariate Ornstein-Uhlenbeck process, which describes stable and non-conservative dynamics, but the formalism can be adapted to other local dynamics for which the Green function is known. We provide applications to classical network examples, such as small-world ring and hierarchical networks. Our theory defines a comprehensive framework that is canonically related to directed and weighted networks, thus paving a way to revise the standards for network analysis, from the pairwise interactions between nodes to the global properties of networks including community detection.

A network-base analysis of CMIP5 "historical" experiments

NASA Astrophysics Data System (ADS)

Bracco, A.; Foudalis, I.; Dovrolis, C.

2012-12-01

In computer science, "complex network analysis" refers to a set of metrics, modeling tools and algorithms commonly used in the study of complex nonlinear dynamical systems. Its main premise is that the underlying topology or network structure of a system has a strong impact on its dynamics and evolution. By allowing to investigate local and non-local statistical interaction, network analysis provides a powerful, but only marginally explored, framework to validate climate models and investigate teleconnections, assessing their strength, range, and impacts on the climate system. In this work we propose a new, fast, robust and scalable methodology to examine, quantify, and visualize climate sensitivity, while constraining general circulation models (GCMs) outputs with observations. The goal of our novel approach is to uncover relations in the climate system that are not (or not fully) captured by more traditional methodologies used in climate science and often adopted from nonlinear dynamical systems analysis, and to explain known climate phenomena in terms of the network structure or its metrics. Our methodology is based on a solid theoretical framework and employs mathematical and statistical tools, exploited only tentatively in climate research so far. Suitably adapted to the climate problem, these tools can assist in visualizing the trade-offs in representing global links and teleconnections among different data sets. Here we present the methodology, and compare network properties for different reanalysis data sets and a suite of CMIP5 coupled GCM outputs. With an extensive model intercomparison in terms of the climate network that each model leads to, we quantify how each model reproduces major teleconnections, rank model performances, and identify common or specific errors in comparing model outputs and observations.

Review of the hydrologic data-collection network in the St Joseph River basin, Indiana

USGS Publications Warehouse

Crompton, E.J.; Peters, J.G.; Miller, R.L.; Stewart, J.A.; Banaszak, K.J.; Shedlock, R.J.

1986-01-01

The St. Joseph River Basin data-collection network in the St. Joseph River for streamflow, lake, ground water, and climatic stations was reviewed. The network review included only the 1700 sq mi part of the basin in Indiana. The streamflow network includes 11 continuous-record gaging stations and one partial-record station. Based on areal distribution, lake effect , contributing drainage area, and flow-record ratio, six of these stations can be used to describe regional hydrology. Gaging stations on lakes are used to collect long-term lake-level data on which to base legal lake levels, and to monitor lake-level fluctuations after legal levels are established. More hydrogeologic data are needed for determining the degree to which grouhd water affects lake levels. The current groundwater network comprises 15 observation wells and has four purposes: (1) to determine the interaction between groundwater and lakes; (2) to measure changes in groundwater levels near irrigation wells; (3) to measure water levels in wells at special purpose sites; and (4) to measure long-term changes in water levels in areas not affected by pumping. Seven wells near three lakes have provided sufficient information for correlating water levels in wells and lakes but are not adequate to quantify the effect of groundwater on lake levels. Water levels in five observation wells located in the vicinity of intensive irrigation are not noticeably affected by seasonal withdrawals. The National Weather Sevice operates eight climatic stations in the basin primarily to characterize regional climatic conditions and to aid in flood forecasting. The network meets network-density guidelines established by the World Meterological Organization for collection of precipitation and evaporation data but not guidelines suggested by the National Weather Service for density of precipitation gages in areas of significant convective rainfalls. (Author 's abstract)

Perfusion network shift during seizures in medial temporal lobe epilepsy.

PubMed

Sequeira, Karen M; Tabesh, Ali; Sainju, Rup K; DeSantis, Stacia M; Naselaris, Thomas; Joseph, Jane E; Ahlman, Mark A; Spicer, Kenneth M; Glazier, Steve S; Edwards, Jonathan C; Bonilha, Leonardo

2013-01-01

Medial temporal lobe epilepsy (MTLE) is associated with limbic atrophy involving the hippocampus, peri-hippocampal and extra-temporal structures. While MTLE is related to static structural limbic compromise, it is unknown whether the limbic system undergoes dynamic regional perfusion network alterations during seizures. In this study, we aimed to investigate state specific (i.e. ictal versus interictal) perfusional limbic networks in patients with MTLE. We studied clinical information and single photon emission computed tomography (SPECT) images obtained with intravenous infusion of the radioactive tracer Technetium- Tc 99 m Hexamethylpropyleneamine Oxime (Tc-99 m HMPAO) during ictal and interictal state confirmed by video-electroencephalography (VEEG) in 20 patients with unilateral MTLE (12 left and 8 right MTLE). Pair-wise voxel-based analyses were used to define global changes in tracer between states. Regional tracer uptake was calculated and state specific adjacency matrices were constructed based on regional correlation of uptake across subjects. Graph theoretical measures were applied to investigate global and regional state specific network reconfigurations. A significant increase in tracer uptake was observed during the ictal state in the medial temporal region, cerebellum, thalamus, insula and putamen. From network analyses, we observed a relative decreased correlation between the epileptogenic temporal region and remaining cortex during the interictal state, followed by a surge of cross-correlated perfusion in epileptogenic temporal-limbic structures during a seizure, corresponding to local network integration. These results suggest that MTLE is associated with a state specific perfusion and possibly functional organization consisting of a surge of limbic cross-correlated tracer uptake during a seizure, with a relative disconnection of the epileptogenic temporal lobe in the interictal period. This pattern of state specific shift in metabolic networks in MTLE may improve the understanding of epileptogenesis and neuropsychological impairments associated with MTLE.

Empirical Reference Distributions for Networks of Different Size

PubMed Central

Smith, Anna; Calder, Catherine A.; Browning, Christopher R.

2016-01-01

Network analysis has become an increasingly prevalent research tool across a vast range of scientific fields. Here, we focus on the particular issue of comparing network statistics, i.e. graph-level measures of network structural features, across multiple networks that differ in size. Although “normalized” versions of some network statistics exist, we demonstrate via simulation why direct comparison is often inappropriate. We consider normalizing network statistics relative to a simple fully parameterized reference distribution and demonstrate via simulation how this is an improvement over direct comparison, but still sometimes problematic. We propose a new adjustment method based on a reference distribution constructed as a mixture model of random graphs which reflect the dependence structure exhibited in the observed networks. We show that using simple Bernoulli models as mixture components in this reference distribution can provide adjusted network statistics that are relatively comparable across different network sizes but still describe interesting features of networks, and that this can be accomplished at relatively low computational expense. Finally, we apply this methodology to a collection of ecological networks derived from the Los Angeles Family and Neighborhood Survey activity location data. PMID:27721556

Kansas ground-water observation-well network, 1985

USGS Publications Warehouse

Dague, B.J.; Stullken, L.E.

1986-01-01

Water level measurements are made in 1,892 selected wells in 73 counties, which currently (1985) comprise the Kansas groundwater observation-well network. These measurements are made on a continuous, monthly, quarterly, or annual basis. Water level measurements have been made in observation wells since 1937 as part of a cooperative program among the Kansas Geological Survey , the Kansas State Board of Agriculture, the city of Wichita, and the U.S. Geological Survey. The objectives of the observation-well cooperative program are: (1) to provide long-term records of water level fluctuations in representative wells, (2) to facilitate the determination of possible water level trends that may indicate future availability of groundwater supplies, (3) to aid in the determination of possible changes in the base flow of streams, and (4) to provide information for use in water-resources research. This report lists for each well in the network the location, the first year of recorded water level measurement, the frequency and number of measurements, the land-surface altitude, hexagon-grid identifiers for wells in the High Plains aquifer, and the principal geologic unit(s) in which the well is completed. (USGS)

Reconstruction of stochastic temporal networks through diffusive arrival times

NASA Astrophysics Data System (ADS)

Li, Xun; Li, Xiang

2017-06-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications.

Humans use compression heuristics to improve the recall of social networks.

PubMed

Brashears, Matthew E

2013-01-01

The ability of primates, including humans, to maintain large social networks appears to depend on the ratio of the neocortex to the rest of the brain. However, observed human network size frequently exceeds predictions based on this ratio (e.g., "Dunbar's Number"), implying that human networks are too large to be cognitively managed. Here I show that humans adaptively use compression heuristics to allow larger amounts of social information to be stored in the same brain volume. I find that human adults can remember larger numbers of relationships in greater detail when a network exhibits triadic closure and kin labels than when it does not. These findings help to explain how humans manage large and complex social networks with finite cognitive resources and suggest that many of the unusual properties of human social networks are rooted in the strategies necessary to cope with cognitive limitations.

Evaluating the effect of aging on interference resolution with time-varying complex networks analysis

PubMed Central

Ariza, Pedro; Solesio-Jofre, Elena; Martínez, Johann H.; Pineda-Pardo, José A.; Niso, Guiomar; Maestú, Fernando; Buldú, Javier M.

2015-01-01

In this study we used graph theory analysis to investigate age-related reorganization of functional networks during the active maintenance of information that is interrupted by external interference. Additionally, we sought to investigate network differences before and after averaging network parameters between both maintenance and interference windows. We compared young and older adults by measuring their magnetoencephalographic recordings during an interference-based working memory task restricted to successful recognitions. Data analysis focused on the topology/temporal evolution of functional networks during both the maintenance and interference windows. We observed that: (a) Older adults require higher synchronization between cortical brain sites in order to achieve a successful recognition, (b) The main differences between age groups arise during the interference window, (c) Older adults show reduced ability to reorganize network topology when interference is introduced, and (d) Averaging network parameters leads to a loss of sensitivity to detect age differences. PMID:26029079

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.

Simultaneous multi-patch-clamp and extracellular-array recordings: Single neuron reflects network activity.

PubMed

Vardi, Roni; Goldental, Amir; Sardi, Shira; Sheinin, Anton; Kanter, Ido

2016-11-08

The increasing number of recording electrodes enhances the capability of capturing the network's cooperative activity, however, using too many monitors might alter the properties of the measured neural network and induce noise. Using a technique that merges simultaneous multi-patch-clamp and multi-electrode array recordings of neural networks in-vitro, we show that the membrane potential of a single neuron is a reliable and super-sensitive probe for monitoring such cooperative activities and their detailed rhythms. Specifically, the membrane potential and the spiking activity of a single neuron are either highly correlated or highly anti-correlated with the time-dependent macroscopic activity of the entire network. This surprising observation also sheds light on the cooperative origin of neuronal burst in cultured networks. Our findings present an alternative flexible approach to the technique based on a massive tiling of networks by large-scale arrays of electrodes to monitor their activity.

NASA Communications Augmentation network

NASA Technical Reports Server (NTRS)

Omidyar, Guy C.; Butler, Thomas E.; Laios, Straton C.

1990-01-01

The NASA Communications (Nascom) Division of the Mission Operations and Data Systems Directorate (MO&DSD) is to undertake a major initiative to develop the Nascom Augmentation (NAUG) network to achieve its long-range service objectives for operational data transport to support the Space Station Freedom Program, the Earth Observing System (EOS), and other projects. The NAUG is the Nascom ground communications network being developed to accommodate the operational traffic of the mid-1990s and beyond. The NAUG network development will be based on the Open Systems Interconnection Reference Model (OSI-RM). This paper describes the NAUG network architecture, subsystems, topology, and services; addresses issues of internetworking the Nascom network with other elements of the Space Station Information System (SSIS); discusses the operations environment. This paper also notes the areas of related research and presents the current conception of how the network will provide broadband services in 1998.

Reconstruction of stochastic temporal networks through diffusive arrival times

PubMed Central

Li, Xun; Li, Xiang

2017-01-01

Temporal networks have opened a new dimension in defining and quantification of complex interacting systems. Our ability to identify and reproduce time-resolved interaction patterns is, however, limited by the restricted access to empirical individual-level data. Here we propose an inverse modelling method based on first-arrival observations of the diffusion process taking place on temporal networks. We describe an efficient coordinate-ascent implementation for inferring stochastic temporal networks that builds in particular but not exclusively on the null model assumption of mutually independent interaction sequences at the dyadic level. The results of benchmark tests applied on both synthesized and empirical network data sets confirm the validity of our algorithm, showing the feasibility of statistically accurate inference of temporal networks only from moderate-sized samples of diffusion cascades. Our approach provides an effective and flexible scheme for the temporally augmented inverse problems of network reconstruction and has potential in a broad variety of applications. PMID:28604687

Feasibility of Using Distributed Wireless Mesh Networks for Medical Emergency Response

PubMed Central

Braunstein, Brian; Trimble, Troy; Mishra, Rajesh; Manoj, B. S.; Rao, Ramesh; Lenert, Leslie

2006-01-01

Achieving reliable, efficient data communications networks at a disaster site is a difficult task. Network paradigms, such as Wireless Mesh Network (WMN) architectures, form one exemplar for providing high-bandwidth, scalable data communication for medical emergency response activity. WMNs are created by self-organized wireless nodes that use multi-hop wireless relaying for data transfer. In this paper, we describe our experience using a mesh network architecture we developed for homeland security and medical emergency applications. We briefly discuss the architecture and present the traffic behavioral observations made by a client-server medical emergency application tested during a large-scale homeland security drill. We present our traffic measurements, describe lessons learned, and offer functional requirements (based on field testing) for practical 802.11 mesh medical emergency response networks. With certain caveats, the results suggest that 802.11 mesh networks are feasible and scalable systems for field communications in disaster settings. PMID:17238308

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.

A 280-Year Long Series of Phenological Observations of Cherry Tree Blossoming Dates for Switzerland

NASA Astrophysics Data System (ADS)

Rutishauser, T.; Luterbacher, J.; Wanner, H.

2003-04-01

Phenology is generally described as the timing of life cycle phases or activities of plants and animals in their temporal occurrence throughout the year (Lieth 1974). Recent studies have shown that meteorological and climatological impacts leave their 'fingerprints' across natural systems in general and strongly influence the seasonal activities of single animal and plant species. During the 20th century, phenological observation networks have been established around the world to document and analyze the influence of the globally changing climate to plants and wildlife. This work presents a first attempt of a unique 280-year long series of phenological observations of cherry tree blossoming dates for the Swiss plateau region. In Switzerland, a nation-wide phenological observation network has been established in 1951 currently documenting 69 phenophases of 26 different plant species. A guidebook seeks to increase objectiveness in the network observations. The observations of the blooming of the cherry tree (prunus avium) were chosen to calculate a mean series for the Swiss plateau region with observations from altitudes ranging between 370 and 860 asl. A total number of 737 observations from 21 stations were used. A linear regression was established between the mean blooming date and altitude in order to correct the data to a reference altitude level. Other ecological parameters were unaccounted for. The selected network data series from 1951 to 2000 was combined and prolonged with observations from various sources back to 1721. These include several historical observation series by farmers, clergymen and teachers, data from various stations collected at the newly established Swiss meteorological network from 1864 to 1873 and the single long series of observations from Liestal starting in 1894. The homogenized time series of observations will be compared with reconstructions of late winter temperatures as well as statistical estimations of blooming time based on long instrumental data from Europe. In addition, the series is one of the few historical phenological records to assess past climate and ecological changes. Lieth, H. (1974). Phenology and Seasonality Modeling. Berlin, Heidelberg, New York, Springer.

Efficient embedding of complex networks to hyperbolic space via their Laplacian

PubMed Central

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-01-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction. PMID:27445157

Efficient embedding of complex networks to hyperbolic space via their Laplacian

NASA Astrophysics Data System (ADS)

Alanis-Lobato, Gregorio; Mier, Pablo; Andrade-Navarro, Miguel A.

2016-07-01

The different factors involved in the growth process of complex networks imprint valuable information in their observable topologies. How to exploit this information to accurately predict structural network changes is the subject of active research. A recent model of network growth sustains that the emergence of properties common to most complex systems is the result of certain trade-offs between node birth-time and similarity. This model has a geometric interpretation in hyperbolic space, where distances between nodes abstract this optimisation process. Current methods for network hyperbolic embedding search for node coordinates that maximise the likelihood that the network was produced by the afore-mentioned model. Here, a different strategy is followed in the form of the Laplacian-based Network Embedding, a simple yet accurate, efficient and data driven manifold learning approach, which allows for the quick geometric analysis of big networks. Comparisons against existing embedding and prediction techniques highlight its applicability to network evolution and link prediction.

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments

PubMed Central

Avalappampatty Sivasamy, Aneetha; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T2 method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T2 statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better. PMID:26357668

A Dynamic Intrusion Detection System Based on Multivariate Hotelling's T2 Statistics Approach for Network Environments.

PubMed

Sivasamy, Aneetha Avalappampatty; Sundan, Bose

2015-01-01

The ever expanding communication requirements in today's world demand extensive and efficient network systems with equally efficient and reliable security features integrated for safe, confident, and secured communication and data transfer. Providing effective security protocols for any network environment, therefore, assumes paramount importance. Attempts are made continuously for designing more efficient and dynamic network intrusion detection models. In this work, an approach based on Hotelling's T(2) method, a multivariate statistical analysis technique, has been employed for intrusion detection, especially in network environments. Components such as preprocessing, multivariate statistical analysis, and attack detection have been incorporated in developing the multivariate Hotelling's T(2) statistical model and necessary profiles have been generated based on the T-square distance metrics. With a threshold range obtained using the central limit theorem, observed traffic profiles have been classified either as normal or attack types. Performance of the model, as evaluated through validation and testing using KDD Cup'99 dataset, has shown very high detection rates for all classes with low false alarm rates. Accuracy of the model presented in this work, in comparison with the existing models, has been found to be much better.

Stability of synchrony against local intermittent fluctuations in tree-like power grids

NASA Astrophysics Data System (ADS)

Auer, Sabine; Hellmann, Frank; Krause, Marie; Kurths, Jürgen

2017-12-01

90% of all Renewable Energy Power in Germany is installed in tree-like distribution grids. Intermittent power fluctuations from such sources introduce new dynamics into the lower grid layers. At the same time, distributed resources will have to contribute to stabilize the grid against these fluctuations in the future. In this paper, we model a system of distributed resources as oscillators on a tree-like, lossy power grid and its ability to withstand desynchronization from localized intermittent renewable infeed. We find a remarkable interplay of the network structure and the position of the node at which the fluctuations are fed in. An important precondition for our findings is the presence of losses in distribution grids. Then, the most network central node splits the network into branches with different influence on network stability. Troublemakers, i.e., nodes at which fluctuations are especially exciting the grid, tend to be downstream branches with high net power outflow. For low coupling strength, we also find branches of nodes vulnerable to fluctuations anywhere in the network. These network regions can be predicted at high confidence using an eigenvector based network measure taking the turbulent nature of perturbations into account. While we focus here on tree-like networks, the observed effects also appear, albeit less pronounced, for weakly meshed grids. On the other hand, the observed effects disappear for lossless power grids often studied in the complex system literature.

Networked web-cameras monitor congruent seasonal development of birches with phenological field observations

NASA Astrophysics Data System (ADS)

Peltoniemi, Mikko; Aurela, Mika; Böttcher, Kristin; Kolari, Pasi; Loehr, John; Karhu, Jouni; Kubin, Eero; Linkosalmi, Maiju; Melih Tanis, Cemal; Nadir Arslan, Ali

2017-04-01

Ecosystems' potential to provide services, e.g. to sequester carbon is largely driven by the phenological cycle of vegetation. Timing of phenological events is required for understanding and predicting the influence of climate change on ecosystems and to support various analyses of ecosystem functioning. We established a network of cameras for automated monitoring of phenological activity of vegetation in boreal ecosystems of Finland. Cameras were mounted on 14 sites, each site having 1-3 cameras. In this study, we used cameras at 11 of these sites to investigate how well networked cameras detect phenological development of birches (Betula spp.) along the latitudinal gradient. Birches are interesting focal species for the analyses as they are common throughout Finland. In our cameras they often appear in smaller quantities within dominant species in the images. Here, we tested whether small scattered birch image elements allow reliable extraction of color indices and changes therein. We compared automatically derived phenological dates from these birch image elements to visually determined dates from the same image time series, and to independent observations recorded in the phenological monitoring network from the same region. Automatically extracted season start dates based on the change of green color fraction in the spring corresponded well with the visually interpreted start of season, and field observed budburst dates. During the declining season, red color fraction turned out to be superior over green color based indices in predicting leaf yellowing and fall. The latitudinal gradients derived using automated phenological date extraction corresponded well with gradients based on phenological field observations from the same region. We conclude that already small and scattered birch image elements allow reliable extraction of key phenological dates for birch species. Devising cameras for species specific analyses of phenological timing will be useful for explaining variation of time series of satellite based indices, and it will also benefit models describing ecosystem functioning at species or plant functional type level. With the contribution of the LIFE+ financial instrument of the European Union (LIFE12 ENV/FI/000409 Monimet, http://monimet.fmi.fi)

Automated sensor networks to advance ocean science

NASA Astrophysics Data System (ADS)

Schofield, O.; Orcutt, J. A.; Arrott, M.; Vernon, F. L.; Peach, C. L.; Meisinger, M.; Krueger, I.; Kleinert, J.; Chao, Y.; Chien, S.; Thompson, D. R.; Chave, A. D.; Balasuriya, A.

2010-12-01

The National Science Foundation has funded the Ocean Observatories Initiative (OOI), which over the next five years will deploy infrastructure to expand scientist’s ability to remotely study the ocean. The deployed infrastructure will be linked by a robust cyberinfrastructure (CI) that will integrate marine observatories into a coherent system-of-systems. OOI is committed to engaging the ocean sciences community during the construction pahse. For the CI, this is being enabled by using a “spiral design strategy” allowing for input throughout the construction phase. In Fall 2009, the OOI CI development team used an existing ocean observing network in the Mid-Atlantic Bight (MAB) to test OOI CI software. The objective of this CI test was to aggregate data from ships, autonomous underwater vehicles (AUVs), shore-based radars, and satellites and make it available to five different data-assimilating ocean forecast models. Scientists used these multi-model forecasts to automate future glider missions in order to demonstrate the feasibility of two-way interactivity between the sensor web and predictive models. The CI software coordinated and prioritized the shared resources that allowed for the semi-automated reconfiguration of assett-tasking, and thus enabled an autonomous execution of observation plans for the fixed and mobile observation platforms. Efforts were coordinated through a web portal that provided an access point for the observational data and model forecasts. Researchers could use the CI software in tandem with the web data portal to assess the performance of individual numerical model results, or multi-model ensembles, through real-time comparisons with satellite, shore-based radar, and in situ robotic measurements. The resulting sensor net will enable a new means to explore and study the world’s oceans by providing scientists a responsive network in the world’s oceans that can be accessed via any wireless network.

CAPILLARY NETWORK ANOMALIES IN BRANCH RETINAL VEIN OCCLUSION ON OPTICAL COHERENCE TOMOGRAPHY ANGIOGRAPHY.

PubMed

Rispoli, Marco; Savastano, Maria Cristina; Lumbroso, Bruno

2015-11-01

To analyze the foveal microvasculature features in eyes with branch retinal vein occlusion (BRVO) using optical coherence tomography angiography based on split spectrum amplitude decorrelation angiography technology. A total of 10 BRVO eyes (mean age 64.2 ± 8.02 range between 52 years and 76 years) were evaluated by optical coherence tomography angiography (XR-Avanti; Optovue). The macular angiography scan protocol covered a 3 mm × 3 mm area. The focus of angiography analysis were two retinal layers: superficial vascular network and deep vascular network. The following vascular morphological congestion parameters were assessed in the vein occlusion area in both the superficial and deep networks: foveal avascular zone enlargement, capillary non-perfusion occurrence, microvascular abnormalities appearance, and vascular congestion signs. Image analyses were performed by 2 masked observers and interobserver agreement of image analyses was 0.90 (κ = 0.225, P < 0.01). In both superficial and deep network of BRVO, a decrease in capillary density with foveal avascular zone enlargement, capillary non-perfusion occurrence, and microvascular abnormalities appearance was observed (P < 0.01). The deep network showed the main vascular congestion at the boundary between healthy and nonperfused retina. Optical coherence tomography angiography in BRVO allows to detect foveal avascular zone enlargement, capillary nonperfusion, microvascular abnormalities, and vascular congestion signs both in the superficial and deep capillary network in all eyes. Optical coherence tomography angiography technology is a potential clinical tool for BRVO diagnosis and follow-up, providing stratigraphic vascular details that have not been previously observed by standard fluorescein angiography. The normal retinal vascular nets and areas of nonperfusion and congestion can be identified at various retinal levels. Optical coherence tomography angiography provides noninvasive images of the retinal capillaries and vascular networks.

Investigation on changes of modularity and robustness by edge-removal mutations in signaling networks.

PubMed

Truong, Cong-Doan; Kwon, Yung-Keun

2017-12-21

Biological networks consisting of molecular components and interactions are represented by a graph model. There have been some studies based on that model to analyze a relationship between structural characteristics and dynamical behaviors in signaling network. However, little attention has been paid to changes of modularity and robustness in mutant networks. In this paper, we investigated the changes of modularity and robustness by edge-removal mutations in three signaling networks. We first observed that both the modularity and robustness increased on average in the mutant network by the edge-removal mutations. However, the modularity change was negatively correlated with the robustness change. This implies that it is unlikely that both the modularity and the robustness values simultaneously increase by the edge-removal mutations. Another interesting finding is that the modularity change was positively correlated with the degree, the number of feedback loops, and the edge betweenness of the removed edges whereas the robustness change was negatively correlated with them. We note that these results were consistently observed in randomly structure networks. Additionally, we identified two groups of genes which are incident to the highly-modularity-increasing and the highly-robustness-decreasing edges with respect to the edge-removal mutations, respectively, and observed that they are likely to be central by forming a connected component of a considerably large size. The gene-ontology enrichment of each of these gene groups was significantly different from the rest of genes. Finally, we showed that the highly-robustness-decreasing edges can be promising edgetic drug-targets, which validates the usefulness of our analysis. Taken together, the analysis of changes of robustness and modularity against edge-removal mutations can be useful to unravel novel dynamical characteristics underlying in signaling networks.

The relevance of network micro-structure for neural dynamics.

PubMed

Pernice, Volker; Deger, Moritz; Cardanobile, Stefano; Rotter, Stefan

2013-01-01

The activity of cortical neurons is determined by the input they receive from presynaptic neurons. Many previous studies have investigated how specific aspects of the statistics of the input affect the spike trains of single neurons and neurons in recurrent networks. However, typically very simple random network models are considered in such studies. Here we use a recently developed algorithm to construct networks based on a quasi-fractal probability measure which are much more variable than commonly used network models, and which therefore promise to sample the space of recurrent networks in a more exhaustive fashion than previously possible. We use the generated graphs as the underlying network topology in simulations of networks of integrate-and-fire neurons in an asynchronous and irregular state. Based on an extensive dataset of networks and neuronal simulations we assess statistical relations between features of the network structure and the spiking activity. Our results highlight the strong influence that some details of the network structure have on the activity dynamics of both single neurons and populations, even if some global network parameters are kept fixed. We observe specific and consistent relations between activity characteristics like spike-train irregularity or correlations and network properties, for example the distributions of the numbers of in- and outgoing connections or clustering. Exploiting these relations, we demonstrate that it is possible to estimate structural characteristics of the network from activity data. We also assess higher order correlations of spiking activity in the various networks considered here, and find that their occurrence strongly depends on the network structure. These results provide directions for further theoretical studies on recurrent networks, as well as new ways to interpret spike train recordings from neural circuits.

EgoNet: identification of human disease ego-network modules

PubMed Central

2014-01-01

Background Mining novel biomarkers from gene expression profiles for accurate disease classification is challenging due to small sample size and high noise in gene expression measurements. Several studies have proposed integrated analyses of microarray data and protein-protein interaction (PPI) networks to find diagnostic subnetwork markers. However, the neighborhood relationship among network member genes has not been fully considered by those methods, leaving many potential gene markers unidentified. The main idea of this study is to take full advantage of the biological observation that genes associated with the same or similar diseases commonly reside in the same neighborhood of molecular networks. Results We present EgoNet, a novel method based on egocentric network-analysis techniques, to exhaustively search and prioritize disease subnetworks and gene markers from a large-scale biological network. When applied to a triple-negative breast cancer (TNBC) microarray dataset, the top selected modules contain both known gene markers in TNBC and novel candidates, such as RAD51 and DOK1, which play a central role in their respective ego-networks by connecting many differentially expressed genes. Conclusions Our results suggest that EgoNet, which is based on the ego network concept, allows the identification of novel biomarkers and provides a deeper understanding of their roles in complex diseases. PMID:24773628

Three Degrees of Inclusion: the Gossip-Effect in Human Networks

NASA Astrophysics Data System (ADS)

Szekfu˝, Balázs; Szvetelszky, Zsuzsanna

2005-06-01

Using the scientific definition of gossip, an ancient and ubiquitous phenomenon of the social networks, we present our preliminary study and its results on how to measure the networks based on dissemination of connections and information. We try to accurately calculate the gossip-effects in networks with our hypothesis of "three degrees of inclusion". Our preliminary study on the subject of "three degrees of inclusion" gives latency to a very important property of social networks. Observing the human communication of closely knit social groups we came to the conclusion that the human networks are based on not more than three degrees of links. Taking the strong human ties into account our research indicates that whoever is on the fourth degree rarely counts as an in-group member. Our close friend's close friend's close friend — that is about the farthest — three steps — our network can reach out when it comes to telling a story or asking for a favor. Up to now no investigations have been performed to see whether the effects of gossip lead to the phase transition of the content of network's self-organizing communication. Our conclusion is that the gossip-effect must be considered as the prefactor of the news and opinions diffusion and dynamics at the social level.

Impact assessment of extreme storm events using a Bayesian network

USGS Publications Warehouse

den Heijer, C.(Kees); Knipping, Dirk T.J.A.; Plant, Nathaniel G.; van Thiel de Vries, Jaap S. M.; Baart, Fedor; van Gelder, Pieter H. A. J. M.

2012-01-01

This paper describes an investigation on the usefulness of Bayesian Networks in the safety assessment of dune coasts. A network has been created that predicts the erosion volume based on hydraulic boundary conditions and a number of cross-shore profile indicators. Field measurement data along a large part of the Dutch coast has been used to train the network. Corresponding storm impact on the dunes was calculated with an empirical dune erosion model named duros+. Comparison between the Bayesian Network predictions and the original duros+ results, here considered as observations, results in a skill up to 0.88, provided that the training data covers the range of predictions. Hence, the predictions from a deterministic model (duros+) can be captured in a probabilistic model (Bayesian Network) such that both the process knowledge and uncertainties can be included in impact and vulnerability assessments.

Empirical evaluation of neutral interactions in host-parasite networks.

PubMed

Canard, E F; Mouquet, N; Mouillot, D; Stanko, M; Miklisova, D; Gravel, D

2014-04-01

While niche-based processes have been invoked extensively to explain the structure of interaction networks, recent studies propose that neutrality could also be of great importance. Under the neutral hypothesis, network structure would simply emerge from random encounters between individuals and thus would be directly linked to species abundance. We investigated the impact of species abundance distributions on qualitative and quantitative metrics of 113 host-parasite networks. We analyzed the concordance between neutral expectations and empirical observations at interaction, species, and network levels. We found that species abundance accurately predicts network metrics at all levels. Despite host-parasite systems being constrained by physiology and immunology, our results suggest that neutrality could also explain, at least partially, their structure. We hypothesize that trait matching would determine potential interactions between species, while abundance would determine their realization.

Variability in Cumulative Habitual Sleep Duration Predicts Waking Functional Connectivity.

PubMed

Khalsa, Sakh; Mayhew, Stephen D; Przezdzik, Izabela; Wilson, Rebecca; Hale, Joanne; Goldstone, Aimee; Bagary, Manny; Bagshaw, Andrew P

2016-01-01

We examined whether interindividual differences in habitual sleep patterns, quantified as the cumulative habitual total sleep time (cTST) over a 2-w period, were reflected in waking measurements of intranetwork and internetwork functional connectivity (FC) between major nodes of three intrinsically connected networks (ICNs): default mode network (DMN), salience network (SN), and central executive network (CEN). Resting state functional magnetic resonance imaging (fMRI) study using seed-based FC analysis combined with 14-d wrist actigraphy, sleep diaries, and subjective questionnaires (N = 33 healthy adults, mean age 34.3, standard deviation ± 11.6 y). Data were statistically analyzed using multiple linear regression. Fourteen consecutive days of wrist actigraphy in participant's home environment and fMRI scanning on day 14 at the Birmingham University Imaging Centre. Seed-based FC analysis on ICNs from resting-state fMRI data and multiple linear regression analysis performed for each ICN seed and target. cTST was used to predict FC (controlling for age). cTST was specific predictor of intranetwork FC when the mesial prefrontal cortex (MPFC) region of the DMN was used as a seed for FC, with a positive correlation between FC and cTST observed. No significant relationship between FC and cTST was seen for any pair of nodes not including the MPFC. Internetwork FC between the DMN (MPFC) and SN (right anterior insula) was also predicted by cTST, with a negative correlation observed between FC and cTST. This study improves understanding of the relationship between intranetwork and internetwork functional connectivity of intrinsically connected networks (ICNs) in relation to habitual sleep quality and duration. The cumulative amount of sleep that participants achieved over a 14-d period was significantly predictive of intranetwork and inter-network functional connectivity of ICNs, an observation that may underlie the link between sleep status and cognitive performance. © 2016 Associated Professional Sleep Societies, LLC.

Where’s the Noise? Key Features of Spontaneous Activity and Neural Variability Arise through Learning in a Deterministic Network

PubMed Central

Hartmann, Christoph; Lazar, Andreea; Nessler, Bernhard; Triesch, Jochen

2015-01-01

Even in the absence of sensory stimulation the brain is spontaneously active. This background “noise” seems to be the dominant cause of the notoriously high trial-to-trial variability of neural recordings. Recent experimental observations have extended our knowledge of trial-to-trial variability and spontaneous activity in several directions: 1. Trial-to-trial variability systematically decreases following the onset of a sensory stimulus or the start of a motor act. 2. Spontaneous activity states in sensory cortex outline the region of evoked sensory responses. 3. Across development, spontaneous activity aligns itself with typical evoked activity patterns. 4. The spontaneous brain activity prior to the presentation of an ambiguous stimulus predicts how the stimulus will be interpreted. At present it is unclear how these observations relate to each other and how they arise in cortical circuits. Here we demonstrate that all of these phenomena can be accounted for by a deterministic self-organizing recurrent neural network model (SORN), which learns a predictive model of its sensory environment. The SORN comprises recurrently coupled populations of excitatory and inhibitory threshold units and learns via a combination of spike-timing dependent plasticity (STDP) and homeostatic plasticity mechanisms. Similar to balanced network architectures, units in the network show irregular activity and variable responses to inputs. Additionally, however, the SORN exhibits sequence learning abilities matching recent findings from visual cortex and the network’s spontaneous activity reproduces the experimental findings mentioned above. Intriguingly, the network’s behaviour is reminiscent of sampling-based probabilistic inference, suggesting that correlates of sampling-based inference can develop from the interaction of STDP and homeostasis in deterministic networks. We conclude that key observations on spontaneous brain activity and the variability of neural responses can be accounted for by a simple deterministic recurrent neural network which learns a predictive model of its sensory environment via a combination of generic neural plasticity mechanisms. PMID:26714277

Long-Term Network Experiments and Interdisciplinary Campaigns Conducted by the USDA-Agricultural Research Service

NASA Astrophysics Data System (ADS)

Goodrich, D. C.; Kustas, W. P.; Cosh, M. H.; Moran, S. M.; Marks, D. G.; Jackson, T. J.; Bosch, D. D.; Rango, A.; Seyfried, M. S.; Scott, R. L.; Prueger, J. H.; Starks, P. J.; Walbridge, M. R.

2014-12-01

The USDA-Agricultural Research Service has led, or been integrally involved in, a myriad of interdisciplinary field campaigns in a wide range of locations both nationally and internationally. Many of the shorter campaigns were anchored over the existing national network of ARS Experimental Watersheds and Rangelands. These long-term outdoor laboratories provided a critical knowledge base for designing the campaigns as well as historical data, hydrologic and meteorological infrastructure coupled with shop, laboratory, and visiting scientist facilities. This strong outdoor laboratory base enabled cost-efficient campaigns informed by historical context, local knowledge, and detailed existing watershed characterization. These long-term experimental facilities have also enabled much longer term lower intensity experiments, observing and building an understanding of both seasonal and inter-annual biosphere-hydrosphere-atmosphere interactions across a wide range of conditions. A sampling of these experiments include MONSOON'90, SGP97, SGP99, Washita'92, Washita'94, SMEX02-05 and JORNEX series of experiments, SALSA, CLASIC and longer-term efforts over the ARS Little Washita, Walnut Gulch, Little River, Reynolds Creek, and OPE3 Experimental Watersheds. This presentation will review some of the highlights and key findings of these campaigns and long-term efforts including the inclusion of many of the experimental watersheds and ranges in the Long-Term Agro-ecosystems Research (LTAR) network. The LTAR network also contains several locations that are also part of other observational networks including the CZO, LTER, and NEON networks. Lessons learned will also be provided for scientists initiating their participation in large-scale, multi-site interdisciplinary science.

On the contributions of topological features to transcriptional regulatory network robustness

PubMed Central

2012-01-01

Background Because biological networks exhibit a high-degree of robustness, a systemic understanding of their architecture and function requires an appraisal of the network design principles that confer robustness. In this project, we conduct a computational study of the contribution of three degree-based topological properties (transcription factor-target ratio, degree distribution, cross-talk suppression) and their combinations on the robustness of transcriptional regulatory networks. We seek to quantify the relative degree of robustness conferred by each property (and combination) and also to determine the extent to which these properties alone can explain the robustness observed in transcriptional networks. Results To study individual properties and their combinations, we generated synthetic, random networks that retained one or more of the three properties with values derived from either the yeast or E. coli gene regulatory networks. Robustness of these networks were estimated through simulation. Our results indicate that the combination of the three properties we considered explains the majority of the structural robustness observed in the real transcriptional networks. Surprisingly, scale-free degree distribution is, overall, a minor contributor to robustness. Instead, most robustness is gained through topological features that limit the complexity of the overall network and increase the transcription factor subnetwork sparsity. Conclusions Our work demonstrates that (i) different types of robustness are implemented by different topological aspects of the network and (ii) size and sparsity of the transcription factor subnetwork play an important role for robustness induction. Our results are conserved across yeast and E Coli, which suggests that the design principles examined are present within an array of living systems. PMID:23194062

Exploring Hitherto Uncharted Planet Territory with Lucky-imaging Microlensing Observations

NASA Astrophysics Data System (ADS)

Dominik, Martin; Jørgensen, U. G.; Hessman, F. V.; Horne, K.; Harpsøe, K.; Skottfelt, J.; MiNDSTEp Consortium

2011-09-01

Leading the agenda for pushing the planet sensitivity limit towards the mass of the Moon, we will report first results from our 2011 MiNDSTEp (Microlensing Network for the Detection of Small Terrestrial Exoplanets) lucky-imaging microlensing follow-up campaign with the Danish 1.54m at ESO La Silla. It serves as a precursor to observations with a global network comprising the LCOGT/SUPAscope, SONG, and MONET 1m-class robotic telescope networks gradually deployed from 2011 to 2014. As for observations from space, the lucky-imaging technique allows us to get around the atmospheric image blurring and to obtain a resolution near the diffraction limit. This enables high-precision photometry on considerably fainter (smaller) stars in the crowded fields towards the Galactic bulge than obtainable from ground-based surveys. Monitoring smaller source stars in turn provides sensitivity to planets with smaller masses orbiting the lens star. M.D. is supported by a Royal Society University Research Fellowship

Conversion of National Health Insurance Service-National Sample Cohort (NHIS-NSC) Database into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM).

PubMed

You, Seng Chan; Lee, Seongwon; Cho, Soo-Yeon; Park, Hojun; Jung, Sungjae; Cho, Jaehyeong; Yoon, Dukyong; Park, Rae Woong

2017-01-01

It is increasingly necessary to generate medical evidence applicable to Asian people compared to those in Western countries. Observational Health Data Sciences a Informatics (OHDSI) is an international collaborative which aims to facilitate generating high-quality evidence via creating and applying open-source data analytic solutions to a large network of health databases across countries. We aimed to incorporate Korean nationwide cohort data into the OHDSI network by converting the national sample cohort into Observational Medical Outcomes Partnership-Common Data Model (OMOP-CDM). The data of 1.13 million subjects was converted to OMOP-CDM, resulting in average 99.1% conversion rate. The ACHILLES, open-source OMOP-CDM-based data profiling tool, was conducted on the converted database to visualize data-driven characterization and access the quality of data. The OMOP-CDM version of National Health Insurance Service-National Sample Cohort (NHIS-NSC) can be a valuable tool for multiple aspects of medical research by incorporation into the OHDSI research network.

Earthquake Related Variation of Total Electron Content in Ionosphere over Chinese Mainland Derived from Observations of a Nationwide GNSS Network

NASA Astrophysics Data System (ADS)

Gan, Weijun

2016-07-01

Crustal Movement Observation Network of China (CMONOC) is a key national scientific infrastructure project carried out during 1997-2012 with 2 phases. The network is composed of 260 continuously observed GNSS stations (CORS) and 2081 campaign mode GNSS stations, with the main purpose to monitor the crustal movement, perceptible water vapor (PWV), total electron content (TEC), and many other tectonic and environmental elements around mainland China, by mainly using the Global Navigation Satellite System (GNSS) technology. Here, based on the GNSS data of 260 CORS of COMNOC for about 5 years, we investigated the characteristics of TEC in ionosphere over Chinese Mainland and discussed if there was any abnormal change of TEC before and after a big earthquake. our preliminary results show that it is hard to see any convincing precursor of TEC before a big earthquake. However, the huge energy released by a big earthquake can obviously disturb the TEC over meizoseismal area.

The North Alabama Severe Thunderstorm Observations, Research, and Monitoring Network (STORMnet)

NASA Technical Reports Server (NTRS)

Goodman, S. J.; Blakeslee, R.; Christian, H.; Boccippio, D.; Koshak, W.; Bailey, J.; Hall, J.; Bateman, M.; McCaul, E.; Buechler, D.;

Regional application of multi-layer artificial neural networks in 3-D ionosphere tomography

NASA Astrophysics Data System (ADS)

Ghaffari Razin, Mir Reza; Voosoghi, Behzad

2016-08-01

Tomography is a very cost-effective method to study physical properties of the ionosphere. In this paper, residual minimization training neural network (RMTNN) is used in voxel-based tomography to reconstruct of 3-D ionosphere electron density with high spatial resolution. For numerical experiments, observations collected at 37 GPS stations from Iranian permanent GPS network (IPGN) are used. A smoothed TEC approach was used for absolute STEC recovery. To improve the vertical resolution, empirical orthogonal functions (EOFs) obtained from international reference ionosphere 2012 (IRI-2012) used as object function in training neural network. Ionosonde observations is used for validate reliability of the proposed method. Minimum relative error for RMTNN is 1.64% and maximum relative error is 15.61%. Also root mean square error (RMSE) of 0.17 × 1011 (electrons/m3) is computed for RMTNN which is less than RMSE of IRI2012. The results show that RMTNN has higher accuracy and compiles speed than other ionosphere reconstruction methods.

RTML: remote telescope markup language and you

NASA Astrophysics Data System (ADS)

Hessman, F. V.

2001-12-01

In order to coordinate the use of robotic and remotely operated telescopes in networks -- like Göttingen's MOnitoring NEtwork of Telescopes (MONET) -- a standard format for the exchange of observing requests and reports is needed. I describe the benefits of Remote Telescope Markup Language (RTML), an XML-based protocol originally developed by the Hands-On Universe Project, which is being used and further developed by several robotic telescope projects and firms.

Telescope networking and user support via Remote Telescope Markup Language

NASA Astrophysics Data System (ADS)

Hessman, Frederic V.; Pennypacker, Carlton R.; Romero-Colmenero, Encarni; Tuparev, Georg

2004-09-01

Remote Telescope Markup Language (RTML) is an XML-based interface/document format designed to facilitate the exchange of astronomical observing requests and results between investigators and observatories as well as within networks of observatories. While originally created to support simple imaging telescope requests (Versions 1.0-2.1), RTML Version 3.0 now supports a wide range of applications, from request preparation, exposure calculation, spectroscopy, and observation reports to remote telescope scheduling, target-of-opportunity observations and telescope network administration. The elegance of RTML is that all of this is made possible using a public XML Schema which provides a general-purpose, easily parsed, and syntax-checked medium for the exchange of astronomical and user information while not restricting or otherwise constraining the use of the information at either end. Thus, RTML can be used to connect heterogeneous systems and their users without requiring major changes in existing local resources and procedures. Projects as very different as a number of advanced amateur observatories, the global Hands-On Universe project, the MONET network (robotic imaging), the STELLA consortium (robotic spectroscopy), and the 11-m Southern African Large Telescope are now using or intending to use RTML in various forms and for various purposes.

Social network analysis of the genetic structure of Pacific islanders.

PubMed

Terrell, John Edward

2010-05-01

Social network analysis (SNA) is a body of theory and a set of relatively new computer-aided techniques used in the analysis and study of relational data. Recent studies of autosomal markers from over 40 human populations in the south-western Pacific have further documented the remarkable degree of genetic diversity in this part of the world. I report additional analysis using SNA methods contributing new controlled observations on the structuring of genetic diversity among these islanders. These SNA mappings are then compared with model-based network expectations derived from the geographic distances among the same populations. Previous studies found that genetic divergence among island Melanesian populations is organised by island, island size/topography, and position (coastal vs. inland), and that similarities observed correlate only weakly with an isolation-by-distance model. Using SNA methods, however, improves the resolution of among population comparison, and suggests that isolation by distance constrained by social networks together with position (coastal/inland) accounts for much of the population structuring observed. The multilocus data now available is also in accord with current thinking on the impact of major biogeographical transformations on prehistoric colonisation and post-settlement human interaction in Oceania.

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

DOE PAGES

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin; ...

2017-01-10

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less

Evaluating the streamflow simulation capability of PERSIANN-CDR daily rainfall products in two river basins on the Tibetan Plateau

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

Liu, Xiaomang; Yang, Tiantian; Hsu, Koulin

On the Tibetan Plateau, the limited ground-based rainfall information owing to a harsh environment has brought great challenges to hydrological studies. Satellite-based rainfall products, which allow for a better coverage than both radar network and rain gauges on the Tibetan Plateau, can be suitable alternatives for studies on investigating the hydrological processes and climate change. In this study, a newly developed daily satellite-based precipitation product, termed Precipitation Estimation from Remotely Sensed Information Using Artificial Neural Networks $-$ Climate Data Record (PERSIANN-CDR), is used as input for a hydrologic model to simulate streamflow in the upper Yellow and Yangtze River basinsmore » on the Tibetan Plateau. The results show that the simulated streamflows using PERSIANN-CDR precipitation and the Global Land Data Assimilation System (GLDAS) precipitation are closer to observation than that using limited gauge-based precipitation interpolation in the upper Yangtze River basin. The simulated streamflow using gauge-based precipitation are higher than the streamflow observation during the wet season. In the upper Yellow River basin, gauge-based precipitation, GLDAS precipitation, and PERSIANN-CDR precipitation have similar good performance in simulating streamflow. Finally, the evaluation of streamflow simulation capability in this study partly indicates that the PERSIANN-CDR rainfall product has good potential to be a reliable dataset and an alternative information source of a limited gauge network for conducting long-term hydrological and climate studies on the Tibetan Plateau.« less

Analytical reasoning task reveals limits of social learning in networks

PubMed Central

Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

2014-01-01

Social learning—by observing and copying others—is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an ‘unreflective copying bias’, which limits their social learning to the output, rather than the process, of their peers’ reasoning—even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning. PMID:24501275

Analytical reasoning task reveals limits of social learning in networks.

PubMed

Rahwan, Iyad; Krasnoshtan, Dmytro; Shariff, Azim; Bonnefon, Jean-François

2014-04-06

Social learning-by observing and copying others-is a highly successful cultural mechanism for adaptation, outperforming individual information acquisition and experience. Here, we investigate social learning in the context of the uniquely human capacity for reflective, analytical reasoning. A hallmark of the human mind is its ability to engage analytical reasoning, and suppress false associative intuitions. Through a set of laboratory-based network experiments, we find that social learning fails to propagate this cognitive strategy. When people make false intuitive conclusions and are exposed to the analytic output of their peers, they recognize and adopt this correct output. But they fail to engage analytical reasoning in similar subsequent tasks. Thus, humans exhibit an 'unreflective copying bias', which limits their social learning to the output, rather than the process, of their peers' reasoning-even when doing so requires minimal effort and no technical skill. In contrast to much recent work on observation-based social learning, which emphasizes the propagation of successful behaviour through copying, our findings identify a limit on the power of social networks in situations that require analytical reasoning.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations.

PubMed

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-03-03

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2-3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling.

A Unified Model for BDS Wide Area and Local Area Augmentation Positioning Based on Raw Observations

PubMed Central

Tu, Rui; Zhang, Rui; Lu, Cuixian; Zhang, Pengfei; Liu, Jinhai; Lu, Xiaochun

2017-01-01

In this study, a unified model for BeiDou Navigation Satellite System (BDS) wide area and local area augmentation positioning based on raw observations has been proposed. Applying this model, both the Real-Time Kinematic (RTK) and Precise Point Positioning (PPP) service can be realized by performing different corrections at the user end. This algorithm was assessed and validated with the BDS data collected at four regional stations from Day of Year (DOY) 080 to 083 of 2016. When the users are located within the local reference network, the fast and high precision RTK service can be achieved using the regional observation corrections, revealing a convergence time of about several seconds and a precision of about 2–3 cm. For the users out of the regional reference network, the global broadcast State-Space Represented (SSR) corrections can be utilized to realize the global PPP service which shows a convergence time of about 25 min for achieving an accuracy of 10 cm. With this unified model, it can not only integrate the Network RTK (NRTK) and PPP into a seamless positioning service, but also recover the ionosphere Vertical Total Electronic Content (VTEC) and Differential Code Bias (DCB) values that are useful for the ionosphere monitoring and modeling. PMID:28273814

Benefits of Delay Tolerant Networking for Earth Science Missions

NASA Technical Reports Server (NTRS)

Davis, Faith; Marquart, Jane; Menke, Greg

2012-01-01

To date there has been much discussion about the value of Delay Tolerant Networking (DTN) for space missions. Claims of various benefits, based on paper analysis, are good; however a benefits statement with empirical evidence to support is even better. This paper presents potential and actual advantages of using DTN for Earth science missions based on results from multiple demonstrations, conducted by the Communications, Standards, and Technology Laboratory (CSTL) at NASA Goddard Space Flight Center (GSFC). Demonstrations included two flight demonstrations using the Earth Observing Mission 1 (EO-1) and the Near Earth Network (NEN), a ground based demonstration over satellite links to the Internet Router in Space (IRIS) payload on Intelsat-14, and others using the NASA Tracking Data Relay Satellite System (TDRSS). Real and potential findings include increased flexibility and efficiency in science campaigns, reduced latency in a collaborative science scenario, and improved scientist-instrument communication and control.

X-Chromosome Effects on Attention Networks: Insights from Imaging Resting-State Networks in Turner Syndrome.

PubMed

Green, Tamar; Saggar, Manish; Ishak, Alexandra; Hong, David S; Reiss, Allan L

2017-07-18

Attention deficit hyperactivity disorder (ADHD) is strongly affected by sex, but sex chromosomes' effect on brain attention networks and cognition are difficult to examine in humans. This is due to significant etiologic heterogeneity among diagnosed individuals. In contrast, individuals with Turner syndrome (TS), who have substantially increased risk for ADHD symptoms, share a common genetic risk factor related to the absence of the X-chromosome, thus serving as a more homogeneous genetic model. Resting-state functional MRI was employed to examine differences in attention networks between girls with TS (n = 40) and age- sex- and Tanner-matched controls (n = 33). We compared groups on resting-state functional connectivity measures from data-driven independent components analysis (ICA) and hypothesis-based seed analysis. Using ICA, reduced connectivity was observed in both frontoparietal and dorsal attention networks. Similarly, using seeds in the bilateral intraparietal sulcus (IPS), reduced connectivity was observed between IPS and frontal and cerebellar regions. Finally, we observed a brain-behavior correlation between IPS-cerebellar connectivity and cognitive attention measures. These findings indicate that X-monosomy contributes affects to attention networks and cognitive dysfunction that might increase risk for ADHD. Our findings not only have clinical relevance for girls with TS, but might also serve as a biological marker in future research examining the effects of the intervention that targets attention skills. © The Author 2017. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Ground-based Network and Supersite Measurements for Studying Aerosol Properties and Aerosol-Cloud Interactions

NASA Technical Reports Server (NTRS)

Tsay, Si-Chee; Holben, Brent N.

2008-01-01

From radiometric principles, it is expected that the retrieved properties of extensive aerosols and clouds from reflected/emitted measurements by satellite (and/or aircraft) should be consistent with those retrieved from transmitted/emitted radiance observed at the surface. Although space-borne remote sensing observations contain large spatial domain, they are often plagued by contamination of surface signatures. Thus, ground-based in-situ and remote-sensing measurements, where signals come directly from atmospheric constituents, the sun, and the Earth-atmosphere interactions, provide additional information content for comparisons that confirm quantitatively the usefulness of the integrated surface, aircraft, and satellite datasets. The development and deployment of AERONET (AErosol RObotic NETwork) sunphotometer network and SMART-COMMIT (Surface-sensing Measurements for Atmospheric Radiative Transfer - Chemical, Optical & Microphysical Measurements of In-situ Troposphere) mobile supersite are aimed for the optimal utilization of collocated ground-based observations as constraints to yield higher fidelity satellite retrievals and to determine any sampling bias due to target conditions. To characterize the regional natural and anthropogenic aerosols, AERONET is an internationally federated network of unique sunphotometry that contains more than 250 permanent sites worldwide. Since 1993, there are more than 480 million aerosol optical depth observations and about 15 sites have continuous records longer than 10 years for annual/seasonal trend analyses. To quantify the energetics of the surface-atmosphere system and the atmospheric processes, SMART-COMMIT instrument into three categories: flux radiometer, radiance sensor and in-situ probe. Through participation in many satellite remote-sensing/retrieval and validation projects over eight years, SMART-COMMIT have gradually refine( and been proven vital for field deployment. In this paper, we will demonstrate the capability of AERONET SMART-COMMIT in current Asian Monsoon Year-2008 campaigns that are designed and being executed to study the compelling variability in temporal scale of both anthropogenic and natural aerosols (e.g., airborne dust, smoke, mega-city pollutant). Feedback mechanisms between aerosol radiative effects and monsoon dynamics have been recently proposed, however there is a lack of consensus on whether aerosol forcing would be more likely to enhance or reduce the strength of the monsoon circulation. We envision robust approaches which well-collocated ground-based measurements and space-borne observations will greatly advance our understanding of absorbing aerosols (e.g., "Global Dimming" vs. "Elevated Heat-Pump" effects) on aerosol cloud water cycle interactions.

Qualitative Discovery in Medical Databases

NASA Technical Reports Server (NTRS)

Maluf, David A.

2000-01-01

Implication rules have been used in uncertainty reasoning systems to confirm and draw hypotheses or conclusions. However a major bottleneck in developing such systems lies in the elicitation of these rules. This paper empirically examines the performance of evidential inferencing with implication networks generated using a rule induction tool called KAT. KAT utilizes an algorithm for the statistical analysis of empirical case data, and hence reduces the knowledge engineering efforts and biases in subjective implication certainty assignment. The paper describes several experiments in which real-world diagnostic problems were investigated; namely, medical diagnostics. In particular, it attempts to show that: (1) with a limited number of case samples, KAT is capable of inducing implication networks useful for making evidential inferences based on partial observations, and (2) observation driven by a network entropy optimization mechanism is effective in reducing the uncertainty of predicted events.

Application of SNODAS and hydrologic models to enhance entropy-based snow monitoring network design

NASA Astrophysics Data System (ADS)

Keum, Jongho; Coulibaly, Paulin; Razavi, Tara; Tapsoba, Dominique; Gobena, Adam; Weber, Frank; Pietroniro, Alain

2018-06-01

Snow has a unique characteristic in the water cycle, that is, snow falls during the entire winter season, but the discharge from snowmelt is typically delayed until the melting period and occurs in a relatively short period. Therefore, reliable observations from an optimal snow monitoring network are necessary for an efficient management of snowmelt water for flood prevention and hydropower generation. The Dual Entropy and Multiobjective Optimization is applied to design snow monitoring networks in La Grande River Basin in Québec and Columbia River Basin in British Columbia. While the networks are optimized to have the maximum amount of information with minimum redundancy based on entropy concepts, this study extends the traditional entropy applications to the hydrometric network design by introducing several improvements. First, several data quantization cases and their effects on the snow network design problems were explored. Second, the applicability the Snow Data Assimilation System (SNODAS) products as synthetic datasets of potential stations was demonstrated in the design of the snow monitoring network of the Columbia River Basin. Third, beyond finding the Pareto-optimal networks from the entropy with multi-objective optimization, the networks obtained for La Grande River Basin were further evaluated by applying three hydrologic models. The calibrated hydrologic models simulated discharges using the updated snow water equivalent data from the Pareto-optimal networks. Then, the model performances for high flows were compared to determine the best optimal network for enhanced spring runoff forecasting.

Role of Ongoing, Intrinsic Activity of Neuronal Populations for Quantitative Neuroimaging of Functional Magnetic Resonance Imaging–Based Networks

PubMed Central

Herman, Peter; Sanganahalli, Basavaraju G.; Coman, Daniel; Blumenfeld, Hal; Rothman, Douglas L.

2011-01-01

Abstract A primary objective in neuroscience is to determine how neuronal populations process information within networks. In humans and animal models, functional magnetic resonance imaging (fMRI) is gaining increasing popularity for network mapping. Although neuroimaging with fMRI—conducted with or without tasks—is actively discovering new brain networks, current fMRI data analysis schemes disregard the importance of the total neuronal activity in a region. In task fMRI experiments, the baseline is differenced away to disclose areas of small evoked changes in the blood oxygenation level-dependent (BOLD) signal. In resting-state fMRI experiments, the spotlight is on regions revealed by correlations of tiny fluctuations in the baseline (or spontaneous) BOLD signal. Interpretation of fMRI-based networks is obscured further, because the BOLD signal indirectly reflects neuronal activity, and difference/correlation maps are thresholded. Since the small changes of BOLD signal typically observed in cognitive fMRI experiments represent a minimal fraction of the total energy/activity in a given area, the relevance of fMRI-based networks is uncertain, because the majority of neuronal energy/activity is ignored. Thus, another alternative for quantitative neuroimaging of fMRI-based networks is a perspective in which the activity of a neuronal population is accounted for by the demanded oxidative energy (CMRO2). In this article, we argue that network mapping can be improved by including neuronal energy/activity of both the information about baseline and small differences/fluctuations of BOLD signal. Thus, total energy/activity information can be obtained through use of calibrated fMRI to quantify differences of ΔCMRO2 and through resting-state positron emission tomography/magnetic resonance spectroscopy measurements for average CMRO2. PMID:22433047

Stability of whole brain and regional network topology within and between resting and cognitive states.

PubMed

Rzucidlo, Justyna K; Roseman, Paige L; Laurienti, Paul J; Dagenbach, Dale

2013-01-01

Graph-theory based analyses of resting state functional Magnetic Resonance Imaging (fMRI) data have been used to map the network organization of the brain. While numerous analyses of resting state brain organization exist, many questions remain unexplored. The present study examines the stability of findings based on this approach over repeated resting state and working memory state sessions within the same individuals. This allows assessment of stability of network topology within the same state for both rest and working memory, and between rest and working memory as well. fMRI scans were performed on five participants while at rest and while performing the 2-back working memory task five times each, with task state alternating while they were in the scanner. Voxel-based whole brain network analyses were performed on the resulting data along with analyses of functional connectivity in regions associated with resting state and working memory. Network topology was fairly stable across repeated sessions of the same task, but varied significantly between rest and working memory. In the whole brain analysis, local efficiency, Eloc, differed significantly between rest and working memory. Analyses of network statistics for the precuneus and dorsolateral prefrontal cortex revealed significant differences in degree as a function of task state for both regions and in local efficiency for the precuneus. Conversely, no significant differences were observed across repeated sessions of the same state. These findings suggest that network topology is fairly stable within individuals across time for the same state, but also fluid between states. Whole brain voxel-based network analyses may prove to be a valuable tool for exploring how functional connectivity changes in response to task demands.

Status of NGS CORS Network and Its Contribution to the GGOS Infrastructure

NASA Astrophysics Data System (ADS)

Choi, K. K.; Haw, D.; Sun, L.

2017-12-01

Recent advancement of Satellite Geodesy techniques can now contribute to the global frame realization needed to improve worldwide accuracies. These techniques rely on coordinates computed using continuously observed GPS data and corresponding satellite orbits. The GPS-based reference system continues to depend on the physical stability of a ground-based network of points as the primary foundation for these observations. NOAA's National Geodetic Survey (NGS) has been operating Continuously Operating Reference Stations (CORS) to provide direct access to the National Spatial Reference System (NSRS). By virtue of NGS' scientific reputation and leadership in national and international geospatial issues, NGS has determined to increase its participation in the maintenance of the U.S. component of the global GPS tracking network in order to realize a long-term stable national terrestrial reference frame. NGS can do so by leveraging its national leadership role coupled with NGS' scientific expertise, in designating and upgrading a subset of the current tracking network for this purpose. This subset of stations must have the highest operational standards to serve the dual functions: being the U.S. contribution to the international frame, along with providing the link to the national datum. These stations deserve special attention to ensure that the highest possible levels of quality and stability are maintained. To meet this need, NGS is working with the international scientific groups to add and designate these reference stations based on scientific merit such as: colocation with other geodetic techniques, geographic area, and monumentation stability.

Spatiotemporal Phase Synchronization in Adaptive Reconfiguration from Action Observation Network to Mentalizing Network for Understanding Other's Action Intention.

PubMed

Zhang, Li; Gan, John Q; Zheng, Wenming; Wang, Haixian

2018-05-01

In action intention understanding, the mirror system is involved in perception-action matching process and the mentalizing system underlies higher-level intention inference. By analyzing the dynamic functional connectivity in α (8-12 Hz) and β (12-30 Hz) frequency bands over a "hand-cup interaction" observation task, this study investigates the topological transition from the action observation network (AON) to the mentalizing network (MZN), and estimates their functional relevance for intention identification from other's different action kinematics. Sequential brain microstates were extracted based on event-related potentials (ERPs), in which significantly differing neuronal responses were found in N170-P200 related to perceptually matching kinematic profiles and P400-700 involved in goal inference. Inter-electrode weighted phase lag index analysis on the ERP microstates revealed a shift of hub centrality salient in α frequency band, from the AON dominated by left-lateral frontal-premotor-temporal and temporal-parietooccipital synchronizations to the MZN consisting of more bilateral frontal-parietal and temporal-parietal synchronizations. As compared with usual actions, intention identification of unintelligible actions induces weaker synchronizations in the AON but dramatically increased connectivity in right frontal-temporal-parietal regions of the MZN, indicating a spatiotemporally complementary effect between the functional network configurations involved in mirror and mentalizing processes. Perceptual processing in observing usual/unintelligible actions decreases/increases requirements for intention inference, which would induce less/greater functional network reorganization on the way to mentalization. From the comparison, our study suggests that the adaptive topological changes from the AON to the MZN indicate implicit causal association between the mirror and mentalizing systems for decoding others' intentionality.

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network

PubMed Central

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information. PMID:25938760

A Fault Diagnosis Methodology for Gear Pump Based on EEMD and Bayesian Network.

PubMed

Liu, Zengkai; Liu, Yonghong; Shan, Hongkai; Cai, Baoping; Huang, Qing

2015-01-01

This paper proposes a fault diagnosis methodology for a gear pump based on the ensemble empirical mode decomposition (EEMD) method and the Bayesian network. Essentially, the presented scheme is a multi-source information fusion based methodology. Compared with the conventional fault diagnosis with only EEMD, the proposed method is able to take advantage of all useful information besides sensor signals. The presented diagnostic Bayesian network consists of a fault layer, a fault feature layer and a multi-source information layer. Vibration signals from sensor measurement are decomposed by the EEMD method and the energy of intrinsic mode functions (IMFs) are calculated as fault features. These features are added into the fault feature layer in the Bayesian network. The other sources of useful information are added to the information layer. The generalized three-layer Bayesian network can be developed by fully incorporating faults and fault symptoms as well as other useful information such as naked eye inspection and maintenance records. Therefore, diagnostic accuracy and capacity can be improved. The proposed methodology is applied to the fault diagnosis of a gear pump and the structure and parameters of the Bayesian network is established. Compared with artificial neural network and support vector machine classification algorithms, the proposed model has the best diagnostic performance when sensor data is used only. A case study has demonstrated that some information from human observation or system repair records is very helpful to the fault diagnosis. It is effective and efficient in diagnosing faults based on uncertain, incomplete information.

Datum maintenance of the main Egyptian geodetic control networks by utilizing Precise Point Positioning "PPP" technique

NASA Astrophysics Data System (ADS)

Rabah, Mostafa; Elmewafey, Mahmoud; Farahan, Magda H.

2016-06-01

A geodetic control network is the wire-frame or the skeleton on which continuous and consistent mapping, Geographic Information Systems (GIS), and surveys are based. Traditionally, geodetic control points are established as permanent physical monuments placed in the ground and precisely marked, located, and documented. With the development of satellite surveying methods and their availability and high degree of accuracy, a geodetic control network could be established by using GNSS and referred to an international terrestrial reference frame used as a three-dimensional geocentric reference system for a country. Based on this concept, in 1992, the Egypt Survey Authority (ESA) established two networks, namely High Accuracy Reference Network (HARN) and the National Agricultural Cadastral Network (NACN). To transfer the International Terrestrial Reference Frame to the HARN, the HARN was connected with four IGS stations. The processing results were 1:10,000,000 (Order A) for HARN and 1:1,000,000 (Order B) for NACN relative network accuracy standard between stations defined in ITRF1994 Epoch1996. Since 1996, ESA did not perform any updating or maintaining works for these networks. To see how non-performing maintenance degrading the values of the HARN and NACN, the available HARN and NACN stations in the Nile Delta were observed. The Processing of the tested part was done by CSRS-PPP Service based on utilizing Precise Point Positioning "PPP" and Trimble Business Center "TBC". The study shows the feasibility of Precise Point Positioning in updating the absolute positioning of the HARN network and its role in updating the reference frame (ITRF). The study also confirmed the necessity of the absent role of datum maintenance of Egypt networks.

Rank-based pooling for deep convolutional neural networks.

PubMed

Shi, Zenglin; Ye, Yangdong; Wu, Yunpeng

2016-11-01

Pooling is a key mechanism in deep convolutional neural networks (CNNs) which helps to achieve translation invariance. Numerous studies, both empirically and theoretically, show that pooling consistently boosts the performance of the CNNs. The conventional pooling methods are operated on activation values. In this work, we alternatively propose rank-based pooling. It is derived from the observations that ranking list is invariant under changes of activation values in a pooling region, and thus rank-based pooling operation may achieve more robust performance. In addition, the reasonable usage of rank can avoid the scale problems encountered by value-based methods. The novel pooling mechanism can be regarded as an instance of weighted pooling where a weighted sum of activations is used to generate the pooling output. This pooling mechanism can also be realized as rank-based average pooling (RAP), rank-based weighted pooling (RWP) and rank-based stochastic pooling (RSP) according to different weighting strategies. As another major contribution, we present a novel criterion to analyze the discriminant ability of various pooling methods, which is heavily under-researched in machine learning and computer vision community. Experimental results on several image benchmarks show that rank-based pooling outperforms the existing pooling methods in classification performance. We further demonstrate better performance on CIFAR datasets by integrating RSP into Network-in-Network. Copyright © 2016 Elsevier Ltd. All rights reserved.

Spreading paths in partially observed social networks

NASA Astrophysics Data System (ADS)

Onnela, Jukka-Pekka; Christakis, Nicholas A.

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.

Spreading paths in partially observed social networks.

PubMed

Onnela, Jukka-Pekka; Christakis, Nicholas A

2012-03-01

Understanding how and how far information, behaviors, or pathogens spread in social networks is an important problem, having implications for both predicting the size of epidemics, as well as for planning effective interventions. There are, however, two main challenges for inferring spreading paths in real-world networks. One is the practical difficulty of observing a dynamic process on a network, and the other is the typical constraint of only partially observing a network. Using static, structurally realistic social networks as platforms for simulations, we juxtapose three distinct paths: (1) the stochastic path taken by a simulated spreading process from source to target; (2) the topologically shortest path in the fully observed network, and hence the single most likely stochastic path, between the two nodes; and (3) the topologically shortest path in a partially observed network. In a sampled network, how closely does the partially observed shortest path (3) emulate the unobserved spreading path (1)? Although partial observation inflates the length of the shortest path, the stochastic nature of the spreading process also frequently derails the dynamic path from the shortest path. We find that the partially observed shortest path does not necessarily give an inflated estimate of the length of the process path; in fact, partial observation may, counterintuitively, make the path seem shorter than it actually is.