Refinery evaluation of optical imaging to locate fugitive emissions.

PubMed

Robinson, Donald R; Luke-Boone, Ronke; Aggarwal, Vineet; Harris, Buzz; Anderson, Eric; Ranum, David; Kulp, Thomas J; Armstrong, Karla; Sommers, Ricky; McRae, Thomas G; Ritter, Karin; Siegell, Jeffrey H; Van Pelt, Doug; Smylie, Mike

2007-07-01

Fugitive emissions account for approximately 50% of total hydrocarbon emissions from process plants. Federal and state regulations aiming at controlling these emissions require refineries and petrochemical plants in the United States to implement a Leak Detection and Repair Program (LDAR). The current regulatory work practice, U.S. Environment Protection Agency Method 21, requires designated components to be monitored individually at regular intervals. The annual costs of these LDAR programs in a typical refinery can exceed US$1,000,000. Previous studies have shown that a majority of controllable fugitive emissions come from a very small fraction of components. The Smart LDAR program aims to find cost-effective methods to monitor and reduce emissions from these large leakers. Optical gas imaging has been identified as one such technology that can help achieve this objective. This paper discusses a refinery evaluation of an instrument based on backscatter absorption gas imaging technology. This portable camera allows an operator to scan components more quickly and image gas leaks in real time. During the evaluation, the instrument was able to identify leaking components that were the source of 97% of the total mass emissions from leaks detected. More than 27,000 components were monitored. This was achieved in far less time than it would have taken using Method 21. In addition, the instrument was able to find leaks from components that are not required to be monitored by the current LDAR regulations. The technology principles and the parameters that affect instrument performance are also discussed in the paper.

Efficient Processing of Data for Locating Lightning Strikes

NASA Technical Reports Server (NTRS)

Medelius, Pedro J.; Starr, Stan

2003-01-01

Two algorithms have been devised to increase the efficiency of processing of data in lightning detection and ranging (LDAR) systems so as to enable the accurate location of lightning strikes in real time. In LDAR, the location of a lightning strike is calculated by solving equations for the differences among the times of arrival (DTOAs) of the lightning signals at multiple antennas as functions of the locations of the antennas and the speed of light. The most difficult part of the problem is computing the DTOAs from digitized versions of the signals received by the various antennas. One way (a time-domain approach) to determine the DTOAs is to compute cross-correlations among variously differentially delayed replicas of the digitized signals and to select, as the DTOAs, those differential delays that yield the maximum correlations. Another way (a frequency-domain approach) to determine the DTOAs involves the computation of cross-correlations among Fourier transforms of variously differentially phased replicas of the digitized signals, along with utilization of the relationship among phase difference, time delay, and frequency.

Classification of Small Negative Lightning Reports at the KSC-ER

NASA Technical Reports Server (NTRS)

Ward, Jennifer G.; Cummins, Kenneth L.; Krider, Philip

2008-01-01

The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) operate an extensive suite of lightning sensors because Florida experiences the highest area density of ground strikes in the United States, with area densities approaching 16 fl/sq km/yr when accumulated in 10x10 km (100 sq km) grids. The KSC-ER use data derived from two cloud-to-ground (CG) lightning detection networks, the "Cloud-to-Ground Lightning Surveillance System" (CGLSS) and the U.S. National Lightning Detection Network (TradeMark) (NLDN) plus a 3-dimensional lightning mapping system, the Lightning Detection and Ranging (LDAR) system, to provide warnings for ground operations and to insure mission safety during space launches. For operational applications at the KSC-ER it is important to understand the performance of each lightning detection system in considerable detail. In this work we examine a specific subset of the CGLSS stroke reports that have low values of the negative inferred peak current, Ip, i.e. values between 0 and -7 kA, and were thought to produce a new ground contact (NGC). When possible, the NLDN and LDAR systems were used to validate the CGLSS classification and to determine how many of these reported strokes were first strokes, subsequent strokes in a pre-existing channel (PEC), or cloud pulses that the CGLSS misclassified as CG strokes. It is scientifically important to determine the smallest current that can reach the ground either in the form of a first stroke or by way of a subsequent stroke that creates a new ground contact. In Biagi et al (2007), 52 low amplitude, negative return strokes ([Ip] < or = 10 kA) were evaluated in southern Arizona, northern Texas, and southern Oklahoma. The authors found that 50-87% of the small NLDN reports could be classified as CG (either first or subsequent strokes) on the basis of video and waveform recordings. Low amplitude return strokes are interesting because they are usually difficult to detect, and they are thought to bypass conventional lightning protection that relies on a sufficient attractive radius to prevent "shielding failure" (Golde, 1977). They also have larger location errors compared to the larger current events. In this study, we use the estimated peak current provided by the CGLSS and the results of our classification to determine the minimum Ip for each category of CG stroke and its probability of occurrence. Where possible, these results are compared to the findings in the literature.

Lightning electromagnetics

NASA Technical Reports Server (NTRS)

Wahid, Parveen

1995-01-01

This project involved the determination of the effective radiated power of lightning sources and the polarization of the radiating source. This requires the computation of the antenna patterns at all the LDAR site receiving antennas. The known radiation patterns and RF signal levels measured at the antennas will be used to determine the effective radiated power of the lightning source. The azimuth and elevation patterns of the antennas in the LDAR system were computed using flight test data that was gathered specifically for this purpose. The results presented in this report deal with the azimuth patterns for all the antennas and the elevation patterns for three of the seven sites.

Lightning Detection and Ranging system LDAR system description and performance objectives

NASA Technical Reports Server (NTRS)

Poehler, H. A.; Lennon, C. L.

1979-01-01

The instruments used at the six remote stations to measure both the time-of-arrival of the envelope of the pulsed 60 MHz to 80 MHz portion of the RF signal emitted by lightning, and the electric field waveforms are described as well as the two methods of transmitting the signal to the central station. Other topics discussed include data processing, recording, and reduction techniques and the software used for the 2100S, 2114, and 2116 computers.

Small negative cloud-to-ground lightning reports at the NASA Kennedy Space Center and Air Force Eastern Range

NASA Astrophysics Data System (ADS)

Wilson, Jennifer G.; Cummins, Kenneth L.; Krider, E. Philip

2009-12-01

The NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground (CG) lightning detection networks, the Cloud-to-Ground Lightning Surveillance System (CGLSS) and the U.S. National Lightning Detection Network™ (NLDN), and a volumetric lightning mapping array, the Lightning Detection and Ranging (LDAR) system, to monitor and characterize lightning that is potentially hazardous to launch or ground operations. Data obtained from these systems during June-August 2006 have been examined to check the classification of small, negative CGLSS reports that have an estimated peak current, ∣Ip∣ less than 7 kA, and to determine the smallest values of Ip that are produced by first strokes, by subsequent strokes that create a new ground contact (NGC), and by subsequent strokes that remain in a preexisting channel (PEC). The results show that within 20 km of the KSC-ER, 21% of the low-amplitude negative CGLSS reports were produced by first strokes, with a minimum Ip of -2.9 kA; 31% were by NGCs, with a minimum Ip of -2.0 kA; and 14% were by PECs, with a minimum Ip of -2.2 kA. The remaining 34% were produced by cloud pulses or lightning events that we were not able to classify.

Is Optical Gas Imaging Effective for Detecting Fugitive Methane Emissions? - A Technological and Policy Perspective

NASA Astrophysics Data System (ADS)

Ravikumar, A. P.; Wang, J.; Brandt, A. R.

2016-12-01

Mitigating fugitive methane emissions from the oil and gas industry has become an important concern for both businesses and regulators. While recent studies have improved our understanding of emissions from all sectors of the natural gas supply chain, cost-effectively identifying leaks over expansive natural gas infrastructure remains a significant challenge. Recently, the Environmental Protection Agency (EPA) has recommended the use of optical gas imaging (OGI) technologies to be used in industry-wide leak detection and repair (LDAR) programs. However, there has been little to no systematic study of the effectiveness of infrared-camera-based OGI technology for leak detection applications. Here, we develop a physics-based model that simulates a passive infrared camera imaging a methane leak against varying background and ambient conditions. We verify the simulation tool through a series of large-volume controlled release field experiments wherein known quantities of methane were released and imaged from a range of distances. After simulator verification, we analyze the effects of environmental conditions like temperature, wind, and imaging background on the amount of methane detected from a statistically representative survey program. We also examine the effects of LDAR design parameters like imaging distance, leak size distribution, and gas composition. We show that imaging distance strongly affects leak detection - EPA's expectation of a 60% reduction in fugitive emissions based on a semi-annual LDAR survey will be realized only if leaks are imaged at a distance less than 10 m from the source under ideal environmental conditions. Local wind speed is also shown to be important. We show that minimum detection limits are 3 to 4 times higher for wet-gas compositions that contain a significant fraction of ethane and propane, resulting a significantly large leakage rate. We also explore the importance of `super-emitters' on the performance of an OGI-based leak detection program, and show that OGI technology can be used as an approximate leak-quantification method to selectively target the biggest leaks. Finally, we also provide recommendations and best-practices guidelines for achieving expected methane mitigation.

76 FR 39899 - Notice of Lodging of Consent Decree Under the Clean Air Act

Federal Register 2010, 2011, 2012, 2013, 2014

2011-07-07

... emissions of nitrogen oxides, sulfur dioxide, volatile organic compounds, and benzene. Among other things... refinery's benzene monitoring program is enhanced, and the refinery's leak-detection-and-repair (LDAR...

77 FR 43614 - Notice of Lodging of Consent Decree Pursuant to the Clean Air Act

Federal Register 2010, 2011, 2012, 2013, 2014

2012-07-25

... (``LDAR'') for hazardous air pollutants, 40 CFR Part 63, Subparts A, H and CC, at an asphalt petroleum... penalty. In addition, although the plant has not refined asphalt since 2008, Chevron agrees to implement...

Small Negative Cloud-to-Ground Lightning Reports at the KSC-ER

NASA Technical Reports Server (NTRS)

Wilson, Jennifer G.; Cummins, Kenneth L.; Krider, E. Philip

2009-01-01

'1he NASA Kennedy Space Center (KSC) and Air Force Eastern Range (ER) use data from two cloud-to-ground (CG) lightning detection networks, the CGLSS and the NLDN, and a volumetric lightning mapping array, LDAR, to monitor and characterize lightning that is potentially hazardous to ground or launch operations. Data obtained from these systems during June-August 2006 have been examined to check the classification of small, negative CGLSS reports that have an estimated peak current, [I(sup p)] less than 7 kA, and to determine the smallest values of I(sup p), that are produced by first strokes, by subsequent strokes that create a new ground contact (NGC), and by subsequent strokes that remain in a pre-existing channel (PEC). The results show that within 20 km of the KSC-ER, 21% of the low-amplitude negative CGLSS reports were produced by first strokes, with a minimum I(sup p) of-2.9 kA; 31% were by NGCs, with a minimum I(sup p) of-2.0 kA; and 14% were by PECs, with a minimum I(sup p) of -2.2 kA. The remaining 34% were produced by cloud pulses or lightning events that we were not able to classify.

Development of cost effective fenceline monitoring methods to support advanced leak detection and repair strategies

EPA Science Inventory

Improved mitigation of fugitive emissions of hazardous air pollutants (HAPs), volatile organic compounds (VOCs), and greenhouse gas (GHG) emissions is an important emerging topic in many industrial sectors. Efficacious leak detection and repair (LDAR) programs of the future yiel...

Development of cost effective fenceline monitoring approaches to support advanced leak detection and repair strategies

EPA Science Inventory

Cost-effective fence line and process monitoring systems to support advanced leak detection and repair (LDAR) strategies can enhance protection of public health, facilitate worker safety, and help companies realize cost savings by reducing lost product. The U.S. EPA Office of Re...

PORTABLE IMAGING DEVICES FOR INDUSTRIAL LEAK DETECTION AT PETROLEUM REFINERIES AND CHEMICAL PLANTS

EPA Science Inventory

Undiscovered gas leaks, or fugitive emissions, in chemical plants and refinery operations can impact regional air quality as well as being a public health problem. Surveying a facility for potential gas leaks can be a daunting task. Industrial Leak Detection and Repair (LDAR) pro...

75 FR 78733 - Notice of Lodging of Consent Decree Under the Clean Air Act

Federal Register 2010, 2011, 2012, 2013, 2014

2010-12-16

... alleges that U.S. Oil violated the National Emission Standards for Hazardous Air Pollutants for Benzene Waste Operations (the ``Benzene NESHAP''), 40 CFR part 61, Subpart FF, the National Emission Standards... Benzene NESHAP compliance program; and (4) implement measures, in addition to compliance with the LDAR...

Of Detection Limits and Effective Mitigation: The Use of Infrared Cameras for Methane Leak Detection

NASA Astrophysics Data System (ADS)

Ravikumar, A. P.; Wang, J.; McGuire, M.; Bell, C.; Brandt, A. R.

2017-12-01

Mitigating methane emissions, a short-lived and potent greenhouse gas, is critical to limiting global temperature rise to two degree Celsius as outlined in the Paris Agreement. A major source of anthropogenic methane emissions in the United States is the oil and gas sector. To this effect, state and federal governments have recommended the use of optical gas imaging systems in periodic leak detection and repair (LDAR) surveys to detect for fugitive emissions or leaks. The most commonly used optical gas imaging systems (OGI) are infrared cameras. In this work, we systematically evaluate the limits of infrared (IR) camera based OGI system for use in methane leak detection programs. We analyze the effect of various parameters that influence the minimum detectable leak rates of infrared cameras. Blind leak detection tests were carried out at the Department of Energy's MONITOR natural gas test-facility in Fort Collins, CO. Leak sources included natural gas wellheads, separators, and tanks. With an EPA mandated 60 g/hr leak detection threshold for IR cameras, we test leak rates ranging from 4 g/hr to over 350 g/hr at imaging distances between 5 ft and 70 ft from the leak source. We perform these experiments over the course of a week, encompassing a wide range of wind and weather conditions. Using repeated measurements at a given leak rate and imaging distance, we generate detection probability curves as a function of leak-size for various imaging distances, and measurement conditions. In addition, we estimate the median detection threshold - leak-size at which the probability of detection is 50% - under various scenarios to reduce uncertainty in mitigation effectiveness. Preliminary analysis shows that the median detection threshold varies from 3 g/hr at an imaging distance of 5 ft to over 150 g/hr at 50 ft (ambient temperature: 80 F, winds < 4 m/s). Results from this study can be directly used to improve OGI based LDAR protocols and reduce uncertainty in estimated mitigation effectiveness. Furthermore, detection limits determined in this study can be used as standards to compare new detection technologies.

Observations of Total Lightning Associated with Severe Convection During the Wet Season in Central Florida

NASA Technical Reports Server (NTRS)

Sharp, D.; Williams, E.; Weber, M.; Goodman, Steven J.; Raghavan, R.; Matlin, A.; Boldi, B.

1998-01-01

This paper will discuss findings of a collaborative lightning research project between National Aeronautics and Space Administration, the Massachusetts Institute of Technology and the National Weather Service office In Melbourne Florida. In August 1996, NWS/MLB received a workstation which incorporates data from the KMLB WSR-88D, Cloud to Ground (CG) stroke data from the National Lightning Detection Network (NLDN), and 3D volumetric lightning data collected from the Kennedy Space Centers' Lightning Detection And Ranging (LDAR) lightning system. The two primary objectives of this lightning workstation, called Lightning Imaging Sensor Data Applications Display (USDAD), are to: observe how total lightning relates to severe convective storm morphology over central Florida, and compare ground based total lightning data (LDAR) to a satellite based lightning detection system. This presentation will focus on objective #1. The LISDAD system continuously displays CG and total lighting activity overlaid on top of the KMLB composite reflectivity product. This allows forecasters to monitor total lightning activity associated with convective cells occurring over the central Florida peninsula and adjacent coastal waters. The LISDAD system also keeps track of the amount of total lightning data, and associated KMLB radar products with individual convective cells occurring over the region. By clicking on an individual cell, a history table displays flash rate information (CG and total lightning) in one minute increments, along with radar parameter trends (echo tops, maximum dBz and height of maximum dBz) every 5 minutes. This history table Is updated continuously, without user intervention, as long as the cell is identified. Reviewing data collected during the 1997 wet season (21 cases) revealed that storms which produced severe weather (hall greater or = 0.75 in. or wind damage) typically showed a rapid rise In total lightning prior to the onset of severe weather. On average, flash rate increases of 25 FPM per minute over a time scale of approximately 5 minutes were common. These pulse severe storms typically reached values of 150 to 200 FPM with some cells exceeding 400 FPM. One finding which could have a direct application to the warning process is that the rapid increase in lightning typically occurred in advance of the warning issuance time. Comparisons between the ending time of the rapid rate increase and the time of when the warning was issued by NWS/MLB meteorologist exhibited a lead time of 8 minutes. It is conceivable that if close monitoring of the LISDAD system by operational meteorologist is routinely performed, warnings for pulse severe storms could be issued up to 4 to 6 minutes earlier than what is issued currently.

Three-Dimensional Radar and Total Lightning Characteristics of Mesoscale Convective Systems

NASA Astrophysics Data System (ADS)

McCormick, T. L.; Carey, L. D.; Murphy, M. J.; Demetriades, N. W.

2002-12-01

Preliminary analysis of three-dimensional radar and total lightning characteristics for two mesoscale convective systems (MCSs) occurring in the Dallas-Fort Worth, Texas area during 12-13 October 2001 and 7-8 April 2002 are presented. This study utilizes WSR-88D Level II radar (KFWS), Vaisala GAI Inc. Lightning Detection and Ranging II (LDAR II), and National Lightning Detection Network (NLDN) data to gain a better understanding of the structure and evolution of MCSs, with special emphasis on total lightning. More specifically, this research examines the following topics: 1) the characteristics and evolution of total lightning in MCS's, 2) the correlation between radar reflectivity and lightning flash origins in MCSs, 3) the evolution of the dominant cloud-to-ground (CG) lightning polarity and peak current in both the stratiform and convective regions of MCSs, and 4) the similarities and differences in mesoscale structure and lightning behavior between the two MCSs being studied. Results thus far are in good agreement with previous studies. For example, CG lightning polarity in both MCSs is predominately negative (~90%). Also, the storm cells within the MCSs that exhibit very strong updrafts, identified by high (> 50 dBZ) radar reflectivities, weak echo regions, hook echoes, and/or confirmed severe reports, have higher mean lightning flash origin heights than storm cells with weaker updrafts. Finally, a significant increase in total lightning production (from ~10 to ~18 flashes/min) followed by a significant decrease (from ~18 to ~12 to ~5 flashes/min) is evident approximately one-half hour and ten minutes, respectively, prior to tornado touchdown from a severe storm cell located behind the main convective squall line of the 12-13 October 2001 MCS. These preliminary results, as well as other total lightning and radar characteristics of two MCSs, will be presented.

Scanning, standoff TDLAS leak imaging and quantification

NASA Astrophysics Data System (ADS)

Wainner, Richard T.; Aubut, Nicholas F.; Laderer, Matthew C.; Frish, Michael B.

2017-05-01

This paper reports a novel quantitative gas plume imaging tool, based on active near-infrared Backscatter Tunable Diode Laser Absorption Spectroscopy (b-TDLAS) technology, designed for upstream natural gas leak applications. The new tool integrates low-cost laser sensors with video cameras to create a highly sensitive gas plume imager that also quantifies emission rate, all in a lightweight handheld ergonomic package. It is intended to serve as a lower-cost, higherperformance, enhanced functionality replacement for traditional passive non-quantitative mid-infrared Optical Gas Imagers (OGI) which are utilized by industry to comply with natural gas infrastructure Leak Detection and Repair (LDAR) requirements. It addresses the need for reliable, robust, low-cost sensors to detect and image methane leaks, and to quantify leak emission rates, focusing on inspections of upstream oil and gas operations, such as well pads, compressors, and gas plants. It provides: 1) Colorized quantified images of path-integrated methane concentration. The images depict methane plumes (otherwise invisible to the eye) actively interrogated by the laser beam overlaid on a visible camera image of the background. The detection sensitivity exceeds passive OGI, thus simplifying the manual task of leak detection and location; and 2) Data and algorithms for using the quantitative information gathered by the active detection technique to deduce plume flux (i.e. methane emission rate). This key capability will enable operators to prioritize leak repairs and thereby minimize the value of lost product, as well as to quantify and minimize greenhouse gas emissions, using a tool that meets EPA LDAR imaging equipment requirements.

The Design and Evaluation of the Lighting Imaging Sensor Data Applications Display (LISDAD)

NASA Technical Reports Server (NTRS)

Boldi, B.; Hodanish, S.; Sharp, D.; Williams, E.; Goodman, Steven; Raghavan, R.; Matlin, A.; Weber, M.

1998-01-01

The design and evaluation of the Lightning Imaging Sensor Data Applications Display (LISDAD). The ultimate goal of the LISDAD system is to quantify the utility of total lightning information in short-term, severe-weather forecasting operations. To this end, scientists from NASA, NWS, and MIT organized an effort to study the relationship of lightning and severe-weather on a storm-by-storm, and even cell-by-cell basis for as many storms as possible near Melbourne, Florida. Melbourne was chosen as it offers a unique combination of high probability of severe weather and proximity to major relevant sensors - specifically: NASA's total lightning mapping system at Kennedy Space Center (the LDAR system at KSC); a NWS/NEXRAD radar (at Melbourne); and a prototype Integrated Terminal Weather System (ITWS, at Orlando), which obtains cloud-to-ground lightning Information from the National Lightning Detection Network (NLDN), and also uses NSSL's Severe Storm Algorithm (NSSL/SSAP) to obtain information about various storm-cell parameters. To assist in realizing this project's goal, an interactive, real-time data processing system (the LISDAD system) has been developed that supports both operational short-term weather forecasting and post facto severe-storm research. Suggestions have been drawn from the operational users (NWS/Melbourne) in the design of the data display and its salient behavior. The initial concept for the users Graphical Situation Display (GSD) was simply to overlay radar data with lightning data, but as the association between rapid upward trends in the total lightning rate and severe weather became evident, the display was significantly redesigned. The focus changed to support the display of time series of storm-parameter data and the automatic recognition of cells that display rapid changes in the total-lightning flash rate. The latter is calculated by grouping discrete LDAR radiation sources into lightning flashes using a time-space association algorithm. Specifically, the GSD presents the user with the Composite Maximum Reflectivity obtained from the NWS/NEXRAD. Superimposed upon this background image are placed small black circles indicating the locations of storm cells identified by the NSSL/SSA. The circles become cyan if lightning is detected within the storm-cell; if the cell has lightning rates indicative of a severe-storm, the circle turns red. This paper will: (1) review the design of LISDAD system; (2) present some examples of its data display; and shown results of the lightning based severe-weather prediction algorithm.

The 29 July 1994 Merritt Island, Fl Microburst: A Case Study Intercomparing Kennedy Space Center Three-Dimensional Lightning Data (LDAR) and WSR-88D Radar Data

NASA Technical Reports Server (NTRS)

Hoffert, Steven G.; Pearce, Matt L.

1996-01-01

Many researchers have shown that the development and evolution of electrical discharges within convective clouds is fundamentally related to the growth and dynamics of precipitation particles aloft. In the presence of strong updrafts above the freezing level collisions among mixed-phase particles (i.e., hail. ice, supercooled water) promote the necessary charge separation needed to initiate intra-cloud lightning. A precipitation core that descends below the freezing level is often accompanied by a change in the electrical structure of the cloud. Consequently, more Cloud-to-Ground (CG) than Intra-Cloud (IC) lightning flashes appear. Descending precipitation cores can also play a significant role in the evolution of mesoscale features at the surface (e.g., microbursts, downbursts) because of latent heat and mass loading effects of water and ice. For this reason, some believe that lightning and microbursts are fundamentally linked by the presence of ice particles in thunderstorms. Several radar and lightning studies of microburst thunderstorms from COHMEX in 1986 showed that the peak IC lightning systematically occurred ten minutes before the onset of a microburst. In contrast, most CG lightning occurred at the time of the microburst. Many of the preceding studies have been done using high-resolution research radars and experimental lightning detection systems in focused field projects. In addition, these studies could only determine the vertical origin or occurrence of IC lightning, and not a true three-dimensional representation. Currently, the WSR-88D radar system and a real-time, state-of-the-art lightning system (LDAR) at the Kennedy Space Center (KSC) in Florida provide an opportunity to extend these kinds of studies in a more meaningful operational setting.

Effects of a Longer Detection Window in VHF Time-of-Arrival Lightning Detection Systems

NASA Astrophysics Data System (ADS)

Murphy, M.; Holle, R.; Demetriades, N.

2003-12-01

Lightning detection systems that operate by measuring the times of arrival (TOA) of short bursts of radiation at VHF can produce huge volumes of data. The first automated system of this kind, the NASA Kennedy Space Center LDAR network, is capable of producing one detection every 100 usec from each of seven sensors (Lennon and Maier, 1991), where each detection consists of the time and amplitude of the highest-amplitude peak observed within the 100 usec window. More modern systems have been shown to produce very detailed information with one detection every 10 usec (Rison et al., 2001). Operating such systems in real time, however, can become expensive because of the large data communications rates required. One solution to this problem is to use a longer detection window, say 500 usec. In principle, this has little or no effect on the flash detection efficiency because each flash typically produces a very large number of these VHF bursts (known as sources). By simply taking the largest-amplitude peak from every 500-usec interval instead of every 100-usec interval, we should detect the largest 20{%} of the sources that would have been detected using the 100-usec window. However, questions remain about the exact effect of a longer detection window on the source detection efficiency with distance from the network, its effects on how well flashes are represented in space, and how well the reduced information represents the parent thunderstorm. The latter issue is relevant for automated location and tracking of thunderstorm cells using data from VHF TOA lightning detection networks, as well as for understanding relationships between lightning and severe weather. References Lennon, C.L. and L.M. Maier, Lightning mapping system. Proceedings, Intl. Aerospace and Ground Conf. on Lightning and Static Elec., Cocoa Beach, Fla., NASA Conf. Pub. 3106, vol. II, pp. 89-1 - 89-10, 1991. Rison, W., P. Krehbiel, R. Thomas, T. Hamlin, J. Harlin, High time resolution lightning mapping observations of a small thunderstorm during STEPS. Eos Trans. AGU, 82 (47), Fall Meet. Suppl., Abstract AE12A-83, 2001.

Modeling Stepped Leaders Using a Time Dependent Multi-dipole Model and High-speed Video Data

NASA Astrophysics Data System (ADS)

Karunarathne, S.; Marshall, T.; Stolzenburg, M.; Warner, T. A.; Orville, R. E.

2012-12-01

In summer of 2011, we collected lightning data with 10 stations of electric field change meters (bandwidth of 0.16 Hz - 2.6 MHz) on and around NASA/Kennedy Space Center (KSC) covering nearly 70 km × 100 km area. We also had a high-speed video (HSV) camera recording 50,000 images per second collocated with one of the electric field change meters. In this presentation we describe our use of these data to model the electric field change caused by stepped leaders. Stepped leaders of a cloud to ground lightning flash typically create the initial path for the first return stroke (RS). Most of the time, stepped leaders have multiple complex branches, and one of these branches will create the ground connection for the RS to start. HSV data acquired with a short focal length lens at ranges of 5-25 km from the flash are useful for obtaining the 2-D location of these multiple branches developing at the same time. Using HSV data along with data from the KSC Lightning Detection and Ranging (LDAR2) system and the Cloud to Ground Lightning Surveillance System (CGLSS), the 3D path of a leader may be estimated. Once the path of a stepped leader is obtained, the time dependent multi-dipole model [ Lu, Winn,and Sonnenfeld, JGR 2011] can be used to match the electric field change at various sensor locations. Based on this model, we will present the time-dependent charge distribution along a leader channel and the total charge transfer during the stepped leader phase.

Lightning activity related to satellite and radar observations of a mesoscale convective system over Texas on 7 8 April 2002

NASA Astrophysics Data System (ADS)

Dotzek, Nikolai; Rabin, Robert M.; Carey, Lawrence D.; MacGorman, Donald R.; McCormick, Tracy L.; Demetriades, Nicholas W.; Murphy, Martin J.; Holle, Ronald L.

2005-07-01

A multi-sensor study of the leading-line, trailing-stratiform (LLTS) mesoscale convective system (MCS) that developed over Texas in the afternoon of 7 April 2002 is presented. The analysis relies mainly on operationally available data sources such as GOES East satellite imagery, WSR-88D radar data and NLDN cloud-to-ground flash data. In addition, total lightning information in three dimensions from the LDAR II network in the Dallas-Ft. Worth region is used. GOES East satellite imagery revealed several ring-like cloud top structures with a diameter of about 100 km during MCS formation. The Throckmorton tornadic supercell, which had formed just ahead of the developing linear MCS, was characterized by a high CG+ percentage below a V-shaped cloud top overshoot north of the tornado swath. There were indications of the presence of a tilted electrical dipole in this storm. Also this supercell had low average CG- first stroke currents and flash multiplicities. Interestingly, especially the average CG+ flash multiplicity in the Throckmorton storm showed oscillations with an estimated period of about 15 min. Later on, in the mature LLTS MCS, the radar versus lightning activity comparison revealed two dominant discharge regions at the back of the convective leading edge and a gentle descent of the upper intracloud lightning region into the trailing stratiform region, apparently coupled to hydrometeor sedimentation. There was evidence for an inverted dipole in the stratiform region of the LLTS MCS, and CG+ flashes from the stratiform region had high first return stroke peak currents.

Drones in Automation - Secured Unmanned Aerial Systems

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

Morales Rodriguez, Marissa E.; Rooke, Sterling; Fuhr, Peter L.

Factories, refineries, utilities (water/wastewater, electric) and related industrial sites are complex systems and structures with inspection and maintenance procedures required for optimal operation and regulatory compliance. As a specific example, consider just the bulk electric power system which is comprised of more than 200,000 miles of highvoltage transmission lines, thousands of generation plants and millions of digital controls. More than 1,800 entities own and operate portions of the grid system, with thousands more involved in the operation of distribution networks across North America. The interconnected and interdependent nature of the bulk power system requires a consistent and systematic application ofmore » risk mitigation across the entire grid system to be truly effective. Similar situations are found throughout automation where frequently an aging infrastructure is in place too. Consider, for example, the situation present in a refinery or chemical processing setting with the requirement for leak detection inspection of pipes, interconnects and systems stretching across the plant. The current practices and challenges relating just to this task - leak detection and repair (LDAR) – of detecting any fugitive emissions present and documenting all measurements thereby meeting air compliance regulations are typically “handled” by a small army of individuals with handheld or backpack-sized detectors who crawl through piping racks conducting measurements at each flange. Such work is performed in difficult conditions (temperature, humidity, physically challenging) with frequently a high level of employee turnover. Finaly, enter low cost sensors and mobile platforms – in other words unmanned aerial systems (UASs, or drones) with enhanced sensing capabilities.« less

Drones in Automation - Secured Unmanned Aerial Systems

DOE PAGES

Morales Rodriguez, Marissa E.; Rooke, Sterling; Fuhr, Peter L.; ...

2017-05-01

Factories, refineries, utilities (water/wastewater, electric) and related industrial sites are complex systems and structures with inspection and maintenance procedures required for optimal operation and regulatory compliance. As a specific example, consider just the bulk electric power system which is comprised of more than 200,000 miles of highvoltage transmission lines, thousands of generation plants and millions of digital controls. More than 1,800 entities own and operate portions of the grid system, with thousands more involved in the operation of distribution networks across North America. The interconnected and interdependent nature of the bulk power system requires a consistent and systematic application ofmore » risk mitigation across the entire grid system to be truly effective. Similar situations are found throughout automation where frequently an aging infrastructure is in place too. Consider, for example, the situation present in a refinery or chemical processing setting with the requirement for leak detection inspection of pipes, interconnects and systems stretching across the plant. The current practices and challenges relating just to this task - leak detection and repair (LDAR) – of detecting any fugitive emissions present and documenting all measurements thereby meeting air compliance regulations are typically “handled” by a small army of individuals with handheld or backpack-sized detectors who crawl through piping racks conducting measurements at each flange. Such work is performed in difficult conditions (temperature, humidity, physically challenging) with frequently a high level of employee turnover. Finaly, enter low cost sensors and mobile platforms – in other words unmanned aerial systems (UASs, or drones) with enhanced sensing capabilities.« less

Developing empirical lightning cessation forecast guidance for the Kennedy Space Center

NASA Astrophysics Data System (ADS)

Stano, Geoffrey T.

The Kennedy Space Center in east Central Florida is one of the few locations in the country that issues lightning advisories. These forecasts are vital to the daily operations of the Space Center and take on even greater significance during launch operations. The U.S. Air Force's 45th Weather Squadron (45WS), who provides forecasts for the Space Center, has a good record of forecasting the initiation of lightning near their locations of special concern. However, the remaining problem is knowing when to cancel a lightning advisory. Without specific scientific guidelines detailing cessation activity, the Weather Squadron must keep advisories in place longer than necessary to ensure the safety of personnel and equipment. This unnecessary advisory time costs the Space Center millions of dollars in lost manpower each year. This research presents storm and environmental characteristics associated with lightning cessation that then are utilized to create lightning cessation guidelines for isolated thunderstorms for use by the 45WS during the warm season months of May through September. The research uses data from the Lightning Detection and Ranging (LDAR) network at the Kennedy Space Center, which can observe intra-cloud and portions of cloud-to-ground lightning strikes. Supporting data from the Cloud-to-Ground Lightning Surveillance System (CGLSS), radar observations from the Melbourne WSR-88D, and Cape Canaveral morning radiosonde launches also are included. Characteristics of 116 thunderstorms comprising our dataset are presented. Most of these characteristics are based on LDAR-derived spark and flash data and have not been described previously. In particular, the first lightning activity is quantified as either cloud-to-ground (CG) or intra-cloud (IC). Only 10% of the storms in this research are found to initiate with a CG strike. Conversely, only 16% of the storms end with a CG strike. Another characteristic is the average horizontal extent of all the flashes comprising a storm. Our average is 12-14 km, while the greatest flash extends 26 km. Comparisons between the starting altitude of the median and last flashes of a storm are analyzed, with only 37% of the storms having a higher last flash initiating altitude. Additional observations are made of the total lightning flash rate, percentage of CG to IC lightning, trends of individual flash initiation altitudes versus the average initiation altitude, the average inter-flash time distribution, and time series of inter-flash times. Five schemes to forecast lightning cessation are developed and evaluated. 100 of the 116 storms were randomly selected as the dependent sample, while the remaining 16 storms were used for verification. The schemes included a correlation and regression tree analysis, multiple linear regression, trends of storm duration, trend of the altitude of the greatest reflectivity to the time of the final flash, and a percentile scheme. Surprisingly, the percentile method was found to be the most effective technique and the simplest. The inclusion of real time storm parameters is found to have little effect on the results, suggesting that different forecast predictors, such as microphysical data from polarimetric radar, will be necessary to produce improved skill. When the percentile method used a confidence level of 99.5%, it successfully maintained lightning advisories for all 16 independent storms on which the schemes were tested. Since the computed wait time was 25 min, compared to the 45WS' most conservative and accurate wait time of 30 min, the percentile method saves 5 min for each advisory. This 5 min of savings safely shortens the Weather Squadron's advisories and saves money. Additionally, these results are the first to evaluate the 30/30 rule that is used commonly. The success of the percentile method is surprising since it out performs more complex procedures involving correlation and regression tree analysis and regression schemes. These more sophisticated statistical analyses were expected to perform better since they include more predictors in the forecasts. However, with the predictors available to us, this was not the case. While not the expected result, the percentile method succeeds in creating a safe and expedited forecast.

Survey of Large Methane Emitters in North America

NASA Astrophysics Data System (ADS)

Deiker, S.

2017-12-01

It has been theorized that methane emissions in the oil and gas industry follow log normal or "fat tail" distributions, with large numbers of small sources for every very large source. Such distributions would have significant policy and operational implications. Unfortunately, by their very nature such distributions would require large sample sizes to verify. Until recently, such large-scale studies would be prohibitively expensive. The largest public study to date sampled 450 wells, an order of magnitude too low to effectively constrain these models. During 2016 and 2017, Kairos Aerospace conducted a series of surveys the LeakSurveyor imaging spectrometer, mounted on light aircraft. This small, lightweight instrument was designed to rapidly locate large emission sources. The resulting survey covers over three million acres of oil and gas production. This includes over 100,000 wells, thousands of storage tanks and over 7,500 miles of gathering lines. This data set allows us to now probe the distribution of large methane emitters. Results of this survey, and implications for methane emission distribution, methane policy and LDAR will be discussed.

The Characteristics of Total Lightning Activity in Severe Florida Thunderstorms

NASA Technical Reports Server (NTRS)

Williams, E.; Goodman, S. J.; Raghavan, R.; Boldi, R.; Matlin, A.; Weber, M.; Hodanish, S.; Sharp, D.

1997-01-01

Severe thunderstorms are defined by specific exceedance criteria regarding either wind speed (greater than or equal to 50 kts), hailstone diameter (greater than or equal to 3/4 inch), the occurrence of a tornado, or any combination thereof. Although traditional radar signatures of severe thunderstorms have been well documented, the characteristics of associated total lightning activity (both intracloud and cloud-to-ground) of severe thunderstorms remain poorly established. The reason for this are (1) less than 1% of all storms are actually severe, (2) intracloud lightning, which is typically the dominant form of electrical discharge within thunderstorms, is not routinely measured or recorded, (3) direct visual observations of intracloud lightning are obscured during the daytime, and (4) the migratory nature of many severe thunderstorms can make the accurate detection and mapping of intracloud lightning difficult when using fixed-location sensors. The recent establishment of LISDAD (Lightning Imaging Sensor Data Acquisition and Display - discussed in Goodman et al, this Meeting) has substantially addressed these limitations in east central Florida (ECFL). Analysis of total lightning flash Count histories using the LDAR (Lightning Detection And Ranging) system for known severe thunderstorms (currently irrespective of seasonal aspects and severe storm-type) has revealed flash rates exceeding 1 per second. This appears to be a necessary, but not sufficient,condition for most ECFL severe storm cases. The differences in radar-observed storm structure for high flash rate storms (to include both severe and non-severe categories) will be described together with the timing of peak flash rate vs. the timing of the severe weather manifestation. Comparisons with the satellite-bases OTD (Optical Transient Detector) overhead passes will also be presented when possible.

Control range: a controllability-based index for node significance in directed networks

NASA Astrophysics Data System (ADS)

Wang, Bingbo; Gao, Lin; Gao, Yong

2012-04-01

While a large number of methods for module detection have been developed for undirected networks, it is difficult to adapt them to handle directed networks due to the lack of consensus criteria for measuring the node significance in a directed network. In this paper, we propose a novel structural index, the control range, motivated by recent studies on the structural controllability of large-scale directed networks. The control range of a node quantifies the size of the subnetwork that the node can effectively control. A related index, called the control range similarity, is also introduced to measure the structural similarity between two nodes. When applying the index of control range to several real-world and synthetic directed networks, it is observed that the control range of the nodes is mainly influenced by the network's degree distribution and that nodes with a low degree may have a high control range. We use the index of control range similarity to detect and analyze functional modules in glossary networks and the enzyme-centric network of homo sapiens. Our results, as compared with other approaches to module detection such as modularity optimization algorithm, dynamic algorithm and clique percolation method, indicate that the proposed indices are effective and practical in depicting structural and modular characteristics of sparse directed networks.

Dynamic range in small-world networks of Hodgkin-Huxley neurons with chemical synapses

NASA Astrophysics Data System (ADS)

Batista, C. A. S.; Viana, R. L.; Lopes, S. R.; Batista, A. M.

2014-09-01

According to Stevens' law the relationship between stimulus and response is a power-law within an interval called the dynamic range. The dynamic range of sensory organs is found to be larger than that of a single neuron, suggesting that the network structure plays a key role in the behavior of both the scaling exponent and the dynamic range of neuron assemblies. In order to verify computationally the relationships between stimulus and response for spiking neurons, we investigate small-world networks of neurons described by the Hodgkin-Huxley equations connected by chemical synapses. We found that the dynamic range increases with the network size, suggesting that the enhancement of the dynamic range observed in sensory organs, with respect to single neurons, is an emergent property of complex network dynamics.

Body area network--a key infrastructure element for patient-centered telemedicine.

PubMed

Norgall, Thomas; Schmidt, Robert; von der Grün, Thomas

2004-01-01

The Body Area Network (BAN) extends the range of existing wireless network technologies by an ultra-low range, ultra-low power network solution optimised for long-term or continuous healthcare applications. It enables wireless radio communication between several miniaturised, intelligent Body Sensor (or actor) Units (BSU) and a single Body Central Unit (BCU) worn at the human body. A separate wireless transmission link from the BCU to a network access point--using different technology--provides for online access to BAN components via usual network infrastructure. The BAN network protocol maintains dynamic ad-hoc network configuration scenarios and co-existence of multiple networks.BAN is expected to become a basic infrastructure element for electronic health services: By integrating patient-attached sensors and mobile actor units, distributed information and data processing systems, the range of medical workflow can be extended to include applications like wireless multi-parameter patient monitoring and therapy support. Beyond clinical use and professional disease management environments, private personal health assistance scenarios (without financial reimbursement by health agencies / insurance companies) enable a wide range of applications and services in future pervasive computing and networking environments.

Aging and functional brain networks

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

Tomasi D.; Tomasi, D.; Volkow, N.D.

2011-07-11

Aging is associated with changes in human brain anatomy and function and cognitive decline. Recent studies suggest the aging decline of major functional connectivity hubs in the 'default-mode' network (DMN). Aging effects on other networks, however, are largely unknown. We hypothesized that aging would be associated with a decline of short- and long-range functional connectivity density (FCD) hubs in the DMN. To test this hypothesis, we evaluated resting-state data sets corresponding to 913 healthy subjects from a public magnetic resonance imaging database using functional connectivity density mapping (FCDM), a voxelwise and data-driven approach, together with parallel computing. Aging was associatedmore » with pronounced long-range FCD decreases in DMN and dorsal attention network (DAN) and with increases in somatosensory and subcortical networks. Aging effects in these networks were stronger for long-range than for short-range FCD and were also detected at the level of the main functional hubs. Females had higher short- and long-range FCD in DMN and lower FCD in the somatosensory network than males, but the gender by age interaction effects were not significant for any of the networks or hubs. These findings suggest that long-range connections may be more vulnerable to aging effects than short-range connections and that, in addition to the DMN, the DAN is also sensitive to aging effects, which could underlie the deterioration of attention processes that occurs with aging.« less

Network feedback regulates motor output across a range of modulatory neuron activity

PubMed Central

Spencer, Robert M.

2016-01-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5–35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. PMID:27030739

Selective Activation of Resting-State Networks following Focal Stimulation in a Connectome-Based Network Model of the Human Brain

PubMed Central

2016-01-01

Abstract When the brain is stimulated, for example, by sensory inputs or goal-oriented tasks, the brain initially responds with activities in specific areas. The subsequent pattern formation of functional networks is constrained by the structural connectivity (SC) of the brain. The extent to which information is processed over short- or long-range SC is unclear. Whole-brain models based on long-range axonal connections, for example, can partly describe measured functional connectivity dynamics at rest. Here, we study the effect of SC on the network response to stimulation. We use a human whole-brain network model comprising long- and short-range connections. We systematically activate each cortical or thalamic area, and investigate the network response as a function of its short- and long-range SC. We show that when the brain is operating at the edge of criticality, stimulation causes a cascade of network recruitments, collapsing onto a smaller space that is partly constrained by SC. We found both short- and long-range SC essential to reproduce experimental results. In particular, the stimulation of specific areas results in the activation of one or more resting-state networks. We suggest that the stimulus-induced brain activity, which may indicate information and cognitive processing, follows specific routes imposed by structural networks explaining the emergence of functional networks. We provide a lookup table linking stimulation targets and functional network activations, which potentially can be useful in diagnostics and treatments with brain stimulation. PMID:27752540

Network feedback regulates motor output across a range of modulatory neuron activity.

PubMed

Spencer, Robert M; Blitz, Dawn M

2016-06-01

Modulatory projection neurons alter network neuron synaptic and intrinsic properties to elicit multiple different outputs. Sensory and other inputs elicit a range of modulatory neuron activity that is further shaped by network feedback, yet little is known regarding how the impact of network feedback on modulatory neurons regulates network output across a physiological range of modulatory neuron activity. Identified network neurons, a fully described connectome, and a well-characterized, identified modulatory projection neuron enabled us to address this issue in the crab (Cancer borealis) stomatogastric nervous system. The modulatory neuron modulatory commissural neuron 1 (MCN1) activates and modulates two networks that generate rhythms via different cellular mechanisms and at distinct frequencies. MCN1 is activated at rates of 5-35 Hz in vivo and in vitro. Additionally, network feedback elicits MCN1 activity time-locked to motor activity. We asked how network activation, rhythm speed, and neuron activity levels are regulated by the presence or absence of network feedback across a physiological range of MCN1 activity rates. There were both similarities and differences in responses of the two networks to MCN1 activity. Many parameters in both networks were sensitive to network feedback effects on MCN1 activity. However, for most parameters, MCN1 activity rate did not determine the extent to which network output was altered by the addition of network feedback. These data demonstrate that the influence of network feedback on modulatory neuron activity is an important determinant of network output and feedback can be effective in shaping network output regardless of the extent of network modulation. Copyright © 2016 the American Physiological Society.

A network of experimental forests and ranges: Providing soil solutions for a changing world

Treesearch

Mary Beth Adams

2010-01-01

The network of experimental forests and ranges of the USDA Forest Service represents significant opportunities to provide soil solutions to critical issues of a changing world. This network of 81 experimental forests and ranges encompasses broad geographic, biological, climatic and physical scales, and includes long-term data sets, and long-term experimental...

Maximum Interconnectedness and Availability for Directional Airborne Range Extension Networks

DTIC Science & Technology

2016-08-29

2 IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS I. INTRODUCTION Tactical military networks both on land and at sea often have restricted transmission ...ranges due to limits on terminal transmission power , geographic features that block line-of-sight, and poor over-the-horizon signal propagation...IEEE TRANSACTIONS ON WIRELESS COMMUNICATIONS 1 Maximum Interconnectedness and Availability for Directional Airborne Range Extension Networks Thomas

Porous Networks Through Colloidal Templates

NASA Astrophysics Data System (ADS)

Li, Qin; Retsch, Markus; Wang, Jianjun; Knoll, Wolfgang; Jonas, Ulrich

Porous networks represent a class of materials with interconnected voids with specific properties concerning adsorption, mass and heat transport, and spatial confinement, which lead to a wide range of applications ranging from oil recovery and water purification to tissue engineering. Porous networks with well-defined, highly ordered structure and periodicities around the wavelength of light can furthermore show very sophisticated optical properties. Such networks can be fabricated from a very large range of materials by infiltration of a sacrificial colloidal crystal template and subsequent removal of the template. The preparation procedures reported in the literature are discussed in this review and the resulting porous networks are presented with respect to the underlying material class. Furthermore, methods for hierarchical superstructure formation and functionalization of the network walls are discussed.

Opinion formation on multiplex scale-free networks

NASA Astrophysics Data System (ADS)

Nguyen, Vu Xuan; Xiao, Gaoxi; Xu, Xin-Jian; Li, Guoqi; Wang, Zhen

2018-01-01

Most individuals, if not all, live in various social networks. The formation of opinion systems is an outcome of social interactions and information propagation occurring in such networks. We study the opinion formation with a new rule of pairwise interactions in the novel version of the well-known Deffuant model on multiplex networks composed of two layers, each of which is a scale-free network. It is found that in a duplex network composed of two identical layers, the presence of the multiplexity helps either diminish or enhance opinion diversity depending on the relative magnitudes of tolerance ranges characterizing the degree of openness/tolerance on both layers: there is a steady separation between different regions of tolerance range values on two network layers where multiplexity plays two different roles, respectively. Additionally, the two critical tolerance ranges follow a one-sum rule; that is, each of the layers reaches a complete consensus only if the sum of the tolerance ranges on the two layers is greater than a constant approximately equaling 1, the double of the critical bound on a corresponding isolated network. A further investigation of the coupling between constituent layers quantified by a link overlap parameter reveals that as the layers are loosely coupled, the two opinion systems co-evolve independently, but when the inter-layer coupling is sufficiently strong, a monotonic behavior is observed: an increase in the tolerance range of a layer causes a decline in the opinion diversity on the other layer regardless of the magnitudes of tolerance ranges associated with the layers in question.

Cortical Thinning in Network-Associated Regions in Cognitively Normal and Below-Normal Range Schizophrenia

PubMed Central

Pinnock, Farena; Parlar, Melissa; Hawco, Colin; Hanford, Lindsay; Hall, Geoffrey B.

2017-01-01

This study assessed whether cortical thickness across the brain and regionally in terms of the default mode, salience, and central executive networks differentiates schizophrenia patients and healthy controls with normal range or below-normal range cognitive performance. Cognitive normality was defined using the MATRICS Consensus Cognitive Battery (MCCB) composite score (T = 50 ± 10) and structural magnetic resonance imaging was used to generate cortical thickness data. Whole brain analysis revealed that cognitively normal range controls (n = 39) had greater cortical thickness than both cognitively normal (n = 17) and below-normal range (n = 49) patients. Cognitively normal controls also demonstrated greater thickness than patients in regions associated with the default mode and salience, but not central executive networks. No differences on any thickness measure were found between cognitively normal range and below-normal range controls (n = 24) or between cognitively normal and below-normal range patients. In addition, structural covariance between network regions was high and similar across subgroups. Positive and negative symptom severity did not correlate with thickness values. Cortical thinning across the brain and regionally in relation to the default and salience networks may index shared aspects of the psychotic psychopathology that defines schizophrenia with no relation to cognitive impairment. PMID:28348889

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Mateos, José L.

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Fractional quantum mechanics on networks: Long-range dynamics and quantum transport.

PubMed

Riascos, A P; Mateos, José L

2015-11-01

In this paper we study the quantum transport on networks with a temporal evolution governed by the fractional Schrödinger equation. We generalize the dynamics based on continuous-time quantum walks, with transitions to nearest neighbors on the network, to the fractional case that allows long-range displacements. By using the fractional Laplacian matrix of a network, we establish a formalism that combines a long-range dynamics with the quantum superposition of states; this general approach applies to any type of connected undirected networks, including regular, random, and complex networks, and can be implemented from the spectral properties of the Laplacian matrix. We study the fractional dynamics and its capacity to explore the network by means of the transition probability, the average probability of return, and global quantities that characterize the efficiency of this quantum process. As a particular case, we explore analytically these quantities for circulant networks such as rings, interacting cycles, and complete graphs.

Role of long- and short-range hydrophobic, hydrophilic and charged residues contact network in protein’s structural organization

PubMed Central

2012-01-01

Background The three-dimensional structure of a protein can be described as a graph where nodes represent residues and the strength of non-covalent interactions between them are edges. These protein contact networks can be separated into long and short-range interactions networks depending on the positions of amino acids in primary structure. Long-range interactions play a distinct role in determining the tertiary structure of a protein while short-range interactions could largely contribute to the secondary structure formations. In addition, physico chemical properties and the linear arrangement of amino acids of the primary structure of a protein determines its three dimensional structure. Here, we present an extensive analysis of protein contact subnetworks based on the London van der Waals interactions of amino acids at different length scales. We further subdivided those networks in hydrophobic, hydrophilic and charged residues networks and have tried to correlate their influence in the overall topology and organization of a protein. Results The largest connected component (LCC) of long (LRN)-, short (SRN)- and all-range (ARN) networks within proteins exhibit a transition behaviour when plotted against different interaction strengths of edges among amino acid nodes. While short-range networks having chain like structures exhibit highly cooperative transition; long- and all-range networks, which are more similar to each other, have non-chain like structures and show less cooperativity. Further, the hydrophobic residues subnetworks in long- and all-range networks have similar transition behaviours with all residues all-range networks, but the hydrophilic and charged residues networks don’t. While the nature of transitions of LCC’s sizes is same in SRNs for thermophiles and mesophiles, there exists a clear difference in LRNs. The presence of larger size of interconnected long-range interactions in thermophiles than mesophiles, even at higher interaction strength between amino acids, give extra stability to the tertiary structure of the thermophiles. All the subnetworks at different length scales (ARNs, LRNs and SRNs) show assortativity mixing property of their participating amino acids. While there exists a significant higher percentage of hydrophobic subclusters over others in ARNs and LRNs; we do not find the assortative mixing behaviour of any the subclusters in SRNs. The clustering coefficient of hydrophobic subclusters in long-range network is the highest among types of subnetworks. There exist highly cliquish hydrophobic nodes followed by charged nodes in LRNs and ARNs; on the other hand, we observe the highest dominance of charged residues cliques in short-range networks. Studies on the perimeter of the cliques also show higher occurrences of hydrophobic and charged residues’ cliques. Conclusions The simple framework of protein contact networks and their subnetworks based on London van der Waals force is able to capture several known properties of protein structure as well as can unravel several new features. The thermophiles do not only have the higher number of long-range interactions; they also have larger cluster of connected residues at higher interaction strengths among amino acids, than their mesophilic counterparts. It can reestablish the significant role of long-range hydrophobic clusters in protein folding and stabilization; at the same time, it shed light on the higher communication ability of hydrophobic subnetworks over the others. The results give an indication of the controlling role of hydrophobic subclusters in determining protein’s folding rate. The occurrences of higher perimeters of hydrophobic and charged cliques imply the role of charged residues as well as hydrophobic residues in stabilizing the distant part of primary structure of a protein through London van der Waals interaction. PMID:22720789

Relationships Between Long-Range Lightning Networks and TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cummins, Kenneth L.; Cummins, Kenneth L.; Blakeslee, Richard J.; Goodman, Steven J.

2012-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. The present study intercompares long-range lightning data with observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study examines network detection efficiency and location accuracy relative to LIS observations, describes spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by the long-range networks. Improved knowledge of relationships between these datasets will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Neural networks application to divergence-based passive ranging

NASA Technical Reports Server (NTRS)

Barniv, Yair

1992-01-01

The purpose of this report is to summarize the state of knowledge and outline the planned work in divergence-based/neural networks approach to the problem of passive ranging derived from optical flow. Work in this and closely related areas is reviewed in order to provide the necessary background for further developments. New ideas about devising a monocular passive-ranging system are then introduced. It is shown that image-plan divergence is independent of image-plan location with respect to the focus of expansion and of camera maneuvers because it directly measures the object's expansion which, in turn, is related to the time-to-collision. Thus, a divergence-based method has the potential of providing a reliable range complementing other monocular passive-ranging methods which encounter difficulties in image areas close to the focus of expansion. Image-plan divergence can be thought of as some spatial/temporal pattern. A neural network realization was chosen for this task because neural networks have generally performed well in various other pattern recognition applications. The main goal of this work is to teach a neural network to derive the divergence from the imagery.

General formulation of long-range degree correlations in complex networks

NASA Astrophysics Data System (ADS)

Fujiki, Yuka; Takaguchi, Taro; Yakubo, Kousuke

2018-06-01

We provide a general framework for analyzing degree correlations between nodes separated by more than one step (i.e., beyond nearest neighbors) in complex networks. One joint and four conditional probability distributions are introduced to fully describe long-range degree correlations with respect to degrees k and k' of two nodes and shortest path length l between them. We present general relations among these probability distributions and clarify the relevance to nearest-neighbor degree correlations. Unlike nearest-neighbor correlations, some of these probability distributions are meaningful only in finite-size networks. Furthermore, as a baseline to determine the existence of intrinsic long-range degree correlations in a network other than inevitable correlations caused by the finite-size effect, the functional forms of these probability distributions for random networks are analytically evaluated within a mean-field approximation. The utility of our argument is demonstrated by applying it to real-world networks.

Hierarchical classification with a competitive evolutionary neural tree.

PubMed

Adams, R G.; Butchart, K; Davey, N

1999-04-01

A new, dynamic, tree structured network, the Competitive Evolutionary Neural Tree (CENT) is introduced. The network is able to provide a hierarchical classification of unlabelled data sets. The main advantage that the CENT offers over other hierarchical competitive networks is its ability to self determine the number, and structure, of the competitive nodes in the network, without the need for externally set parameters. The network produces stable classificatory structures by halting its growth using locally calculated heuristics. The results of network simulations are presented over a range of data sets, including Anderson's IRIS data set. The CENT network demonstrates its ability to produce a representative hierarchical structure to classify a broad range of data sets.

Predicting the global spread range via small subnetworks

NASA Astrophysics Data System (ADS)

Sun, Jiachen; Dong, Junyou; Ma, Xiao; Feng, Ling; Hu, Yanqing

2017-04-01

Modern online social network platforms are replacing traditional media due to their effectiveness in both spreading information and communicating opinions. One of the key problems in these online platforms is to predict the global spread range of any given information. Due to its gigantic size as well as time-varying dynamics, an online social network's global structure, however, is usually inaccessible to most researchers. Thus, it raises the very important issue of how to use solely small subnetworks to predict the global influence. In this paper, based on percolation theory, we show that the global spread range can be predicted well from only two small subnetworks. We test our methods in an artificial network and three empirical online social networks, such as the full Sina Weibo network with 99546027 nodes.

Expanding the vision of the Experimental Forest and Range network to urban areas

Treesearch

J. Morgan Grove

2014-01-01

After 100 years, the USDA Forest Service has emerging opportunities to expand the Experimental Forest and Range (EFR) network to urban areas. The purpose of this expansion would be to broaden the types of ecosystems studied, interdisciplinary approaches used, and relevance to society of the EFR network through long-term and large-scale social-ecological projects in...

Coevolution of Cooperation and Partner Rewiring Range in Spatial Social Networks

NASA Astrophysics Data System (ADS)

Khoo, Tommy; Fu, Feng; Pauls, Scott

2016-11-01

In recent years, there has been growing interest in the study of coevolutionary games on networks. Despite much progress, little attention has been paid to spatially embedded networks, where the underlying geographic distance, rather than the graph distance, is an important and relevant aspect of the partner rewiring process. It thus remains largely unclear how individual partner rewiring range preference, local vs. global, emerges and affects cooperation. Here we explicitly address this issue using a coevolutionary model of cooperation and partner rewiring range preference in spatially embedded social networks. In contrast to local rewiring, global rewiring has no distance restriction but incurs a one-time cost upon establishing any long range link. We find that under a wide range of model parameters, global partner switching preference can coevolve with cooperation. Moreover, the resulting partner network is highly degree-heterogeneous with small average shortest path length while maintaining high clustering, thereby possessing small-world properties. We also discover an optimum availability of reputation information for the emergence of global cooperators, who form distant partnerships at a cost to themselves. From the coevolutionary perspective, our work may help explain the ubiquity of small-world topologies arising alongside cooperation in the real world.

The relative efficiency of modular and non-modular networks of different size

PubMed Central

Tosh, Colin R.; McNally, Luke

2015-01-01

Most biological networks are modular but previous work with small model networks has indicated that modularity does not necessarily lead to increased functional efficiency. Most biological networks are large, however, and here we examine the relative functional efficiency of modular and non-modular neural networks at a range of sizes. We conduct a detailed analysis of efficiency in networks of two size classes: ‘small’ and ‘large’, and a less detailed analysis across a range of network sizes. The former analysis reveals that while the modular network is less efficient than one of the two non-modular networks considered when networks are small, it is usually equally or more efficient than both non-modular networks when networks are large. The latter analysis shows that in networks of small to intermediate size, modular networks are much more efficient that non-modular networks of the same (low) connective density. If connective density must be kept low to reduce energy needs for example, this could promote modularity. We have shown how relative functionality/performance scales with network size, but the precise nature of evolutionary relationship between network size and prevalence of modularity will depend on the costs of connectivity. PMID:25631996

Predicting commuter flows in spatial networks using a radiation model based on temporal ranges

NASA Astrophysics Data System (ADS)

Ren, Yihui; Ercsey-Ravasz, Mária; Wang, Pu; González, Marta C.; Toroczkai, Zoltán

2014-11-01

Understanding network flows such as commuter traffic in large transportation networks is an ongoing challenge due to the complex nature of the transportation infrastructure and human mobility. Here we show a first-principles based method for traffic prediction using a cost-based generalization of the radiation model for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events.

Human Behavior Modeling in Network Science

DTIC Science & Technology

2010-03-01

in Network Science bringing three distinct research areas together, communication networks, information networks, and social /cognitive networks. The...researchers. A critical part of the social /cognitive network effort is the modeling of human behavior. The modeling efforts range from organizational...behavior to social cognitive trust to explore and refine the theoretical and applied network relationships between and among the human

Heterogeneous fractionation profiles of meta-analytic coactivation networks.

PubMed

Laird, Angela R; Riedel, Michael C; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L; Eickhoff, Simon B; Smith, Stephen M; Fox, Peter T; Sutherland, Matthew T

2017-04-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d=20-300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how "parent" functional brain systems decompose into constituent "child" sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. Copyright © 2017 Elsevier Inc. All rights reserved.

Heterogeneous fractionation profiles of meta-analytic coactivation networks

PubMed Central

Laird, Angela R.; Riedel, Michael C.; Okoe, Mershack; Jianu, Radu; Ray, Kimberly L.; Eickhoff, Simon B.; Smith, Stephen M.; Fox, Peter T.; Sutherland, Matthew T.

2017-01-01

Computational cognitive neuroimaging approaches can be leveraged to characterize the hierarchical organization of distributed, functionally specialized networks in the human brain. To this end, we performed large-scale mining across the BrainMap database of coordinate-based activation locations from over 10,000 task-based experiments. Meta-analytic coactivation networks were identified by jointly applying independent component analysis (ICA) and meta-analytic connectivity modeling (MACM) across a wide range of model orders (i.e., d = 20 to 300). We then iteratively computed pairwise correlation coefficients for consecutive model orders to compare spatial network topologies, ultimately yielding fractionation profiles delineating how “parent” functional brain systems decompose into constituent “child” sub-networks. Fractionation profiles differed dramatically across canonical networks: some exhibited complex and extensive fractionation into a large number of sub-networks across the full range of model orders, whereas others exhibited little to no decomposition as model order increased. Hierarchical clustering was applied to evaluate this heterogeneity, yielding three distinct groups of network fractionation profiles: high, moderate, and low fractionation. BrainMap-based functional decoding of resultant coactivation networks revealed a multi-domain association regardless of fractionation complexity. Rather than emphasize a cognitive-motor-perceptual gradient, these outcomes suggest the importance of inter-lobar connectivity in functional brain organization. We conclude that high fractionation networks are complex and comprised of many constituent sub-networks reflecting long-range, inter-lobar connectivity, particularly in fronto-parietal regions. In contrast, low fractionation networks may reflect persistent and stable networks that are more internally coherent and exhibit reduced inter-lobar communication. PMID:28222386

US Forest Service Experimental Forests and Ranges Network: a continental research platform for catchment-scale research

Treesearch

Daniel Neary; Deborah Hayes; Lindsey Rustad; James Vose; Gerald Gottfried; Stephen Sebesteyn; Sherri Johnson; Fred Swanson; Mary Adams

2012-01-01

The US Forest Service initiated its catchment research program in 1909 with the first paired catchment study at Wagon Wheel Gap, Colorado, USA. It has since developed the Experimental Forests and Ranges Network, with over 80 long-term research study sites located across the contiguous USA, Alaska, Hawaii, and the Caribbean. This network provides a unique, powerful...

Dynamics of wood in stream networks of the western Cascades Range, Oregon

Treesearch

Nicole M. Czarnomski; David M. Dreher; Kai U. Snyder; Julia A. Jones; Frederick J. Swanson

2008-01-01

We develop and test a conceptual model of wood dynamics in stream networks that considers legacies of forest management practices, floods, and debris flows. We combine an observational study of wood in 25 km of 2nd- through 5th-order streams in a steep, forested watershed of the western Cascade Range of Oregon with whole-network studies of forest cutting, roads, and...

Inversion of parameters for semiarid regions by a neural network

NASA Technical Reports Server (NTRS)

Zurk, Lisa M.; Davis, Daniel; Njoku, Eni G.; Tsang, Leung; Hwang, Jenq-Neng

1992-01-01

Microwave brightness temperatures obtained from a passive radiative transfer model are inverted through use of a neural network. The model is applicable to semiarid regions and produces dual-polarized brightness temperatures for 6.6-, 10.7-, and 37-GHz frequencies. A range of temperatures is generated by varying three geophysical parameters over acceptable ranges: soil moisture, vegetation moisture, and soil temperature. A multilayered perceptron (MLP) neural network is trained with a subset of the generated temperatures, and the remaining temperatures are inverted using a backpropagation method. Several synthetic terrains are devised and inverted by the network under local constraints. All the inversions show good agreement with the original geophysical parameters, falling within 5 percent of the actual value of the parameter range.

NASA Satellite Laser Ranging Network

NASA Technical Reports Server (NTRS)

Carter, David L.

2004-01-01

I will be participating in the International Workshop on Laser Ranging. I will be presenting to the International Laser Ranging Service (ILRS) general body meeting on the recent accomplishments and status of the NASA Satellite Laser Ranging (SLR) Network. The recent accomplishments and NASA's future plans will be outlined and the benefits to the scientific community will be addressed. I am member of the ILRS governing board, the Missions working group, and the Networks & Engineering working group. I am the chairman of the Missions Working and will be hosting a meeting during the week of the workshop. I will also represent the NASA SLR program at the ILRS governing board and other working group meetings.

High-Rate Wireless Airborne Network Demonstration (HiWAND) Flight Test Results

NASA Technical Reports Server (NTRS)

Franz, Russell

2008-01-01

An increasing number of flight research and airborne science experiments now contain network-ready systems that could benefit from a high-rate bidirectional air-to-ground network link. A prototype system, the High-Rate Wireless Airborne Network Demonstration, was developed from commercial off-the-shelf components while leveraging the existing telemetry infrastructure on the Western Aeronautical Test Range. This approach resulted in a cost-effective, long-range, line-of-sight network link over the S and the L frequency bands using both frequency modulation and shaped-offset quadrature phase-shift keying modulation. This report discusses system configuration and the flight test results.

High-Rate Wireless Airborne Network Demonstration (HiWAND) Flight Test Results

NASA Technical Reports Server (NTRS)

Franz, Russell

2007-01-01

An increasing number of flight research and airborne science experiments now contain network-ready systems that could benefit from a high-rate bidirectional air-to-ground network link. A prototype system, the High-Rate Wireless Airborne Network Demonstration, was developed from commercial off-the-shelf components while leveraging the existing telemetry infrastructure on the Western Aeronautical Test Range. This approach resulted in a cost-effective, long-range, line-of-sight network link over the S and the L frequency bands using both frequency modulation and shaped-offset quadrature phase-shift keying modulation. This paper discusses system configuration and the flight test results.

Brain states recognition during visual perception by means of artificial neural network in the different EEG frequency ranges

NASA Astrophysics Data System (ADS)

Musatov, V. Yu.; Runnova, A. E.; Andreev, A. V.; Zhuravlev, M. O.

2018-04-01

In the present paper, the possibility of classification by artificial neural networks of a certain architecture of ambiguous images is investigated using the example of the Necker cube from the experimentally obtained EEG recording data of several operators. The possibilities of artificial neural network classification of ambiguous images are investigated in the different frequency ranges of EEG recording signals.

Evaluating the Connectivity of a Protected Areas' Network under the Prism of Global Change: The Efficiency of the European Natura 2000 Network for Four Birds of Prey

PubMed Central

Mazaris, Antonios D.; Papanikolaou, Alexandra D.; Barbet-Massin, Morgane; Kallimanis, Athanasios S.; Jiguet, Frédéric; Schmeller, Dirk S.; Pantis, John D.

2013-01-01

Climate and land use changes are major threats to biodiversity. To preserve biodiversity, networks of protected areas have been established worldwide, like the Natura 2000 network across the European Union (EU). Currently, this reserve network consists of more than 26000 sites covering more than 17% of EU terrestrial territory. Its efficiency to mitigate the detrimental effects of land use and climate change remains an open research question. Here, we examined the potential current and future geographical ranges of four birds of prey under scenarios of both land use and climate changes. By using graph theory, we examined how the current Natura 2000 network will perform in regard to the conservation of these species. This approach determines the importance of a site in regard to the total network and its connectivity. We found that sites becoming unsuitable due to climate change are not a random sample of the network, but are less connected and contribute less to the overall connectivity than the average site and thus their loss does not disrupt the full network. Hence, the connectivity of the remaining network changed only slightly from present day conditions. Our findings highlight the need to establish species-specific management plans with flexible conservation strategies ensuring protection under potential future range expansions. Aquila pomarina is predicted to disappear from the southern part of its range and to become restricted to northeastern Europe. Gyps fulvus, Aquila chrysaetos, and Neophron percnopterus are predicted to locally lose some suitable sites; hence, some isolated small populations may become extinct. However, their geographical range and metapopulation structure will remain relatively unaffected throughout Europe. These species would benefit more from an improved habitat quality and management of the existing network of protected areas than from increased connectivity or assisted migration. PMID:23527237

Transport and percolation in complex networks

NASA Astrophysics Data System (ADS)

Li, Guanliang

To design complex networks with optimal transport properties such as flow efficiency, we consider three approaches to understanding transport and percolation in complex networks. We analyze the effects of randomizing the strengths of connections, randomly adding long-range connections to regular lattices, and percolation of spatially constrained networks. Various real-world networks often have links that are differentiated in terms of their strength, intensity, or capacity. We study the distribution P(σ) of the equivalent conductance for Erdoḧs-Rényi (ER) and scale-free (SF) weighted resistor networks with N nodes, for which links are assigned with conductance σ i ≡ e-axi, where xi is a random variable with 0 < xi < 1. We find, both analytically and numerically, that P(σ) for ER networks exhibits two regimes: (i) For σ < e-apc, P(σ) is independent of N and scales as a power law P(σ) ˜ sk/a-1 . Here pc = 1/ is the critical percolation threshold of the network and is the average degree of the network. (ii) For σ > e -apc, P(σ) has strong N dependence and scales as P(σ) ˜ f(σ, apc/N1/3). Transport properties are greatly affected by the topology of networks. We investigate the transport problem in lattices with long-range connections and subject to a cost constraint, seeking design principles for optimal transport networks. Our network is built from a regular d-dimensional lattice to be improved by adding long-range connections with probability Pij ˜ r-aij , where rij is the lattice distance between site i and j. We introduce a cost constraint on the total length of the additional links and find optimal transport in the system for α = d + 1, established here for d = 1, 2 and 3 for regular lattices and df for fractals. Remarkably, this cost constraint approach remains optimal, regardless of the strategy used for transport, whether based on local or global knowledge of the network structure. To further understand the role that long-range connections play in optimizing the transport of complex systems, we study the percolation of spatially constrained networks. We now consider originally empty lattices embedded in d dimensions by adding long-range connections with the same power law probability p(r) ˜ r -α. We find that, for α ≤ d, the percolation transition belongs to the universality class of percolation in ER networks, while for α > 2d it belongs to the universality class of percolation in regular lattices (for one-dimensional linear chain, there is no percolation transition). However for d < α < 2d, the percolation properties show new intermediate behavior different from ER networks, with critical exponents that depend on α.

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)...

Ranking in evolving complex networks

NASA Astrophysics Data System (ADS)

Liao, Hao; Mariani, Manuel Sebastian; Medo, Matúš; Zhang, Yi-Cheng; Zhou, Ming-Yang

2017-05-01

Complex networks have emerged as a simple yet powerful framework to represent and analyze a wide range of complex systems. The problem of ranking the nodes and the edges in complex networks is critical for a broad range of real-world problems because it affects how we access online information and products, how success and talent are evaluated in human activities, and how scarce resources are allocated by companies and policymakers, among others. This calls for a deep understanding of how existing ranking algorithms perform, and which are their possible biases that may impair their effectiveness. Many popular ranking algorithms (such as Google's PageRank) are static in nature and, as a consequence, they exhibit important shortcomings when applied to real networks that rapidly evolve in time. At the same time, recent advances in the understanding and modeling of evolving networks have enabled the development of a wide and diverse range of ranking algorithms that take the temporal dimension into account. The aim of this review is to survey the existing ranking algorithms, both static and time-aware, and their applications to evolving networks. We emphasize both the impact of network evolution on well-established static algorithms and the benefits from including the temporal dimension for tasks such as prediction of network traffic, prediction of future links, and identification of significant nodes.

Topographical maps as complex networks

NASA Astrophysics Data System (ADS)

da Fontoura Costa, Luciano; Diambra, Luis

2005-02-01

The neuronal networks in the mammalian cortex are characterized by the coexistence of hierarchy, modularity, short and long range interactions, spatial correlations, and topographical connections. Particularly interesting, the latter type of organization implies special demands on developing systems in order to achieve precise maps preserving spatial adjacencies, even at the expense of isometry. Although the object of intensive biological research, the elucidation of the main anatomic-functional purposes of the ubiquitous topographical connections in the mammalian brain remains an elusive issue. The present work reports on how recent results from complex network formalism can be used to quantify and model the effect of topographical connections between neuronal cells over the connectivity of the network. While the topographical mapping between two cortical modules is achieved by connecting nearest cells from each module, four kinds of network models are adopted for implementing intramodular connections, including random, preferential-attachment, short-range, and long-range networks. It is shown that, though spatially uniform and simple, topographical connections between modules can lead to major changes in the network properties in some specific cases, depending on intramodular connections schemes, fostering more effective intercommunication between the involved neuronal cells and modules. The possible implications of such effects on cortical operation are discussed.

Deep neural networks for direct, featureless learning through observation: The case of two-dimensional spin models

NASA Astrophysics Data System (ADS)

Mills, Kyle; Tamblyn, Isaac

2018-03-01

We demonstrate the capability of a convolutional deep neural network in predicting the nearest-neighbor energy of the 4 ×4 Ising model. Using its success at this task, we motivate the study of the larger 8 ×8 Ising model, showing that the deep neural network can learn the nearest-neighbor Ising Hamiltonian after only seeing a vanishingly small fraction of configuration space. Additionally, we show that the neural network has learned both the energy and magnetization operators with sufficient accuracy to replicate the low-temperature Ising phase transition. We then demonstrate the ability of the neural network to learn other spin models, teaching the convolutional deep neural network to accurately predict the long-range interaction of a screened Coulomb Hamiltonian, a sinusoidally attenuated screened Coulomb Hamiltonian, and a modified Potts model Hamiltonian. In the case of the long-range interaction, we demonstrate the ability of the neural network to recover the phase transition with equivalent accuracy to the numerically exact method. Furthermore, in the case of the long-range interaction, the benefits of the neural network become apparent; it is able to make predictions with a high degree of accuracy, and do so 1600 times faster than a CUDA-optimized exact calculation. Additionally, we demonstrate how the neural network succeeds at these tasks by looking at the weights learned in a simplified demonstration.

Summarisation of weighted networks

NASA Astrophysics Data System (ADS)

Zhou, Fang; Qu, Qiang; Toivonen, Hannu

2017-09-01

Networks often contain implicit structure. We introduce novel problems and methods that look for structure in networks, by grouping nodes into supernodes and edges to superedges, and then make this structure visible to the user in a smaller generalised network. This task of finding generalisations of nodes and edges is formulated as 'network Summarisation'. We propose models and algorithms for networks that have weights on edges, on nodes or on both, and study three new variants of the network summarisation problem. In edge-based weighted network summarisation, the summarised network should preserve edge weights as well as possible. A wider class of settings is considered in path-based weighted network summarisation, where the resulting summarised network should preserve longer range connectivities between nodes. Node-based weighted network summarisation in turn allows weights also on nodes and summarisation aims to preserve more information related to high weight nodes. We study theoretical properties of these problems and show them to be NP-hard. We propose a range of heuristic generalisation algorithms with different trade-offs between complexity and quality of the result. Comprehensive experiments on real data show that weighted networks can be summarised efficiently with relatively little error.

Fiber fault location utilizing traffic signal in optical network.

PubMed

Zhao, Tong; Wang, Anbang; Wang, Yuncai; Zhang, Mingjiang; Chang, Xiaoming; Xiong, Lijuan; Hao, Yi

2013-10-07

We propose and experimentally demonstrate a method for fault location in optical communication network. This method utilizes the traffic signal transmitted across the network as probe signal, and then locates the fault by correlation technique. Compared with conventional techniques, our method has a simple structure and low operation expenditure, because no additional device is used, such as light source, modulator and signal generator. The correlation detection in this method overcomes the tradeoff between spatial resolution and measurement range in pulse ranging technique. Moreover, signal extraction process can improve the location result considerably. Experimental results show that we achieve a spatial resolution of 8 cm and detection range of over 23 km with -8-dBm mean launched power in optical network based on synchronous digital hierarchy protocols.

Prediction of Flow Stress in Cadmium Using Constitutive Equation and Artificial Neural Network Approach

NASA Astrophysics Data System (ADS)

Sarkar, A.; Chakravartty, J. K.

2013-10-01

A model is developed to predict the constitutive flow behavior of cadmium during compression test using artificial neural network (ANN). The inputs of the neural network are strain, strain rate, and temperature, whereas flow stress is the output. Experimental data obtained from compression tests in the temperature range -30 to 70 °C, strain range 0.1 to 0.6, and strain rate range 10-3 to 1 s-1 are employed to develop the model. A three-layer feed-forward ANN is trained with Levenberg-Marquardt training algorithm. It has been shown that the developed ANN model can efficiently and accurately predict the deformation behavior of cadmium. This trained network could predict the flow stress better than a constitutive equation of the type.

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks.

PubMed

Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

2008-11-24

Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper.

On a growth model for complex networks capable of producing power-law out-degree distributions with wide range exponents

PubMed Central

Esquivel-Gómez, J.; Arjona-Villicaña, P. D.; Stevens-Navarro, E.; Pineda-Rico, U.; Balderas-Navarro, R. E.; Acosta-Elias, J.

2015-01-01

The out-degree distribution is one of the most reported topological properties to characterize real complex networks. This property describes the probability that a node in the network has a particular number of outgoing links. It has been found that in many real complex networks the out-degree has a behavior similar to a power-law distribution, therefore some network growth models have been proposed to approximate this behavior. This paper introduces a new growth model that allows to produce out-degree distributions that decay as a power-law with an exponent in the range from 1 to ∞. PMID:25765763

Critical configurations (determinantal loci) for range and range difference satellite networks

NASA Technical Reports Server (NTRS)

Tsimis, E.

1973-01-01

The observational modes of Geometric Satellite Geodesy are discussed. The geometrical analysis of the problem yielded a regression model for the adjustment of the observations along with a suitable and convenient metric for the least-squares criterion. The determinantal loci (critical configurations) for range networks are analyzed. An attempt is made to apply elements of the theory of variants for this purpose. The use of continuously measured range differences for loci determination is proposed.

A generalized approach to complex networks

NASA Astrophysics Data System (ADS)

Costa, L. Da F.; da Rocha, L. E. C.

2006-03-01

This work describes how the formalization of complex network concepts in terms of discrete mathematics, especially mathematical morphology, allows a series of generalizations and important results ranging from new measurements of the network topology to new network growth models. First, the concepts of node degree and clustering coefficient are extended in order to characterize not only specific nodes, but any generic subnetwork. Second, the consideration of distance transform and rings are used to further extend those concepts in order to obtain a signature, instead of a single scalar measurement, ranging from the single node to whole graph scales. The enhanced discriminative potential of such extended measurements is illustrated with respect to the identification of correspondence between nodes in two complex networks, namely a protein-protein interaction network and a perturbed version of it.

Long-range fluctuations and multifractality in connectivity density time series of a wind speed monitoring network

NASA Astrophysics Data System (ADS)

Laib, Mohamed; Telesca, Luciano; Kanevski, Mikhail

2018-03-01

This paper studies the daily connectivity time series of a wind speed-monitoring network using multifractal detrended fluctuation analysis. It investigates the long-range fluctuation and multifractality in the residuals of the connectivity time series. Our findings reveal that the daily connectivity of the correlation-based network is persistent for any correlation threshold. Further, the multifractality degree is higher for larger absolute values of the correlation threshold.

Computational model of electrically coupled, intrinsically distinct pacemaker neurons.

PubMed

Soto-Treviño, Cristina; Rabbah, Pascale; Marder, Eve; Nadim, Farzan

2005-07-01

Electrical coupling between neurons with similar properties is often studied. Nonetheless, the role of electrical coupling between neurons with widely different intrinsic properties also occurs, but is less well understood. Inspired by the pacemaker group of the crustacean pyloric network, we developed a multicompartment, conductance-based model of a small network of intrinsically distinct, electrically coupled neurons. In the pyloric network, a small intrinsically bursting neuron, through gap junctions, drives 2 larger, tonically spiking neurons to reliably burst in-phase with it. Each model neuron has 2 compartments, one responsible for spike generation and the other for producing a slow, large-amplitude oscillation. We illustrate how these compartments interact and determine the dynamics of the model neurons. Our model captures the dynamic oscillation range measured from the isolated and coupled biological neurons. At the network level, we explore the range of coupling strengths for which synchronous bursting oscillations are possible. The spatial segregation of ionic currents significantly enhances the ability of the 2 neurons to burst synchronously, and the oscillation range of the model pacemaker network depends not only on the strength of the electrical synapse but also on the identity of the neuron receiving inputs. We also compare the activity of the electrically coupled, distinct neurons with that of a network of coupled identical bursting neurons. For small to moderate coupling strengths, the network of identical elements, when receiving asymmetrical inputs, can have a smaller dynamic range of oscillation than that of its constituent neurons in isolation.

Mapping cortical hubs in tinnitus

PubMed Central

2009-01-01

Background Subjective tinnitus is the perception of a sound in the absence of any physical source. It has been shown that tinnitus is associated with hyperactivity of the auditory cortices. Accompanying this hyperactivity, changes in non-auditory brain structures have also been reported. However, there have been no studies on the long-range information flow between these regions. Results Using Magnetoencephalography, we investigated the long-range cortical networks of chronic tinnitus sufferers (n = 23) and healthy controls (n = 24) in the resting state. A beamforming technique was applied to reconstruct the brain activity at source level and the directed functional coupling between all voxels was analyzed by means of Partial Directed Coherence. Within a cortical network, hubs are brain structures that either influence a great number of other brain regions or that are influenced by a great number of other brain regions. By mapping the cortical hubs in tinnitus and controls we report fundamental group differences in the global networks, mainly in the gamma frequency range. The prefrontal cortex, the orbitofrontal cortex and the parieto-occipital region were core structures in this network. The information flow from the global network to the temporal cortex correlated positively with the strength of tinnitus distress. Conclusion With the present study we suggest that the hyperactivity of the temporal cortices in tinnitus is integrated in a global network of long-range cortical connectivity. Top-down influence from the global network on the temporal areas relates to the subjective strength of the tinnitus distress. PMID:19930625

An agent-based model of centralized institutions, social network technology, and revolution.

PubMed

Makowsky, Michael D; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change.

On the Topologic Properties of River Networks

NASA Astrophysics Data System (ADS)

Sarker, S.; Singh, A.

2017-12-01

River network is an important landscape feature and has been studied extensively from a range of geomorphological and hydrological perspective. However, quantifying topologic dynamics and reorganization of river networks is becoming more and more challenging under changing natural and anthropogenic forcings. Here, we use a graph-theoretical approach to study topologic properties of natural and simulated river networks for a range of climatic and tectonic conditions. Among other metrics, we use betweeness and eigenvector centrality distributions computed using adjacency matrix of river networks and show their dependence on energy exponent γ that characterizes mechanism of erosional processes on a landscape. We further compare these topologic characteristics of landscape to geomorphic features such as slope-area curve and drainage density. Furthermore, we identify locations of critical nodes and links on a network as a function of energy exponent γ to understand network robustness and vulnerability under external attacks.

Advancing reversible shape memory by tuning the polymer network architecture

DOE PAGES

Li, Qiaoxi; Zhou, Jing; Vatankhah-Varnoosfaderani, Mohammad; ...

2016-02-02

Because of counteraction of a chemical network and a crystalline scaffold, semicrystalline polymer networks exhibit a peculiar behavior—reversible shape memory (RSM), which occurs naturally without applying any external force and particular structural design. There are three RSM properties: (i) range of reversible strain, (ii) rate of strain recovery, and (iii) decay of reversibility with time, which can be improved by tuning the architecture of the polymer network. Different types of poly(octylene adipate) networks were synthesized, allowing for control of cross-link density and network topology, including randomly cross-linked network by free-radical polymerization, thiol–ene clicked network with enhanced mesh uniformity, and loosemore » network with deliberately incorporated dangling chains. It is shown that the RSM properties are controlled by average cross-link density and crystal size, whereas topology of a network greatly affects its extensibility. In conclusion, we have achieved 80% maximum reversible range, 15% minimal decrease in reversibility, and fast strain recovery rate up to 0.05 K –1, i.e., ca. 5% per 10 s at a cooling rate of 5 K/min.« less

Variation in antibiotic prescribing and its impact on recovery in patients with acute cough in primary care: prospective study in 13 countries

PubMed Central

Hood, K; Verheij, T; Little, P; Melbye, H; Nuttall, J; Kelly, M J; Mölstad, S; Godycki-Cwirko, M; Almirall, J; Torres, A; Gillespie, D; Rautakorpi, U; Coenen, S; Goossens, H

2009-01-01

Objective To describe variation in antibiotic prescribing for acute cough in contrasting European settings and the impact on recovery. Design Cross sectional observational study with clinicians from 14 primary care research networks in 13 European countries who recorded symptoms on presentation and management. Patients followed up for 28 days with patient diaries. Setting Primary care. Participants Adults with a new or worsening cough or clinical presentation suggestive of lower respiratory tract infection. Main outcome measures Prescribing of antibiotics by clinicians and total symptom severity scores over time. Results 3402 patients were recruited (clinicians completed a case report form for 99% (3368) of participants and 80% (2714) returned a symptom diary). Mean symptom severity scores at presentation ranged from 19 (scale range 0 to 100) in networks based in Spain and Italy to 38 in the network based in Sweden. Antibiotic prescribing by networks ranged from 20% to nearly 90% (53% overall), with wide variation in classes of antibiotics prescribed. Amoxicillin was overall the most common antibiotic prescribed, but this ranged from 3% of antibiotics prescribed in the Norwegian network to 83% in the English network. While fluoroquinolones were not prescribed at all in three networks, they were prescribed for 18% in the Milan network. After adjustment for clinical presentation and demographics, considerable differences remained in antibiotic prescribing, ranging from Norway (odds ratio 0.18, 95% confidence interval 0.11 to 0.30) to Slovakia (11.2, 6.20 to 20.27) compared with the overall mean (proportion prescribed: 0.53). The rate of recovery was similar for patients who were and were not prescribed antibiotics (coefficient −0.01, P<0.01) once clinical presentation was taken into account. Conclusions Variation in clinical presentation does not explain the considerable variation in antibiotic prescribing for acute cough in Europe. Variation in antibiotic prescribing is not associated with clinically important differences in recovery. Trial registration Clinicaltrials.gov NCT00353951. PMID:19549995

Effects of global financial crisis on network structure in a local stock market

NASA Astrophysics Data System (ADS)

Nobi, Ashadun; Maeng, Seong Eun; Ha, Gyeong Gyun; Lee, Jae Woo

2014-08-01

This study considers the effects of the 2008 global financial crisis on threshold networks of a local Korean financial market around the time of the crisis. Prices of individual stocks belonging to KOSPI 200 (Korea Composite Stock Price Index 200) are considered for three time periods, namely before, during, and after the crisis. Threshold networks are constructed from fully connected cross-correlation networks, and thresholds of cross-correlation coefficients are assigned to obtain threshold networks. At the high threshold, only one large cluster consisting of firms in the financial sector, heavy industry, and construction is observed during the crisis. However, before and after the crisis, there are several fragmented clusters belonging to various sectors. The power law of the degree distribution in threshold networks is observed within the limited range of thresholds. Threshold networks are fatter during the crisis than before or after the crisis. The clustering coefficient of the threshold network follows the power law in the scaling range.

SSL: Signal Similarity-Based Localization for Ocean Sensor Networks.

PubMed

Chen, Pengpeng; Ma, Honglu; Gao, Shouwan; Huang, Yan

2015-11-24

Nowadays, wireless sensor networks are often deployed on the sea surface for ocean scientific monitoring. One of the important challenges is to localize the nodes' positions. Existing localization schemes can be roughly divided into two types: range-based and range-free. The range-based localization approaches heavily depend on extra hardware capabilities, while range-free ones often suffer from poor accuracy and low scalability, far from the practical ocean monitoring applications. In response to the above limitations, this paper proposes a novel signal similarity-based localization (SSL) technology, which localizes the nodes' positions by fully utilizing the similarity of received signal strength and the open-air characteristics of the sea surface. In the localization process, we first estimate the relative distance between neighboring nodes through comparing the similarity of received signal strength and then calculate the relative distance for non-neighboring nodes with the shortest path algorithm. After that, the nodes' relative relation map of the whole network can be obtained. Given at least three anchors, the physical locations of nodes can be finally determined based on the multi-dimensional scaling (MDS) technology. The design is evaluated by two types of ocean experiments: a zonal network and a non-regular network using 28 nodes. Results show that the proposed design improves the localization accuracy compared to typical connectivity-based approaches and also confirm its effectiveness for large-scale ocean sensor networks.

Long-range correlations improve understanding of the influence of network structure on contact dynamics.

PubMed

Peyrard, N; Dieckmann, U; Franc, A

2008-05-01

Models of infectious diseases are characterized by a phase transition between extinction and persistence. A challenge in contemporary epidemiology is to understand how the geometry of a host's interaction network influences disease dynamics close to the critical point of such a transition. Here we address this challenge with the help of moment closures. Traditional moment closures, however, do not provide satisfactory predictions close to such critical points. We therefore introduce a new method for incorporating longer-range correlations into existing closures. Our method is technically simple, remains computationally tractable and significantly improves the approximation's performance. Our extended closures thus provide an innovative tool for quantifying the influence of interaction networks on spatially or socially structured disease dynamics. In particular, we examine the effects of a network's clustering coefficient, as well as of new geometrical measures, such as a network's square clustering coefficients. We compare the relative performance of different closures from the literature, with or without our long-range extension. In this way, we demonstrate that the normalized version of the Bethe approximation-extended to incorporate long-range correlations according to our method-is an especially good candidate for studying influences of network structure. Our numerical results highlight the importance of the clustering coefficient and the square clustering coefficient for predicting disease dynamics at low and intermediate values of transmission rate, and demonstrate the significance of path redundancy for disease persistence.

Soft network materials with isotropic negative Poisson's ratios over large strains.

PubMed

Liu, Jianxing; Zhang, Yihui

2018-01-31

Auxetic materials with negative Poisson's ratios have important applications across a broad range of engineering areas, such as biomedical devices, aerospace engineering and automotive engineering. A variety of design strategies have been developed to achieve artificial auxetic materials with controllable responses in the Poisson's ratio. The development of designs that can offer isotropic negative Poisson's ratios over large strains can open up new opportunities in emerging biomedical applications, which, however, remains a challenge. Here, we introduce deterministic routes to soft architected materials that can be tailored precisely to yield the values of Poisson's ratio in the range from -1 to 1, in an isotropic manner, with a tunable strain range from 0% to ∼90%. The designs rely on a network construction in a periodic lattice topology, which incorporates zigzag microstructures as building blocks to connect lattice nodes. Combined experimental and theoretical studies on broad classes of network topologies illustrate the wide-ranging utility of these concepts. Quantitative mechanics modeling under both infinitesimal and finite deformations allows the development of a rigorous design algorithm that determines the necessary network geometries to yield target Poisson ratios over desired strain ranges. Demonstrative examples in artificial skin with both the negative Poisson's ratio and the nonlinear stress-strain curve precisely matching those of the cat's skin and in unusual cylindrical structures with engineered Poisson effect and shape memory effect suggest potential applications of these network materials.

On the structural properties of small-world networks with range-limited shortcut links

NASA Astrophysics Data System (ADS)

Jia, Tao; Kulkarni, Rahul V.

2013-12-01

We explore a new variant of Small-World Networks (SWNs), in which an additional parameter (r) sets the length scale over which shortcuts are uniformly distributed. When r=0 we have an ordered network, whereas r=1 corresponds to the original Watts-Strogatz SWN model. These limited range SWNs have a similar degree distribution and scaling properties as the original SWN model. We observe the small-world phenomenon for r≪1, indicating that global shortcuts are not necessary for the small-world effect. For limited range SWNs, the average path length changes nonmonotonically with system size, whereas for the original SWN model it increases monotonically. We propose an expression for the average path length for limited range SWNs based on numerical simulations and analytical approximations.

Role of Hydrophobic Clusters and Long-Range Contact Networks in the Folding of (α/β)8 Barrel Proteins

PubMed Central

Selvaraj, S.; Gromiha, M. Michael

2003-01-01

Analysis on the three dimensional structures of (α/β)8 barrel proteins provides ample light to understand the factors that are responsible for directing and maintaining their common fold. In this work, the hydrophobically enriched clusters are identified in 92% of the considered (α/β)8 barrel proteins. The residue segments with hydrophobic clusters have high thermal stability. Further, these clusters are formed and stabilized through long-range interactions. Specifically, a network of long-range contacts connects adjacent β-strands of the (α/β)8 barrel domain and the hydrophobic clusters. The implications of hydrophobic clusters and long-range networks in providing a feasible common mechanism for the folding of (α/β)8 barrel proteins are proposed. PMID:12609894

Advancing the State of the Art in Applying Network Science to C2

DTIC Science & Technology

2014-06-01

technological networks to include information , cognitive and social networks, they have yet to apply the full range of theoretical instruments now...robustness, and processes. While NEC researchers extended their coverage from technological networks to include information , cognitive and social networks...can be found in a wide variety of domains. For example, Newman (2003) surveys work on biological, technological , information , and social networks

Evolutionary games on multilayer networks: a colloquium

NASA Astrophysics Data System (ADS)

Wang, Zhen; Wang, Lin; Szolnoki, Attila; Perc, Matjaž

2015-05-01

Networks form the backbone of many complex systems, ranging from the Internet to human societies. Accordingly, not only is the range of our interactions limited and thus best described and modeled by networks, it is also a fact that the networks that are an integral part of such models are often interdependent or even interconnected. Networks of networks or multilayer networks are therefore a more apt description of social systems. This colloquium is devoted to evolutionary games on multilayer networks, and in particular to the evolution of cooperation as one of the main pillars of modern human societies. We first give an overview of the most significant conceptual differences between single-layer and multilayer networks, and we provide basic definitions and a classification of the most commonly used terms. Subsequently, we review fascinating and counterintuitive evolutionary outcomes that emerge due to different types of interdependencies between otherwise independent populations. The focus is on coupling through the utilities of players, through the flow of information, as well as through the popularity of different strategies on different network layers. The colloquium highlights the importance of pattern formation and collective behavior for the promotion of cooperation under adverse conditions, as well as the synergies between network science and evolutionary game theory.

Toward a model of school inspections in a polycentric system.

PubMed

Janssens, Frans J G; Ehren, Melanie C M

2016-06-01

Many education systems are developing towards more lateral structures where schools collaborate in networks to improve and provide (inclusive) education. These structures call for bottom-up models of network evaluation and accountability instead of the current hierarchical arrangements where single schools are evaluated by a central agency. This paper builds on available research about network effectiveness to present evolving models of network evaluation. Network effectiveness can be defined as the achievement of positive network level outcomes that cannot be attained by individual organizational participants acting alone. Models of network evaluation need to take into account the relations between network members, the structure of the network, its processes and its internal mechanism to enforce norms in order to understand the achievement and outcomes of the network and how these may evolve over time. A range of suitable evaluation models are presented in this paper, as well as a tentative school inspection framework which is inspired by these models. The final section will present examples from Inspectorates of Education in Northern Ireland and Scotland who have developed newer inspection models to evaluate the effectiveness of a range of different networks. Copyright © 2016 Elsevier Ltd. All rights reserved.

Wireless Cooperative Networks: Self-Configuration and Optimization

DTIC Science & Technology

2011-09-09

TERMS wireless sensor networks , wireless cooperative networks, resource optimization, ultra-wideband, localization, ranging 16. SECURITY...Communications We consider two prevalent relay protocols for wireless sensor networks : decode-and-forward (DF) and amplify-and-forward (AF). To... sensor networks where each node may have its own sensing data to transmit, since they can maximally conserve energy while helping others as relays

Parameterization of Keeling's network generation algorithm.

PubMed

Badham, Jennifer; Abbass, Hussein; Stocker, Rob

2008-09-01

Simulation is increasingly being used to examine epidemic behaviour and assess potential management options. The utility of the simulations rely on the ability to replicate those aspects of the social structure that are relevant to epidemic transmission. One approach is to generate networks with desired social properties. Recent research by Keeling and his colleagues has generated simulated networks with a range of properties, and examined the impact of these properties on epidemic processes occurring over the network. However, published work has included only limited analysis of the algorithm itself and the way in which the network properties are related to the algorithm parameters. This paper identifies some relationships between the algorithm parameters and selected network properties (mean degree, degree variation, clustering coefficient and assortativity). Our approach enables users of the algorithm to efficiently generate a network with given properties, thereby allowing realistic social networks to be used as the basis of epidemic simulations. Alternatively, the algorithm could be used to generate social networks with a range of property values, enabling analysis of the impact of these properties on epidemic behaviour.

Reliability analysis of interdependent lattices

NASA Astrophysics Data System (ADS)

Limiao, Zhang; Daqing, Li; Pengju, Qin; Bowen, Fu; Yinan, Jiang; Zio, Enrico; Rui, Kang

2016-06-01

Network reliability analysis has drawn much attention recently due to the risks of catastrophic damage in networked infrastructures. These infrastructures are dependent on each other as a result of various interactions. However, most of the reliability analyses of these interdependent networks do not consider spatial constraints, which are found important for robustness of infrastructures including power grid and transport systems. Here we study the reliability properties of interdependent lattices with different ranges of spatial constraints. Our study shows that interdependent lattices with strong spatial constraints are more resilient than interdependent Erdös-Rényi networks. There exists an intermediate range of spatial constraints, at which the interdependent lattices have minimal resilience.

An Agent-Based Model of Centralized Institutions, Social Network Technology, and Revolution

PubMed Central

Makowsky, Michael D.; Rubin, Jared

2013-01-01

This paper sheds light on the general mechanisms underlying large-scale social and institutional change. We employ an agent-based model to test the impact of authority centralization and social network technology on preference falsification and institutional change. We find that preference falsification is increasing with centralization and decreasing with social network range. This leads to greater cascades of preference revelation and thus more institutional change in highly centralized societies and this effect is exacerbated at greater social network ranges. An empirical analysis confirms the connections that we find between institutional centralization, social radius, preference falsification, and institutional change. PMID:24278280

Large Scale Environmental Monitoring through Integration of Sensor and Mesh Networks

PubMed Central

Jurdak, Raja; Nafaa, Abdelhamid; Barbirato, Alessio

2008-01-01

Monitoring outdoor environments through networks of wireless sensors has received interest for collecting physical and chemical samples at high spatial and temporal scales. A central challenge to environmental monitoring applications of sensor networks is the short communication range of the sensor nodes, which increases the complexity and cost of monitoring commodities that are located in geographically spread areas. To address this issue, we propose a new communication architecture that integrates sensor networks with medium range wireless mesh networks, and provides users with an advanced web portal for managing sensed information in an integrated manner. Our architecture adopts a holistic approach targeted at improving the user experience by optimizing the system performance for handling data that originates at the sensors, traverses the mesh network, and resides at the server for user consumption. This holistic approach enables users to set high level policies that can adapt the resolution of information collected at the sensors, set the preferred performance targets for their application, and run a wide range of queries and analysis on both real-time and historical data. All system components and processes will be described in this paper. PMID:27873941

International Network of Passive Correlation Ranging for Orbit Determination of a Geostationary Satellite

NASA Astrophysics Data System (ADS)

Kaliuzhnyi, Mykola; Bushuev, Felix; Shulga, Oleksandr; Sybiryakova, Yevgeniya; Shakun, Leonid; Bezrukovs, Vladislavs; Moskalenko, Sergiy; Kulishenko, Vladislav; Malynovskyi, Yevgen

2016-12-01

An international network of passive correlation ranging of a geostationary telecommunication satellite is considered in the article. The network is developed by the RI "MAO". The network consists of five spatially separated stations of synchronized reception of DVB-S signals of digital satellite TV. The stations are located in Ukraine and Latvia. The time difference of arrival (TDOA) on the network stations of the DVB-S signals, radiated by the satellite, is a measured parameter. The results of TDOA estimation obtained by the network in May-August 2016 are presented in the article. Orbital parameters of the tracked satellite are determined using measured values of the TDOA and two models of satellite motion: the analytical model SGP4/SDP4 and the model of numerical integration of the equations of satellite motion. Both models are realized using the free low-level space dynamics library OREKIT (ORbit Extrapolation KIT).

Ultrafast and Wide Range Analysis of DNA Molecules Using Rigid Network Structure of Solid Nanowires

PubMed Central

Rahong, Sakon; Yasui, Takao; Yanagida, Takeshi; Nagashima, Kazuki; Kanai, Masaki; Klamchuen, Annop; Meng, Gang; He, Yong; Zhuge, Fuwei; Kaji, Noritada; Kawai, Tomoji; Baba, Yoshinobu

2014-01-01

Analyzing sizes of DNA via electrophoresis using a gel has played an important role in the recent, rapid progress of biology and biotechnology. Although analyzing DNA over a wide range of sizes in a short time is desired, no existing electrophoresis methods have been able to fully satisfy these two requirements. Here we propose a novel method using a rigid 3D network structure composed of solid nanowires within a microchannel. This rigid network structure enables analysis of DNA under applied DC electric fields for a large DNA size range (100 bp–166 kbp) within 13 s, which are much wider and faster conditions than those of any existing methods. The network density is readily varied for the targeted DNA size range by tailoring the number of cycles of the nanowire growth only at the desired spatial position within the microchannel. The rigid dense 3D network structure with spatial density control plays an important role in determining the capability for analyzing DNA. Since the present method allows the spatial location and density of the nanostructure within the microchannels to be defined, this unique controllability offers a new strategy to develop an analytical method not only for DNA but also for other biological molecules. PMID:24918865

Ultrafast and Wide Range Analysis of DNA Molecules Using Rigid Network Structure of Solid Nanowires

NASA Astrophysics Data System (ADS)

Rahong, Sakon; Yasui, Takao; Yanagida, Takeshi; Nagashima, Kazuki; Kanai, Masaki; Klamchuen, Annop; Meng, Gang; He, Yong; Zhuge, Fuwei; Kaji, Noritada; Kawai, Tomoji; Baba, Yoshinobu

2014-06-01

Analyzing sizes of DNA via electrophoresis using a gel has played an important role in the recent, rapid progress of biology and biotechnology. Although analyzing DNA over a wide range of sizes in a short time is desired, no existing electrophoresis methods have been able to fully satisfy these two requirements. Here we propose a novel method using a rigid 3D network structure composed of solid nanowires within a microchannel. This rigid network structure enables analysis of DNA under applied DC electric fields for a large DNA size range (100 bp-166 kbp) within 13 s, which are much wider and faster conditions than those of any existing methods. The network density is readily varied for the targeted DNA size range by tailoring the number of cycles of the nanowire growth only at the desired spatial position within the microchannel. The rigid dense 3D network structure with spatial density control plays an important role in determining the capability for analyzing DNA. Since the present method allows the spatial location and density of the nanostructure within the microchannels to be defined, this unique controllability offers a new strategy to develop an analytical method not only for DNA but also for other biological molecules.

[Correlations between features of social network and outcomes in those suffering from schizophrenia seven years from the first hospitalisation].

PubMed

Cechnicki, Andrzej; Wojciechowska, Anna

2007-01-01

A research had been conducted upon the correlations between selected parameters of social networks of 64 patients ill with schizophrenia who were diagnosed according to DSM-III, and the aims of treatment such as: motivation to receive treatment, insight, compliance in taking medication, satisfaction with treatment, and treatment outcomes in the area of clinical and social functioning as well as family functioning seven years after the first admission. The indices of social networks were studied with Bizon's questionnaire. It serves storing of data on persons who have supportive functions as well as allows to work out characteristic properties of the support system such as: range of the network, size of the extra-familial network, level and localisation of the support, network and support system age. A compound system of social support and large social network, with a high level of support, correlate in a beneficial way with higher subjective satisfaction with treatment. Whereas a large extra-familial network with high level of support, correlates with better insight into illness. The larger the social network was (its range to be precise), including extra-familial network and the high level of incoming support, the fewer positive and negative symptoms the patients had and much more remissions appeared then. The larger network's range correlates with smaller number of relapses and global time of being hospitalised. People with a larger network, with high level of support located in family and outside the family, have been rarely hospitalised. The connection between network's parameters and number of daily hospitalisations had been rated. People with a larger network, including extra-familial network, with high level of social support function better in the society didn't become regressive in their professional lives and they have smaller burden in their family life. The high level of social support correlates with better family function. In families of people ill with schizophrenia having larger extra-familial network with a high level of support there is less deterioration and disintegration, criticism and rejection.

Research and emulation of ranging in BPON system

NASA Astrophysics Data System (ADS)

Yang, Guangxiang; Tao, Dexin; He, Yan

2005-12-01

Ranging is one of the key technologies in Broadband Passive Optical Network based on the ATM (BPON) system. It is complex for software designers and difficult to test. In order to simplify the ranging procedure, enhance its efficiency, and find an appropriate method to verify it, a new ranging procedure that completely satisfies the requirements specified in ITU-T G.983.1 and one verifying method is proposed in this paper. A kind of ranging procedure without serial number (SN) searching function, called one-by-one ranging are developed under the condition of cold PON, cold Optical Network Termination (ONU). The ranging procedure includes the use of OLT and ONU flow charts respectively. By using the network emulation software OPNET, the BPON system is modeled and the ranging procedure is simulated. The emulation experimental results show that the presented ranging procedure can effectively eliminate the collision of burst mode signals between ONUs, which can be ranged one-by-one under the controlling of OLT, while also enhancing the ranging efficiency. As all of the message formats used in this research conform with the ITU-T G.983.1, the ranging procedure can meet the protocol specifications with good interoperability, and is very compatible with products of other manufacturer. According to the present study of ranging procedures, guidelines and principles are provided, Also some design difficulties are eliminated in the software design.

Scalable Online Network Modeling and Simulation

DTIC Science & Technology

2005-08-01

ONLINE NETWORK MODELING AND SIMULATION 6. AUTHOR(S) Boleslaw Szymanski , Shivkumar Kalyanaraman, Biplab Sikdar and Christopher Carothers 5...performance for a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature ...a wide range of parameter values (parameter sensitivity), understanding of protocol stability and dynamics, and studying feature interactions

Development of distinct control networks through segregation and integration

PubMed Central

Fair, Damien A.; Dosenbach, Nico U. F.; Church, Jessica A.; Cohen, Alexander L.; Brahmbhatt, Shefali; Miezin, Francis M.; Barch, Deanna M.; Raichle, Marcus E.; Petersen, Steven E.; Schlaggar, Bradley L.

2007-01-01

Human attentional control is unrivaled. We recently proposed that adults depend on distinct frontoparietal and cinguloopercular networks for adaptive online task control versus more stable set control, respectively. During development, both experience-dependent evoked activity and spontaneous waves of synchronized cortical activity are thought to support the formation and maintenance of neural networks. Such mechanisms may encourage tighter “integration” of some regions into networks over time while “segregating” other sets of regions into separate networks. Here we use resting state functional connectivity MRI, which measures correlations in spontaneous blood oxygenation level-dependent signal fluctuations between brain regions to compare previously identified control networks between children and adults. We find that development of the proposed adult control networks involves both segregation (i.e., decreased short-range connections) and integration (i.e., increased long-range connections) of the brain regions that comprise them. Delay/disruption in the developmental processes of segregation and integration may play a role in disorders of control, such as autism, attention deficit hyperactivity disorder, and Tourette's syndrome. PMID:17679691

Power Aware Management Middleware for Multiple Radio Interfaces

NASA Astrophysics Data System (ADS)

Friedman, Roy; Kogan, Alex

Modern mobile phones and laptops are equipped with multiple wireless communication interfaces, such as WiFi and Bluetooth (BT), enabling the creation of ad-hoc networks. These interfaces significantly differ from one another in their power requirements, transmission range, bandwidth, etc. For example, BT is an order of magnitude more power efficient than WiFi, but its transmission range is an order of magnitude shorter. This paper introduces a management middleware that establishes a power efficient overlay for such ad-hoc networks, in which most devices can shut down their long range power hungry wireless interface (e.g., WiFi). Yet, the resulting overlay is fully connected, and for capacity and latency needs, no message ever travels more than 2k short range (e.g., BT) hops, where k is an arbitrary parameter. The paper describes the architecture of the solution and the management protocol, as well as a detailed simulations based performance study. The simulations largely validate the ability of the management infrastructure to obtain considerable power savings while keeping the network connected and maintaining reasonable latency. The performance study covers both static and mobile networks.

Abnormal resting-state cortical coupling in chronic tinnitus

PubMed Central

Schlee, Winfried; Hartmann, Thomas; Langguth, Berthold; Weisz, Nathan

2009-01-01

Background Subjective tinnitus is characterized by an auditory phantom perception in the absence of any physical sound source. Consequently, in a quiet environment, tinnitus patients differ from control participants because they constantly perceive a sound whereas controls do not. We hypothesized that this difference is expressed by differential activation of distributed cortical networks. Results The analysis was based on a sample of 41 participants: 21 patients with chronic tinnitus and 20 healthy control participants. To investigate the architecture of these networks, we used phase locking analysis in the 1–90 Hz frequency range of a minute of resting-state MEG recording. We found: 1) For tinnitus patients: A significant decrease of inter-areal coupling in the alpha (9–12 Hz) band and an increase of inter-areal coupling in the 48–54 Hz gamma frequency range relative to the control group. 2) For both groups: an inverse relationship (r = -.71) of the alpha and gamma network coupling. 3) A discrimination of 83% between the patient and the control group based on the alpha and gamma networks. 4) An effect of manifestation on the distribution of the gamma network: In patients with a tinnitus history of less than 4 years, the left temporal cortex was predominant in the gamma network whereas in patients with tinnitus duration of more than 4 years, the gamma network was more widely distributed including more frontal and parietal regions. Conclusion In the here presented data set we found strong support for an alteration of long-range coupling in tinnitus. Long-range coupling in the alpha frequency band was decreased for tinnitus patients while long-range gamma coupling was increased. These changes discriminate well between tinnitus and control participants. We propose a tinnitus model that integrates this finding in the current knowledge about tinnitus. Furthermore we discuss the impact of this finding to tinnitus therapies using Transcranial Magnetic Stimulation (TMS). PMID:19228390

Modelling opinion formation driven communities in social networks

NASA Astrophysics Data System (ADS)

Iñiguez, Gerardo; Barrio, Rafael A.; Kertész, János; Kaski, Kimmo K.

2011-09-01

In a previous paper we proposed a model to study the dynamics of opinion formation in human societies by a co-evolution process involving two distinct time scales of fast transaction and slower network evolution dynamics. In the transaction dynamics we take into account short range interactions as discussions between individuals and long range interactions to describe the attitude to the overall mood of society. The latter is handled by a uniformly distributed parameter α, assigned randomly to each individual, as quenched personal bias. The network evolution dynamics is realised by rewiring the societal network due to state variable changes as a result of transaction dynamics. The main consequence of this complex dynamics is that communities emerge in the social network for a range of values in the ratio between time scales. In this paper we focus our attention on the attitude parameter α and its influence on the conformation of opinion and the size of the resulting communities. We present numerical studies and extract interesting features of the model that can be interpreted in terms of social behaviour.

Network science and the human brain: Using graph theory to understand the brain and one of its hubs, the amygdala, in health and disease.

PubMed

Mears, David; Pollard, Harvey B

2016-06-01

Over the past 15 years, the emerging field of network science has revealed the key features of brain networks, which include small-world topology, the presence of highly connected hubs, and hierarchical modularity. The value of network studies of the brain is underscored by the range of network alterations that have been identified in neurological and psychiatric disorders, including epilepsy, depression, Alzheimer's disease, schizophrenia, and many others. Here we briefly summarize the concepts of graph theory that are used to quantify network properties and describe common experimental approaches for analysis of brain networks of structural and functional connectivity. These range from tract tracing to functional magnetic resonance imaging, diffusion tensor imaging, electroencephalography, and magnetoencephalography. We then summarize the major findings from the application of graph theory to nervous systems ranging from Caenorhabditis elegans to more complex primate brains, including man. Focusing, then, on studies involving the amygdala, a brain region that has attracted intense interest as a center for emotional processing, fear, and motivation, we discuss the features of the amygdala in brain networks for fear conditioning and emotional perception. Finally, to highlight the utility of graph theory for studying dysfunction of the amygdala in mental illness, we review data with regard to changes in the hub properties of the amygdala in brain networks of patients with depression. We suggest that network studies of the human brain may serve to focus attention on regions and connections that act as principal drivers and controllers of brain function in health and disease. Published 2016. This article is a U.S. Government work and is in the public domain in the USA.

Visibility in the topology of complex networks

NASA Astrophysics Data System (ADS)

Tsiotas, Dimitrios; Charakopoulos, Avraam

2018-09-01

Taking its inspiration from the visibility algorithm, which was proposed by Lacasa et al. (2008) to convert a time-series into a complex network, this paper develops and proposes a novel expansion of this algorithm that allows generating a visibility graph from a complex network instead of a time-series that is currently applicable. The purpose of this approach is to apply the idea of visibility from the field of time-series to complex networks in order to interpret the network topology as a landscape. Visibility in complex networks is a multivariate property producing an associated visibility graph that maps the ability of a node "to see" other nodes in the network that lie beyond the range of its neighborhood, in terms of a control-attribute. Within this context, this paper examines the visibility topology produced by connectivity (degree) in comparison with the original (source) network, in order to detect what patterns or forces describe the mechanism under which a network is converted to a visibility graph. The overall analysis shows that visibility is a property that increases the connectivity in networks, it may contribute to pattern recognition (among which the detection of the scale-free topology) and it is worth to be applied to complex networks in order to reveal the potential of signal processing beyond the range of its neighborhood. Generally, this paper promotes interdisciplinary research in complex networks providing new insights to network science.

Node property of weighted networks considering connectability to nodes within two degrees of separation.

PubMed

Amano, Sun-Ichi; Ogawa, Ken-Ichiro; Miyake, Yoshihiro

2018-05-31

Weighted networks have been extensively studied because they can represent various phenomena in which the diversity of edges is essential. To investigate the properties of weighted networks, various centrality measures have been proposed, such as strength, weighted clustering coefficients, and weighted betweenness centrality. In such measures, only direct connections or entire network connectivity from arbitrary nodes have been used to calculate the connectivity of each node. However, in weighted networks composed of autonomous elements such as humans, middle ranges from each node are also considered to be meaningful for characterizing each node's connectability. In this study, we define a new node property in weighted networks to consider connectability to nodes within a range of two degrees of separation, then apply this new centrality to face-to-face human communication networks in corporate organizations. Our results show that the proposed centrality distinguishes inherent communities corresponding to the job types in each organization with a high degree of accuracy. This indicates the possibility that connectability to nodes within two degrees of separation reveals potential trends of weighted networks that are not apparent from conventional measures.

Collective relaxation dynamics of small-world networks

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N , average degree k , and topological randomness q . We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q , including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Trace saver: A tool for network service improvement and personalised analysis of user centric statistics

NASA Astrophysics Data System (ADS)

Bilal, Muhammad; Asfand-e-Yar, Mockford, Steve; Khan, Wasiq; Awan, Irfan

2012-11-01

Mobile technology is among the fastest growing technologies in today's world with low cost and highly effective benefits. Most important and entertaining areas in mobile technology development and usage are location based services, user friendly networked applications and gaming applications. However, concern towards network operator service provision and improvement has been very low. The portable applications available for a range of mobile operating systems which help improve the network operator services are desirable by the mobile operators. This paper proposes a state of the art mobile application Tracesaver, which provides a great achievement over the barriers in gathering device and network related information, for network operators to improve their network service provision. Tracesaver is available for a broad range of mobile devices with different mobile operating systems and computational capabilities. The availability of Tracesaver in market has proliferated over the last year since it was published. The survey and results show that Tracesaver is being used by millions of mobile users and provides novel ways of network service improvement with its highly user friendly interface.

Collective relaxation dynamics of small-world networks.

PubMed

Grabow, Carsten; Grosskinsky, Stefan; Kurths, Jürgen; Timme, Marc

2015-05-01

Complex networks exhibit a wide range of collective dynamic phenomena, including synchronization, diffusion, relaxation, and coordination processes. Their asymptotic dynamics is generically characterized by the local Jacobian, graph Laplacian, or a similar linear operator. The structure of networks with regular, small-world, and random connectivities are reasonably well understood, but their collective dynamical properties remain largely unknown. Here we present a two-stage mean-field theory to derive analytic expressions for network spectra. A single formula covers the spectrum from regular via small-world to strongly randomized topologies in Watts-Strogatz networks, explaining the simultaneous dependencies on network size N, average degree k, and topological randomness q. We present simplified analytic predictions for the second-largest and smallest eigenvalue, and numerical checks confirm our theoretical predictions for zero, small, and moderate topological randomness q, including the entire small-world regime. For large q of the order of one, we apply standard random matrix theory, thereby overarching the full range from regular to randomized network topologies. These results may contribute to our analytic and mechanistic understanding of collective relaxation phenomena of network dynamical systems.

Role of local network oscillations in resting-state functional connectivity.

PubMed

Cabral, Joana; Hugues, Etienne; Sporns, Olaf; Deco, Gustavo

2011-07-01

Spatio-temporally organized low-frequency fluctuations (<0.1 Hz), observed in BOLD fMRI signal during rest, suggest the existence of underlying network dynamics that emerge spontaneously from intrinsic brain processes. Furthermore, significant correlations between distinct anatomical regions-or functional connectivity (FC)-have led to the identification of several widely distributed resting-state networks (RSNs). This slow dynamics seems to be highly structured by anatomical connectivity but the mechanism behind it and its relationship with neural activity, particularly in the gamma frequency range, remains largely unknown. Indeed, direct measurements of neuronal activity have revealed similar large-scale correlations, particularly in slow power fluctuations of local field potential gamma frequency range oscillations. To address these questions, we investigated neural dynamics in a large-scale model of the human brain's neural activity. A key ingredient of the model was a structural brain network defined by empirically derived long-range brain connectivity together with the corresponding conduction delays. A neural population, assumed to spontaneously oscillate in the gamma frequency range, was placed at each network node. When these oscillatory units are integrated in the network, they behave as weakly coupled oscillators. The time-delayed interaction between nodes is described by the Kuramoto model of phase oscillators, a biologically-based model of coupled oscillatory systems. For a realistic setting of axonal conduction speed, we show that time-delayed network interaction leads to the emergence of slow neural activity fluctuations, whose patterns correlate significantly with the empirically measured FC. The best agreement of the simulated FC with the empirically measured FC is found for a set of parameters where subsets of nodes tend to synchronize although the network is not globally synchronized. Inside such clusters, the simulated BOLD signal between nodes is found to be correlated, instantiating the empirically observed RSNs. Between clusters, patterns of positive and negative correlations are observed, as described in experimental studies. These results are found to be robust with respect to a biologically plausible range of model parameters. In conclusion, our model suggests how resting-state neural activity can originate from the interplay between the local neural dynamics and the large-scale structure of the brain. Copyright © 2011 Elsevier Inc. All rights reserved.

Similarity networks as a knowledge representation for space applications

NASA Technical Reports Server (NTRS)

Bailey, David; Thompson, Donna; Feinstein, Jerald

1987-01-01

Similarity networks are a powerful form of knowledge representation that are useful for many artificial intelligence applications. Similarity networks are used in applications ranging from information analysis and case based reasoning to machine learning and linking symbolic to neural processing. Strengths of similarity networks include simple construction, intuitive object storage, and flexible retrieval techniques that facilitate inferencing. Therefore, similarity networks provide great potential for space applications.

Range Measurement as Practiced in the Deep Space Network

NASA Technical Reports Server (NTRS)

Berner, Jeff B.; Bryant, Scott H.; Kinman, Peter W.

2007-01-01

Range measurements are used to improve the trajectory models of spacecraft tracked by the Deep Space Network. The unique challenge of deep-space ranging is that the two-way delay is long, typically many minutes, and the signal-to-noise ratio is small. Accurate measurements are made under these circumstances by means of long correlations that incorporate Doppler rate-aiding. This processing is done with commercial digital signal processors, providing a flexibility in signal design that can accommodate both the traditional sequential ranging signal and pseudonoise range codes. Accurate range determination requires the calibration of the delay within the tracking station. Measurements with a standard deviation of 1 m have been made.

Hydraulic and Condition Assessment of Existing Sewerage Network: A Case Study of an Educational Institute

NASA Astrophysics Data System (ADS)

Sourabh, Nishant; Timbadiya, P. V.

2018-04-01

The hydraulic simulation of the existing sewerage network provides various information about critical points to assess the deteriorating condition and help in rehabilitation of existing network and future expansion. In the present study, hydraulic and condition assessment of existing network of educational Institute (i.e. Sardar Vallabhbhai National Institute of Technology-Surat, Gujarat, India), having an area of 100 ha and ground levels in range of 5.0-9.0 m above mean sea level, has been carried out using sewage flow simulation for existing and future scenarios analysis using SewerGEMS v8i. The paper describes the features of 4.79 km long sewerage network of institute followed by network model simulation for aforesaid scenarios and recommendations on improvement of the existing network for future use. The total sewer loads for present and future scenarios are 1.67 million litres per day (MLD) and 3.62 MLD, considering the peak factor of 3 on the basis of population. The hydraulic simulation of the existing scenario indicated depth by diameter (d/D) ratio in the range of 0.02-0.48 and velocity range of 0.08-0.53 m/s for existing network for present scenario. For the future scenario, the existing network is needed to be modified and it was found that total of 11 conduits (length: 464.8 m) should be replaced to the next higher diameter available, i.e., 350 mm for utilization of existing network for future scenario. The present study provides the methodology for condition assessment of existing network and its utilization as per guidelines provided by Central Public Health and Environmental Engineering Organization, 2013. The methodology presented in this paper can be used by municipal/public health engineer for the assessment of existing sewerage network for its serviceability and improvement in future.

Long-range wireless mesh network for weather monitoring in unfriendly geographic conditions.

PubMed

Toledano-Ayala, Manuel; Herrera-Ruiz, Gilberto; Soto-Zarazúa, Genaro M; Rivas-Araiza, Edgar A; Bazán Trujillo, Rey D; Porrás-Trejo, Rafael E

2011-01-01

In this paper a long-range wireless mesh network system is presented. It consists of three main parts: Remote Terminal Units (RTUs), Base Terminal Units (BTUs) and a Central Server (CS). The RTUs share a wireless network transmitting in the industrial, scientific and medical applications ISM band, which reaches up to 64 Km in a single point-to-point communication. A BTU controls the traffic within the network and has as its main task interconnecting it to a Ku-band satellite link using an embedded microcontroller-based gateway. Collected data is stored in a CS and presented to the final user in a numerical and a graphical form in a web portal.

Experimental forests and ranges as a network for for long-term data

Treesearch

Martin Vavra; John Mitchell

2010-01-01

In the new millennium, national leaders and policymakers are facing profound issues regarding people and the environment. Experimental Forests and Ranges (EFRs), managed by the Forest Service, U.S. Department of Agriculture (USDA), form a network of locations amenable to the development of long-term data collection across many major ecosystems of the continental United...

An optimally evolved connective ratio of neural networks that maximizes the occurrence of synchronized bursting behavior

PubMed Central

2012-01-01

Background Synchronized bursting activity (SBA) is a remarkable dynamical behavior in both ex vivo and in vivo neural networks. Investigations of the underlying structural characteristics associated with SBA are crucial to understanding the system-level regulatory mechanism of neural network behaviors. Results In this study, artificial pulsed neural networks were established using spike response models to capture fundamental dynamics of large scale ex vivo cortical networks. Network simulations with synaptic parameter perturbations showed the following two findings. (i) In a network with an excitatory ratio (ER) of 80-90%, its connective ratio (CR) was within a range of 10-30% when the occurrence of SBA reached the highest expectation. This result was consistent with the experimental observation in ex vivo neuronal networks, which were reported to possess a matured inhibitory synaptic ratio of 10-20% and a CR of 10-30%. (ii) No SBA occurred when a network does not contain any all-positive-interaction feedback loop (APFL) motif. In a neural network containing APFLs, the number of APFLs presented an optimal range corresponding to the maximal occurrence of SBA, which was very similar to the optimal CR. Conclusions In a neural network, the evolutionarily selected CR (10-30%) optimizes the occurrence of SBA, and APFL serves a pivotal network motif required to maximize the occurrence of SBA. PMID:22462685

Identifying Functional Mechanisms of Gene and Protein Regulatory Networks in Response to a Broader Range of Environmental Stresses

PubMed Central

Li, Cheng-Wei; Chen, Bor-Sen

2010-01-01

Cellular responses to sudden environmental stresses or physiological changes provide living organisms with the opportunity for final survival and further development. Therefore, it is an important topic to understand protective mechanisms against environmental stresses from the viewpoint of gene and protein networks. We propose two coupled nonlinear stochastic dynamic models to reconstruct stress-activated gene and protein regulatory networks via microarray data in response to environmental stresses. According to the reconstructed gene/protein networks, some possible mutual interactions, feedforward and feedback loops are found for accelerating response and filtering noises in these signaling pathways. A bow-tie core network is also identified to coordinate mutual interactions and feedforward loops, feedback inhibitions, feedback activations, and cross talks to cope efficiently with a broader range of environmental stresses with limited proteins and pathways. PMID:20454442

Rank-dependent deactivation in network evolution.

PubMed

Xu, Xin-Jian; Zhou, Ming-Chen

2009-12-01

A rank-dependent deactivation mechanism is introduced to network evolution. The growth dynamics of the network is based on a finite memory of individuals, which is implemented by deactivating one site at each time step. The model shows striking features of a wide range of real-world networks: power-law degree distribution, high clustering coefficient, and disassortative degree correlation.

Neural Network Classifier Architectures for Phoneme Recognition. CRC Technical Note No. CRC-TN-92-001.

ERIC Educational Resources Information Center

Treurniet, William

A study applied artificial neural networks, trained with the back-propagation learning algorithm, to modelling phonemes extracted from the DARPA TIMIT multi-speaker, continuous speech data base. A number of proposed network architectures were applied to the phoneme classification task, ranging from the simple feedforward multilayer network to more…

Topology Control in Aerial Multi-Beam Directional Networks

DTIC Science & Technology

2017-04-24

underlying challenges to topology control in multi -beam direction networks. Two topology control algorithms are developed: a centralized algorithm...main beam, the gain is negligible. Thus, for topology control in a multi -beam system, two nodes that are being simultaneously transmitted to or...the network. As the network size is larger than the communication range, even the original network will require some multi -hop traffic. The second two

Analysis and Testing of Mobile Wireless Networks

NASA Technical Reports Server (NTRS)

Alena, Richard; Evenson, Darin; Rundquist, Victor; Clancy, Daniel (Technical Monitor)

2002-01-01

Wireless networks are being used to connect mobile computing elements in more applications as the technology matures. There are now many products (such as 802.11 and 802.11b) which ran in the ISM frequency band and comply with wireless network standards. They are being used increasingly to link mobile Intranet into Wired networks. Standard methods of analyzing and testing their performance and compatibility are needed to determine the limits of the technology. This paper presents analytical and experimental methods of determining network throughput, range and coverage, and interference sources. Both radio frequency (BE) domain and network domain analysis have been applied to determine wireless network throughput and range in the outdoor environment- Comparison of field test data taken under optimal conditions, with performance predicted from RF analysis, yielded quantitative results applicable to future designs. Layering multiple wireless network- sooners can increase performance. Wireless network components can be set to different radio frequency-hopping sequences or spreading functions, allowing more than one sooner to coexist. Therefore, we ran multiple 802.11-compliant systems concurrently in the same geographical area to determine interference effects and scalability, The results can be used to design of more robust networks which have multiple layers of wireless data communication paths and provide increased throughput overall.

Large-Scale Simulation Network Design Study

DTIC Science & Technology

1983-10-01

video displays: three for the tank commander, three for the driver, one for the loader, and one for the gunner. The solid angles subtended by these...Newman Inc Range Sortr This process sorts the expanded display lists into range order for drawing according to the "painter’s algorithm’" The range sorter ...session could then be continued as soon as the network recovered. and the elapsed session time would not be wasted . The SimNet design is much more tolerant

Topology Control and Routing in Ad Hoc Networks

NASA Astrophysics Data System (ADS)

Carr-Motyckova, Lenka; Navarra, Alfredo; Johansson, Tomas; Unger, Walter

Mobile nodes with the ability to communicate with radio signals may form an ad hoc network. In this chapter special problems arising for these ad hoc networks are considered. These include range control, the reduction of interferences, regulation of power consumption, and localization.

Noise Tolerance of Attractor and Feedforward Memory Models

PubMed Central

Lim, Sukbin; Goldman, Mark S.

2017-01-01

In short-term memory networks, transient stimuli are represented by patterns of neural activity that persist long after stimulus offset. Here, we compare the performance of two prominent classes of memory networks, feedback-based attractor networks and feedforward networks, in conveying information about the amplitude of a briefly presented stimulus in the presence of gaussian noise. Using Fisher information as a metric of memory performance, we find that the optimal form of network architecture depends strongly on assumptions about the forms of nonlinearities in the network. For purely linear networks, we find that feedforward networks outperform attractor networks because noise is continually removed from feedforward networks when signals exit the network; as a result, feedforward networks can amplify signals they receive faster than noise accumulates over time. By contrast, attractor networks must operate in a signal-attenuating regime to avoid the buildup of noise. However, if the amplification of signals is limited by a finite dynamic range of neuronal responses or if noise is reset at the time of signal arrival, as suggested by recent experiments, we find that attractor networks can out-perform feedforward ones. Under a simple model in which neurons have a finite dynamic range, we find that the optimal attractor networks are forgetful if there is no mechanism for noise reduction with signal arrival but nonforgetful (perfect integrators) in the presence of a strong reset mechanism. Furthermore, we find that the maximal Fisher information for the feedforward and attractor networks exhibits power law decay as a function of time and scales linearly with the number of neurons. These results highlight prominent factors that lead to trade-offs in the memory performance of networks with different architectures and constraints, and suggest conditions under which attractor or feedforward networks may be best suited to storing information about previous stimuli. PMID:22091664

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.

Ion-photon entanglement and quantum frequency conversion with trapped Ba⁺ ions.

PubMed

Siverns, J D; Li, X; Quraishi, Q

2017-01-20

Trapped ions are excellent candidates for quantum nodes, as they possess many desirable features of a network node including long lifetimes, on-site processing capability, and production of photonic flying qubits. However, unlike classical networks in which data may be transmitted in optical fibers and where the range of communication is readily extended with amplifiers, quantum systems often emit photons that have a limited propagation range in optical fibers and, by virtue of the nature of a quantum state, cannot be noiselessly amplified. Here, we first describe a method to extract flying qubits from a Ba⁺ trapped ion via shelving to a long-lived, low-lying D-state with higher entanglement probabilities compared with current strong and weak excitation methods. We show a projected fidelity of ≈89% of the ion-photon entanglement. We compare several methods of ion-photon entanglement generation, and we show how the fidelity and entanglement probability varies as a function of the photon collection optic's numerical aperture. We then outline an approach for quantum frequency conversion of the photons emitted by the Ba⁺ ion to the telecommunication range for long-distance networking and to 780 nm for potential entanglement with rubidium-based quantum memories. Our approach is significant for extending the range of quantum networks and for the development of hybrid quantum networks compromised of different types of quantum memories.

Cross-modal integration of lexical-semantic features during word processing: evidence from oscillatory dynamics during EEG.

PubMed

van Ackeren, Markus J; Rueschemeyer, Shirley-Ann

2014-01-01

In recent years, numerous studies have provided converging evidence that word meaning is partially stored in modality-specific cortical networks. However, little is known about the mechanisms supporting the integration of this distributed semantic content into coherent conceptual representations. In the current study we aimed to address this issue by using EEG to look at the spatial and temporal dynamics of feature integration during word comprehension. Specifically, participants were presented with two modality-specific features (i.e., visual or auditory features such as silver and loud) and asked to verify whether these two features were compatible with a subsequently presented target word (e.g., WHISTLE). Each pair of features described properties from either the same modality (e.g., silver, tiny  =  visual features) or different modalities (e.g., silver, loud  =  visual, auditory). Behavioral and EEG data were collected. The results show that verifying features that are putatively represented in the same modality-specific network is faster than verifying features across modalities. At the neural level, integrating features across modalities induces sustained oscillatory activity around the theta range (4-6 Hz) in left anterior temporal lobe (ATL), a putative hub for integrating distributed semantic content. In addition, enhanced long-range network interactions in the theta range were seen between left ATL and a widespread cortical network. These results suggest that oscillatory dynamics in the theta range could be involved in integrating multimodal semantic content by creating transient functional networks linking distributed modality-specific networks and multimodal semantic hubs such as left ATL.

Systematic Evaluation of Molecular Networks for Discovery of Disease Genes. | Office of Cancer Genomics

Cancer.gov

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.

Object class segmentation of RGB-D video using recurrent convolutional neural networks.

PubMed

Pavel, Mircea Serban; Schulz, Hannes; Behnke, Sven

2017-04-01

Object class segmentation is a computer vision task which requires labeling each pixel of an image with the class of the object it belongs to. Deep convolutional neural networks (DNN) are able to learn and take advantage of local spatial correlations required for this task. They are, however, restricted by their small, fixed-sized filters, which limits their ability to learn long-range dependencies. Recurrent Neural Networks (RNN), on the other hand, do not suffer from this restriction. Their iterative interpretation allows them to model long-range dependencies by propagating activity. This property is especially useful when labeling video sequences, where both spatial and temporal long-range dependencies occur. In this work, a novel RNN architecture for object class segmentation is presented. We investigate several ways to train such a network. We evaluate our models on the challenging NYU Depth v2 dataset for object class segmentation and obtain competitive results. Copyright © 2017 Elsevier Ltd. All rights reserved.

Wireless Sensors Network (Sensornet)

NASA Technical Reports Server (NTRS)

Perotti, J.

2003-01-01

The Wireless Sensor Network System presented in this paper provides a flexible reconfigurable architecture that could be used in a broad range of applications. It also provides a sensor network with increased reliability; decreased maintainability costs, and assured data availability by autonomously and automatically reconfiguring to overcome communication interferences.

Topology Design for Directional Range Extension Networks with Antenna Blockage

DTIC Science & Technology

2017-03-19

introduced by pod-based antenna blockages. Using certain modeling approximations, the paper presents a quantitative analysis showing design trade-offs...parameters. Sec- tion IV develops quantitative relationships among key design elements and performance metrics. Section V considers some implications of the...Topology Design for Directional Range Extension Networks with Antenna Blockage Thomas Shake MIT Lincoln Laboratory shake@ll.mit.edu Abstract

Optimization of Close Range Photogrammetry Network Design Applying Fuzzy Computation

NASA Astrophysics Data System (ADS)

Aminia, A. S.

2017-09-01

Measuring object 3D coordinates with optimum accuracy is one of the most important issues in close range photogrammetry. In this context, network design plays an important role in determination of optimum position of imaging stations. This is, however, not a trivial task due to various geometric and radiometric constraints affecting the quality of the measurement network. As a result, most camera stations in the network are defined on a try and error basis based on the user's experience and generic network concept. In this paper, we propose a post-processing task to investigate the quality of camera positions right after image capturing to achieve the best result. To do this, a new fuzzy reasoning approach is adopted, in which the constraints affecting the network design are all modeled. As a result, the position of all camera locations is defined based on fuzzy rules and inappropriate stations are determined. The experiments carried out show that after determination and elimination of the inappropriate images using the proposed fuzzy reasoning system, the accuracy of measurements is improved and enhanced about 17% for the latter network.

Seamless interworking architecture for WBAN in heterogeneous wireless networks with QoS guarantees.

PubMed

Khan, Pervez; Ullah, Niamat; Ullah, Sana; Kwak, Kyung Sup

2011-10-01

The IEEE 802.15.6 standard is a communication standard optimized for low-power and short-range in-body/on-body nodes to serve a variety of medical, consumer electronics and entertainment applications. Providing high mobility with guaranteed Quality of Service (QoS) to a WBAN user in heterogeneous wireless networks is a challenging task. A WBAN uses a Personal Digital Assistant (PDA) to gather data from body sensors and forwards it to a remote server through wide range wireless networks. In this paper, we present a coexistence study of WBAN with Wireless Local Area Networks (WLAN) and Wireless Wide Area Networks (WWANs). The main issue is interworking of WBAN in heterogenous wireless networks including seamless handover, QoS, emergency services, cooperation and security. We propose a Seamless Interworking Architecture (SIA) for WBAN in heterogenous wireless networks based on a cost function. The cost function is based on power consumption and data throughput costs. Our simulation results show that the proposed scheme outperforms typical approaches in terms of throughput, delay and packet loss rate.

Delay Analysis of GTS Bridging between IEEE 802.15.4 and IEEE 802.11 Networks for Healthcare Applications

PubMed Central

Mišić, Jelena; (Sherman) Shen, Xuemin

2009-01-01

We consider interconnection of IEEE 802.15.4 beacon-enabled network cluster with IEEE 802.11b network. This scenario is important in healthcare applications where IEEE 802.15.4 nodes comprise patient's body area network (BAN) and are involved in sensing some health-related data. BAN nodes have very short communication range in order to avoid harming patient's health and save energy. Sensed data needs to be transmitted to an access point in the ward room using wireless technology with higher transmission range and rate such as IEEE 802.11b. We model the interconnected network where IEEE 802.15.4-based BAN operates in guaranteed time slot (GTS) mode, and IEEE 802.11b part of the bridge conveys GTS superframe to the 802.11b access point. We then analyze the network delays. Performance analysis is performed using EKG traffic from continuous telemetry, and we discuss the delays of communication due the increasing number of patients. PMID:19107184

A range-based predictive localization algorithm for WSID networks

NASA Astrophysics Data System (ADS)

Liu, Yuan; Chen, Junjie; Li, Gang

2017-11-01

Most studies on localization algorithms are conducted on the sensor networks with densely distributed nodes. However, the non-localizable problems are prone to occur in the network with sparsely distributed sensor nodes. To solve this problem, a range-based predictive localization algorithm (RPLA) is proposed in this paper for the wireless sensor networks syncretizing the RFID (WSID) networks. The Gaussian mixture model is established to predict the trajectory of a mobile target. Then, the received signal strength indication is used to reduce the residence area of the target location based on the approximate point-in-triangulation test algorithm. In addition, collaborative localization schemes are introduced to locate the target in the non-localizable situations. Simulation results verify that the RPLA achieves accurate localization for the network with sparsely distributed sensor nodes. The localization accuracy of the RPLA is 48.7% higher than that of the APIT algorithm, 16.8% higher than that of the single Gaussian model-based algorithm and 10.5% higher than that of the Kalman filtering-based algorithm.

Delay Analysis of GTS Bridging between IEEE 802.15.4 and IEEE 802.11 Networks for Healthcare Applications.

PubMed

Misić, Jelena; Sherman Shen, Xuemin

2009-01-01

We consider interconnection of IEEE 802.15.4 beacon-enabled network cluster with IEEE 802.11b network. This scenario is important in healthcare applications where IEEE 802.15.4 nodes comprise patient's body area network (BAN) and are involved in sensing some health-related data. BAN nodes have very short communication range in order to avoid harming patient's health and save energy. Sensed data needs to be transmitted to an access point in the ward room using wireless technology with higher transmission range and rate such as IEEE 802.11b. We model the interconnected network where IEEE 802.15.4-based BAN operates in guaranteed time slot (GTS) mode, and IEEE 802.11b part of the bridge conveys GTS superframe to the 802.11b access point. We then analyze the network delays. Performance analysis is performed using EKG traffic from continuous telemetry, and we discuss the delays of communication due the increasing number of patients.

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.

Resolving Structural Variability in Network Models and the Brain

PubMed Central

Klimm, Florian; Bassett, Danielle S.; Carlson, Jean M.; Mucha, Peter J.

2014-01-01

Large-scale white matter pathways crisscrossing the cortex create a complex pattern of connectivity that underlies human cognitive function. Generative mechanisms for this architecture have been difficult to identify in part because little is known in general about mechanistic drivers of structured networks. Here we contrast network properties derived from diffusion spectrum imaging data of the human brain with 13 synthetic network models chosen to probe the roles of physical network embedding and temporal network growth. We characterize both the empirical and synthetic networks using familiar graph metrics, but presented here in a more complete statistical form, as scatter plots and distributions, to reveal the full range of variability of each measure across scales in the network. We focus specifically on the degree distribution, degree assortativity, hierarchy, topological Rentian scaling, and topological fractal scaling—in addition to several summary statistics, including the mean clustering coefficient, the shortest path-length, and the network diameter. The models are investigated in a progressive, branching sequence, aimed at capturing different elements thought to be important in the brain, and range from simple random and regular networks, to models that incorporate specific growth rules and constraints. We find that synthetic models that constrain the network nodes to be physically embedded in anatomical brain regions tend to produce distributions that are most similar to the corresponding measurements for the brain. We also find that network models hardcoded to display one network property (e.g., assortativity) do not in general simultaneously display a second (e.g., hierarchy). This relative independence of network properties suggests that multiple neurobiological mechanisms might be at play in the development of human brain network architecture. Together, the network models that we develop and employ provide a potentially useful starting point for the statistical inference of brain network structure from neuroimaging data. PMID:24675546

Network geometry with flavor: From complexity to quantum geometry

NASA Astrophysics Data System (ADS)

Bianconi, Ginestra; Rahmede, Christoph

2016-03-01

Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d -dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s =-1 ,0 ,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d . In d =1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d >1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t . Interestingly the NGF remains fully classical but its statistical properties reveal the relation to its quantum mechanical description. In fact the δ -dimensional faces of the NGF have generalized degrees that follow either the Fermi-Dirac, Boltzmann, or Bose-Einstein statistics depending on the flavor s and the dimensions d and δ .

Network geometry with flavor: From complexity to quantum geometry.

PubMed

Bianconi, Ginestra; Rahmede, Christoph

2016-03-01

Network geometry is attracting increasing attention because it has a wide range of applications, ranging from data mining to routing protocols in the Internet. At the same time advances in the understanding of the geometrical properties of networks are essential for further progress in quantum gravity. In network geometry, simplicial complexes describing the interaction between two or more nodes play a special role. In fact these structures can be used to discretize a geometrical d-dimensional space, and for this reason they have already been widely used in quantum gravity. Here we introduce the network geometry with flavor s=-1,0,1 (NGF) describing simplicial complexes defined in arbitrary dimension d and evolving by a nonequilibrium dynamics. The NGF can generate discrete geometries of different natures, ranging from chains and higher-dimensional manifolds to scale-free networks with small-world properties, scale-free degree distribution, and nontrivial community structure. The NGF admits as limiting cases both the Bianconi-Barabási models for complex networks, the stochastic Apollonian network, and the recently introduced model for complex quantum network manifolds. The thermodynamic properties of NGF reveal that NGF obeys a generalized area law opening a new scenario for formulating its coarse-grained limit. The structure of NGF is strongly dependent on the dimensionality d. In d=1 NGFs grow complex networks for which the preferential attachment mechanism is necessary in order to obtain a scale-free degree distribution. Instead, for NGF with dimension d>1 it is not necessary to have an explicit preferential attachment rule to generate scale-free topologies. We also show that NGF admits a quantum mechanical description in terms of associated quantum network states. Quantum network states evolve by a Markovian dynamics and a quantum network state at time t encodes all possible NGF evolutions up to time t. Interestingly the NGF remains fully classical but its statistical properties reveal the relation to its quantum mechanical description. In fact the δ-dimensional faces of the NGF have generalized degrees that follow either the Fermi-Dirac, Boltzmann, or Bose-Einstein statistics depending on the flavor s and the dimensions d and δ.

Designing Robust and Resilient Tactical MANETs

DTIC Science & Technology

2014-09-25

Bounds on the Throughput Efficiency of Greedy Maximal Scheduling in Wireless Networks , IEEE/ACM Transactions on Networking , (06 2011): 0. doi: N... Wireless Sensor Networks and Effects of Long Range Dependant Data, Special IWSM Issue of Sequential Analysis, (11 2012): 0. doi: A. D. Dominguez...Bushnell, R. Poovendran. A Convex Optimization Approach for Clone Detection in Wireless Sensor Networks , Pervasive and Mobile Computing, (01 2012

The Structure of Male Adolescent Peer Networks and Risk for Intimate Partner Violence Perpetration: Findings from a National Sample

ERIC Educational Resources Information Center

Casey, Erin A.; Beadnell, Blair

2010-01-01

Although peer networks have been implicated as influential in a range of adolescent behaviors, little is known about relationships between peer network structures and risk for intimate partner violence (IPV) among youth. This study is a descriptive analysis of how peer network "types" may be related to subsequent risk for IPV…

A hierarchical clustering scheme approach to assessment of IP-network traffic using detrended fluctuation analysis

NASA Astrophysics Data System (ADS)

Takuma, Takehisa; Masugi, Masao

2009-03-01

This paper presents an approach to the assessment of IP-network traffic in terms of the time variation of self-similarity. To get a comprehensive view in analyzing the degree of long-range dependence (LRD) of IP-network traffic, we use a hierarchical clustering scheme, which provides a way to classify high-dimensional data with a tree-like structure. Also, in the LRD-based analysis, we employ detrended fluctuation analysis (DFA), which is applicable to the analysis of long-range power-law correlations or LRD in non-stationary time-series signals. Based on sequential measurements of IP-network traffic at two locations, this paper derives corresponding values for the LRD-related parameter α that reflects the degree of LRD of measured data. In performing the hierarchical clustering scheme, we use three parameters: the α value, average throughput, and the proportion of network traffic that exceeds 80% of network bandwidth for each measured data set. We visually confirm that the traffic data can be classified in accordance with the network traffic properties, resulting in that the combined depiction of the LRD and other factors can give us an effective assessment of network conditions at different times.

Networking—a statistical physics perspective

NASA Astrophysics Data System (ADS)

Yeung, Chi Ho; Saad, David

2013-03-01

Networking encompasses a variety of tasks related to the communication of information on networks; it has a substantial economic and societal impact on a broad range of areas including transportation systems, wired and wireless communications and a range of Internet applications. As transportation and communication networks become increasingly more complex, the ever increasing demand for congestion control, higher traffic capacity, quality of service, robustness and reduced energy consumption requires new tools and methods to meet these conflicting requirements. The new methodology should serve for gaining better understanding of the properties of networking systems at the macroscopic level, as well as for the development of new principled optimization and management algorithms at the microscopic level. Methods of statistical physics seem best placed to provide new approaches as they have been developed specifically to deal with nonlinear large-scale systems. This review aims at presenting an overview of tools and methods that have been developed within the statistical physics community and that can be readily applied to address the emerging problems in networking. These include diffusion processes, methods from disordered systems and polymer physics, probabilistic inference, which have direct relevance to network routing, file and frequency distribution, the exploration of network structures and vulnerability, and various other practical networking applications.

Relationship between inter-stimulus-intervals and intervals of autonomous activities in a neuronal network.

PubMed

Ito, Hidekatsu; Minoshima, Wataru; Kudoh, Suguru N

2015-08-01

To investigate relationships between neuronal network activity and electrical stimulus, we analyzed autonomous activity before and after electrical stimulus. Recordings of autonomous activity were performed using dissociated culture of rat hippocampal neurons on a multi-electrodes array (MEA) dish. Single stimulus and pared stimuli were applied to a cultured neuronal network. Single stimulus was applied every 1 min, and paired stimuli was performed by two sequential stimuli every 1 min. As a result, the patterns of synchronized activities of a neuronal network were changed after stimulus. Especially, long range synchronous activities were induced by paired stimuli. When 1 s inter-stimulus-intervals (ISI) and 1.5 s ISI paired stimuli are applied to a neuronal network, relatively long range synchronous activities expressed in case of 1.5 s ISI. Temporal synchronous activity of neuronal network is changed according to inter-stimulus-intervals (ISI) of electrical stimulus. In other words, dissociated neuronal network can maintain given information in temporal pattern and a certain type of an information maintenance mechanism was considered to be implemented in a semi-artificial dissociated neuronal network. The result is useful toward manipulation technology of neuronal activity in a brain system.

Assortativeness and information in scale-free networks

NASA Astrophysics Data System (ADS)

Piraveenan, M.; Prokopenko, M.; Zomaya, A. Y.

2009-02-01

We analyze Shannon information of scale-free networks in terms of their assortativeness, and identify classes of networks according to the dependency of the joint remaining degree distribution on the assortativeness. We conjecture that these classes comprise minimalistic and maximalistic networks in terms of Shannon information. For the studied classes, the information is shown to depend non-linearly on the absolute value of the assortativeness, with the dominant term of the relationship being a power-law. We exemplify this dependency using a range of real-world networks. Optimization of scale-free networks according to information they contain depends on the landscape of parameters’ search-space, and we identify two regions of interest: a slope region and a stability region. In the slope region, there is more freedom to generate and evaluate candidate networks since the information content can be changed easily by modifying only the assortativeness, while even a small change in the power-law’s scaling exponent brings a reward in a higher rate of information change. This feature may explain why the exponents of real-world scale-free networks are within a certain range, defined by the slope and stability regions.

Computational Characterization of Type I collagen-based Extra-cellular Matrix

NASA Astrophysics Data System (ADS)

Liang, Long; Jones, Christopher Allen Rucksack; Lin, Daniel; Jiao, Yang; Sun, Bo

2015-03-01

A model of extracellular matrix (ECM) of collagen fibers has been built, in which cells could communicate with distant partners via fiber-mediated long-range-transmitted stress states. The ECM is modeled as a spring-like fiber network derived from skeletonized confocal microscopy data. Different local and global perturbations have been performed on the network, each followed by an optimized global Monte-Carlo (MC) energy minimization leading to the deformed network in response to the perturbations. In the optimization, a highly efficient local energy update procedure is employed and force-directed MC moves are used, which results in a convergence to the energy minimum state 20 times faster than the commonly used random displacement trial moves in MC. Further analysis and visualization of the distribution and correlation of the resulting force network reveal that local perturbations can give rise to global impacts: the force chains formed with a linear extent much further than the characteristic length scale associated with the perturbation sites and average fiber length. This behavior provides a strong evidence for our hypothesis of fiber-mediated long-range force transmission in ECM networks and the resulting long-range cell-cell mechanical signaling. ASU Seed Grant.

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

PubMed

Zhang, J W; Rangan, A V

2015-04-01

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

Performance analysis and improvement of WPAN MAC for home networks.

PubMed

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking.

Performance Analysis and Improvement of WPAN MAC for Home Networks

PubMed Central

Mehta, Saurabh; Kwak, Kyung Sup

2010-01-01

The wireless personal area network (WPAN) is an emerging wireless technology for future short range indoor and outdoor communication applications. The IEEE 802.15.3 medium access control (MAC) is proposed to coordinate the access to the wireless medium among the competing devices, especially for short range and high data rate applications in home networks. In this paper we use analytical modeling to study the performance analysis of WPAN (IEEE 802.15.3) MAC in terms of throughput, efficient bandwidth utilization, and delay with various ACK policies under error channel condition. This allows us to introduce a K-Dly-ACK-AGG policy, payload size adjustment mechanism, and Improved Backoff algorithm to improve the performance of the WPAN MAC. Performance evaluation results demonstrate the impact of our improvements on network capacity. Moreover, these results can be very useful to WPAN application designers and protocol architects to easily and correctly implement WPAN for home networking. PMID:22319274

Physical limits to biomechanical sensing in disordered fibre networks

NASA Astrophysics Data System (ADS)

Beroz, Farzan; Jawerth, Louise M.; Münster, Stefan; Weitz, David A.; Broedersz, Chase P.; Wingreen, Ned S.

2017-07-01

Cells actively probe and respond to the stiffness of their surroundings. Since mechanosensory cells in connective tissue are surrounded by a disordered network of biopolymers, their in vivo mechanical environment can be extremely heterogeneous. Here we investigate how this heterogeneity impacts mechanosensing by modelling the cell as an idealized local stiffness sensor inside a disordered fibre network. For all types of networks we study, including experimentally-imaged collagen and fibrin architectures, we find that measurements applied at different points yield a strikingly broad range of local stiffnesses, spanning roughly two decades. We verify via simulations and scaling arguments that this broad range of local stiffnesses is a generic property of disordered fibre networks. Finally, we show that to obtain optimal, reliable estimates of global tissue stiffness, a cell must adjust its size, shape, and position to integrate multiple stiffness measurements over extended regions of space.

Small Worldness in Dense and Weighted Connectomes

NASA Astrophysics Data System (ADS)

Colon-Perez, Luis; Couret, Michelle; Triplett, William; Price, Catherine; Mareci, Thomas

2016-05-01

The human brain is a heterogeneous network of connected functional regions; however, most brain network studies assume that all brain connections can be described in a framework of binary connections. The brain is a complex structure of white matter tracts connected by a wide range of tract sizes, which suggests a broad range of connection strengths. Therefore, the assumption that the connections are binary yields an incomplete picture of the brain. Various thresholding methods have been used to remove spurious connections and reduce the graph density in binary networks. But these thresholds are arbitrary and make problematic the comparison of networks created at different thresholds. The heterogeneity of connection strengths can be represented in graph theory by applying weights to the network edges. Using our recently introduced edge weight parameter, we estimated the topological brain network organization using a complimentary weighted connectivity framework to the traditional framework of a binary network. To examine the reproducibility of brain networks in a controlled condition, we studied the topological network organization of a single healthy individual by acquiring 10 repeated diffusion-weighted magnetic resonance image datasets, over a one-month period on the same scanner, and analyzing these networks with deterministic tractography. We applied a threshold to both the binary and weighted networks and determined that the extra degree of freedom that comes with the framework of weighting network connectivity provides a robust result as any threshold level. The proposed weighted connectivity framework provides a stable result and is able to demonstrate the small world property of brain networks in situations where the binary framework is inadequate and unable to demonstrate this network property.

Network structure of SiO2 and MgSiO3 in amorphous and liquid States

NASA Astrophysics Data System (ADS)

Lan, Mai Thi; Thuy Duong, Tran; Viet Huy, Nguyen; Van Hong, Nguyen

2017-03-01

Network structure of SiO2 and MgSiO3 at 300 K and 3200 K is investigated by molecular dynamics simulation and visualization of simulation data. Structural organization of SiO2 and MgSiO3 is clarified via analysis the short range order (SRO) and intermediate range order (IRO). Network topology is determined via analyzing the bond between structural units, the cluster of structural units as well as spatial distribution of structural units. The polyamorphism as well as structural and dynamic heterogeneities are also discussed in this work.

Range-wide network of priority areas for greater sage-grouse - a design for conserving connected distributions or isolating individual zoos?

USGS Publications Warehouse

Crist, Michele R.; Knick, Steven T.; Hanser, Steven E.

2015-09-08

The network of areas delineated in 11 Western States for prioritizing management of greater sage-grouse (Centrocercus urophasianus) represents a grand experiment in conservation biology and reserve design. We used centrality metrics from social network theory to gain insights into how this priority area network might function. The network was highly centralized. Twenty of 188 priority areas accounted for 80 percent of the total centrality scores. These priority areas, characterized by large size and a central location in the range-wide distribution, are strongholds for greater sage-grouse populations and also might function as sources. Mid-ranking priority areas may serve as stepping stones because of their location between large central and smaller peripheral priority areas. The current network design and conservation strategy has risks. The contribution of almost one-half (n = 93) of the priority areas combined for less than 1 percent of the cumulative centrality scores for the network. These priority areas individually are likely too small to support viable sage-grouse populations within their boundary. Without habitat corridors to connect small priority areas either to larger priority areas or as a clustered group within the network, their isolation could lead to loss of sage-grouse within these regions of the network. 

Learning Data Set Influence on Identification Accuracy of Gas Turbine Neural Network Model

NASA Astrophysics Data System (ADS)

Kuznetsov, A. V.; Makaryants, G. M.

2018-01-01

There are many gas turbine engine identification researches via dynamic neural network models. It should minimize errors between model and real object during identification process. Questions about training data set processing of neural networks are usually missed. This article presents a study about influence of data set type on gas turbine neural network model accuracy. The identification object is thermodynamic model of micro gas turbine engine. The thermodynamic model input signal is the fuel consumption and output signal is the engine rotor rotation frequency. Four types input signals was used for creating training and testing data sets of dynamic neural network models - step, fast, slow and mixed. Four dynamic neural networks were created based on these types of training data sets. Each neural network was tested via four types test data sets. In the result 16 transition processes from four neural networks and four test data sets from analogous solving results of thermodynamic model were compared. The errors comparison was made between all neural network errors in each test data set. In the comparison result it was shown error value ranges of each test data set. It is shown that error values ranges is small therefore the influence of data set types on identification accuracy is low.

Spectral properties of the temporal evolution of brain network structure.

PubMed

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Spectral properties of the temporal evolution of brain network structure

NASA Astrophysics Data System (ADS)

Wang, Rong; Zhang, Zhen-Zhen; Ma, Jun; Yang, Yong; Lin, Pan; Wu, Ying

2015-12-01

The temporal evolution properties of the brain network are crucial for complex brain processes. In this paper, we investigate the differences in the dynamic brain network during resting and visual stimulation states in a task-positive subnetwork, task-negative subnetwork, and whole-brain network. The dynamic brain network is first constructed from human functional magnetic resonance imaging data based on the sliding window method, and then the eigenvalues corresponding to the network are calculated. We use eigenvalue analysis to analyze the global properties of eigenvalues and the random matrix theory (RMT) method to measure the local properties. For global properties, the shifting of the eigenvalue distribution and the decrease in the largest eigenvalue are linked to visual stimulation in all networks. For local properties, the short-range correlation in eigenvalues as measured by the nearest neighbor spacing distribution is not always sensitive to visual stimulation. However, the long-range correlation in eigenvalues as evaluated by spectral rigidity and number variance not only predicts the universal behavior of the dynamic brain network but also suggests non-consistent changes in different networks. These results demonstrate that the dynamic brain network is more random for the task-positive subnetwork and whole-brain network under visual stimulation but is more regular for the task-negative subnetwork. Our findings provide deeper insight into the importance of spectral properties in the functional brain network, especially the incomparable role of RMT in revealing the intrinsic properties of complex systems.

Enabling parallel simulation of large-scale HPC network systems

DOE PAGES

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.; ...

2016-04-07

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

Enabling parallel simulation of large-scale HPC network systems

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

Mubarak, Misbah; Carothers, Christopher D.; Ross, Robert B.

Here, with the increasing complexity of today’s high-performance computing (HPC) architectures, simulation has become an indispensable tool for exploring the design space of HPC systems—in particular, networks. In order to make effective design decisions, simulations of these systems must possess the following properties: (1) have high accuracy and fidelity, (2) produce results in a timely manner, and (3) be able to analyze a broad range of network workloads. Most state-of-the-art HPC network simulation frameworks, however, are constrained in one or more of these areas. In this work, we present a simulation framework for modeling two important classes of networks usedmore » in today’s IBM and Cray supercomputers: torus and dragonfly networks. We use the Co-Design of Multi-layer Exascale Storage Architecture (CODES) simulation framework to simulate these network topologies at a flit-level detail using the Rensselaer Optimistic Simulation System (ROSS) for parallel discrete-event simulation. Our simulation framework meets all the requirements of a practical network simulation and can assist network designers in design space exploration. First, it uses validated and detailed flit-level network models to provide an accurate and high-fidelity network simulation. Second, instead of relying on serial time-stepped or traditional conservative discrete-event simulations that limit simulation scalability and efficiency, we use the optimistic event-scheduling capability of ROSS to achieve efficient and scalable HPC network simulations on today’s high-performance cluster systems. Third, our models give network designers a choice in simulating a broad range of network workloads, including HPC application workloads using detailed network traces, an ability that is rarely offered in parallel with high-fidelity network simulations« less

A novel PON-based mobile distributed cluster of antennas approach to provide impartial and broadband services to end users

NASA Astrophysics Data System (ADS)

Sana, Ajaz; Saddawi, Samir; Moghaddassi, Jalil; Hussain, Shahab; Zaidi, Syed R.

2010-01-01

In this research paper we propose a novel Passive Optical Network (PON) based Mobile Worldwide Interoperability for Microwave Access (WiMAX) access network architecture to provide high capacity and performance multimedia services to mobile WiMAX users. Passive Optical Networks (PON) networks do not require powered equipment; hence they cost lower and need less network management. WiMAX technology emerges as a viable candidate for the last mile solution. In the conventional WiMAX access networks, the base stations and Multiple Input Multiple Output (MIMO) antennas are connected by point to point lines. Ideally in theory, the Maximum WiMAX bandwidth is assumed to be 70 Mbit/s over 31 miles. In reality, WiMAX can only provide one or the other as when operating over maximum range, bit error rate increases and therefore it is required to use lower bit rate. Lowering the range allows a device to operate at higher bit rates. Our focus in this research paper is to increase both range and bit rate by utilizing distributed cluster of MIMO antennas connected to WiMAX base stations with PON based topologies. A novel quality of service (QoS) algorithm is also proposed to provide admission control and scheduling to serve classified traffic. The proposed architecture presents flexible and scalable system design with different performance requirements and complexity.

EPA Library Network Communication Strategies

EPA Pesticide Factsheets

To establish Agency-wide procedures for the EPA National Library Network libraries to communicate, using a range of established mechanisms, with other EPA libraries, EPA staff, organizations and the public.

Age Factor in Business Education Students' Use of Social Networking Sites in Tertiary Institutions in Anambra State, Nigeria

ERIC Educational Resources Information Center

Ementa, Christiana Ngozi; Ile, Chika Madu

2015-01-01

There are diverse social networking sites which range from those that provide social sharing and interaction to those that provide networks for professionals within same and other fields. Social networking sites require a user to sign up, create a profile and begin sending short messages about what the user is doing or thinking. The study sought…

Capacity Building for Research and Education in GIS/GPS Technology and Systems

DTIC Science & Technology

2015-05-20

In multi- sensor area Wireless Sensor Networking (WSN) fields will be explored. As a step forward the research to be conducted in WSN field is to...Agriculture Using Technology for Crops Scouting in Agriculture Application of Technology in Precision Agriculture Wireless Sensor Network (WSN) in...Cooperative Engagement Capability Range based algorithms for Wireless Sensor Network Self-configurable Wireless Sensor Network Energy Efficient Wireless

Maintaining Limited-Range Connectivity Among Second-Order Agents

DTIC Science & Technology

2016-07-07

we consider ad-hoc networks of robotic agents with double integrator dynamics. For such networks, the connectivity maintenance problems are: (i) do...hoc networks of mobile autonomous agents. This loose ter- minology refers to groups of robotic agents with limited mobility and communica- tion...connectivity can be preserved. 3.1. Networks of robotic agents with second-order dynamics and the connectivity maintenance problem. We begin by

Comparative-effectiveness research in distributed health data networks.

PubMed

Toh, S; Platt, R; Steiner, J F; Brown, J S

2011-12-01

Comparative-effectiveness research (CER) can be conducted within a distributed health data network. Such networks allow secure access to separate data sets from different data partners and overcome many practical obstacles related to patient privacy, data security, and proprietary concerns. A scalable network architecture supports a wide range of CER activities and meets the data infrastructure needs envisioned by the Federal Coordinating Council for Comparative Effectiveness Research.

The Australian SuperSite Network: A continental, long-term terrestrial ecosystem observatory.

PubMed

Karan, Mirko; Liddell, Michael; Prober, Suzanne M; Arndt, Stefan; Beringer, Jason; Boer, Matthias; Cleverly, James; Eamus, Derek; Grace, Peter; Van Gorsel, Eva; Hero, Jean-Marc; Hutley, Lindsay; Macfarlane, Craig; Metcalfe, Dan; Meyer, Wayne; Pendall, Elise; Sebastian, Alvin; Wardlaw, Tim

2016-10-15

Ecosystem monitoring networks aim to collect data on physical, chemical and biological systems and their interactions that shape the biosphere. Here we introduce the Australian SuperSite Network that, along with complementary facilities of Australia's Terrestrial Ecosystem Research Network (TERN), delivers field infrastructure and diverse, ecosystem-related datasets for use by researchers, educators and policy makers. The SuperSite Network uses infrastructure replicated across research sites in different biomes, to allow comparisons across ecosystems and improve scalability of findings to regional, continental and global scales. This conforms with the approaches of other ecosystem monitoring networks such as Critical Zone Observatories, the U.S. National Ecological Observatory Network; Analysis and Experimentation on Ecosystems, Europe; Chinese Ecosystem Research Network; International Long Term Ecological Research network and the United States Long Term Ecological Research Network. The Australian SuperSite Network currently involves 10 SuperSites across a diverse range of biomes, including tropical rainforest, grassland and savanna; wet and dry sclerophyll forest and woodland; and semi-arid grassland, woodland and savanna. The focus of the SuperSite Network is on using vegetation, faunal and biophysical monitoring to develop a process-based understanding of ecosystem function and change in Australian biomes; and to link this with data streams provided by the series of flux towers across the network. The Australian SuperSite Network is also intended to support a range of auxiliary researchers who contribute to the growing body of knowledge within and across the SuperSite Network, public outreach and education to promote environmental awareness and the role of ecosystem monitoring in the management of Australian environments. Copyright © 2016 Elsevier B.V. All rights reserved.

Regional and local networks of horizontal control, Cerro Prieto geothermal area

USGS Publications Warehouse

Massey, B.L.

1979-01-01

The Cerro Prieto geothermal area in the Mexicali Valley 30 km southeast of Mexicali, Baja California, is probably deforming due to (1) the extraction of large volumes of steam and hot water, and (2) active tectonism. Two networks of precise horizontal control were established in Mexicali Valley by the U.S. Geological Survey in 1977 - 1978 to measure both types of movement as they occur. These networks consisted of (1) a regional trilateration net brought into the mountain ranges west of the geothermal area from survey stations on an existing U.S. Geological Survey crustal-strain network north of the international border, and (2) a local net tied to stations in the regional net and encompassing the area of present and planned geothermal production. Survey lines in this net were selected to span areas of probable ground-surface movements in and around the geothermal area. Electronic distance measuring (EDM) instruments, operating with a modulated laser beam, were used to measure the distances between stations in both networks. The regional net was run using a highly precise long-range EDM instrument, helicopters for transportation of men and equipment to inaccessible stations on mountain peaks, and a fixed wing airplane flying along the line of sight. Precision of measurements with this complex long-range system approached 0-2 ppm of line length. The local net was measured with a medium-range EDM instrument requiring minimal ancillary equipment. Precision of measurements with this less complex system approached 3 ppm for the shorter line lengths. The detection and analysis of ground-surface movements resulting from tectonic strains or induced by geothermal fluid withdrawal is dependent on subsequent resurveys of these networks. ?? 1979.

Quantifying the Spatial Ecology of Wide-Ranging Marine Species in the Gulf of California: Implications for Marine Conservation Planning

PubMed Central

Anadón, José Daniel; D'Agrosa, Caterina; Gondor, Anne; Gerber, Leah R.

2011-01-01

There is growing interest in systematic establishment of marine protected area (MPA) networks and representative conservation sites. This movement toward networks of no-take zones requires that reserves are deliberately and adequately spaced for connectivity. Here, we test the network functionality of an ecoregional assessment configuration of marine conservation areas by evaluating the habitat protection and connectivity offered to wide-ranging fauna in the Gulf of California (GOC, Mexico). We first use expert opinion to identify representative species of wide-ranging fauna of the GOC. These include leopard grouper, hammerhead sharks, California brown pelicans and green sea turtles. Analyzing habitat models with both structural and functional connectivity indexes, our results indicate that the configuration includes large proportions of biologically important habitat for the four species considered (25–40%), particularly, the best quality habitats (46–57%). Our results also show that connectivity levels offered by the conservation area design for these four species may be similar to connectivity levels offered by the entire Gulf of California, thus indicating that connectivity offered by the areas may resemble natural connectivity. The selected focal species comprise different life histories among marine or marine-related vertebrates and are associated with those habitats holding the most biodiversity values (i.e. coastal habitats); our results thus suggest that the proposed configuration may function as a network for connectivity and may adequately represent the marine megafauna in the GOC, including the potential connectivity among habitat patches. This work highlights the range of approaches that can be used to quantify habitat protection and connectivity for wide-ranging marine species in marine reserve networks. PMID:22163013

Quantifying the spatial ecology of wide-ranging marine species in the Gulf of California: implications for marine conservation planning.

PubMed

Anadón, José Daniel; D'Agrosa, Caterina; Gondor, Anne; Gerber, Leah R

2011-01-01

There is growing interest in systematic establishment of marine protected area (MPA) networks and representative conservation sites. This movement toward networks of no-take zones requires that reserves are deliberately and adequately spaced for connectivity. Here, we test the network functionality of an ecoregional assessment configuration of marine conservation areas by evaluating the habitat protection and connectivity offered to wide-ranging fauna in the Gulf of California (GOC, Mexico). We first use expert opinion to identify representative species of wide-ranging fauna of the GOC. These include leopard grouper, hammerhead sharks, California brown pelicans and green sea turtles. Analyzing habitat models with both structural and functional connectivity indexes, our results indicate that the configuration includes large proportions of biologically important habitat for the four species considered (25-40%), particularly, the best quality habitats (46-57%). Our results also show that connectivity levels offered by the conservation area design for these four species may be similar to connectivity levels offered by the entire Gulf of California, thus indicating that connectivity offered by the areas may resemble natural connectivity. The selected focal species comprise different life histories among marine or marine-related vertebrates and are associated with those habitats holding the most biodiversity values (i.e. coastal habitats); our results thus suggest that the proposed configuration may function as a network for connectivity and may adequately represent the marine megafauna in the GOC, including the potential connectivity among habitat patches. This work highlights the range of approaches that can be used to quantify habitat protection and connectivity for wide-ranging marine species in marine reserve networks.

Network survivability performance

NASA Astrophysics Data System (ADS)

1993-11-01

This technical report has been developed to address the survivability of telecommunications networks including services. It responds to the need for a common understanding of, and assessment techniques for network survivability, availability, integrity, and reliability. It provides a basis for designing and operating telecommunications networks to user expectations for network survivability and a foundation for continuing industry activities in the subject area. This report focuses on the survivability of both public and private networks and covers a wide range of users. Two frameworks are established for quantifying and categorizing service outages, and for classifying network survivability techniques and measures. The performance of the network survivability techniques is considered; however, recommended objectives are not established for network survivability performance.

Four-state rock-paper-scissors games in constrained Newman-Watts networks.

PubMed

Zhang, Guo-Yong; Chen, Yong; Qi, Wei-Kai; Qing, Shao-Meng

2009-06-01

We study the cyclic dominance of three species in two-dimensional constrained Newman-Watts networks with a four-state variant of the rock-paper-scissors game. By limiting the maximal connection distance Rmax in Newman-Watts networks with the long-range connection probability p , we depict more realistically the stochastic interactions among species within ecosystems. When we fix mobility and vary the value of p or Rmax, the Monte Carlo simulations show that the spiral waves grow in size, and the system becomes unstable and biodiversity is lost with increasing p or Rmax. These results are similar to recent results of Reichenbach et al. [Nature (London) 448, 1046 (2007)], in which they increase the mobility only without including long-range interactions. We compared extinctions with or without long-range connections and computed spatial correlation functions and correlation length. We conclude that long-range connections could improve the mobility of species, drastically changing their crossover to extinction and making the system more unstable.

Characterizing structural association alterations within brain networks in normal aging using Gaussian Bayesian networks.

PubMed

Guo, Xiaojuan; Wang, Yan; Chen, Kewei; Wu, Xia; Zhang, Jiacai; Li, Ke; Jin, Zhen; Yao, Li

2014-01-01

Recent multivariate neuroimaging studies have revealed aging-related alterations in brain structural networks. However, the sensory/motor networks such as the auditory, visual and motor networks, have obtained much less attention in normal aging research. In this study, we used Gaussian Bayesian networks (BN), an approach investigating possible inter-regional directed relationship, to characterize aging effects on structural associations between core brain regions within each of these structural sensory/motor networks using volumetric MRI data. We then further examined the discriminability of BN models for the young (N = 109; mean age =22.73 years, range 20-28) and old (N = 82; mean age =74.37 years, range 60-90) groups. The results of the BN modeling demonstrated that structural associations exist between two homotopic brain regions from the left and right hemispheres in each of the three networks. In particular, compared with the young group, the old group had significant connection reductions in each of the three networks and lesser connection numbers in the visual network. Moreover, it was found that the aging-related BN models could distinguish the young and old individuals with 90.05, 73.82, and 88.48% accuracy for the auditory, visual, and motor networks, respectively. Our findings suggest that BN models can be used to investigate the normal aging process with reliable statistical power. Moreover, these differences in structural inter-regional interactions may help elucidate the neuronal mechanism of anatomical changes in normal aging.

Formation of nanoscale networks: selectively swelling amphiphilic block copolymers with CO2-expanded liquids

NASA Astrophysics Data System (ADS)

Gong, Jianliang; Zhang, Aijuan; Bai, Hua; Zhang, Qingkun; Du, Can; Li, Lei; Hong, Yanzhen; Li, Jun

2013-01-01

Polymeric films with nanoscale networks were prepared by selectively swelling an amphiphilic diblock copolymer, polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP), with the CO2-expanded liquid (CXL), CO2-methanol. The phase behavior of the CO2-methanol system was investigated by both theoretical calculation and experiments, revealing that methanol can be expanded by CO2, forming homogeneous CXL under the experimental conditions. When treated with the CO2-methanol system, the spin cast compact PS-b-P4VP film was transformed into a network with interconnected pores, in a pressure range of 12-20 MPa and a temperature range of 45-60 °C. The formation mechanism of the network, involving plasticization of PS and selective swelling of P4VP, was proposed. Because the diblock copolymer diffusion process is controlled by the activated hopping of individual block copolymer chains with the thermodynamic barrier for moving PVP segments from one to another, the formation of the network structures is achieved in a short time scale and shows ``thermodynamically restricted'' character. Furthermore, the resulting polymer networks were employed as templates, for the preparation of polypyrrole networks, by an electrochemical polymerization process. The prepared porous polypyrrole film was used to fabricate a chemoresistor-type gas sensor which showed high sensitivity towards ammonia.Polymeric films with nanoscale networks were prepared by selectively swelling an amphiphilic diblock copolymer, polystyrene-block-poly(4-vinylpyridine) (PS-b-P4VP), with the CO2-expanded liquid (CXL), CO2-methanol. The phase behavior of the CO2-methanol system was investigated by both theoretical calculation and experiments, revealing that methanol can be expanded by CO2, forming homogeneous CXL under the experimental conditions. When treated with the CO2-methanol system, the spin cast compact PS-b-P4VP film was transformed into a network with interconnected pores, in a pressure range of 12-20 MPa and a temperature range of 45-60 °C. The formation mechanism of the network, involving plasticization of PS and selective swelling of P4VP, was proposed. Because the diblock copolymer diffusion process is controlled by the activated hopping of individual block copolymer chains with the thermodynamic barrier for moving PVP segments from one to another, the formation of the network structures is achieved in a short time scale and shows ``thermodynamically restricted'' character. Furthermore, the resulting polymer networks were employed as templates, for the preparation of polypyrrole networks, by an electrochemical polymerization process. The prepared porous polypyrrole film was used to fabricate a chemoresistor-type gas sensor which showed high sensitivity towards ammonia. Electronic supplementary information (ESI) available. See DOI: 10.1039/c2nr33188h

Photodiode Preamplifier for Laser Ranging With Weak Signals

NASA Technical Reports Server (NTRS)

Abramovici, Alexander; Chapsky, Jacob

2007-01-01

An improved preamplifier circuit has been designed for processing the output of an avalanche photodiode (APD) that is used in a high-resolution laser ranging system to detect laser pulses returning from a target. The improved circuit stands in contrast to prior such circuits in which the APD output current pulses are made to pass, variously, through wide-band or narrow-band load networks before preamplification. A major disadvantage of the prior wide-band load networks is that they are highly susceptible to noise, which degrades timing resolution. A major disadvantage of the prior narrow-band load networks is that they make it difficult to sample the amplitudes of the narrow laser pulses ordinarily used in ranging. In the improved circuit, a load resistor is connected to the APD output and its value is chosen so that the time constant defined by this resistance and the APD capacitance is large, relative to the duration of a laser pulse. The APD capacitance becomes initially charged by the pulse of current generated by a return laser pulse, so that the rise time of the load-network output is comparable to the duration of the return pulse. Thus, the load-network output is characterized by a fast-rising leading edge, which is necessary for accurate pulse timing. On the other hand, the resistance-capacitance combination constitutes a lowpass filter, which helps to suppress noise. The long time constant causes the load network output pulse to have a long shallow-sloping trailing edge, which makes it easy to sample the amplitude of the return pulse. The output of the load network is fed to a low-noise, wide-band amplifier. The amplifier must be a wide-band one in order to preserve the sharp pulse rise for timing. The suppression of noise and the use of a low-noise amplifier enable the ranging system to detect relatively weak return pulses.

Dynamic Control of Adsorption Sensitivity for Photo-EMF-Based Ammonia Gas Sensors Using a Wireless Network

PubMed Central

Vashpanov, Yuriy; Choo, Hyunseung; Kim, Dongsoo Stephen

2011-01-01

This paper proposes an adsorption sensitivity control method that uses a wireless network and illumination light intensity in a photo-electromagnetic field (EMF)-based gas sensor for measurements in real time of a wide range of ammonia concentrations. The minimum measurement error for a range of ammonia concentration from 3 to 800 ppm occurs when the gas concentration magnitude corresponds with the optimal intensity of the illumination light. A simulation with LabView-engineered modules for automatic control of a new intelligent computer system was conducted to improve measurement precision over a wide range of gas concentrations. This gas sensor computer system with wireless network technology could be useful in the chemical industry for automatic detection and measurement of hazardous ammonia gas levels in real time. PMID:22346680

Simulation and modeling of the temporal performance of path-based restoration schemes in planar mesh networks

NASA Astrophysics Data System (ADS)

Bhardwaj, Manish; McCaughan, Leon; Olkhovets, Anatoli; Korotky, Steven K.

2006-12-01

We formulate an analytic framework for the restoration performance of path-based restoration schemes in planar mesh networks. We analyze various switch architectures and signaling schemes and model their total restoration interval. We also evaluate the network global expectation value of the time to restore a demand as a function of network parameters. We analyze a wide range of nominally capacity-optimal planar mesh networks and find our analytic model to be in good agreement with numerical simulation data.

An investigation of the impact of using different methods for network meta-analysis: a protocol for an empirical evaluation.

PubMed

Karahalios, Amalia Emily; Salanti, Georgia; Turner, Simon L; Herbison, G Peter; White, Ian R; Veroniki, Areti Angeliki; Nikolakopoulou, Adriani; Mckenzie, Joanne E

2017-06-24

Network meta-analysis, a method to synthesise evidence from multiple treatments, has increased in popularity in the past decade. Two broad approaches are available to synthesise data across networks, namely, arm- and contrast-synthesis models, with a range of models that can be fitted within each. There has been recent debate about the validity of the arm-synthesis models, but to date, there has been limited empirical evaluation comparing results using the methods applied to a large number of networks. We aim to address this gap through the re-analysis of a large cohort of published networks of interventions using a range of network meta-analysis methods. We will include a subset of networks from a database of network meta-analyses of randomised trials that have been identified and curated from the published literature. The subset of networks will include those where the primary outcome is binary, the number of events and participants are reported for each direct comparison, and there is no evidence of inconsistency in the network. We will re-analyse the networks using three contrast-synthesis methods and two arm-synthesis methods. We will compare the estimated treatment effects, their standard errors, treatment hierarchy based on the surface under the cumulative ranking (SUCRA) curve, the SUCRA value, and the between-trial heterogeneity variance across the network meta-analysis methods. We will investigate whether differences in the results are affected by network characteristics and baseline risk. The results of this study will inform whether, in practice, the choice of network meta-analysis method matters, and if it does, in what situations differences in the results between methods might arise. The results from this research might also inform future simulation studies.

Does landscape connectivity shape local and global social network structure in white-tailed deer?

PubMed Central

Koen, Erin L.; Tosa, Marie I.; Nielsen, Clayton K.; Schauber, Eric M.

2017-01-01

Intraspecific social behavior can be influenced by both intrinsic and extrinsic factors. While much research has focused on how characteristics of individuals influence their roles in social networks, we were interested in the role that landscape structure plays in animal sociality at both individual (local) and population (global) levels. We used female white-tailed deer (Odocoileus virginianus) in Illinois, USA, to investigate the potential effect of landscape on social network structure by weighting the edges of seasonal social networks with association rate (based on proximity inferred from GPS collar data). At the local level, we found that sociality among female deer in neighboring social groups (n = 36) was mainly explained by their home range overlap, with two exceptions: 1) during fawning in an area of mixed forest and grassland, deer whose home ranges had low forest connectivity were more social than expected; and 2) during the rut in an area of intensive agriculture, deer inhabiting home ranges with high amount and connectedness of agriculture were more social than expected. At the global scale, we found that deer populations (n = 7) in areas with highly connected forest-agriculture edge, a high proportion of agriculture, and a low proportion of forest tended to have higher weighted network closeness, although low sample size precluded statistical significance. This result implies that infectious disease could spread faster in deer populations inhabiting such landscapes. Our work advances the general understanding of animal social networks, demonstrating how landscape features can underlie differences in social behavior both within and among wildlife social networks. PMID:28306748

Self-localization of wireless sensor networks using self-organizing maps

NASA Astrophysics Data System (ADS)

Ertin, Emre; Priddy, Kevin L.

2005-03-01

Recently there has been a renewed interest in the notion of deploying large numbers of networked sensors for applications ranging from environmental monitoring to surveillance. In a typical scenario a number of sensors are distributed in a region of interest. Each sensor is equipped with sensing, processing and communication capabilities. The information gathered from the sensors can be used to detect, track and classify objects of interest. For a number of locations the sensors location is crucial in interpreting the data collected from those sensors. Scalability requirements dictate sensor nodes that are inexpensive devices without a dedicated localization hardware such as GPS. Therefore the network has to rely on information collected within the network to self-localize. In the literature a number of algorithms has been proposed for network localization which uses measurements informative of range, angle, proximity between nodes. Recent work by Patwari and Hero relies on sensor data without explicit range estimates. The assumption is that the correlation structure in the data is a monotone function of the intersensor distances. In this paper we propose a new method based on unsupervised learning techniques to extract location information from the sensor data itself. We consider a grid consisting of virtual nodes and try to fit grid in the actual sensor network data using the method of self organizing maps. Then known sensor network geometry can be used to rotate and scale the grid to a global coordinate system. Finally, we illustrate how the virtual nodes location information can be used to track a target.

Designing allostery-inspired response in mechanical networks

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

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are then able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ~1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individualmore » response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.« less

Designing allostery-inspired response in mechanical networks

DOE PAGES

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard; ...

2017-02-21

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are then able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ~1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individualmore » response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.« less

Monitoring air quality in mountains: Designing an effective network

USGS Publications Warehouse

Peterson, D.L.

2000-01-01

A quantitatively robust yet parsimonious air-quality monitoring network in mountainous regions requires special attention to relevant spatial and temporal scales of measurement and inference. The design of monitoring networks should focus on the objectives required by public agencies, namely: 1) determine if some threshold has been exceeded (e.g., for regulatory purposes), and 2) identify spatial patterns and temporal trends (e.g., to protect natural resources). A short-term, multi-scale assessment to quantify spatial variability in air quality is a valuable asset in designing a network, in conjunction with an evaluation of existing data and simulation-model output. A recent assessment in Washington state (USA) quantified spatial variability in tropospheric ozone distribution ranging from a single watershed to the western third of the state. Spatial and temporal coherence in ozone exposure modified by predictable elevational relationships ( 1.3 ppbv ozone per 100 m elevation gain) extends from urban areas to the crest of the Cascade Range. This suggests that a sparse network of permanent analyzers is sufficient at all spatial scales, with the option of periodic intensive measurements to validate network design. It is imperative that agencies cooperate in the design of monitoring networks in mountainous regions to optimize data collection and financial efficiencies.

Designing allostery-inspired response in mechanical networks

PubMed Central

Rocks, Jason W.; Pashine, Nidhi; Bischofberger, Irmgard; Goodrich, Carl P.; Liu, Andrea J.; Nagel, Sidney R.

2017-01-01

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ∼1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks. PMID:28223534

Designing allostery-inspired response in mechanical networks.

PubMed

Rocks, Jason W; Pashine, Nidhi; Bischofberger, Irmgard; Goodrich, Carl P; Liu, Andrea J; Nagel, Sidney R

2017-03-07

Recent advances in designing metamaterials have demonstrated that global mechanical properties of disordered spring networks can be tuned by selectively modifying only a small subset of bonds. Here, using a computationally efficient approach, we extend this idea to tune more general properties of networks. With nearly complete success, we are able to produce a strain between any two target nodes in a network in response to an applied source strain on any other pair of nodes by removing only ∼1% of the bonds. We are also able to control multiple pairs of target nodes, each with a different individual response, from a single source, and to tune multiple independent source/target responses simultaneously into a network. We have fabricated physical networks in macroscopic 2D and 3D systems that exhibit these responses. This work is inspired by the long-range coupled conformational changes that constitute allosteric function in proteins. The fact that allostery is a common means for regulation in biological molecules suggests that it is a relatively easy property to develop through evolution. In analogy, our results show that long-range coupled mechanical responses are similarly easy to achieve in disordered networks.

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.

BiNA: A Visual Analytics Tool for Biological Network Data

PubMed Central

Gerasch, Andreas; Faber, Daniel; Küntzer, Jan; Niermann, Peter; Kohlbacher, Oliver; Lenhof, Hans-Peter; Kaufmann, Michael

2014-01-01

Interactive visual analysis of biological high-throughput data in the context of the underlying networks is an essential task in modern biomedicine with applications ranging from metabolic engineering to personalized medicine. The complexity and heterogeneity of data sets require flexible software architectures for data analysis. Concise and easily readable graphical representation of data and interactive navigation of large data sets are essential in this context. We present BiNA - the Biological Network Analyzer - a flexible open-source software for analyzing and visualizing biological networks. Highly configurable visualization styles for regulatory and metabolic network data offer sophisticated drawings and intuitive navigation and exploration techniques using hierarchical graph concepts. The generic projection and analysis framework provides powerful functionalities for visual analyses of high-throughput omics data in the context of networks, in particular for the differential analysis and the analysis of time series data. A direct interface to an underlying data warehouse provides fast access to a wide range of semantically integrated biological network databases. A plugin system allows simple customization and integration of new analysis algorithms or visual representations. BiNA is available under the 3-clause BSD license at http://bina.unipax.info/. PMID:24551056

Defense Acquisitions: Assessments of Selected Weapon Programs

DTIC Science & Technology

2017-03-01

PAC-3 MSE) 81 Warfighter Information Network-Tactical (WIN-T) Increment 2 83 Improved Turbine Engine Program (ITEP) 85 Long Range Precision Fires...Unmanned Air System 05/2018 —- O  Joint Surveillance Target Attack Radar System Recapitalization 10/2017 —- O  Improved Turbine Engine Program TBD...Network-Tactical (WIN-T) Increment 2 83 1-page assessments Improved Turbine Engine Program (ITEP) 85 Long Range Precision Fires (LRPF) 86

Neurocomputation by Reaction Diffusion

NASA Astrophysics Data System (ADS)

Liang, Ping

1995-08-01

This Letter demonstrates the possible role nonsynaptic diffusion neurotransmission may play in neurocomputation using an artificial neural network model. A reaction-diffusion neural network model with field-based information-processing mechanisms is proposed. The advantages of nonsynaptic field neurotransmission from a computational viewpoint demonstrated in this Letter include long-range inhibition using only local interaction, nonhardwired and changeable (target specific) long-range communication pathways, and multiple simultaneous spatiotemporal organization processes in the same medium.

An Energy Efficient Power Control Protocol for Ad Hoc Networks Using Directional Antennas

NASA Astrophysics Data System (ADS)

Quiroz-Perez, Carlos; Gulliver, T. Aaron

A wireless ad hoc network is a collection of mobile nodes that can communicate with each other. Typically, nodes employ omnidirectional antennas. The use of directional antennas can increase spatial reuse, reduce the number of hops to a destination, reduce interference, and increase the transmission range in a specific direction. This is because omnidirectional antennas radiate equally in all directions, limiting the transmission range.

A Sensible Approach to Wireless Networking.

ERIC Educational Resources Information Center

Ahmed, S. Faruq

2002-01-01

Discusses radio frequency (R.F.) wireless technology, including industry standards, range (coverage) and throughput (data rate), wireless compared to wired networks, and considerations before embarking on a large-scale wireless project. (EV)

Stochastic Control of Multi-Scale Networks: Modeling, Analysis and Algorithms

DTIC Science & Technology

2014-10-20

Theory, (02 2012): 0. doi: B. T. Swapna, Atilla Eryilmaz, Ness B. Shroff. Throughput-Delay Analysis of Random Linear Network Coding for Wireless ... Wireless Sensor Networks and Effects of Long-Range Dependent Data, Sequential Analysis , (10 2012): 0. doi: 10.1080/07474946.2012.719435 Stefano...Sequential Analysis , (10 2012): 0. doi: John S. Baras, Shanshan Zheng. Sequential Anomaly Detection in Wireless Sensor Networks andEffects of Long

Soft matter: rubber and networks

NASA Astrophysics Data System (ADS)

McKenna, Gregory B.

2018-06-01

Rubber networks are important and form the basis for materials with properties ranging from rubber tires to super absorbents and contact lenses. The development of the entropy ideas of rubber deformation thermodynamics provides a powerful framework from which to understand and to use these materials. In addition, swelling of the rubber in the presence of small molecule liquids or solvents leads to materials that are very soft and ‘gel’ like in nature. The review covers the thermodynamics of polymer networks and gels from the perspective of the thermodynamics and mechanics of the strain energy density function. Important relationships are presented and experimental results show that the continuum ideas contained in the phenomenological thermodynamics are valid, but that the molecular bases for some of them remain to be fully elucidated. This is particularly so in the case of the entropic gels or swollen networks. The review is concluded with some perspectives on other networks, ranging from entropic polymer networks such as thermoplastic elastomers to physical gels in which cross-link points are formed by glassy or crystalline domains. A discussion is provided for other physical gels in which the network forms a spinodal-like decomposition, both in thermoplastic polymers that form a glassy network upon phase separation and for colloidal gels that seem to have a similar behavior.

Optimizing targeted vaccination across cyber-physical networks: an empirically based mathematical simulation study.

PubMed

Mones, Enys; Stopczynski, Arkadiusz; Pentland, Alex 'Sandy'; Hupert, Nathaniel; Lehmann, Sune

2018-01-01

Targeted vaccination, whether to minimize the forward transmission of infectious diseases or their clinical impact, is one of the 'holy grails' of modern infectious disease outbreak response, yet it is difficult to achieve in practice due to the challenge of identifying optimal targets in real time. If interruption of disease transmission is the goal, targeting requires knowledge of underlying person-to-person contact networks. Digital communication networks may reflect not only virtual but also physical interactions that could result in disease transmission, but the precise overlap between these cyber and physical networks has never been empirically explored in real-life settings. Here, we study the digital communication activity of more than 500 individuals along with their person-to-person contacts at a 5-min temporal resolution. We then simulate different disease transmission scenarios on the person-to-person physical contact network to determine whether cyber communication networks can be harnessed to advance the goal of targeted vaccination for a disease spreading on the network of physical proximity. We show that individuals selected on the basis of their closeness centrality within cyber networks (what we call 'cyber-directed vaccination') can enhance vaccination campaigns against diseases with short-range (but not full-range) modes of transmission. © 2018 The Author(s).

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

Control of Multilayer Networks

PubMed Central

Menichetti, Giulia; Dall’Asta, Luca; Bianconi, Ginestra

2016-01-01

The controllability of a network is a theoretical problem of relevance in a variety of contexts ranging from financial markets to the brain. Until now, network controllability has been characterized only on isolated networks, while the vast majority of complex systems are formed by multilayer networks. Here we build a theoretical framework for the linear controllability of multilayer networks by mapping the problem into a combinatorial matching problem. We found that correlating the external signals in the different layers can significantly reduce the multiplex network robustness to node removal, as it can be seen in conjunction with a hybrid phase transition occurring in interacting Poisson networks. Moreover we observe that multilayer networks can stabilize the fully controllable multiplex network configuration that can be stable also when the full controllability of the single network is not stable. PMID:26869210

Flash Detection Efficiencies of Long Range Lightning Detection Networks During GRIP

NASA Technical Reports Server (NTRS)

Mach, Douglas M.; Bateman, Monte G.; Blakeslee, Richard J.

2012-01-01

We flew our Lightning Instrument Package (LIP) on the NASA Global Hawk as a part of the Genesis and Rapid Intensification Processes (GRIP) field program. The GRIP program was a NASA Earth science field experiment during the months of August and September, 2010. During the program, the LIP detected lighting from 48 of the 213 of the storms overflown by the Global Hawk. The time and location of tagged LIP flashes can be used as a "ground truth" dataset for checking the detection efficiency of the various long or extended range ground-based lightning detection systems available during the GRIP program. The systems analyzed included Vaisala Long Range (LR), Vaisala GLD360, the World Wide Lightning Location Network (WWLLN), and the Earth Networks Total Lightning Network (ENTLN). The long term goal of our research is to help understand the advantages and limitations of these systems so that we can utilize them for both proxy data applications and cross sensor validation of the GOES-R Geostationary Lightning Mapper (GLM) sensor when it is launched in the 2015 timeframe.

A Mathematical Model of the Illinois Interlibrary Loan Network. Project Report Number 4.

ERIC Educational Resources Information Center

Rouse, William B.; Rouse, Sandra H.

Relatively recent developments, ranging from microfilm catalogs to networked circulation systems, have the potential of removing much of the uncertainty from the routing of interlibrary loan requests. The opportunity of purchasing location and availability information changes the performance of a model interlibrary loan network. Data collected in…

Knowledge Wisdom and Networks: A Project Management Centre of Excellence Example

ERIC Educational Resources Information Center

Walker, Derek H. T.; Christenson, Dale

2005-01-01

Purpose: This conceptual paper aims to explain how "project management centres of excellence (CoEs)", a particular class of knowledge network, can be viewed as providing great potential for assisting project management (PM) teams to make wise decisions. Design/methodology/approach: The paper presents a range of knowledge network types and…

The Curriculum Prerequisite Network: Modeling the Curriculum as a Complex System

ERIC Educational Resources Information Center

Aldrich, Preston R.

2015-01-01

This article advances the prerequisite network as a means to visualize the hidden structure in an academic curriculum. Networks have been used to represent a variety of complex systems ranging from social systems to biochemical pathways and protein interactions. Here, I treat the academic curriculum as a complex system with nodes representing…

U.S. National PM2.5 Chemical Speciation Monitoring Networks – CSN and IMPROVE: Description of Networks

EPA Science Inventory

The U.S. Environmental Protection Agency (EPA) initiated the national PM2.5 Chemical Speciation Monitoring Network (CSN) in 2000 to support evaluation of long-term trends and to better quantify the impact of sources on particulate matter (PM) concentrations in the size range belo...

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.

Human tracking over camera networks: a review

NASA Astrophysics Data System (ADS)

Hou, Li; Wan, Wanggen; Hwang, Jenq-Neng; Muhammad, Rizwan; Yang, Mingyang; Han, Kang

2017-12-01

In recent years, automated human tracking over camera networks is getting essential for video surveillance. The tasks of tracking human over camera networks are not only inherently challenging due to changing human appearance, but also have enormous potentials for a wide range of practical applications, ranging from security surveillance to retail and health care. This review paper surveys the most widely used techniques and recent advances for human tracking over camera networks. Two important functional modules for the human tracking over camera networks are addressed, including human tracking within a camera and human tracking across non-overlapping cameras. The core techniques of human tracking within a camera are discussed based on two aspects, i.e., generative trackers and discriminative trackers. The core techniques of human tracking across non-overlapping cameras are then discussed based on the aspects of human re-identification, camera-link model-based tracking and graph model-based tracking. Our survey aims to address existing problems, challenges, and future research directions based on the analyses of the current progress made toward human tracking techniques over camera networks.

Elastic properties and short-range structural order in mixed network former glasses.

PubMed

Wang, Weimin; Christensen, Randilynn; Curtis, Brittany; Hynek, David; Keizer, Sydney; Wang, James; Feller, Steve; Martin, Steve W; Kieffer, John

2017-06-21

Elastic properties of alkali containing glasses are of great interest not only because they provide information about overall structural integrity but also they are related to other properties such as thermal conductivity and ion mobility. In this study, we investigate two mixed-network former glass systems, sodium borosilicate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)SiO 2 ] and sodium borogermanate 0.2Na 2 O + 0.8[xBO 1.5 + (1 - x)GeO 2 ] glasses. By mixing network formers, the network topology can be changed while keeping the network modifier concentration constant, which allows for the effect of network structure on elastic properties to be analyzed over a wide parametric range. In addition to non-linear, non-additive mixed-glass former effects, maxima are observed in longitudinal, shear and Young's moduli with increasing atomic number density. By combining results from NMR spectroscopy and Brillouin light scattering with a newly developed statistical thermodynamic reaction equilibrium model, it is possible to determine the relative proportions of all network structural units. This new analysis reveals that the structural characteristic predominantly responsible for effective mechanical load transmission in these glasses is a high density of network cations coordinated by four or more bridging oxygens, as it provides for establishing a network of covalent bonds among these cations with connectivity in three dimensions.

Towards a comprehensive understanding of emerging dynamics and function of pancreatic islets: A complex network approach. Comment on "Network science of biological systems at different scales: A review" by Gosak et al.

NASA Astrophysics Data System (ADS)

Loppini, Alessandro

2018-03-01

Complex network theory represents a comprehensive mathematical framework to investigate biological systems, ranging from sub-cellular and cellular scales up to large-scale networks describing species interactions and ecological systems. In their exhaustive and comprehensive work [1], Gosak et al. discuss several scenarios in which the network approach was able to uncover general properties and underlying mechanisms of cells organization and regulation, tissue functions and cell/tissue failure in pathology, by the study of chemical reaction networks, structural networks and functional connectivities.

Network biology: Describing biological systems by complex networks. Comment on "Network science of biological systems at different scales: A review" by M. Gosak et al.

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2018-03-01

I enjoyed reading Gosak et al. review on analysing biological systems from network science perspective [1]. Network science, first started within Physics community, is now a mature multidisciplinary field of science with many applications ranging from Ecology to biology, medicine, social sciences, engineering and computer science. Gosak et al. discussed how biological systems can be modelled and described by complex network theory which is an important application of network science. Although there has been considerable progress in network biology over the past two decades, this is just the beginning and network science has a great deal to offer to biology and medical sciences.

Optimization-based method for automated road network extraction

DOT National Transportation Integrated Search

2001-09-18

Automated road information extraction has significant applicability in transportation. : It provides a means for creating, maintaining, and updating transportation network databases that : are needed for purposes ranging from traffic management to au...

Body Area Network BAN--a key infrastructure element for patient-centered medical applications.

PubMed

Schmidt, Robert; Norgall, Thomas; Mörsdorf, Joachim; Bernhard, Josef; von der Grün, Thomas

2002-01-01

The Body Area Network (BAN) concept enables wireless communication between several miniaturized, intelligent Body Sensor (or actor) Units (BSU) and a single Body Central Unit (BCU) worn at the human body. A separate wireless transmission link from the BCU to a network access point--using different technology--provides for online access to BAN data via usual network infrastructure. BAN is expected to become a basic infrastructure element for service-based electronic health assistance: By integrating patient-attached sensors and control of mobile dedicated actor units, the range of medical workflow can be extended by wireless patient monitoring and therapy support. Beyond clinical use, professional disease management environments, and private personal health assistance scenarios (without financial reimbursement by health agencies/insurance companies), BAN enables a wide range of health care applications and related services.

Effect of a large gaming neighborhood and a strategy adaptation neighborhood for bolstering network reciprocity in a prisoner's dilemma game

NASA Astrophysics Data System (ADS)

Ogasawara, Takashi; Tanimoto, Jun; Fukuda, Eriko; Hagishima, Aya; Ikegaya, Naoki

2014-12-01

In 2 × 2 prisoner's dilemma (PD) games, network reciprocity is one mechanism for adding social viscosity, leading to a cooperative equilibrium. In this paper, we explain how gaming neighborhoods and strategy-adaptation neighborhoods affect network reciprocity independently in spatial PD games. We explore an appropriate range of strategy adaptation neighborhoods as opposed to the conventional method of making the gaming and strategy adaptation neighborhoods coincide to enhance the level of cooperation. In cases of expanding gaming neighborhoods, network reciprocity falls to a low level relative to the conventional setting. In the discussion below, which is based on the results of our simulation, we explore how these enhancements come about. Essentially, varying the range of the neighborhoods influences how cooperative clusters form and expand in the evolutionary process.

Toughening of Thermoresponsive Arrested Networks of Elastin-Like Polypeptides To Engineer Cytocompatible Tissue Scaffolds.

PubMed

Glassman, Matthew J; Avery, Reginald K; Khademhosseini, Ali; Olsen, Bradley D

2016-02-08

Formulation of tissue engineering or regenerative scaffolds from simple bioactive polymers with tunable structure and mechanics is crucial for the regeneration of complex tissues, and hydrogels from recombinant proteins, such as elastin-like polypeptides (ELPs), are promising platforms to support these applications. The arrested phase separation of ELPs has been shown to yield remarkably stiff, biocontinuous, nanostructured networks, but these gels are limited in applications by their relatively brittle nature. Here, a gel-forming ELP is chain-extended by telechelic oxidative coupling, forming extensible, tough hydrogels. Small angle scattering indicates that the chain-extended polypeptides form a fractal network of nanoscale aggregates over a broad concentration range, accessing moduli ranging from 5 kPa to over 1 MPa over a concentration range of 5-30 wt %. These networks exhibited excellent erosion resistance and allowed for the diffusion and release of encapsulated particles consistent with a bicontinuous, porous structure with a broad distribution of pore sizes. Biofunctionalized, toughened networks were found to maintain the viability of human mesenchymal stem cells (hMSCs) in 2D, demonstrating signs of osteogenesis even in cell media without osteogenic molecules. Furthermore, chondrocytes could be readily mixed into these gels via thermoresponsive assembly and remained viable in extended culture. These studies demonstrate the ability to engineer ELP-based arrested physical networks on the molecular level to form reinforced, cytocompatible hydrogel matrices, supporting the promise of these new materials as candidates for the engineering and regeneration of stiff tissues.

De novo design of protein homo-oligomers with modular hydrogen bond network-mediated specificity

PubMed Central

Boyken, Scott E.; Chen, Zibo; Groves, Benjamin; Langan, Robert A.; Oberdorfer, Gustav; Ford, Alex; Gilmore, Jason; Xu, Chunfu; DiMaio, Frank; Pereira, Jose Henrique; Sankaran, Banumathi; Seelig, Georg; Zwart, Peter H.; Baker, David

2017-01-01

In nature, structural specificity in DNA and proteins is encoded quite differently: in DNA, specificity arises from modular hydrogen bonds in the core of the double helix, whereas in proteins, specificity arises largely from buried hydrophobic packing complemented by irregular peripheral polar interactions. Here we describe a general approach for designing a wide range of protein homo-oligomers with specificity determined by modular arrays of central hydrogen bond networks. We use the approach to design dimers, trimers, and tetramers consisting of two concentric rings of helices, including previously not seen triangular, square, and supercoiled topologies. X-ray crystallography confirms that the structures overall, and the hydrogen bond networks in particular, are nearly identical to the design models, and the networks confer interaction specificity in vivo. The ability to design extensive hydrogen bond networks with atomic accuracy is a milestone for protein design and enables the programming of protein interaction specificity for a broad range of synthetic biology applications. PMID:27151862

Learning vector quantization neural networks improve accuracy of transcranial color-coded duplex sonography in detection of middle cerebral artery spasm--preliminary report.

PubMed

Swiercz, Miroslaw; Kochanowicz, Jan; Weigele, John; Hurst, Robert; Liebeskind, David S; Mariak, Zenon; Melhem, Elias R; Krejza, Jaroslaw

2008-01-01

To determine the performance of an artificial neural network in transcranial color-coded duplex sonography (TCCS) diagnosis of middle cerebral artery (MCA) spasm. TCCS was prospectively acquired within 2 h prior to routine cerebral angiography in 100 consecutive patients (54M:46F, median age 50 years). Angiographic MCA vasospasm was classified as mild (<25% of vessel caliber reduction), moderate (25-50%), or severe (>50%). A Learning Vector Quantization neural network classified MCA spasm based on TCCS peak-systolic, mean, and end-diastolic velocity data. During a four-class discrimination task, accurate classification by the network ranged from 64.9% to 72.3%, depending on the number of neurons in the Kohonen layer. Accurate classification of vasospasm ranged from 79.6% to 87.6%, with an accuracy of 84.7% to 92.1% for the detection of moderate-to-severe vasospasm. An artificial neural network may increase the accuracy of TCCS in diagnosis of MCA spasm.

Endogenous network of firms and systemic risk

NASA Astrophysics Data System (ADS)

Ma, Qianting; He, Jianmin; Li, Shouwei

2018-02-01

We construct an endogenous network characterized by commercial credit relationships connecting the upstream and downstream firms. Simulation results indicate that the endogenous network model displays a scale-free property which exists in real-world firm systems. In terms of the network structure, with the expansion of the scale of network nodes, the systemic risk increases significantly, while the heterogeneities of network nodes have no effect on systemic risk. As for firm micro-behaviors, including the selection range of trading partners, actual output, labor requirement, price of intermediate products and employee salaries, increase of all these parameters will lead to higher systemic risk.

Gene network inference and visualization tools for biologists: application to new human transcriptome datasets

PubMed Central

Hurley, Daniel; Araki, Hiromitsu; Tamada, Yoshinori; Dunmore, Ben; Sanders, Deborah; Humphreys, Sally; Affara, Muna; Imoto, Seiya; Yasuda, Kaori; Tomiyasu, Yuki; Tashiro, Kosuke; Savoie, Christopher; Cho, Vicky; Smith, Stephen; Kuhara, Satoru; Miyano, Satoru; Charnock-Jones, D. Stephen; Crampin, Edmund J.; Print, Cristin G.

2012-01-01

Gene regulatory networks inferred from RNA abundance data have generated significant interest, but despite this, gene network approaches are used infrequently and often require input from bioinformaticians. We have assembled a suite of tools for analysing regulatory networks, and we illustrate their use with microarray datasets generated in human endothelial cells. We infer a range of regulatory networks, and based on this analysis discuss the strengths and limitations of network inference from RNA abundance data. We welcome contact from researchers interested in using our inference and visualization tools to answer biological questions. PMID:22121215

C4I Community of Interest C2 Roadmap

DTIC Science & Technology

2015-03-24

QoS -based services – Digital policy-based prioritization – Dynamic bandwidth allocation – Automated network management April 15 Slide 9...Co-Site Mitigation) NC-3 • LPD/LPI Comms NC-4 • Increased Range NC-7 • Increased Loss Tolerance & Recovery NC-7 • Mobile Ad Hoc Networking NC-8...Algorithms and Software • Systems and Processes Networks and Communications • Radios and Apertures • Networks • Information April 15 Slide 8

An Artificial Immune System-Inspired Multiobjective Evolutionary Algorithm with Application to the Detection of Distributed Computer Network Intrusions

DTIC Science & Technology

2007-03-01

Intelligence AIS Artificial Immune System ANN Artificial Neural Networks API Application Programming Interface BFS Breadth-First Search BIS Biological...problem domain is too large for only one algorithm’s application . It ranges from network - based sniffer systems, responsible for Enterprise-wide coverage...options to network administrators in choosing detectors to employ in future ID applications . Objectives Our hypothesis validity is based on a set

Implicitly Defined Neural Networks for Sequence Labeling

DTIC Science & Technology

2017-07-31

network are coupled together, in order to improve perfor- mance on complex, long-range dependencies in either direction of a sequence. We contrast our...struc- ture. 1.1 Related Work Long-range dependencies have been an issue as long as there have been NLP tasks, and there are many ef- fective approaches...retain informa- tion about dependencies . The Bidirectional LSTM (b- LSTM) (Graves and Schmidhuber, 2005), a natural ex- tension of (Schuster and Paliwal

Estimating order statistics of network degrees

NASA Astrophysics Data System (ADS)

Chu, J.; Nadarajah, S.

2018-01-01

We model the order statistics of network degrees of big data sets by a range of generalised beta distributions. A three parameter beta distribution due to Libby and Novick (1982) is shown to give the best overall fit for at least four big data sets. The fit of this distribution is significantly better than the fit suggested by Olhede and Wolfe (2012) across the whole range of order statistics for all four data sets.

Evaluation of Long-Range Lightning Detection Networks Using TRMM/LIS Observations

NASA Technical Reports Server (NTRS)

Rudlosky, Scott D.; Holzworth, Robert H.; Carey, Lawrence D.; Schultz, Chris J.; Bateman, Monte; Cecil, Daniel J.; Cummins, Kenneth L.; Petersen, Walter A.; Blakeslee, Richard J.; Goodman, Steven J.

2011-01-01

Recent advances in long-range lightning detection technologies have improved our understanding of thunderstorm evolution in the data sparse oceanic regions. Although the expansion and improvement of long-range lightning datasets have increased their applicability, these applications (e.g., data assimilation, atmospheric chemistry, and aviation weather hazards) require knowledge of the network detection capabilities. Toward this end, the present study evaluates data from the World Wide Lightning Location Network (WWLLN) using observations from the Lightning Imaging Sensor (LIS) aboard the Tropical Rainfall Measurement Mission (TRMM) satellite. The study documents the WWLLN detection efficiency and location accuracy relative to LIS observations, describes the spatial variability in these performance metrics, and documents the characteristics of LIS flashes that are detected by WWLLN. Improved knowledge of the WWLLN detection capabilities will allow researchers, algorithm developers, and operational users to better prepare for the spatial and temporal coverage of the upcoming GOES-R Geostationary Lightning Mapper (GLM).

Latency-Efficient Communication in Wireless Mesh Networks under Consideration of Large Interference Range

NASA Astrophysics Data System (ADS)

Xin, Qin; Yao, Xiaolan; Engelstad, Paal E.

2010-09-01

Wireless Mesh Networking is an emerging communication paradigm to enable resilient, cost-efficient and reliable services for the future-generation wireless networks. We study here the minimum-latency communication primitive of gossiping (all-to-all communication) in multi-hop ad-hoc Wireless Mesh Networks (WMNs). Each mesh node in the WMN is initially given a message and the objective is to design a minimum-latency schedule such that each mesh node distributes its message to all other mesh nodes. Minimum-latency gossiping problem is well known to be NP-hard even for the scenario in which the topology of the WMN is known to all mesh nodes in advance. In this paper, we propose a new latency-efficient approximation scheme that can accomplish gossiping task in polynomial time units in any ad-hoc WMN under consideration of Large Interference Range (LIR), e.g., the interference range is much larger than the transmission range. To the best of our knowledge, it is first time to investigate such a scenario in ad-hoc WMNs under LIR, our algorithm allows the labels (e.g., identifiers) of the mesh nodes to be polynomially large in terms of the size of the WMN, which is the first time that the scenario of large labels has been considered in ad-hoc WMNs under LIR. Furthermore, our gossiping scheme can be considered as a framework which can be easily implied to the scenario under consideration of mobility-related issues since we assume that the mesh nodes have no knowledge on the network topology even for its neighboring mesh nodes.

Understanding auditory distance estimation by humpback whales: a computational approach.

PubMed

Mercado, E; Green, S R; Schneider, J N

2008-02-01

Ranging, the ability to judge the distance to a sound source, depends on the presence of predictable patterns of attenuation. We measured long-range sound propagation in coastal waters to assess whether humpback whales might use frequency degradation cues to range singing whales. Two types of neural networks, a multi-layer and a single-layer perceptron, were trained to classify recorded sounds by distance traveled based on their frequency content. The multi-layer network successfully classified received sounds, demonstrating that the distorting effects of underwater propagation on frequency content provide sufficient cues to estimate source distance. Normalizing received sounds with respect to ambient noise levels increased the accuracy of distance estimates by single-layer perceptrons, indicating that familiarity with background noise can potentially improve a listening whale's ability to range. To assess whether frequency patterns predictive of source distance were likely to be perceived by whales, recordings were pre-processed using a computational model of the humpback whale's peripheral auditory system. Although signals processed with this model contained less information than the original recordings, neural networks trained with these physiologically based representations estimated source distance more accurately, suggesting that listening whales should be able to range singers using distance-dependent changes in frequency content.

Educational Broadcasts of NHK. Special Issue of NHK Today and Tomorrow.

ERIC Educational Resources Information Center

Japan Broadcasting Co., Tokyo

An overview of the full range of educational broadcasts offered by Nippon Hoso Kyokai (NHK) is presented. Nippon Hoso Kyokai, which translates to English as Japan Broadcasting Company, is the only public service broadcasting organization in Japan; it operates two AM radio networks, one FM network, and two television networks and is completely…

Frogs, fish and forestry: An integrated watershed network paradigm conserves biodiversity and ecological services

Treesearch

Hartwell H. Welsh Jr.

2011-01-01

Successfully addressing the multitude of stresses influencing forest catchments, their native biota, and the vital ecological services they provide humanity will require adapting an integrated view that incorporates the full range of natural and anthropogenic disturbances acting on these landscapes and their embedded fluvial networks. The concepts of dendritic networks...

Normal modes of weak colloidal gels

NASA Astrophysics Data System (ADS)

Varga, Zsigmond; Swan, James W.

2018-01-01

The normal modes and relaxation rates of weak colloidal gels are investigated in calculations using different models of the hydrodynamic interactions between suspended particles. The relaxation spectrum is computed for freely draining, Rotne-Prager-Yamakawa, and accelerated Stokesian dynamics approximations of the hydrodynamic mobility in a normal mode analysis of a harmonic network representing several colloidal gels. We find that the density of states and spatial structure of the normal modes are fundamentally altered by long-ranged hydrodynamic coupling among the particles. Short-ranged coupling due to hydrodynamic lubrication affects only the relaxation rates of short-wavelength modes. Hydrodynamic models accounting for long-ranged coupling exhibit a microscopic relaxation rate for each normal mode, λ that scales as l-2, where l is the spatial correlation length of the normal mode. For the freely draining approximation, which neglects long-ranged coupling, the microscopic relaxation rate scales as l-γ, where γ varies between three and two with increasing particle volume fraction. A simple phenomenological model of the internal elastic response to normal mode fluctuations is developed, which shows that long-ranged hydrodynamic interactions play a central role in the viscoelasticity of the gel network. Dynamic simulations of hard spheres that gel in response to short-ranged depletion attractions are used to test the applicability of the density of states predictions. For particle concentrations up to 30% by volume, the power law decay of the relaxation modulus in simulations accounting for long-ranged hydrodynamic interactions agrees with predictions generated by the density of states of the corresponding harmonic networks as well as experimental measurements. For higher volume fractions, excluded volume interactions dominate the stress response, and the prediction from the harmonic network density of states fails. Analogous to the Zimm model in polymer physics, our results indicate that long-ranged hydrodynamic interactions play a crucial role in determining the microscopic dynamics and macroscopic properties of weak colloidal gels.

Towards a global quantum network

NASA Astrophysics Data System (ADS)

Simon, Christoph

2017-11-01

The creation of a global quantum network is now a realistic proposition thanks to developments in satellite and fibre links and quantum memory. Applications will range from secure communication and fundamental physics experiments to a future quantum internet.

Revealing networks from dynamics: an introduction

NASA Astrophysics Data System (ADS)

Timme, Marc; Casadiego, Jose

2014-08-01

What can we learn from the collective dynamics of a complex network about its interaction topology? Taking the perspective from nonlinear dynamics, we briefly review recent progress on how to infer structural connectivity (direct interactions) from accessing the dynamics of the units. Potential applications range from interaction networks in physics, to chemical and metabolic reactions, protein and gene regulatory networks as well as neural circuits in biology and electric power grids or wireless sensor networks in engineering. Moreover, we briefly mention some standard ways of inferring effective or functional connectivity.

Zinc oxide nanowire networks for macroelectronic devices

NASA Astrophysics Data System (ADS)

Unalan, Husnu Emrah; Zhang, Yan; Hiralal, Pritesh; Dalal, Sharvari; Chu, Daping; Eda, Goki; Teo, K. B. K.; Chhowalla, Manish; Milne, William I.; Amaratunga, Gehan A. J.

2009-04-01

Highly transparent zinc oxide (ZnO) nanowire networks have been used as the active material in thin film transistors (TFTs) and complementary inverter devices. A systematic study on a range of networks of variable density and TFT channel length was performed. ZnO nanowire networks provide a less lithographically intense alternative to individual nanowire devices, are always semiconducting, and yield significantly higher mobilites than those achieved from currently used amorphous Si and organic TFTs. These results suggest that ZnO nanowire networks could be ideal for inexpensive large area electronics.

Attacks on public telephone networks: technologies and challenges

NASA Astrophysics Data System (ADS)

Kosloff, T.; Moore, Tyler; Keller, J.; Manes, Gavin W.; Shenoi, Sujeet

2003-09-01

Signaling System 7 (SS7) is vital to signaling and control in America's public telephone networks. This paper describes a class of attacks on SS7 networks involving the insertion of malicious signaling messages via compromised SS7 network components. Three attacks are discussed in detail: IAM flood attacks, redirection attacks and point code spoofing attacks. Depending on their scale of execution, these attacks can produce effects ranging from network congestion to service disruption. Methods for detecting these denial-of-service attacks and mitigating their effects are also presented.

Network analysis of swine shipments in Ontario, Canada, to support disease spread modelling and risk-based disease management.

PubMed

Dorjee, S; Revie, C W; Poljak, Z; McNab, W B; Sanchez, J

2013-10-01

Understanding contact networks are important for modelling and managing the spread and control of communicable diseases in populations. This study characterizes the swine shipment network of a multi-site production system in southwestern Ontario, Canada. Data were extracted from a company's database listing swine shipments among 251 swine farms, including 20 sow, 69 nursery and 162 finishing farms, for the 2-year period of 2006 to 2007. Several network metrics were generated. The number of shipments per week between pairs of farms ranged from 1 to 6. The medians (and ranges) of out-degree were: sow 6 (1-21), nursery 8 (0-25), and finishing 0 (0-4), over the entire 2-year study period. Corresponding estimates for in-degree of nursery and finishing farms were 3 (0-9) and 3 (0-12) respectively. Outgoing and incoming infection chains (OIC and IIC), were also measured. The medians (ranges) of the monthly OIC and IIC were 0 (0-8) and 0 (0-6), respectively, with very similar measures observed for 2-week intervals. Nursery farms exhibited high measures of centrality. This indicates that they pose greater risks of disease spread in the network. Therefore, they should be given a high priority for disease prevention and control measures affecting all age groups alike. The network demonstrated scale-free and small-world topologies as observed in other livestock shipment studies. This heterogeneity in contacts among farm types and network topologies should be incorporated in simulation models to improve their validity. In conclusion, this study provided useful epidemiological information and parameters for the control and modelling of disease spread among swine farms, for the first time from Ontario, Canada. Copyright © 2013 Elsevier B.V. All rights reserved.

Designing optimized multi-species monitoring networks to detect range shifts driven by climate change: a case study with bats in the North of Portugal.

PubMed

Amorim, Francisco; Carvalho, Sílvia B; Honrado, João; Rebelo, Hugo

2014-01-01

Here we develop a framework to design multi-species monitoring networks using species distribution models and conservation planning tools to optimize the location of monitoring stations to detect potential range shifts driven by climate change. For this study, we focused on seven bat species in Northern Portugal (Western Europe). Maximum entropy modelling was used to predict the likely occurrence of those species under present and future climatic conditions. By comparing present and future predicted distributions, we identified areas where each species is likely to gain, lose or maintain suitable climatic space. We then used a decision support tool (the Marxan software) to design three optimized monitoring networks considering: a) changes in species likely occurrence, b) species conservation status, and c) level of volunteer commitment. For present climatic conditions, species distribution models revealed that areas suitable for most species occur in the north-eastern part of the region. However, areas predicted to become climatically suitable in the future shifted towards west. The three simulated monitoring networks, adaptable for an unpredictable volunteer commitment, included 28, 54 and 110 sampling locations respectively, distributed across the study area and covering the potential full range of conditions where species range shifts may occur. Our results show that our framework outperforms the traditional approach that only considers current species ranges, in allocating monitoring stations distributed across different categories of predicted shifts in species distributions. This study presents a straightforward framework to design monitoring schemes aimed specifically at testing hypotheses about where and when species ranges may shift with climatic changes, while also ensuring surveillance of general population trends.

Network rewiring dynamics with convergence towards a star network

PubMed Central

Dick, G.; Parry, M.

2016-01-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz (Nature 393, 440–442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach. PMID:27843396

Network rewiring dynamics with convergence towards a star network.

PubMed

Whigham, P A; Dick, G; Parry, M

2016-10-01

Network rewiring as a method for producing a range of structures was first introduced in 1998 by Watts & Strogatz ( Nature 393 , 440-442. (doi:10.1038/30918)). This approach allowed a transition from regular through small-world to a random network. The subsequent interest in scale-free networks motivated a number of methods for developing rewiring approaches that converged to scale-free networks. This paper presents a rewiring algorithm (RtoS) for undirected, non-degenerate, fixed size networks that transitions from regular, through small-world and scale-free to star-like networks. Applications of the approach to models for the spread of infectious disease and fixation time for a simple genetics model are used to demonstrate the efficacy and application of the approach.

Internet protocol network mapper

DOEpatents

Youd, David W.; Colon III, Domingo R.; Seidl, Edward T.

2016-02-23

A network mapper for performing tasks on targets is provided. The mapper generates a map of a network that specifies the overall configuration of the network. The mapper inputs a procedure that defines how the network is to be mapped. The procedure specifies what, when, and in what order the tasks are to be performed. Each task specifies processing that is to be performed for a target to produce results. The procedure may also specify input parameters for a task. The mapper inputs initial targets that specify a range of network addresses to be mapped. The mapper maps the network by, for each target, executing the procedure to perform the tasks on the target. The results of the tasks represent the mapping of the network defined by the initial targets.

Bluetooth Low Energy Mesh Networks: A Survey.

PubMed

Darroudi, Seyed Mahdi; Gomez, Carles

2017-06-22

Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology. For these reasons, academia, industry, and standards development organizations have been designing solutions to enable BLE mesh networks. Nevertheless, the literature lacks a consolidated view on this emerging area. This paper comprehensively surveys state of the art BLE mesh networking. We first provide a taxonomy of BLE mesh network solutions. We then review the solutions, describing the variety of approaches that leverage existing BLE functionality to enable BLE mesh networks. We identify crucial aspects of BLE mesh network solutions and discuss their advantages and drawbacks. Finally, we highlight currently open issues.

Automating Mid- and Long-Range Scheduling for the NASA Deep Space Network

NASA Technical Reports Server (NTRS)

Johnston, Mark D.; Tran, Daniel

2012-01-01

NASA has recently deployed a new mid-range scheduling system for the antennas of the Deep Space Network (DSN), called Service Scheduling Software, or S(sup 3). This system was designed and deployed as a modern web application containing a central scheduling database integrated with a collaborative environment, exploiting the same technologies as social web applications but applied to a space operations context. This is highly relevant to the DSN domain since the network schedule of operations is developed in a peer-to-peer negotiation process among all users of the DSN. These users represent not only NASA's deep space missions, but also international partners and ground-based science and calibration users. The initial implementation of S(sup 3) is complete and the system has been operational since July 2011. This paper describes some key aspects of the S(sup 3) system and on the challenges of modeling complex scheduling requirements and the ongoing extension of S(sup 3) to encompass long-range planning, downtime analysis, and forecasting, as the next step in developing a single integrated DSN scheduling tool suite to cover all time ranges.

Self-assembled tunable networks of sticky colloidal particles

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

Demortiere, Arnaud; Snezhko, Oleksiy Alexey; Sapozhnikov, Maksim

Self-assembled tunable networks of microscopic polymer fibers ranging from wavy colloidal "fur" to highly interconnected networks are created from polymer systems and an applied electric field. The networks emerge via dynamic self-assembly in an alternating (ac) electric field from a non-aqueous suspension of "sticky" polymeric colloidal particles with a controlled degree of polymerization. The resulting architectures are tuned by the frequency and amplitude of the electric field and surface properties of the particles.

Biological mechanisms beyond network analysis via mathematical modeling. Comment on "Network science of biological systems at different scales: A review" by Marko Gosak et al.

NASA Astrophysics Data System (ADS)

Pedersen, Morten Gram

2018-03-01

Methods from network theory are increasingly used in research spanning from engineering and computer science to psychology and the social sciences. In this issue, Gosak et al. [1] provide a thorough review of network science applications to biological systems ranging from the subcellular world via neuroscience to ecosystems, with special attention to the insulin-secreting beta-cells in pancreatic islets.

Fluctuations of Attentional Networks and Default Mode Network during the Resting State Reflect Variations in Cognitive States: Evidence from a Novel Resting-state Experience Sampling Method.

PubMed

Van Calster, Laurens; D'Argembeau, Arnaud; Salmon, Eric; Peters, Frédéric; Majerus, Steve

2017-01-01

Neuroimaging studies have revealed the recruitment of a range of neural networks during the resting state, which might reflect a variety of cognitive experiences and processes occurring in an individual's mind. In this study, we focused on the default mode network (DMN) and attentional networks and investigated their association with distinct mental states when participants are not performing an explicit task. To investigate the range of possible cognitive experiences more directly, this study proposes a novel method of resting-state fMRI experience sampling, informed by a phenomenological investigation of the fluctuation of mental states during the resting state. We hypothesized that DMN activity would increase as a function of internal mentation and that the activity of dorsal and ventral networks would indicate states of top-down versus bottom-up attention at rest. Results showed that dorsal attention network activity fluctuated as a function of subjective reports of attentional control, providing evidence that activity of this network reflects the perceived recruitment of controlled attentional processes during spontaneous cognition. Activity of the DMN increased when participants reported to be in a subjective state of internal mentation, but not when they reported to be in a state of perception. This study provides direct evidence for a link between fluctuations of resting-state neural activity and fluctuations in specific cognitive processes.

Flow control using audio tones in resonant microfluidic networks: towards cell-phone controlled lab-on-a-chip devices.

PubMed

Phillips, Reid H; Jain, Rahil; Browning, Yoni; Shah, Rachana; Kauffman, Peter; Dinh, Doan; Lutz, Barry R

2016-08-16

Fluid control remains a challenge in development of portable lab-on-a-chip devices. Here, we show that microfluidic networks driven by single-frequency audio tones create resonant oscillating flow that is predicted by equivalent electrical circuit models. We fabricated microfluidic devices with fluidic resistors (R), inductors (L), and capacitors (C) to create RLC networks with band-pass resonance in the audible frequency range available on portable audio devices. Microfluidic devices were fabricated from laser-cut adhesive plastic, and a "buzzer" was glued to a diaphragm (capacitor) to integrate the actuator on the device. The AC flowrate magnitude was measured by imaging oscillation of bead tracers to allow direct comparison to the RLC circuit model across the frequency range. We present a systematic build-up from single-channel systems to multi-channel (3-channel) networks, and show that RLC circuit models predict complex frequency-dependent interactions within multi-channel networks. Finally, we show that adding flow rectifying valves to the network creates pumps that can be driven by amplified and non-amplified audio tones from common audio devices (iPod and iPhone). This work shows that RLC circuit models predict resonant flow responses in multi-channel fluidic networks as a step towards microfluidic devices controlled by audio tones.

Advanced functional network analysis in the geosciences: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Runge, Jakob; Schultz, Hanna C. H.; Wiedermann, Marc; Zech, Alraune; Feldhoff, Jan; Rheinwalt, Aljoscha; Kutza, Hannes; Radebach, Alexander; Marwan, Norbert; Kurths, Jürgen

2013-04-01

Functional networks are a powerful tool for analyzing large geoscientific datasets such as global fields of climate time series originating from observations or model simulations. pyunicorn (pythonic unified complex network and recurrence analysis toolbox) is an open-source, fully object-oriented and easily parallelizable package written in the language Python. It allows for constructing functional networks (aka climate networks) representing the structure of statistical interrelationships in large datasets and, subsequently, investigating this structure using advanced methods of complex network theory such as measures for networks of interacting networks, node-weighted statistics or network surrogates. Additionally, pyunicorn allows to study the complex dynamics of geoscientific systems as recorded by time series by means of recurrence networks and visibility graphs. The range of possible applications of the package is outlined drawing on several examples from climatology.

Annual Research Review: Growth connectomics – the organization and reorganization of brain networks during normal and abnormal development

PubMed Central

Vértes, Petra E; Bullmore, Edward T

2015-01-01

Background We first give a brief introduction to graph theoretical analysis and its application to the study of brain network topology or connectomics. Within this framework, we review the existing empirical data on developmental changes in brain network organization across a range of experimental modalities (including structural and functional MRI, diffusion tensor imaging, magnetoencephalography and electroencephalography in humans). Synthesis We discuss preliminary evidence and current hypotheses for how the emergence of network properties correlates with concomitant cognitive and behavioural changes associated with development. We highlight some of the technical and conceptual challenges to be addressed by future developments in this rapidly moving field. Given the parallels previously discovered between neural systems across species and over a range of spatial scales, we also review some recent advances in developmental network studies at the cellular scale. We highlight the opportunities presented by such studies and how they may complement neuroimaging in advancing our understanding of brain development. Finally, we note that many brain and mind disorders are thought to be neurodevelopmental in origin and that charting the trajectory of brain network changes associated with healthy development also sets the stage for understanding abnormal network development. Conclusions We therefore briefly review the clinical relevance of network metrics as potential diagnostic markers and some recent efforts in computational modelling of brain networks which might contribute to a more mechanistic understanding of neurodevelopmental disorders in future. PMID:25441756

Implanted neural network potentials: Application to Li-Si alloys

NASA Astrophysics Data System (ADS)

Onat, Berk; Cubuk, Ekin D.; Malone, Brad D.; Kaxiras, Efthimios

2018-03-01

Modeling the behavior of materials composed of elements with different bonding and electronic structure character for large spatial and temporal scales and over a large compositional range is a challenging problem. Cases in point are amorphous alloys of Si, a prototypical covalent material, and Li, a prototypical metal, which are being considered as anodes for high-energy-density batteries. To address this challenge, we develop a methodology based on neural networks that extends the conventional training approach to incorporate pre-trained parts that capture the character of different components, into the overall network; we refer to this model as the "implanted neural network" method. We show that this approach works well for the Si-Li amorphous alloys for a wide range of compositions, giving good results for key quantities like the diffusion coefficients. The method is readily generalizable to more complicated situations that involve two or more different elements.

High Efficiency, Transparent, Reusable, and Active PM2.5 Filters by Hierarchical Ag Nanowire Percolation Network.

PubMed

Jeong, Seongmin; Cho, Hyunmin; Han, Seonggeun; Won, Phillip; Lee, Habeom; Hong, Sukjoon; Yeo, Junyeob; Kwon, Jinhyeong; Ko, Seung Hwan

2017-07-12

Air quality has become a major public health issue in Asia including China, Korea, and India. Particulate matters are the major concern in air quality. We present the first environmental application demonstration of Ag nanowire percolation network for a novel, electrical type transparent, reusable, and active PM2.5 air filter although the Ag nanowire percolation network has been studied as a very promising transparent conductor in optoelectronics. Compared with previous particulate matter air filter study using relatively weaker short-range intermolecular force in polar polymeric nanofiber, Ag nanowire percolation network filters use stronger long-range electrostatic force to capture PM2.5, and they are highly efficient (>99.99%), transparent, working on an active mode, low power consumption, antibacterial, and reusable after simple washing. The proposed new particulate matter filter can be applied for a highly efficient, reusable, active and energy efficient filter for wearable electronics application.

Epidemic dynamics and endemic states in complex networks

NASA Astrophysics Data System (ADS)

Pastor-Satorras, Romualdo; Vespignani, Alessandro

2001-06-01

We study by analytical methods and large scale simulations a dynamical model for the spreading of epidemics in complex networks. In networks with exponentially bounded connectivity we recover the usual epidemic behavior with a threshold defining a critical point below that the infection prevalence is null. On the contrary, on a wide range of scale-free networks we observe the absence of an epidemic threshold and its associated critical behavior. This implies that scale-free networks are prone to the spreading and the persistence of infections whatever spreading rate the epidemic agents might possess. These results can help understanding computer virus epidemics and other spreading phenomena on communication and social networks.

Jimena: efficient computing and system state identification for genetic regulatory networks.

PubMed

Karl, Stefan; Dandekar, Thomas

2013-10-11

Boolean networks capture switching behavior of many naturally occurring regulatory networks. For semi-quantitative modeling, interpolation between ON and OFF states is necessary. The high degree polynomial interpolation of Boolean genetic regulatory networks (GRNs) in cellular processes such as apoptosis or proliferation allows for the modeling of a wider range of node interactions than continuous activator-inhibitor models, but suffers from scaling problems for networks which contain nodes with more than ~10 inputs. Many GRNs from literature or new gene expression experiments exceed those limitations and a new approach was developed. (i) As a part of our new GRN simulation framework Jimena we introduce and setup Boolean-tree-based data structures; (ii) corresponding algorithms greatly expedite the calculation of the polynomial interpolation in almost all cases, thereby expanding the range of networks which can be simulated by this model in reasonable time. (iii) Stable states for discrete models are efficiently counted and identified using binary decision diagrams. As application example, we show how system states can now be sampled efficiently in small up to large scale hormone disease networks (Arabidopsis thaliana development and immunity, pathogen Pseudomonas syringae and modulation by cytokinins and plant hormones). Jimena simulates currently available GRNs about 10-100 times faster than the previous implementation of the polynomial interpolation model and even greater gains are achieved for large scale-free networks. This speed-up also facilitates a much more thorough sampling of continuous state spaces which may lead to the identification of new stable states. Mutants of large networks can be constructed and analyzed very quickly enabling new insights into network robustness and behavior.

Epidemic spreading in networks with nonrandom long-range interactions

NASA Astrophysics Data System (ADS)

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An “infection,” understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both “close” contacts and “casual” encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called “conductance” controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

Epidemic spreading in networks with nonrandom long-range interactions.

PubMed

Estrada, Ernesto; Kalala-Mutombo, Franck; Valverde-Colmeiro, Alba

2011-09-01

An "infection," understood here in a very broad sense, can be propagated through the network of social contacts among individuals. These social contacts include both "close" contacts and "casual" encounters among individuals in transport, leisure, shopping, etc. Knowing the first through the study of the social networks is not a difficult task, but having a clear picture of the network of casual contacts is a very hard problem in a society of increasing mobility. Here we assume, on the basis of several pieces of empirical evidence, that the casual contacts between two individuals are a function of their social distance in the network of close contacts. Then, we assume that we know the network of close contacts and infer the casual encounters by means of nonrandom long-range (LR) interactions determined by the social proximity of the two individuals. This approach is then implemented in a susceptible-infected-susceptible (SIS) model accounting for the spread of infections in complex networks. A parameter called "conductance" controls the feasibility of those casual encounters. In a zero conductance network only contagion through close contacts is allowed. As the conductance increases the probability of having casual encounters also increases. We show here that as the conductance parameter increases, the rate of propagation increases dramatically and the infection is less likely to die out. This increment is particularly marked in networks with scale-free degree distributions, where infections easily become epidemics. Our model provides a general framework for studying epidemic spreading in networks with arbitrary topology with and without casual contacts accounted for by means of LR interactions.

The New Generation Russian VLBI Network

NASA Technical Reports Server (NTRS)

Finkelstein, Andrey; Ipatov, Alexander; Smolentsev, Sergey; Mardyshkin, Vyacheslav; Fedotov, Leonid; Surkis, Igor; Ivanov, Dmitrij; Gayazov, Iskander

2010-01-01

This paper deals with a new project of the Russian VLBI Network dedicated for Universal Time determinations in quasi on-line mode. The basic principles of the network design and location of antennas are explained. Variants of constructing receiving devices, digital data acquisition system, and phase calibration system are specially considered. The frequency ranges and expected values of noise temperature are given.

Quality in Family Child Care Networks: An Evaluation of All Our Kin Provider Quality

ERIC Educational Resources Information Center

Porter, Toni; Reiman, Kayla; Nelson, Christina; Sager, Jessica; Wagner, Janna

2016-01-01

This article presents findings from a quasi-experimental evaluation of quality with a sample of 28 family child care providers in the All Our Kin Family Child Care Network, a staffed family child care network which offers a range of services including relationship-based intensive consultation, and 20 family child care providers who had no…

Small diameter symmetric networks from linear groups

NASA Technical Reports Server (NTRS)

Campbell, Lowell; Carlsson, Gunnar E.; Dinneen, Michael J.; Faber, Vance; Fellows, Michael R.; Langston, Michael A.; Moore, James W.; Multihaupt, Andrew P.; Sexton, Harlan B.

1992-01-01

In this note is reported a collection of constructions of symmetric networks that provide the largest known values for the number of nodes that can be placed in a network of a given degree and diameter. Some of the constructions are in the range of current potential engineering significance. The constructions are Cayley graphs of linear groups obtained by experimental computation.

Multimedia Network Design Study

DTIC Science & Technology

1989-09-30

manipulation and analysis of the equations involved, thereby providing the application of the great range of powerful mathematical optimization...be treated by this analysis. First, all arrivals to the network have the Poisson distribution, and separate traffic classes may have separate qrrival...different for open and closed networks, so these two situations will be treated separately in the following subsections. 2.3.1 The Computational Process in

Reaching Rural Schools Using an Interactive Satellite Based Educational Network: Evaluating TI-IN Network's First Year.

ERIC Educational Resources Information Center

Pease, Pamela S.; Tinsley, Patsy J.

The paper details development, implementation, and user research/evaluation of TI-IN Network, Inc., the first private, interactive satellite based educational system in the United States developed for public schools and offering a total systems approach by providing both user technology and a wide range of course offerings. An overview of specific…

Learning and innovative elements of strategy adoption rules expand cooperative network topologies.

PubMed

Wang, Shijun; Szalay, Máté S; Zhang, Changshui; Csermely, Peter

2008-04-09

Cooperation plays a key role in the evolution of complex systems. However, the level of cooperation extensively varies with the topology of agent networks in the widely used models of repeated games. Here we show that cooperation remains rather stable by applying the reinforcement learning strategy adoption rule, Q-learning on a variety of random, regular, small-word, scale-free and modular network models in repeated, multi-agent Prisoner's Dilemma and Hawk-Dove games. Furthermore, we found that using the above model systems other long-term learning strategy adoption rules also promote cooperation, while introducing a low level of noise (as a model of innovation) to the strategy adoption rules makes the level of cooperation less dependent on the actual network topology. Our results demonstrate that long-term learning and random elements in the strategy adoption rules, when acting together, extend the range of network topologies enabling the development of cooperation at a wider range of costs and temptations. These results suggest that a balanced duo of learning and innovation may help to preserve cooperation during the re-organization of real-world networks, and may play a prominent role in the evolution of self-organizing, complex systems.

Stuck in default mode: inefficient cross-frequency synchronization may lead to age-related short-term memory decline.

PubMed

Pinal, Diego; Zurrón, Montserrat; Díaz, Fernando; Sauseng, Paul

2015-04-01

Aging-related decline in short-term memory capacity seems to be caused by deficient balancing of task-related and resting state brain networks activity; however, the exact neural mechanism underlying this deficit remains elusive. Here, we studied brain oscillatory activity in healthy young and old adults during visual information maintenance in a delayed match-to-sample task. Particular emphasis was on long range phase:amplitude coupling of frontal alpha (8-12 Hz) and posterior fast oscillatory activity (>30 Hz). It is argued that through posterior fast oscillatory activity nesting into the excitatory or the inhibitory phase of frontal alpha wave, long-range networks can be efficiently coupled or decoupled, respectively. On the basis of this mechanism, we show that healthy, elderly participants exhibit a lack of synchronization in task-relevant networks while maintaining synchronized regions of the resting state network. Lacking disconnection of this resting state network is predictive of aging-related short-term memory decline. These results support the idea of inefficient orchestration of competing brain networks in the aging human brain and identify the neural mechanism responsible for this control breakdown. Copyright © 2015 Elsevier Inc. All rights reserved.

Resistance to alveolar shape change limits range of force propagation in lung parenchyma.

PubMed

Ma, Baoshun; Smith, Bradford J; Bates, Jason H T

2015-06-01

We have recently shown that if the lung parenchyma is modeled in 2 dimensions as a network of springs arranged in a pattern of repeating hexagonal cells, the distortional forces around a contracting airway propagate much further from the airway wall than classic continuum theory predicts. In the present study we tested the hypothesis that this occurs because of the negligible shear modulus of a hexagonal spring network. We simulated the narrowing of an airway embedded in a hexagonal network of elastic alveolar walls when the hexagonal cells of the network offered some resistance to a change in shape. We found that as the forces resisting shape change approach about 10% of the forces resisting length change of an individual spring the range of distortional force propagation in the spring network fell of rapidly as in an elastic continuum. We repeated these investigations in a 3-dimensional spring network composed of space-filling polyhedral cells and found similar results. This suggests that force propagation away from a point of local parenchymal distortion also falls off rapidly in real lung tissue. Copyright © 2015 Elsevier B.V. All rights reserved.

Energy loss, range, and bremsstrahlung yield for 10-keV to 100-MeV electrons in various elements and chemical compounds

NASA Astrophysics Data System (ADS)

Pages, Lucien; Bertel, Evelyne; Joffre, Henri; Sklavenitis, Laodamas

2012-12-01

Even though the United States lacks a national climate policy, significant action has occurred at the local and regional levels. Some of the most aggressive climate change policies have occurred at the state and local levels and in interagency cooperation on specific management issues. While there is a long history of partnerships in dealing with a wide variety of policy issues, the uncertainty and the political debate surrounding climate change has generated new challenges to establishing effective policy networks. This paper investigates the formation of climate policy networks in the State of Nevada. It presents a methodology based on social network analysis for assessing the structure and function of local policy networks across a range of substantive climate impacted resources (water, landscape management, conservation, forestry and others). It draws from an emerging literature on federalism and climate policy, public sector innovation, and institutional analysis in socio-ecological systems. Comparisons across different policy issue networks in the state are used to highlight the influence of network structure, connectivity, bridging across vertical and horizontal organizational units, organizational diversity, and flows between organizational nodes.

Range Scheduling Aid (RSA)

NASA Technical Reports Server (NTRS)

Logan, J. R.; Pulvermacher, M. K.

1991-01-01

Range Scheduling Aid (RSA) is presented in the form of the viewgraphs. The following subject areas are covered: satellite control network; current and new approaches to range scheduling; MITRE tasking; RSA features; RSA display; constraint based analytic capability; RSA architecture; and RSA benefits.

A transmission power optimization with a minimum node degree for energy-efficient wireless sensor networks with full-reachability.

PubMed

Chen, Yi-Ting; Horng, Mong-Fong; Lo, Chih-Cheng; Chu, Shu-Chuan; Pan, Jeng-Shyang; Liao, Bin-Yih

2013-03-20

Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments.

A Transmission Power Optimization with a Minimum Node Degree for Energy-Efficient Wireless Sensor Networks with Full-Reachability

PubMed Central

Chen, Yi-Ting; Horng, Mong-Fong; Lo, Chih-Cheng; Chu, Shu-Chuan; Pan, Jeng-Shyang; Liao, Bin-Yih

2013-01-01

Transmission power optimization is the most significant factor in prolonging the lifetime and maintaining the connection quality of wireless sensor networks. Un-optimized transmission power of nodes either interferes with or fails to link neighboring nodes. The optimization of transmission power depends on the expected node degree and node distribution. In this study, an optimization approach to an energy-efficient and full reachability wireless sensor network is proposed. In the proposed approach, an adjustment model of the transmission range with a minimum node degree is proposed that focuses on topology control and optimization of the transmission range according to node degree and node density. The model adjusts the tradeoff between energy efficiency and full reachability to obtain an ideal transmission range. In addition, connectivity and reachability are used as performance indices to evaluate the connection quality of a network. The two indices are compared to demonstrate the practicability of framework through simulation results. Furthermore, the relationship between the indices under the conditions of various node degrees is analyzed to generalize the characteristics of node densities. The research results on the reliability and feasibility of the proposed approach will benefit the future real deployments. PMID:23519351

The TensorMol-0.1 model chemistry: a neural network augmented with long-range physics.

PubMed

Yao, Kun; Herr, John E; Toth, David W; Mckintyre, Ryker; Parkhill, John

2018-02-28

Traditional force fields cannot model chemical reactivity, and suffer from low generality without re-fitting. Neural network potentials promise to address these problems, offering energies and forces with near ab initio accuracy at low cost. However a data-driven approach is naturally inefficient for long-range interatomic forces that have simple physical formulas. In this manuscript we construct a hybrid model chemistry consisting of a nearsighted neural network potential with screened long-range electrostatic and van der Waals physics. This trained potential, simply dubbed "TensorMol-0.1", is offered in an open-source Python package capable of many of the simulation types commonly used to study chemistry: geometry optimizations, harmonic spectra, open or periodic molecular dynamics, Monte Carlo, and nudged elastic band calculations. We describe the robustness and speed of the package, demonstrating its millihartree accuracy and scalability to tens-of-thousands of atoms on ordinary laptops. We demonstrate the performance of the model by reproducing vibrational spectra, and simulating the molecular dynamics of a protein. Our comparisons with electronic structure theory and experimental data demonstrate that neural network molecular dynamics is poised to become an important tool for molecular simulation, lowering the resource barrier to simulating chemistry.

Long-range synchrony and emergence of neural reentry

NASA Astrophysics Data System (ADS)

Keren, Hanna; Marom, Shimon

2016-11-01

Neural synchronization across long distances is a functionally important phenomenon in health and disease. In order to access the basis of different modes of long-range synchrony, we monitor spiking activities over centimetre scale in cortical networks and show that the mode of synchrony depends upon a length scale, λ, which is the minimal path that activity should propagate through to find its point of origin ready for reactivation. When λ is larger than the physical dimension of the network, distant neuronal populations operate synchronously, giving rise to irregularly occurring network-wide events that last hundreds of milliseconds to several seconds. In contrast, when λ approaches the dimension of the network, a continuous self-sustained reentry propagation emerges, a regular seizure-like mode that is marked by precise spatiotemporal patterns (‘synfire chains’) and may last many minutes. Termination of a reentry phase is preceded by a decrease of propagation speed to a halt. Stimulation decreases both propagation speed and λ values, which modifies the synchrony mode respectively. The results contribute to the understanding of the origin and termination of different modes of neural synchrony as well as their long-range spatial patterns, while hopefully catering to manipulation of the phenomena in pathological conditions.

Genetic networking of the Bemisia tabaci cryptic species complex reveals pattern of biological invasions.

PubMed

De Barro, Paul; Ahmed, Muhammad Z

2011-01-01

A challenge within the context of cryptic species is the delimitation of individual species within the complex. Statistical parsimony network analytics offers the opportunity to explore limits in situations where there are insufficient species-specific morphological characters to separate taxa. The results also enable us to explore the spread in taxa that have invaded globally. Using a 657 bp portion of mitochondrial cytochrome oxidase 1 from 352 unique haplotypes belonging to the Bemisia tabaci cryptic species complex, the analysis revealed 28 networks plus 7 unconnected individual haplotypes. Of the networks, 24 corresponded to the putative species identified using the rule set devised by Dinsdale et al. (2010). Only two species proposed in Dinsdale et al. (2010) departed substantially from the structure suggested by the analysis. The analysis of the two invasive members of the complex, Mediterranean (MED) and Middle East - Asia Minor 1 (MEAM1), showed that in both cases only a small number of haplotypes represent the majority that have spread beyond the home range; one MEAM1 and three MED haplotypes account for >80% of the GenBank records. Israel is a possible source of the globally invasive MEAM1 whereas MED has two possible sources. The first is the eastern Mediterranean which has invaded only the USA, primarily Florida and to a lesser extent California. The second are western Mediterranean haplotypes that have spread to the USA, Asia and South America. The structure for MED supports two home range distributions, a Sub-Saharan range and a Mediterranean range. The MEAM1 network supports the Middle East - Asia Minor region. The network analyses show a high level of congruence with the species identified in a previous phylogenetic analysis. The analysis of the two globally invasive members of the complex support the view that global invasion often involve very small portions of the available genetic diversity.

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.

Random walks with long-range steps generated by functions of Laplacian matrices

NASA Astrophysics Data System (ADS)

Riascos, A. P.; Michelitsch, T. M.; Collet, B. A.; Nowakowski, A. F.; Nicolleau, F. C. G. A.

2018-04-01

In this paper, we explore different Markovian random walk strategies on networks with transition probabilities between nodes defined in terms of functions of the Laplacian matrix. We generalize random walk strategies with local information in the Laplacian matrix, that describes the connections of a network, to a dynamic determined by functions of this matrix. The resulting processes are non-local allowing transitions of the random walker from one node to nodes beyond its nearest neighbors. We find that only two types of Laplacian functions are admissible with distinct behaviors for long-range steps in the infinite network limit: type (i) functions generate Brownian motions, type (ii) functions Lévy flights. For this asymptotic long-range step behavior only the lowest non-vanishing order of the Laplacian function is relevant, namely first order for type (i), and fractional order for type (ii) functions. In the first part, we discuss spectral properties of the Laplacian matrix and a series of relations that are maintained by a particular type of functions that allow to define random walks on any type of undirected connected networks. Once described general properties, we explore characteristics of random walk strategies that emerge from particular cases with functions defined in terms of exponentials, logarithms and powers of the Laplacian as well as relations of these dynamics with non-local strategies like Lévy flights and fractional transport. Finally, we analyze the global capacity of these random walk strategies to explore networks like lattices and trees and different types of random and complex networks.

Sparse short-distance connections enhance calcium wave propagation in a 3D model of astrocyte networks

PubMed Central

Lallouette, Jules; De Pittà, Maurizio; Ben-Jacob, Eshel; Berry, Hugues

2014-01-01

Traditionally, astrocytes have been considered to couple via gap-junctions into a syncytium with only rudimentary spatial organization. However, this view is challenged by growing experimental evidence that astrocytes organize as a proper gap-junction mediated network with more complex region-dependent properties. On the other hand, the propagation range of intercellular calcium waves (ICW) within astrocyte populations is as well highly variable, depending on the brain region considered. This suggests that the variability of the topology of gap-junction couplings could play a role in the variability of the ICW propagation range. Since this hypothesis is very difficult to investigate with current experimental approaches, we explore it here using a biophysically realistic model of three-dimensional astrocyte networks in which we varied the topology of the astrocyte network, while keeping intracellular properties and spatial cell distribution and density constant. Computer simulations of the model suggest that changing the topology of the network is indeed sufficient to reproduce the distinct ranges of ICW propagation reported experimentally. Unexpectedly, our simulations also predict that sparse connectivity and restriction of gap-junction couplings to short distances should favor propagation while long–distance or dense connectivity should impair it. Altogether, our results provide support to recent experimental findings that point toward a significant functional role of the organization of gap-junction couplings into proper astroglial networks. Dynamic control of this topology by neurons and signaling molecules could thus constitute a new type of regulation of neuron-glia and glia-glia interactions. PMID:24795613

Survival in Very Preterm Infants: An International Comparison of 10 National Neonatal Networks.

PubMed

Helenius, Kjell; Sjörs, Gunnar; Shah, Prakesh S; Modi, Neena; Reichman, Brian; Morisaki, Naho; Kusuda, Satoshi; Lui, Kei; Darlow, Brian A; Bassler, Dirk; Håkansson, Stellan; Adams, Mark; Vento, Maximo; Rusconi, Franca; Isayama, Tetsuya; Lee, Shoo K; Lehtonen, Liisa

2017-12-01

To compare survival rates and age at death among very preterm infants in 10 national and regional neonatal networks. A cohort study of very preterm infants, born between 24 and 29 weeks' gestation and weighing <1500 g, admitted to participating neonatal units between 2007 and 2013 in the International Network for Evaluating Outcomes of Neonates. Survival was compared by using standardized ratios (SRs) comparing survival in each network to the survival estimate of the whole population. Network populations differed with respect to rates of cesarean birth, exposure to antenatal steroids and birth in nontertiary hospitals. Network SRs for survival were highest in Japan (SR: 1.10; 99% confidence interval: 1.08-1.13) and lowest in Spain (SR: 0.88; 99% confidence interval: 0.85-0.90). The overall survival differed from 78% to 93% among networks, the difference being highest at 24 weeks' gestation (range 35%-84%). Survival rates increased and differences between networks diminished with increasing gestational age (GA) (range 92%-98% at 29 weeks' gestation); yet, relative differences in survival followed a similar pattern at all GAs. The median age at death varied from 4 days to 13 days across networks. The network ranking of survival rates for very preterm infants remained largely unchanged as GA increased; however, survival rates showed marked variations at lower GAs. The median age at death also varied among networks. These findings warrant further assessment of the representativeness of the study populations, organization of perinatal services, national guidelines, philosophy of care at extreme GAs, and resources used for decision-making. Copyright © 2017 by the American Academy of Pediatrics.

Using obsidian transfer distances to explore social network maintenance in late Pleistocene hunter-gatherers.

PubMed

Pearce, Eiluned; Moutsiou, Theodora

2014-12-01

Social behaviour is notoriously difficult to study archaeologically and it is unclear how large the networks of prehistoric humans were, or how they remained connected. Maintaining social cohesion was crucial for early humans because social networks facilitate cooperation and are imperative for survival and reproduction. Recent hunter-gatherer social organisation typically comprises a number of nested layers, ranging from the nuclear family through to the ~1500-strong ethnolinguistic tribe. Here we compare maximum obsidian transfer distances from the late Pleistocene with ethnographic data on the size of the geographic areas associated with each of these social grouping layers in recent hunter-gatherers. The closest match between the two is taken to indicate the maximum social layer within which contact could be sustained by Pleistocene hominins. Within both the (sub)tropical African and Subarctic biomes, the maximum obsidian transfer distances for Pleistocene modern humans (~200km and ~400km respectively) correspond to the geographic ranges of the outermost tribal layer in recent hunter-gatherers. This suggests that modern humans could potentially sustain the cohesion of their entire tribe at all latitudes, even though networks are more dispersed nearer the poles. Neanderthal obsidian transfer distances (300km) indicate that although Neanderthal home ranges are larger than those of low latitude hominins, Neanderthals travelled shorter distances than modern humans living at the same high latitudes. We argue that, like modern humans, Neanderthals could have maintained tribal cohesion, but that their tribes were substantially smaller than those of contemporary modern humans living in similar environments. The greater time taken to traverse the larger modern human tribal ranges may have limited the frequency of their face-to-face interactions and thus necessitated additional mechanisms to ensure network connectivity, such as the exchange of symbolic artefacts including ornaments and figurines. Such cultural supports may not have been required to the same extent by the Neanderthals due to their smaller tribes and home ranges.

Using obsidian transfer distances to explore social network maintenance in late Pleistocene hunter-gatherers

PubMed Central

Pearce, Eiluned; Moutsiou, Theodora

2014-01-01

Social behaviour is notoriously difficult to study archaeologically and it is unclear how large the networks of prehistoric humans were, or how they remained connected. Maintaining social cohesion was crucial for early humans because social networks facilitate cooperation and are imperative for survival and reproduction. Recent hunter-gatherer social organisation typically comprises a number of nested layers, ranging from the nuclear family through to the ~1500-strong ethnolinguistic tribe. Here we compare maximum obsidian transfer distances from the late Pleistocene with ethnographic data on the size of the geographic areas associated with each of these social grouping layers in recent hunter-gatherers. The closest match between the two is taken to indicate the maximum social layer within which contact could be sustained by Pleistocene hominins. Within both the (sub)tropical African and Subarctic biomes, the maximum obsidian transfer distances for Pleistocene modern humans (~200km and ~400km respectively) correspond to the geographic ranges of the outermost tribal layer in recent hunter-gatherers. This suggests that modern humans could potentially sustain the cohesion of their entire tribe at all latitudes, even though networks are more dispersed nearer the poles. Neanderthal obsidian transfer distances (300km) indicate that although Neanderthal home ranges are larger than those of low latitude hominins, Neanderthals travelled shorter distances than modern humans living at the same high latitudes. We argue that, like modern humans, Neanderthals could have maintained tribal cohesion, but that their tribes were substantially smaller than those of contemporary modern humans living in similar environments. The greater time taken to traverse the larger modern human tribal ranges may have limited the frequency of their face-to-face interactions and thus necessitated additional mechanisms to ensure network connectivity, such as the exchange of symbolic artefacts including ornaments and figurines. Such cultural supports may not have been required to the same extent by the Neanderthals due to their smaller tribes and home ranges. PMID:25214705

Best Practices Handbook: Traffic Engineering in Range Networks

DTIC Science & Technology

2016-03-01

units of measurement. Measurement Methodology - A repeatable measurement technique used to derive one or more metrics of interest . Network...Performance measures - Metrics that provide quantitative or qualitative measures of the performance of systems or subsystems of interest . Performance Metric

Default Network Modulation and Large-Scale Network Interactivity in Healthy Young and Old Adults

PubMed Central

Schacter, Daniel L.

2012-01-01

We investigated age-related changes in default, attention, and control network activity and their interactions in young and old adults. Brain activity during autobiographical and visuospatial planning was assessed using multivariate analysis and with intrinsic connectivity networks as regions of interest. In both groups, autobiographical planning engaged the default network while visuospatial planning engaged the attention network, consistent with a competition between the domains of internalized and externalized cognition. The control network was engaged for both planning tasks. In young subjects, the control network coupled with the default network during autobiographical planning and with the attention network during visuospatial planning. In old subjects, default-to-control network coupling was observed during both planning tasks, and old adults failed to deactivate the default network during visuospatial planning. This failure is not indicative of default network dysfunction per se, evidenced by default network engagement during autobiographical planning. Rather, a failure to modulate the default network in old adults is indicative of a lower degree of flexible network interactivity and reduced dynamic range of network modulation to changing task demands. PMID:22128194

Maneuvering in the Complex Path from Genotype to Phenotype

NASA Astrophysics Data System (ADS)

Strohman, Richard

2002-04-01

Human disease phenotypes are controlled not only by genes but by lawful self-organizing networks that display system-wide dynamics. These networks range from metabolic pathways to signaling pathways that regulate hormone action. When perturbed, networks alter their output of matter and energy which, depending on the environmental context, can produce either a pathological or a normal phenotype. Study of the dynamics of these networks by approaches such as metabolic control analysis may provide new insights into the pathogenesis and treatment of complex diseases.

Warfighter Information Network - Tactical (WIN-T) Increment 2: Second Follow-on Operational Test and Evaluation

DTIC Science & Technology

2015-05-01

HNW line-of-sight network is mounted on a 10-meter telescoping mast located just aft of the TCN’s cab. The flat plate Range Throughput Extension Kit... TAC – Tactical Command Post ATH – At-the-Halt PoP – Point of Presence SNE – Soldier Network Extension NOSC – Network Operations & Security...Survivability/Lethality Analysis Directorate (ARL/SLAD) conducted a Cooperative Vulnerability and Penetration Assessment on WIN-T Increment 2. The Army

Influence of tidal range on the stability of coastal marshland

USGS Publications Warehouse

Kirwan, Matthew L.; Guntenspergen, Glenn R.

2010-01-01

Early comparisons between rates of vertical accretion and sea level rise across marshes in different tidal ranges inspired a paradigm that marshes in high tidal range environments are more resilient to sea level rise than marshes in low tidal range environments. We use field-based observations to propose a relationship between vegetation growth and tidal range and to adapt two numerical models of marsh evolution to explicitly consider the effect of tidal range on the response of the marsh platform channel network system to accelerating rates of sea level rise. We find that the stability of both the channel network and vegetated platform increases with increasing tidal range. Our results support earlier hypotheses that suggest enhanced stability can be directly attributable to a vegetation growth range that expands with tidal range. Accretion rates equilibrate to the rate of sea level rise in all experiments regardless of tidal range, suggesting that comparisons between accretion rate and tidal range will not likely produce a significant relationship. Therefore, our model results offer an explanation to widely inconsistent field-based attempts to quantify this relationship while still supporting the long-held paradigm that high tidal range marshes are indeed more stable.

The Dark Side of Saturn's Gravity

NASA Astrophysics Data System (ADS)

Iess, L.; Racioppa, P.; Durante, D.; Mariani, M., Jr.; Anabtawi, A.; Armstrong, J. W.; Gomez Casajus, L.; Tortora, P.; Zannoni, M.

2017-12-01

On July 19, 2017 the Cassini spacecraft successfully completed its sixth and last pericenter pass devoted to the investigation of Saturn's interior structure and rings. During each pass the spacecraft was tracked for about 24 hours by the antennas of NASA's Deep Space Network and ESA's ESTRACK network, providing high quality measurements of the spacecraft range rate. We report on a preliminary estimate of Saturn's gravity field and ring mass inferred from range rate observables, and discuss the surprising features of our findings.

Adaptive search in mobile peer-to-peer databases

NASA Technical Reports Server (NTRS)

Wolfson, Ouri (Inventor); Xu, Bo (Inventor)

2010-01-01

Information is stored in a plurality of mobile peers. The peers communicate in a peer to peer fashion, using a short-range wireless network. Occasionally, a peer initiates a search for information in the peer to peer network by issuing a query. Queries and pieces of information, called reports, are transmitted among peers that are within a transmission range. For each search additional peers are utilized, wherein these additional peers search and relay information on behalf of the originator of the search.

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.

NDEx: A Community Resource for Sharing and Publishing of Biological Networks.

PubMed

Pillich, Rudolf T; Chen, Jing; Rynkov, Vladimir; Welker, David; Pratt, Dexter

2017-01-01

Networks are a powerful and flexible paradigm that facilitate communication and computation about interactions of any type, whether social, economic, or biological. NDEx, the Network Data Exchange, is an online commons to enable new modes of collaboration and publication using biological networks. NDEx creates an access point and interface to a broad range of networks, whether they express molecular interactions, curated relationships from literature, or the outputs of systematic analysis of big data. Research organizations can use NDEx as a distribution channel for networks they generate or curate. Developers of bioinformatic applications can store and query NDEx networks via a common programmatic interface. NDEx can also facilitate the integration of networks as data in electronic publications, thus making a step toward an ecosystem in which networks bearing data, hypotheses, and findings flow seamlessly between scientists.

Bluetooth Low Energy Mesh Networks: A Survey

PubMed Central

Darroudi, Seyed Mahdi; Gomez, Carles

2017-01-01

Bluetooth Low Energy (BLE) has gained significant momentum. However, the original design of BLE focused on star topology networking, which limits network coverage range and precludes end-to-end path diversity. In contrast, other competing technologies overcome such constraints by supporting the mesh network topology. For these reasons, academia, industry, and standards development organizations have been designing solutions to enable BLE mesh networks. Nevertheless, the literature lacks a consolidated view on this emerging area. This paper comprehensively surveys state of the art BLE mesh networking. We first provide a taxonomy of BLE mesh network solutions. We then review the solutions, describing the variety of approaches that leverage existing BLE functionality to enable BLE mesh networks. We identify crucial aspects of BLE mesh network solutions and discuss their advantages and drawbacks. Finally, we highlight currently open issues. PMID:28640183

Bringing a Network-Oriented Approach to Domestic Violence Services: A Focus Group Exploration of Promising Practices.

PubMed

Goodman, Lisa A; Banyard, Victoria; Woulfe, Julie; Ash, Sarah; Mattern, Grace

2016-01-01

Despite powerful evidence that informal social support contributes to survivors' safety and well-being, mainstream domestic violence (DV) programs have not developed comprehensive models for helping isolated survivors re-engage with these networks. Although many advocates use network-oriented strategies informally, they often do so without resources, funding, or training. This qualitative focus group study explored advocates' use and perceptions of network-oriented strategies. Advocates working in a range of DV programs across one state described the importance of network-oriented work and articulated its five dimensions, including helping survivors build their capacity to form healthy relationships, identify helpful and harmful network members, re-engage with existing networks, develop new relationships, and respond more effectively to network members. © The Author(s) 2015.

Automatic transducer switching provides accurate wide range measurement of pressure differential

NASA Technical Reports Server (NTRS)

Yoder, S. K.

1967-01-01

Automatic pressure transducer switching network sequentially selects any one of a number of limited-range transducers as gas pressure rises or falls, extending the range of measurement and lessening the chances of damage due to high pressure.

Effective contaminant detection networks in uncertain groundwater flow fields.

PubMed

Hudak, P F

2001-01-01

A mass transport simulation model tested seven contaminant detection-monitoring networks under a 40 degrees range of groundwater flow directions. Each monitoring network contained five wells located 40 m from a rectangular landfill. The 40-m distance (lag) was measured in different directions, depending upon the strategy used to design a particular monitoring network. Lagging the wells parallel to the central flow path was more effective than alternative design strategies. Other strategies allowed higher percentages of leaks to migrate between monitoring wells. Results of this study suggest that centrally lagged groundwater monitoring networks perform most effectively in uncertain groundwater-flow fields.

Cross over of recurrence networks to random graphs and random geometric graphs

NASA Astrophysics Data System (ADS)

Jacob, Rinku; Harikrishnan, K. P.; Misra, R.; Ambika, G.

2017-02-01

Recurrence networks are complex networks constructed from the time series of chaotic dynamical systems where the connection between two nodes is limited by the recurrence threshold. This condition makes the topology of every recurrence network unique with the degree distribution determined by the probability density variations of the representative attractor from which it is constructed. Here we numerically investigate the properties of recurrence networks from standard low-dimensional chaotic attractors using some basic network measures and show how the recurrence networks are different from random and scale-free networks. In particular, we show that all recurrence networks can cross over to random geometric graphs by adding sufficient amount of noise to the time series and into the classical random graphs by increasing the range of interaction to the system size. We also highlight the effectiveness of a combined plot of characteristic path length and clustering coefficient in capturing the small changes in the network characteristics.

Mechanically induced intercellular calcium communication in confined endothelial structures.

PubMed

Junkin, Michael; Lu, Yi; Long, Juexuan; Deymier, Pierre A; Hoying, James B; Wong, Pak Kin

2013-03-01

Calcium signaling in the diverse vascular structures is regulated by a wide range of mechanical and biochemical factors to maintain essential physiological functions of the vasculature. To properly transmit information, the intercellular calcium communication mechanism must be robust against various conditions in the cellular microenvironment. Using plasma lithography geometric confinement, we investigate mechanically induced calcium wave propagation in networks of human umbilical vein endothelial cells organized. Endothelial cell networks with confined architectures were stimulated at the single cell level, including using capacitive force probes. Calcium wave propagation in the network was observed using fluorescence calcium imaging. We show that mechanically induced calcium signaling in the endothelial networks is dynamically regulated against a wide range of probing forces and repeated stimulations. The calcium wave is able to propagate consistently in various dimensions from monolayers to individual cell chains, and in different topologies from linear patterns to cell junctions. Our results reveal that calcium signaling provides a robust mechanism for cell-cell communication in networks of endothelial cells despite the diversity of the microenvironmental inputs and complexity of vascular structures. Copyright © 2012 Elsevier Ltd. All rights reserved.

TDM interrogation of intensity-modulated USFBGs network based on multichannel lasers.

PubMed

Rohollahnejad, Jalal; Xia, Li; Cheng, Rui; Ran, Yanli; Rahubadde, Udaya; Zhou, Jiaao; Zhu, Lin

2017-01-23

We report a large-scale multi-channel fiber sensing network, where ultra-short FBGs (USFBGs) instead of conventional narrow-band ultra-weak FBGs are used as the sensors. In the time division multiplexing scheme of the network, each grating response is resolved as three adjacent discrete peaks. The central wavelengths of USFBGs are tracked with the differential detection, which is achieved by calculating the peak-to-peak ratio of two maximum peaks. Compared with previous large-scale hybrid multiplexing sensing networks (e.g., WDM/TDM) which typically have relatively low interrogation speed and very high complexity, the proposed system can achieve interrogation of all channel sensors through very fast and simple intensity measurements with a broad dynamic range. A proof-of-concept experiment with twenty USFBGs, at two wavelength channels, was performed and a fast static strain measurements were demonstrated, with a high average sensitivity of ~0.54dB/µƐ and wide dynamic range of over ~3000µƐ. The channel to channel switching time was 10ms and total network interrogation time was 50ms.

Three-dimensional inversion for Network-Magnetotelluric data

NASA Astrophysics Data System (ADS)

Siripunvaraporn, W.; Uyeshima, M.; Egbert, G.

2004-09-01

Three-dimensional inversion of Network-Magnetotelluric (MT) data has been implemented. The program is based on a conventional 3-D MT inversion code (Siripunvaraporn et al., 2004), which is a data space variant of the OCCAM approach. In addition to modifications required for computing Network-MT responses and sensitivities, the program makes use of Massage Passing Interface (MPI) software, with allowing computations for each period to be run on separate CPU nodes. Here, we consider inversion of synthetic data generated from simple models consisting of a 1 W-m conductive block buried at varying depths in a 100 W-m background. We focus in particular on inversion of long period (320-40,960 seconds) data, because Network-MT data usually have high coherency in these period ranges. Even with only long period data the inversion recovers shallow and deep structures, as long as these are large enough to affect the data significantly. However, resolution of the inversion depends greatly on the geometry of the dipole network, the range of periods used, and the horizontal size of the conductive anomaly.

Tunable impedance matching network fundamental limits and practical considerations

NASA Astrophysics Data System (ADS)

Allen, Wesley N.

As wireless devices continue to increase in utility while decreasing in dimension, design of the RF front-end becomes more complex. It is common for a single handheld device to operate on a plethora of frequency bands, utilize multiple antennae, and be subjected to a variety of environments. One complexity in particular which arises from these factors is that of impedance mismatch. Recently, tunable impedance matching networks have begun to be implemented to address this problem. This dissertation presents the first in-depth study on the frequency tuning range of tunable impedance matching networks. Both the fundamental limitations of ideal networks as well as practical considerations for design and implementation are addressed. Specifically, distributed matching networks with a single tuning element are investigated for use with parallel resistor-capacitor and series resistor-inductor loads. Analytical formulas are developed to directly calculate the frequency tuning range TR of ideal topologies. The theoretical limit of TR for these topologies is presented and discussed. Additional formulas are developed which address limitations in transmission line characteristic impedance and varactor range. Equations to predict loss due to varactor quality factor are demonstrated and the ability of parasitics to both increase and decrease TR are shown. Measured results exemplify i) the potential to develop matching networks with a small impact from parasitics, ii) the need for accurate knowledge of parasitics when designing near transition points in optimal parameters, iii) the importance of using a transmission line with the right characteristic impedance, and iv) the ability to achieve extremely low loss at the design frequency with a lossy varactor under the right conditions (measured loss of -0.07 dB). In the area of application, tunable matching networks are designed and measured for mobile handset antennas, demonstrating up to a 3 dB improvement in power delivered to a planar inverted-F antenna and up to 4--5.6 dB improvement in power delivered to the iPhone(TM) antenna. Additionally, a single-varactor matching network is measured to achieve greater tuning range than a two-varactor matching network (> 824--960 MHz versus 850--915 MHz) and yield higher power handling. Addressing miniaturization, an accurate model of metal loss in planar integrated inductors for low-loss substrates is developed and demonstrated. Finally, immediate future research directions are suggested: i) expanding the topologies, tuning elements, and loads analyzed; ii) performing a deep study into parasitics; and iii) investigating power handling with various varactor technologies.

Design of microstrip patch antennas using knowledge insertion through retraining

NASA Astrophysics Data System (ADS)

Divakar, T. V. S.; Sudhakar, A.

2018-04-01

The traditional way of analyzing/designing neural network is to collect experimental data and train neural network. Then, the trained neural network acts as global approximate function. The network is then used to calculate parameters for unknown configurations. The main drawback of this method is one does not have enough experimental data, cost of prototypes being a major factor [1-4]. Therefore, in this method the author collected training data from available approximate formulas with in full design range and trained the network with it. After successful training, the network is retrained with available measured results. This simple way inserts experimental knowledge into the network [5]. This method is tested for rectangular microstrip antenna and circular microstrip antenna.

Flood Monitoring using X-band Dual-polarization Radar Network

NASA Astrophysics Data System (ADS)

Chandrasekar, V.; Wang, Y.; Maki, M.; Nakane, K.

2009-09-01

A dense weather radar network is an emerging concept advanced by the Center for Collaborative Adaptive Sensing of the Atmosphere (CASA). Using multiple radars observing over a common will create different data outcomes depending on the characteristics of the radar units employed and the network topology. To define this a general framework is developed to describe the radar network space, and formulations are obtained that can be used for weather radar network characterization. Current weather radar surveillance networks are based upon conventional sensing paradigm of widely-separated, standalone sensing systems using long range radars that operate at wavelengths in 5-10 cm range. Such configuration has limited capability to observe close to the surface of the earth because of the earth's curvature but also has poorer resolution at far ranges. The dense network radar system, observes and measures weather phenomenon such as rainfall and severe weather close to the ground at higher spatial and temporal resolution compared to the current paradigm. In addition the dense network paradigm also is easily adaptable to complex terrain. Flooding is one of the most common natural hazards in the world. Especially, excessive development decreases the response time of urban watersheds and complex terrain to rainfall and increases the chance of localized flooding events over a small spatial domain. Successful monitoring of urban floods requires high spatiotemporal resolution, accurate precipitation estimation because of the rapid flood response as well as the complex hydrologic and hydraulic characteristics in an urban environment. This paper reviews various aspects in radar rainfall mapping in urban coverage using dense X-band dual-polarization radar networks. By reducing the maximum range and operating at X-band, one can ensure good azimuthal resolution with a small-size antenna and keep the radar beam closer to the ground. The networked topology helps to achieve satisfactory sensitivity and fast temporal update across the coverage. Strong clutter is expected from buildings in the neighborhood which act as perfect reflectors. The reduction in radar size enables flexible deployment, such as rooftop installation, with small infrastructure requirement, which is critical in a metropolitan region. Dual-polarization based technologies can be implemented for real-time mitigation of rain attenuations and accurate estimation of rainfall. The NSF Engineering Research Center for Collaborative Adaptive Sensing of the Atmosphere (CASA) is developing the technologies and the systems for network centric weather observation. The Differential propagation phase (Kdp) has higher sensitivity at X-band compared to S and C band. It is attractive to use Kdp to derive Quantitative Precipitation Estimation (QPE) because it is immune to rain attenuation, calibration biases, partial beam blockage, and hail contamination. Despite the advantage of Kdp for radar QPE, the estimation of Kdp itself is a challenge as the range derivative of the differential propagation phase profiles. An adaptive Kdp algorithm was implemented in the CASA IP1 testbed that substantially reduces the fluctuation in light rain and the bias at heavy rain. The Kdp estimation also benefits from the higher resolution in the IP1 radar network. The performance of the IP1 QPE product was evaluated for all major rain events against the USDA Agriculture Research Service's gauge network (MicroNet) in the Little Washita watershed, which comprises 20 weather stations in the center of the test bed. The cross-comparison with gauge measurements shows excellent agreement for the storm events during the Spring Experiments of 2007 and 2008. The hourly rainfall estimates compared to the gauge measurements have a very small bias of few percent and a normalized standard error of 21%. The IP1 testbed was designed with overlapping coverage among its radar nodes. The study area is covered by multiple radars and the aspect of network composition is also evaluated. The independence of Kdp on the radar calibration enables flexibility in combining the collocated Kdp estimates from all the radar nodes. Radar QPE can be improved from the composite Kdp field from the radar with lowest beam height and nearest slant range, or from the radar with the best Kdp estimates. More importantly, the data availability is greatly enhanced by the overlapped topology in cases of heavy rainfall, demonstrating the operational strength of the network centric radar system. The National Research Institute for Earth Science and Disaster Prevention (NIED), Japan, is in the process of establishing an X-band radar network (X-Net) in Metropolitan Tokyo area. Colorado State University and NIED have formed a partnership to initiate a joint program for urban flood monitoring using X-band dual-polarization radar network. This paper will also present some preliminary plans for this program.

Design Sensitivity for a Subsonic Aircraft Predicted by Neural Network and Regression Models

NASA Technical Reports Server (NTRS)

Hopkins, Dale A.; Patnaik, Surya N.

2005-01-01

A preliminary methodology was obtained for the design optimization of a subsonic aircraft by coupling NASA Langley Research Center s Flight Optimization System (FLOPS) with NASA Glenn Research Center s design optimization testbed (COMETBOARDS with regression and neural network analysis approximators). The aircraft modeled can carry 200 passengers at a cruise speed of Mach 0.85 over a range of 2500 n mi and can operate on standard 6000-ft takeoff and landing runways. The design simulation was extended to evaluate the optimal airframe and engine parameters for the subsonic aircraft to operate on nonstandard runways. Regression and neural network approximators were used to examine aircraft operation on runways ranging in length from 4500 to 7500 ft.

Memory traces of long-range coordinated oscillations in the sleeping human brain.

PubMed

Piantoni, Giovanni; Van Der Werf, Ysbrand D; Jensen, Ole; Van Someren, Eus J W

2015-01-01

Cognition involves coordinated activity across distributed neuronal networks. Neuronal activity during learning triggers cortical plasticity that allows for reorganization of the neuronal network and integration of new information. Animal studies have shown post-learning reactivation of learning-elicited neuronal network activity during subsequent sleep, supporting consolidation of the reorganization. However, no previous studies, to our knowledge, have demonstrated reactivation of specific learning-elicited long-range functional connectivity during sleep in humans. We here show reactivation of learning-induced long-range synchronization of magnetoencephalography power fluctuations in human sleep. Visuomotor learning elicited a specific profile of long-range cortico-cortical synchronization of slow (0.1 Hz) fluctuations in beta band (12-30 Hz) power. The parieto-occipital part of this synchronization profile reappeared in delta band (1-3.5 Hz) power fluctuations during subsequent sleep, but not during the intervening wakefulness period. Individual differences in the reactivated synchronization predicted postsleep performance improvement. The presleep resting-state synchronization profile was not reactivated during sleep. The findings demonstrate reactivation of long-range coordination of neuronal activity in humans, more specifically of reactivation of coupling of infra-slow fluctuations in oscillatory power. The spatiotemporal profile of delta power fluctuations during sleep may subserve memory consolidation by echoing coordinated activation elicited by prior learning. © 2014 Wiley Periodicals, Inc.

Quantifying the Structure of Free Association Networks across the Life Span

ERIC Educational Resources Information Center

Dubossarsky, Haim; De Deyne, Simon; Hills, Thomas T.

2017-01-01

We investigate how the mental lexicon changes over the life span using free association data from over 8,000 individuals, ranging from 10 to 84 years of age, with more than 400 cue words per age group. Using network analysis, with words as nodes and edges defined by the strength of shared associations, we find that associative networks evolve in a…

Suborbital Telepresence and Over-the-Horizon Networking

NASA Technical Reports Server (NTRS)

Freudinger, Lawrence C.

2007-01-01

A viewgraph presentation describing the suborbital telepresence project utilizing in-flight network computing is shown. The topics include: 1) Motivation; 2) Suborbital Telepresence and Global Test Range; 3) Tropical Composition, Cloud, and Climate Coupling Experiment (TC4); 4) Data Sets for TC4 Real-time Monitoring; 5) TC-4 Notional Architecture; 6) An Application Integration View; 7) Telepresence: Architectural Framework; and 8) Disruption Tolerant Networks.

Identity Management Task Force Report 2008

DTIC Science & Technology

2008-01-01

Telecommunication Grid ( GTG ) consists of the public- switched telecommunications network (PSTN), various forms of Internet protocol (IP) networks...to network providers) to a large community of nomadic users and access devices over a wide range of access technologies. The GTG is notional, and...DOC Dr. Myra Gray , DOD Greg Hall, DNI Celia Hanley, DOD Patrick Hannon, DNI James Hass, IC Linda Hill, SSA Bobby Jones, DOC Patrick Hannon

Real Time Physiological Status Monitoring (RT-PSM): Accomplishments, Requirements, and Research Roadmap

DTIC Science & Technology

2016-03-01

Maneuver Center of Excellence (US Army - Ft. Benning) MINIMEN Minimalist Wearable Mesh Network Mloco Metabolic Costs of Locomotion MOUT Military...detect blast and ballistic wounding events Quantum Applied Science & Research, Inc. Army A05-163 SBIR 2005 Minimalist Short- Range Wearable for...STTR 2005 (Phase 1) 2005 Minimalist Wearable Mesh Network (MINIMEN) System Develop PSM system linking wearable sensors, mesh networking

Optimal Scheduling for Underwater Communications in Multiple-User Scenarios

DTIC Science & Technology

2015-09-30

term goals of this project is to analyze and propose energy-efficient communication techniques for underwater acoustic sensor networks . These...investigate the possibility that these underwater acoustic networks disrupt the behavior of surrounding species of marine mammals. As a consequence of... underwater VHF acoustics , high data rate/short range acoustic communications and networking , and acoustic sensing in the VHF regime. WORK COMPLETED We

Targeting climate diversity in conservation planning to build resilience to climate change

USGS Publications Warehouse

Heller, Nicole E.; Kreitler, Jason R.; Ackerly, David; Weiss, Stuart; Recinos, Amanda; Branciforte, Ryan; Flint, Lorraine E.; Flint, Alan L.; Micheli, Elisabeth

2015-01-01

Climate change is raising challenging concerns for systematic conservation planning. Are methods based on the current spatial patterns of biodiversity effective given long-term climate change? Some conservation scientists argue that planning should focus on protecting the abiotic diversity in the landscape, which drives patterns of biological diversity, rather than focusing on the distribution of focal species, which shift in response to climate change. Climate is one important abiotic driver of biodiversity patterns, as different climates host different biological communities and genetic pools. We propose conservation networks that capture the full range of climatic diversity in a region will improve the resilience of biotic communities to climate change compared to networks that do not. In this study we used historical and future hydro-climate projections from the high resolution Basin Characterization Model to explore the utility of directly targeting climatic diversity in planning. Using the spatial planning tool, Marxan, we designed conservation networks to capture the diversity of climate types, at the regional and sub-regional scale, and compared them to networks we designed to capture the diversity of vegetation types. By focusing on the Conservation Lands Network (CLN) of the San Francisco Bay Area as a real-world case study, we compared the potential resilience of networks by examining two factors: the range of climate space captured, and climatic stability to 18 future climates, reflecting different emission scenarios and global climate models. We found that the climate-based network planned at the sub-regional scale captured a greater range of climate space and showed higher climatic stability than the vegetation and regional based-networks. At the same time, differences among network scenarios are small relative to the variance in climate stability across global climate models. Across different projected futures, topographically heterogeneous areas consistently show greater climate stability than homogenous areas. The analysis suggests that utilizing high-resolution climate and hydrological data in conservation planning improves the likely resilience of biodiversity to climate change. We used these analyses to suggest new conservation priorities for the San Francisco Bay Area.

Ultra-wideband radar sensors and networks

DOEpatents

Leach, Jr., Richard R; Nekoogar, Faranak; Haugen, Peter C

2013-08-06

Ultra wideband radar motion sensors strategically placed in an area of interest communicate with a wireless ad hoc network to provide remote area surveillance. Swept range impulse radar and a heart and respiration monitor combined with the motion sensor further improves discrimination.

Trade-off between Multiple Constraints Enables Simultaneous Formation of Modules and Hubs in Neural Systems

PubMed Central

Chen, Yuhan; Wang, Shengjun; Hilgetag, Claus C.; Zhou, Changsong

2013-01-01

The formation of the complex network architecture of neural systems is subject to multiple structural and functional constraints. Two obvious but apparently contradictory constraints are low wiring cost and high processing efficiency, characterized by short overall wiring length and a small average number of processing steps, respectively. Growing evidence shows that neural networks are results from a trade-off between physical cost and functional value of the topology. However, the relationship between these competing constraints and complex topology is not well understood quantitatively. We explored this relationship systematically by reconstructing two known neural networks, Macaque cortical connectivity and C. elegans neuronal connections, from combinatory optimization of wiring cost and processing efficiency constraints, using a control parameter , and comparing the reconstructed networks to the real networks. We found that in both neural systems, the reconstructed networks derived from the two constraints can reveal some important relations between the spatial layout of nodes and the topological connectivity, and match several properties of the real networks. The reconstructed and real networks had a similar modular organization in a broad range of , resulting from spatial clustering of network nodes. Hubs emerged due to the competition of the two constraints, and their positions were close to, and partly coincided, with the real hubs in a range of values. The degree of nodes was correlated with the density of nodes in their spatial neighborhood in both reconstructed and real networks. Generally, the rebuilt network matched a significant portion of real links, especially short-distant ones. These findings provide clear evidence to support the hypothesis of trade-off between multiple constraints on brain networks. The two constraints of wiring cost and processing efficiency, however, cannot explain all salient features in the real networks. The discrepancy suggests that there are further relevant factors that are not yet captured here. PMID:23505352

Thermal and dynamic range characterization of a photonics-based RF amplifier

NASA Astrophysics Data System (ADS)

Noque, D. F.; Borges, R. M.; Muniz, A. L. M.; Bogoni, A.; Cerqueira S., Arismar, Jr.

2018-05-01

This work reports a thermal and dynamic range characterization of an ultra-wideband photonics-based RF amplifier for microwave and mm-waves future 5G optical-wireless networks. The proposed technology applies the four-wave mixing nonlinear effect to provide RF amplification in analog and digital radio-over-fiber systems. The experimental analysis from 300 kHz to 50 GHz takes into account different figures of merit, such as RF gain, spurious-free dynamic range and RF output power stability as a function of temperature. The thermal characterization from -10 to +70 °C demonstrates a 27 dB flat photonics-assisted RF gain over the entire frequency range under real operational conditions of a base station for illustrating the feasibility of the photonics-assisted RF amplifier for 5G networks.

Spatial networks

NASA Astrophysics Data System (ADS)

Barthélemy, Marc

2011-02-01

Complex systems are very often organized under the form of networks where nodes and edges are embedded in space. Transportation and mobility networks, Internet, mobile phone networks, power grids, social and contact networks, and neural networks, are all examples where space is relevant and where topology alone does not contain all the information. Characterizing and understanding the structure and the evolution of spatial networks is thus crucial for many different fields, ranging from urbanism to epidemiology. An important consequence of space on networks is that there is a cost associated with the length of edges which in turn has dramatic effects on the topological structure of these networks. We will thoroughly explain the current state of our understanding of how the spatial constraints affect the structure and properties of these networks. We will review the most recent empirical observations and the most important models of spatial networks. We will also discuss various processes which take place on these spatial networks, such as phase transitions, random walks, synchronization, navigation, resilience, and disease spread.

Medical education practice-based research networks: Facilitating collaborative research.

PubMed

Schwartz, Alan; Young, Robin; Hicks, Patricia J

2016-01-01

Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). We searched for extant networks through peer-reviewed literature and the world-wide web. We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks.

Medical education practice-based research networks: Facilitating collaborative research

PubMed Central

Schwartz, Alan; Young, Robin; Hicks, Patricia J.; APPD LEARN, For

2016-01-01

Abstract Background: Research networks formalize and institutionalize multi-site collaborations by establishing an infrastructure that enables network members to participate in research, propose new studies, and exploit study data to move the field forward. Although practice-based clinical research networks are now widespread, medical education research networks are rapidly emerging. Aims: In this article, we offer a definition of the medical education practice-based research network, a brief description of networks in existence in July 2014 and their features, and a more detailed case study of the emergence and early growth of one such network, the Association of Pediatric Program Directors Longitudinal Educational Assessment Research Network (APPD LEARN). Methods: We searched for extant networks through peer-reviewed literature and the world-wide web. Results: We identified 15 research networks in medical education founded since 2002 with membership ranging from 8 to 120 programs. Most focus on graduate medical education in primary care or emergency medicine specialties. Conclusions: We offer four recommendations for the further development and spread of medical education research networks: increasing faculty development, obtaining central resources, studying networks themselves, and developing networks of networks. PMID:25319404

Long-range acoustic observations of the Eyjafjallajökull eruption, Iceland, April-May 2010

NASA Astrophysics Data System (ADS)

Matoza, Robin S.; Vergoz, Julien; Le Pichon, Alexis; Ceranna, Lars; Green, David N.; Evers, Läslo G.; Ripepe, Maurizio; Campus, Paola; Liszka, Ludwik; Kvaerna, Tormod; Kjartansson, Einar; Höskuldsson, Ármann

2011-03-01

The April-May 2010 summit eruption of Eyjafjallajökull, Iceland, was recorded by 14 atmospheric infrasound sensor arrays at ranges between 1,700 and 3,700 km, indicating that infrasound from modest-size eruptions can propagate for thousands of kilometers in atmospheric waveguides. Although variations in both atmospheric propagation conditions and background noise levels at the sensors generate fluctuations in signal-to-noise ratios and signal detectability, array processing techniques successfully discriminate between volcanic infrasound and ambient coherent and incoherent noise. The current global infrasound network is significantly more dense and sensitive than any previously operated network and signals from large volcanic explosions are routinely recorded. Because volcanic infrasound is generated during the explosive release of fluid into the atmosphere, it is a strong indicator that an eruption has occurred. Therefore, long-range infrasonic monitoring may aid volcanic explosion detection by complementing other monitoring technologies, especially in remote regions with sparse ground-based instrument networks.

Time delay and long-range connection induced synchronization transitions in Newman-Watts small-world neuronal networks.

PubMed

Qian, Yu

2014-01-01

The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay τ and long-range connection (LRC) probability P have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability P = 1.0 as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability P is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs.

Time Delay and Long-Range Connection Induced Synchronization Transitions in Newman-Watts Small-World Neuronal Networks

PubMed Central

Qian, Yu

2014-01-01

The synchronization transitions in Newman-Watts small-world neuronal networks (SWNNs) induced by time delay and long-range connection (LRC) probability have been investigated by synchronization parameter and space-time plots. Four distinct parameter regions, that is, asynchronous region, transition region, synchronous region, and oscillatory region have been discovered at certain LRC probability as time delay is increased. Interestingly, desynchronization is observed in oscillatory region. More importantly, we consider the spatiotemporal patterns obtained in delayed Newman-Watts SWNNs are the competition results between long-range drivings (LRDs) and neighboring interactions. In addition, for moderate time delay, the synchronization of neuronal network can be enhanced remarkably by increasing LRC probability. Furthermore, lag synchronization has been found between weak synchronization and complete synchronization as LRC probability is a little less than 1.0. Finally, the two necessary conditions, moderate time delay and large numbers of LRCs, are exposed explicitly for synchronization in delayed Newman-Watts SWNNs. PMID:24810595

Traffic sharing algorithms for hybrid mobile networks

NASA Technical Reports Server (NTRS)

Arcand, S.; Murthy, K. M. S.; Hafez, R.

1995-01-01

In a hybrid (terrestrial + satellite) mobile personal communications networks environment, a large size satellite footprint (supercell) overlays on a large number of smaller size, contiguous terrestrial cells. We assume that the users have either a terrestrial only single mode terminal (SMT) or a terrestrial/satellite dual mode terminal (DMT) and the ratio of DMT to the total terminals is defined gamma. It is assumed that the call assignments to and handovers between terrestrial cells and satellite supercells take place in a dynamic fashion when necessary. The objectives of this paper are twofold, (1) to propose and define a class of traffic sharing algorithms to manage terrestrial and satellite network resources efficiently by handling call handovers dynamically, and (2) to analyze and evaluate the algorithms by maximizing the traffic load handling capability (defined in erl/cell) over a wide range of terminal ratios (gamma) given an acceptable range of blocking probabilities. Two of the algorithms (G & S) in the proposed class perform extremely well for a wide range of gamma.

A simple model of bipartite cooperation for ecological and organizational networks.

PubMed

Saavedra, Serguei; Reed-Tsochas, Felix; Uzzi, Brian

2009-01-22

In theoretical ecology, simple stochastic models that satisfy two basic conditions about the distribution of niche values and feeding ranges have proved successful in reproducing the overall structural properties of real food webs, using species richness and connectance as the only input parameters. Recently, more detailed models have incorporated higher levels of constraint in order to reproduce the actual links observed in real food webs. Here, building on previous stochastic models of consumer-resource interactions between species, we propose a highly parsimonious model that can reproduce the overall bipartite structure of cooperative partner-partner interactions, as exemplified by plant-animal mutualistic networks. Our stochastic model of bipartite cooperation uses simple specialization and interaction rules, and only requires three empirical input parameters. We test the bipartite cooperation model on ten large pollination data sets that have been compiled in the literature, and find that it successfully replicates the degree distribution, nestedness and modularity of the empirical networks. These properties are regarded as key to understanding cooperation in mutualistic networks. We also apply our model to an extensive data set of two classes of company engaged in joint production in the garment industry. Using the same metrics, we find that the network of manufacturer-contractor interactions exhibits similar structural patterns to plant-animal pollination networks. This surprising correspondence between ecological and organizational networks suggests that the simple rules of cooperation that generate bipartite networks may be generic, and could prove relevant in many different domains, ranging from biological systems to human society.

Connecting Core Percolation and Controllability of Complex Networks

PubMed Central

Jia, Tao; Pósfai, Márton

2014-01-01

Core percolation is a fundamental structural transition in complex networks related to a wide range of important problems. Recent advances have provided us an analytical framework of core percolation in uncorrelated random networks with arbitrary degree distributions. Here we apply the tools in analysis of network controllability. We confirm analytically that the emergence of the bifurcation in control coincides with the formation of the core and the structure of the core determines the control mode of the network. We also derive the analytical expression related to the controllability robustness by extending the deduction in core percolation. These findings help us better understand the interesting interplay between the structural and dynamical properties of complex networks. PMID:24946797

Competition-Driven Network Dynamics: Emergence of a Scale-Free Leadership Structure and Collective Efficiency

NASA Astrophysics Data System (ADS)

Anghel, M.; Toroczkai, Zoltán; Bassler, Kevin E.; Korniss, G.

2004-02-01

Using the minority game as a model for competition dynamics, we investigate the effects of interagent communications across a network on the global evolution of the game. Agent communication across this network leads to the formation of an influence network, which is dynamically coupled to the evolution of the game, and it is responsible for the information flow driving the agents' actions. We show that the influence network spontaneously develops hubs with a broad distribution of in-degrees, defining a scale-free robust leadership structure. Furthermore, in realistic parameter ranges, facilitated by information exchange on the network, agents can generate a high degree of cooperation making the collective almost maximally efficient.

Analysing Local Sparseness in the Macaque Brain Network

PubMed Central

Singh, Raghavendra; Nagar, Seema; Nanavati, Amit A.

2015-01-01

Understanding the network structure of long distance pathways in the brain is a necessary step towards developing an insight into the brain’s function, organization and evolution. Dense global subnetworks of these pathways have often been studied, primarily due to their functional implications. Instead we study sparse local subnetworks of the pathways to establish the role of a brain area in enabling shortest path communication between its non-adjacent topological neighbours. We propose a novel metric to measure the topological communication load on a vertex due to its immediate neighbourhood, and show that in terms of distribution of this local communication load, a network of Macaque long distance pathways is substantially different from other real world networks and random graph models. Macaque network contains the entire range of local subnetworks, from star-like networks to clique-like networks, while other networks tend to contain a relatively small range of subnetworks. Further, sparse local subnetworks in the Macaque network are not only found across topographical super-areas, e.g., lobes, but also within a super-area, arguing that there is conservation of even relatively short-distance pathways. To establish the communication role of a vertex we borrow the concept of brokerage from social science, and present the different types of brokerage roles that brain areas play, highlighting that not only the thalamus, but also cingulate gyrus and insula often act as “relays” for areas in the neocortex. These and other analysis of communication load and roles of the sparse subnetworks of the Macaque brain provide new insights into the organisation of its pathways. PMID:26437077

Long-range planning cost model for support of future space missions by the deep space network

NASA Technical Reports Server (NTRS)

Sherif, J. S.; Remer, D. S.; Buchanan, H. R.

1990-01-01

A simple model is suggested to do long-range planning cost estimates for Deep Space Network (DSP) support of future space missions. The model estimates total DSN preparation costs and the annual distribution of these costs for long-range budgetary planning. The cost model is based on actual DSN preparation costs from four space missions: Galileo, Voyager (Uranus), Voyager (Neptune), and Magellan. The model was tested against the four projects and gave cost estimates that range from 18 percent above the actual total preparation costs of the projects to 25 percent below. The model was also compared to two other independent projects: Viking and Mariner Jupiter/Saturn (MJS later became Voyager). The model gave cost estimates that range from 2 percent (for Viking) to 10 percent (for MJS) below the actual total preparation costs of these missions.

Collaborative research networks in health: a pragmatic scoping study for the development of an imaging network.

PubMed

Robinson, Tracy Elizabeth; Rankin, Nicole; Janssen, Anna; Mcgregor, Deborah; Grieve, Stuart; Shaw, Timothy

2015-12-09

Collaborative research networks are often touted as a solution for enhancing the translation of knowledge, but questions remain about how to evaluate their impact on health service delivery. This pragmatic scoping study explored the enabling factors for developing and supporting a collaborative imaging network in a metropolitan university in Australia. An advisory group was established to provide governance and to identify key informants and participants. Focus group discussions (n = 2) and semi-structured interviews (n = 22) were facilitated with representatives from a broad range of disciplines. In addition, a survey, a review of relevant websites (n = 15) and a broad review of the literature were undertaken to elicit information on collaborative research networks and perceived needs and factors that would support their involvement in a multi-disciplinary collaborative research network. Findings were de-identified and broad themes were identified. Participants identified human factors as having priority for developing and sustaining a collaborative research network. In particular, leadership, a shared vision and a communication plan that includes social media were identified as crucial for sustaining an imaging network in health research. It is important to develop metrics that map relationships between network members and the role that communication tools can contribute to this process. This study confirms that human factors remain significant across a range of collaborative endeavours. The use of focus group discussions, interviews, and literature and website reviews means we can now strongly recommend the primacy of human factors. More work is needed to identify how the network operates and what specific indicators or metrics help build the capacity of clinicians and scientists to participate in translational research.

Transistor circuit increases range of logarithmic current amplifier

NASA Technical Reports Server (NTRS)

Gilmour, G.

1966-01-01

Circuit increases the range of a logarithmic current amplifier by combining a commercially available amplifier with a silicon epitaxial transistor. A temperature compensating network is provided for the transistor.

On the Dynamics of the Spontaneous Activity in Neuronal Networks

PubMed Central

Bonifazi, Paolo; Ruaro, Maria Elisabetta; Torre, Vincent

2007-01-01

Most neuronal networks, even in the absence of external stimuli, produce spontaneous bursts of spikes separated by periods of reduced activity. The origin and functional role of these neuronal events are still unclear. The present work shows that the spontaneous activity of two very different networks, intact leech ganglia and dissociated cultures of rat hippocampal neurons, share several features. Indeed, in both networks: i) the inter-spike intervals distribution of the spontaneous firing of single neurons is either regular or periodic or bursting, with the fraction of bursting neurons depending on the network activity; ii) bursts of spontaneous spikes have the same broad distributions of size and duration; iii) the degree of correlated activity increases with the bin width, and the power spectrum of the network firing rate has a 1/f behavior at low frequencies, indicating the existence of long-range temporal correlations; iv) the activity of excitatory synaptic pathways mediated by NMDA receptors is necessary for the onset of the long-range correlations and for the presence of large bursts; v) blockage of inhibitory synaptic pathways mediated by GABAA receptors causes instead an increase in the correlation among neurons and leads to a burst distribution composed only of very small and very large bursts. These results suggest that the spontaneous electrical activity in neuronal networks with different architectures and functions can have very similar properties and common dynamics. PMID:17502919

Trend Motif: A Graph Mining Approach for Analysis of Dynamic Complex Networks

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

Jin, R; McCallen, S; Almaas, E

2007-05-28

Complex networks have been used successfully in scientific disciplines ranging from sociology to microbiology to describe systems of interacting units. Until recently, studies of complex networks have mainly focused on their network topology. However, in many real world applications, the edges and vertices have associated attributes that are frequently represented as vertex or edge weights. Furthermore, these weights are often not static, instead changing with time and forming a time series. Hence, to fully understand the dynamics of the complex network, we have to consider both network topology and related time series data. In this work, we propose a motifmore » mining approach to identify trend motifs for such purposes. Simply stated, a trend motif describes a recurring subgraph where each of its vertices or edges displays similar dynamics over a userdefined period. Given this, each trend motif occurrence can help reveal significant events in a complex system; frequent trend motifs may aid in uncovering dynamic rules of change for the system, and the distribution of trend motifs may characterize the global dynamics of the system. Here, we have developed efficient mining algorithms to extract trend motifs. Our experimental validation using three disparate empirical datasets, ranging from the stock market, world trade, to a protein interaction network, has demonstrated the efficiency and effectiveness of our approach.« less

Biologically Derived Soft Conducting Hydrogels Using Heparin-Doped Polymer Networks

PubMed Central

2015-01-01

The emergence of flexible and stretchable electronic components expands the range of applications of electronic devices. Flexible devices are ideally suited for electronic biointerfaces because of mechanically permissive structures that conform to curvilinear structures found in native tissue. Most electronic materials used in these applications exhibit elastic moduli on the order of 0.1–1 MPa. However, many electronically excitable tissues exhibit elasticities in the range of 1–10 kPa, several orders of magnitude smaller than existing components used in flexible devices. This work describes the use of biologically derived heparins as scaffold materials for fabricating networks with hybrid electronic/ionic conductivity and ultracompliant mechanical properties. Photo-cross-linkable heparin–methacrylate hydrogels serve as templates to control the microstructure and doping of in situ polymerized polyaniline structures. Macroscopic heparin-doped polyaniline hydrogel dual networks exhibit impedances as low as Z = 4.17 Ω at 1 kHz and storage moduli of G′ = 900 ± 100 Pa. The conductivity of heparin/polyaniline networks depends on the oxidation state and microstructure of secondary polyaniline networks. Furthermore, heparin/polyaniline networks support the attachment, proliferation, and differentiation of murine myoblasts without any surface treatments. Taken together, these results suggest that heparin/polyaniline hydrogel networks exhibit suitable physical properties as an electronically active biointerface material that can match the mechanical properties of soft tissues composed of excitable cells. PMID:24738911

Organization of brain networks governed by long-range connections index autistic traits in the general population

PubMed Central

2013-01-01

Background The dimensional approach to autism spectrum disorder (ASD) considers ASD as the extreme of a dimension traversing through the entire population. We explored the potential utility of electroencephalography (EEG) functional connectivity as a biomarker. We hypothesized that individual differences in autistic traits of typical subjects would involve a long-range connectivity diminution within the delta band. Methods Resting-state EEG functional connectivity was measured for 74 neurotypical subjects. All participants also provided a questionnaire (Social Responsiveness Scale, SRS) that was completed by an informant who knows the participant in social settings. We conducted multivariate regression between the SRS score and functional connectivity in all EEG frequency bands. We explored modulations of network graph metrics characterizing the optimality of a network using the SRS score. Results Our results show a decay in functional connectivity mainly within the delta and theta bands (the lower part of the EEG spectrum) associated with an increasing number of autistic traits. When inspecting the impact of autistic traits on the global organization of the functional network, we found that the optimal properties of the network are inversely related to the number of autistic traits, suggesting that the autistic dimension, throughout the entire population, modulates the efficiency of functional brain networks. Conclusions EEG functional connectivity at low frequencies and its associated network properties may be associated with some autistic traits in the general population. PMID:23806204

Determinants of fluidlike behavior and effective viscosity in cross-linked actin networks.

PubMed

Kim, Taeyoon; Gardel, Margaret L; Munro, Ed

2014-02-04

The actin cortex has a well-documented ability to rapidly remodel and flow while maintaining long-range connectivity, but how this is achieved remains poorly understood. Here, we use computer simulations to explore how stress relaxation in cross-linked actin networks subjected to extensional stress depends on the interplay between network architecture and turnover. We characterize a regime in which a network response is nonaffine and stress relaxation is governed by the continuous dissipation of elastic energy via cyclic formation, elongation, and turnover of tension-bearing elements. Within this regime, for a wide range of network parameters, we observe a constant deformation (creep) rate that is linearly proportional to the rate of filament turnover, leading to a constant effective viscosity that is inversely proportional to turnover rate. Significantly, we observe a biphasic dependence of the creep rate on applied stress: below a critical stress threshold, the creep rate increases linearly with applied stress; above that threshold, the creep rate becomes independent of applied stress. We show that this biphasic stress dependence can be understood in terms of the nonlinear force-extension behavior of individual force-transmitting network elements. These results have important implications for understanding the origins and control of viscous flows both in the cortex of living cells and in other polymer networks. Copyright © 2014 Biophysical Society. Published by Elsevier Inc. All rights reserved.

Climate Change and Conservation Planning in California: The San Francisco Bay Area Upland Habitat Goals Approach

NASA Astrophysics Data System (ADS)

Branciforte, R.; Weiss, S. B.; Schaefer, N.

2008-12-01

Climate change threatens California's vast and unique biodiversity. The Bay Area Upland Habitat Goals is a comprehensive regional biodiversity assessment of the 9 counties surrounding San Francisco Bay, and is designing conservation land networks that will serve to protect, manage, and restore that biodiversity. Conservation goals for vegetation, rare plants, mammals, birds, fish, amphibians, reptiles, and invertebrates are set, and those goals are met using the optimization algorithm MARXAN. Climate change issues are being considered in the assessment and network design in several ways. The high spatial variability at mesoclimatic and topoclimatic scales in California creates high local biodiversity, and provides some degree of local resiliency to macroclimatic change. Mesoclimatic variability from 800 m scale PRISM climatic norms is used to assess "mesoclimate spaces" in distinct mountain ranges, so that high mesoclimatic variability, especially local extremes that likely support range limits of species and potential climatic refugia, can be captured in the network. Quantitative measures of network resiliency to climate change include the spatial range of key temperature and precipitation variables within planning units. Topoclimatic variability provides a finer-grained spatial patterning. Downscaling to the topoclimatic scale (10-50 m scale) includes modeling solar radiation across DEMs for predicting maximum temperature differentials, and topographic position indices for modeling minimum temperature differentials. PRISM data are also used to differentiate grasslands into distinct warm and cool types. The overall conservation strategy includes local and regional connectivity so that range shifts can be accommodated.

The European VLBI network

NASA Technical Reports Server (NTRS)

Schilizzi, R. T.

1980-01-01

The capabilities of the European very long baseline interferometry (VLBI) network are summarized. The range of baseline parameters, sensitivities, and recording and other equipment available are included. Plans for upgrading the recording facilities and the use of geostationary satellites for signal transfer and clock synchronization are discussed.

What Next for Networks and Netwars

DTIC Science & Technology

2001-01-01

Jacques Derrida , Michel Foucault, 30Standard sources on neorealism include a range of writings by Kenneth Waltz and John Mearshimer in particular. The...address banking networks, and Jacques (1990), which provides a classic defense of the importance of hierarchy in corporate structures. What Next for

Emergence, evolution and scaling of online social networks.

PubMed

Wang, Le-Zhi; Huang, Zi-Gang; Rong, Zhi-Hai; Wang, Xiao-Fan; Lai, Ying-Cheng

2014-01-01

Online social networks have become increasingly ubiquitous and understanding their structural, dynamical, and scaling properties not only is of fundamental interest but also has a broad range of applications. Such networks can be extremely dynamic, generated almost instantaneously by, for example, breaking-news items. We investigate a common class of online social networks, the user-user retweeting networks, by analyzing the empirical data collected from Sina Weibo (a massive twitter-like microblogging social network in China) with respect to the topic of the 2011 Japan earthquake. We uncover a number of algebraic scaling relations governing the growth and structure of the network and develop a probabilistic model that captures the basic dynamical features of the system. The model is capable of reproducing all the empirical results. Our analysis not only reveals the basic mechanisms underlying the dynamics of the retweeting networks, but also provides general insights into the control of information spreading on such networks.

Class network routing

DOEpatents

Bhanot, Gyan [Princeton, NJ; Blumrich, Matthias A [Ridgefield, CT; Chen, Dong [Croton On Hudson, NY; Coteus, Paul W [Yorktown Heights, NY; Gara, Alan G [Mount Kisco, NY; Giampapa, Mark E [Irvington, NY; Heidelberger, Philip [Cortlandt Manor, NY; Steinmacher-Burow, Burkhard D [Mount Kisco, NY; Takken, Todd E [Mount Kisco, NY; Vranas, Pavlos M [Bedford Hills, NY

2009-09-08

Class network routing is implemented in a network such as a computer network comprising a plurality of parallel compute processors at nodes thereof. Class network routing allows a compute processor to broadcast a message to a range (one or more) of other compute processors in the computer network, such as processors in a column or a row. Normally this type of operation requires a separate message to be sent to each processor. With class network routing pursuant to the invention, a single message is sufficient, which generally reduces the total number of messages in the network as well as the latency to do a broadcast. Class network routing is also applied to dense matrix inversion algorithms on distributed memory parallel supercomputers with hardware class function (multicast) capability. This is achieved by exploiting the fact that the communication patterns of dense matrix inversion can be served by hardware class functions, which results in faster execution times.

A human factors approach to range scheduling for satellite control

NASA Technical Reports Server (NTRS)

Wright, Cameron H. G.; Aitken, Donald J.

1991-01-01

Range scheduling for satellite control presents a classical problem: supervisory control of a large-scale dynamic system, with unwieldy amounts of interrelated data used as inputs to the decision process. Increased automation of the task, with the appropriate human-computer interface, is highly desirable. The development and user evaluation of a semi-automated network range scheduling system is described. The system incorporates a synergistic human-computer interface consisting of a large screen color display, voice input/output, a 'sonic pen' pointing device, a touchscreen color CRT, and a standard keyboard. From a human factors standpoint, this development represents the first major improvement in almost 30 years to the satellite control network scheduling task.

Neural Networks for Rapid Design and Analysis

NASA Technical Reports Server (NTRS)

Sparks, Dean W., Jr.; Maghami, Peiman G.

1998-01-01

Artificial neural networks have been employed for rapid and efficient dynamics and control analysis of flexible systems. Specifically, feedforward neural networks are designed to approximate nonlinear dynamic components over prescribed input ranges, and are used in simulations as a means to speed up the overall time response analysis process. To capture the recursive nature of dynamic components with artificial neural networks, recurrent networks, which use state feedback with the appropriate number of time delays, as inputs to the networks, are employed. Once properly trained, neural networks can give very good approximations to nonlinear dynamic components, and by their judicious use in simulations, allow the analyst the potential to speed up the analysis process considerably. To illustrate this potential speed up, an existing simulation model of a spacecraft reaction wheel system is executed, first conventionally, and then with an artificial neural network in place.

Investigating the effects of streamline-based fiber tractography on matrix scaling in brain connective network.

PubMed

Jan, Hengtai; Chao, Yi-Ping; Cho, Kuan-Hung; Kuo, Li-Wei

2013-01-01

Investigating the brain connective network using the modern graph theory has been widely applied in cognitive and clinical neuroscience research. In this study, we aimed to investigate the effects of streamline-based fiber tractography on the change of network properties and established a systematic framework to understand how an adequate network matrix scaling can be determined. The network properties, including degree, efficiency and betweenness centrality, show similar tendency in both left and right hemispheres. By employing the curve-fitting process with exponential law and measuring the residuals, the association between changes of network properties and threshold of track numbers is found and an adequate range of investigating the lateralization of brain network is suggested. The proposed approach can be further applied in clinical applications to improve the diagnostic sensitivity using network analysis with graph theory.

Current Trends and Challenges in Satellite Laser Ranging

NASA Astrophysics Data System (ADS)

Appleby, Graham M.; Bianco, Giuseppe; Noll, Carey E.; Pavlis, Erricos C.; Pearlman, Michael R.

2016-12-01

Satellite Laser Ranging (SLR) is used to measure accurately the distance from ground stations to retro-reflectors on satellites and on the Moon. SLR is one of the fundamental space-geodetic techniques that define the International Terrestrial Reference Frame (ITRF), which is the basis upon which many aspects of global change over space, time, and evolving technology are measured; with VLBI the two techniques define the scale of the ITRF; alone the SLR technique defines its origin (geocenter). The importance of the reference frame has recently been recognized at the inter-governmental level through the United Nations, which adopted in February 2015 the Resolution "Global Geodetic Reference Frame for Sustainable Development." Laser Ranging provides precision orbit determination and instrument calibration and validation for satellite-borne altimeters for the better understanding of sea level change, ocean dynamics, ice mass-balance, and terrestrial topography. It is also a tool to study the dynamics of the Moon and fundamental constants and theories. With the exception of the currently in-orbit GPS constellation, all GNSS satellites now carry retro-reflectors for improved orbit determination, harmonization of reference frames, and in-orbit co-location and system performance validation; the next generation of GPS satellites due for launch from 2019 onwards will also carry retro-reflectors. The ILRS delivers weekly realizations that are accumulated sequentially to extend the ITRF and the Earth Orientation Parameter series with a daily resolution. SLR technology continues to evolve towards the next-generation laser ranging systems and it is expected to successfully meet the challenges of the GGOS2020 program for a future Global Space Geodetic Network. Ranging precision is improving as higher repetition rate, narrower pulse lasers, and faster detectors are implemented within the network. Automation and pass interleaving at some stations is expanding temporal coverage and greatly enhancing efficiency. Discussions are ongoing with some missions that will allow the SLR network stations to provide crucial, but energy-safe, range measurements to optically vulnerable satellites. New retro-reflector designs are improving the signal link and enable daylight ranging that is now the norm for many stations. We discuss many of these laser ranging activities and some of the tough challenges that the SLR network currently faces.

Short-range structure and cation bonding in calcium-aluminum metaphosphate glasses.

PubMed

Schneider, J; Oliveira, S L; Nunes, L A O; Bonk, F; Panepucci, H

2005-01-24

Comprehension of short- and medium-range order of phosphate glasses is a topic of interest, due to the close relation between network structure and mechanical, thermal, and optical properties. In this work, the short-range structure of glasses (1 - x)Ca(PO(3))(2).xAl(PO(3))(3) with 0 < or = x < or = 0.47 was studied using solid-state nuclear magnetic resonance spectroscopy, Raman spectroscopy, density measurements, and differential scanning calorimetry. The bonding between a network modifier species, Al, and the network forming phosphate groups was probed using high-resolution nuclear magnetic resonance spectroscopy of (27)Al and (31)P. Changes in the compositional behavior of the density, glass transition temperature, PO(2) symmetric vibrations, and Al coordination number were verified at around x = 0.30. (31)P NMR spectra show the presence of phosphorus in Q(2) sites with nonbridging oxygens (NBOs) coordinated by Ca ions and also Q(2) sites with one NBO coordinated by Al (namely, Q(2)(1Al)). The changes in the properties as a function of x can be understood by considering the mean coordination number measured for Al and the formation of only Q(2) and Q(2)(1Al) species. It is possible to calculate that a network formed only by Q(2)(1Al) phosphates can just exist up to the upper limit of x = 0.48. Above this value, Q(2)(2Al) species should appear, imposing a major reorganization of the network. Above x = 0.30 the network undergoes a progressive reorganization to incorporate Al ions, maintaining the condition that only Q(2)(1Al) species are formed. These observations support the idea that bonding principles for cationic species inferred originally in binary phosphate glasses can also be extended to ternary systems.

The influence of tie strength on evolutionary games on networks: An empirical investigation

NASA Astrophysics Data System (ADS)

Buesser, Pierre; Peña, Jorge; Pestelacci, Enea; Tomassini, Marco

2011-11-01

Extending previous work on unweighted networks, we present here a systematic numerical investigation of standard evolutionary games on weighted networks. In the absence of any reliable model for generating weighted social networks, we attribute weights to links in a few ways supported by empirical data ranging from totally uncorrelated to weighted bipartite networks. The results of the extensive simulation work on standard complex network models show that, except in a case that does not seem to be common in social networks, taking the tie strength into account does not change in a radical manner the long-run steady-state behavior of the studied games. Besides model networks, we also included a real-life case drawn from a coauthorship network. In this case also, taking the weights into account only changes the results slightly with respect to the raw unweighted graph, although to draw more reliable conclusions on real social networks many more cases should be studied as these weighted networks become available.

Dynamic vs. static social networks in models of parasite transmission: predicting Cryptosporidium spread in wild lemurs.

PubMed

Springer, Andrea; Kappeler, Peter M; Nunn, Charles L

2017-05-01

Social networks provide an established tool to implement heterogeneous contact structures in epidemiological models. Dynamic temporal changes in contact structure and ranging behaviour of wildlife may impact disease dynamics. A consensus has yet to emerge, however, concerning the conditions in which network dynamics impact model outcomes, as compared to static approximations that average contact rates over longer time periods. Furthermore, as many pathogens can be transmitted both environmentally and via close contact, it is important to investigate the relative influence of both transmission routes in real-world populations. Here, we use empirically derived networks from a population of wild primates, Verreaux's sifakas (Propithecus verreauxi), and simulated networks to investigate pathogen spread in dynamic vs. static social networks. First, we constructed a susceptible-exposed-infected-recovered model of Cryptosporidium spread in wild Verreaux's sifakas. We incorporated social and environmental transmission routes and parameterized the model for two different climatic seasons. Second, we used simulated networks and greater variation in epidemiological parameters to investigate the conditions in which dynamic networks produce larger outbreak sizes than static networks. We found that average outbreak size of Cryptosporidium infections in sifakas was larger when the disease was introduced in the dry season than in the wet season, driven by an increase in home range overlap towards the end of the dry season. Regardless of season, dynamic networks always produced larger average outbreak sizes than static networks. Larger outbreaks in dynamic models based on simulated networks occurred especially when the probability of transmission and recovery were low. Variation in tie strength in the dynamic networks also had a major impact on outbreak size, while network modularity had a weaker influence than epidemiological parameters that determine transmission and recovery. Our study adds to emerging evidence that dynamic networks can change predictions of disease dynamics, especially if the disease shows low transmissibility and a long infectious period, and when environmental conditions lead to enhanced between-group contact after an infectious agent has been introduced. © 2016 The Authors. Journal of Animal Ecology published by John Wiley & Sons Ltd on behalf of British Ecological Society.

Moving towards an Integrated Theory of Policy Networks: A Multi-Theoretical Approach for Examining State-Level Policy Change in U.S. Subsystems. Working Paper #45

ERIC Educational Resources Information Center

Galey, Sarah; Youngs, Peter

2014-01-01

Scholars have developed a wide range of theories to explain both stability and change in policy subsystems. In recent years, a burgeoning literature has emerged that focuses on the application of network analysis in policy research, more formally known as Policy Network Analysis (PNA). This approach, while still developing, has great potential as…

Exploring 3D optimal channel networks by multiple organizing principles

NASA Astrophysics Data System (ADS)

Mason, Emanuele; Bizzi, Simone; Cominola, Andrea; Castelletti, Andrea; Paik, Kyungrock

2017-04-01

Catchment topography and flow networks are shaped by the interactions of water and sediment across various spatial and temporal scales. The complexity of these processes hinders the development of models able to assess the validity of general principles governing such phenomena. The theory of Optimal Channel Networks (OCNs) proved that it is possible to generate drainage networks statistically comparable to those observed in nature by minimizing the energy spent by the water flowing through them. So far, the OCN theory has been developed for planar 2D domains, assuming equal energy expenditure per unit area of channel and, correspondingly, a constant slope-discharge relationship. In this work, we apply the OCN theory to 3D problems by introducing a multi-principle minimization starting from an artificial digital elevation model of pyramidal shape. The OCN theory assumption of constant slope-area relationship is relaxed and embedded into a second-order principle. The modelled 3D channel networks achieve lower total energy expenditure corresponding to 2D sub-optimal OCNs bound to specific slope-area relationships. This is the first time we are able to explore accessible 3D OCNs starting from a general DEM. By contrasting the modelled 3D OCNs and natural river networks, we found statistical similarities of two indexes, namely the area exponent index and the profile concavity index. Among the wide range of alternative and sub-optimal river networks, a minimum degree of 3D network organization is found to guarantee the indexes values within the natural range. These networks simultaneously possess topological and topographic properties of real river networks. We found a pivotal functional link between slope-area relationship and accessible sub-optimal 2D river network paths, which suggests that geological and climate conditions producing slope-area relationships in natural basins co-determine the degree of optimality of accessible network paths.

Overcoming the effects of false positives and threshold bias in graph theoretical analyses of neuroimaging data.

PubMed

Drakesmith, M; Caeyenberghs, K; Dutt, A; Lewis, G; David, A S; Jones, D K

2015-09-01

Graph theory (GT) is a powerful framework for quantifying topological features of neuroimaging-derived functional and structural networks. However, false positive (FP) connections arise frequently and influence the inferred topology of networks. Thresholding is often used to overcome this problem, but an appropriate threshold often relies on a priori assumptions, which will alter inferred network topologies. Four common network metrics (global efficiency, mean clustering coefficient, mean betweenness and smallworldness) were tested using a model tractography dataset. It was found that all four network metrics were significantly affected even by just one FP. Results also show that thresholding effectively dampens the impact of FPs, but at the expense of adding significant bias to network metrics. In a larger number (n=248) of tractography datasets, statistics were computed across random group permutations for a range of thresholds, revealing that statistics for network metrics varied significantly more than for non-network metrics (i.e., number of streamlines and number of edges). Varying degrees of network atrophy were introduced artificially to half the datasets, to test sensitivity to genuine group differences. For some network metrics, this atrophy was detected as significant (p<0.05, determined using permutation testing) only across a limited range of thresholds. We propose a multi-threshold permutation correction (MTPC) method, based on the cluster-enhanced permutation correction approach, to identify sustained significant effects across clusters of thresholds. This approach minimises requirements to determine a single threshold a priori. We demonstrate improved sensitivity of MTPC-corrected metrics to genuine group effects compared to an existing approach and demonstrate the use of MTPC on a previously published network analysis of tractography data derived from a clinical population. In conclusion, we show that there are large biases and instability induced by thresholding, making statistical comparisons of network metrics difficult. However, by testing for effects across multiple thresholds using MTPC, true group differences can be robustly identified. Copyright © 2015. Published by Elsevier Inc.

Spatial connections in regional climate model rainfall outputs at different temporal scales: Application of network theory

NASA Astrophysics Data System (ADS)

Naufan, Ihsan; Sivakumar, Bellie; Woldemeskel, Fitsum M.; Raghavan, Srivatsan V.; Vu, Minh Tue; Liong, Shie-Yui

2018-01-01

Understanding the spatial and temporal variability of rainfall has always been a great challenge, and the impacts of climate change further complicate this issue. The present study employs the concepts of complex networks to study the spatial connections in rainfall, with emphasis on climate change and rainfall scaling. Rainfall outputs (during 1961-1990) from a regional climate model (i.e. Weather Research and Forecasting (WRF) model that downscaled the European Centre for Medium-range Weather Forecasts, ECMWF ERA-40 reanalyses) over Southeast Asia are studied, and data corresponding to eight different temporal scales (6-hr, 12-hr, daily, 2-day, 4-day, weekly, biweekly, and monthly) are analyzed. Two network-based methods are applied to examine the connections in rainfall: clustering coefficient (a measure of the network's local density) and degree distribution (a measure of the network's spread). The influence of rainfall correlation threshold (T) on spatial connections is also investigated by considering seven different threshold levels (ranging from 0.5 to 0.8). The results indicate that: (1) rainfall networks corresponding to much coarser temporal scales exhibit properties similar to that of small-world networks, regardless of the threshold; (2) rainfall networks corresponding to much finer temporal scales may be classified as either small-world networks or scale-free networks, depending upon the threshold; and (3) rainfall spatial connections exhibit a transition phase at intermediate temporal scales, especially at high thresholds. These results suggest that the most appropriate model for studying spatial connections may often be different at different temporal scales, and that a combination of small-world and scale-free network models might be more appropriate for rainfall upscaling/downscaling across all scales, in the strict sense of scale-invariance. The results also suggest that spatial connections in the studied rainfall networks in Southeast Asia are weak, especially when more stringent conditions are imposed (i.e. when T is very high), except at the monthly scale.

Propagation and immunization of infection on general networks with both homogeneous and heterogeneous components

NASA Astrophysics Data System (ADS)

Liu, Zonghua; Lai, Ying-Cheng; Ye, Nong

2003-03-01

We consider the entire spectrum of architectures of general networks, ranging from being heterogeneous (scale-free) to homogeneous (random), and investigate the infection dynamics by using a three-state epidemiological model that does not involve the mechanism of self-recovery. This model is relevant to realistic situations such as the propagation of a flu virus or information over a social network. Our heuristic analysis and computations indicate that (1) regardless of the network architecture, there exists a substantial fraction of nodes that can never be infected and (2) heterogeneous networks are relatively more robust against spreads of infection as compared with homogeneous networks. We have also considered the problem of immunization for preventing wide spread of infection, with the result that targeted immunization is effective for heterogeneous networks.

Distributed network scheduling

NASA Technical Reports Server (NTRS)

Clement, Bradley J.; Schaffer, Steven R.

2004-01-01

Distributed Network Scheduling is the scheduling of future communications of a network by nodes in the network. This report details software for doing this onboard spacecraft in a remote network. While prior work on distributed scheduling has been applied to remote spacecraft networks, the software reported here focuses on modeling communication activities in greater detail and including quality of service constraints. Our main results are based on a Mars network of spacecraft and include identifying a maximum opportunity of improving traverse exploration rate a factor of three; a simulation showing reduction in one-way delivery times from a rover to Earth from as much as 5 to 1.5 hours; simulated response to unexpected events averaging under an hour onboard; and ground schedule generation ranging from seconds to 50 minutes for 15 to 100 communication goals.

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.

Research of Ad Hoc Networks Access Algorithm

NASA Astrophysics Data System (ADS)

Xiang, Ma

With the continuous development of mobile communication technology, Ad Hoc access network has become a hot research, Ad Hoc access network nodes can be used to expand capacity of multi-hop communication range of mobile communication system, even business adjacent to the community, improve edge data rates. When the ad hoc network is the access network of the internet, the gateway discovery protocol is very important to choose the most appropriate gateway to guarantee the connectivity between ad hoc network and IP based fixed networks. The paper proposes a QoS gateway discovery protocol which uses the time delay and stable route to the gateway selection conditions. And according to the gateway discovery protocol, it also proposes a fast handover scheme which can decrease the handover time and improve the handover efficiency.

Bridges in complex networks

NASA Astrophysics Data System (ADS)

Wu, Ang-Kun; Tian, Liang; Liu, Yang-Yu

2018-01-01

A bridge in a graph is an edge whose removal disconnects the graph and increases the number of connected components. We calculate the fraction of bridges in a wide range of real-world networks and their randomized counterparts. We find that real networks typically have more bridges than their completely randomized counterparts, but they have a fraction of bridges that is very similar to their degree-preserving randomizations. We define an edge centrality measure, called bridgeness, to quantify the importance of a bridge in damaging a network. We find that certain real networks have a very large average and variance of bridgeness compared to their degree-preserving randomizations and other real networks. Finally, we offer an analytical framework to calculate the bridge fraction and the average and variance of bridgeness for uncorrelated random networks with arbitrary degree distributions.

Functional Alignment of Metabolic Networks.

PubMed

Mazza, Arnon; Wagner, Allon; Ruppin, Eytan; Sharan, Roded

2016-05-01

Network alignment has become a standard tool in comparative biology, allowing the inference of protein function, interaction, and orthology. However, current alignment techniques are based on topological properties of networks and do not take into account their functional implications. Here we propose, for the first time, an algorithm to align two metabolic networks by taking advantage of their coupled metabolic models. These models allow us to assess the functional implications of genes or reactions, captured by the metabolic fluxes that are altered following their deletion from the network. Such implications may spread far beyond the region of the network where the gene or reaction lies. We apply our algorithm to align metabolic networks from various organisms, ranging from bacteria to humans, showing that our alignment can reveal functional orthology relations that are missed by conventional topological alignments.

Discrete-time BAM neural networks with variable delays

NASA Astrophysics Data System (ADS)

Liu, Xin-Ge; Tang, Mei-Lan; Martin, Ralph; Liu, Xin-Bi

2007-07-01

This Letter deals with the global exponential stability of discrete-time bidirectional associative memory (BAM) neural networks with variable delays. Using a Lyapunov functional, and linear matrix inequality techniques (LMI), we derive a new delay-dependent exponential stability criterion for BAM neural networks with variable delays. As this criterion has no extra constraints on the variable delay functions, it can be applied to quite general BAM neural networks with a broad range of time delay functions. It is also easy to use in practice. An example is provided to illustrate the theoretical development.

Measure-valued solutions to nonlocal transport equations on networks

NASA Astrophysics Data System (ADS)

Camilli, Fabio; De Maio, Raul; Tosin, Andrea

2018-06-01

Aiming to describe traffic flow on road networks with long-range driver interactions, we study a nonlinear transport equation defined on an oriented network where the velocity field depends not only on the state variable but also on the distribution of the population. We prove existence, uniqueness and continuous dependence results of the solution intended in a suitable measure-theoretic sense. We also provide a representation formula in terms of the push-forward of the initial and boundary data along the network and discuss an explicit example of nonlocal velocity field fitting our framework.

Optimal multi-community network modularity for information diffusion

NASA Astrophysics Data System (ADS)

Wu, Jiaocan; Du, Ruping; Zheng, Yingying; Liu, Dong

2016-02-01

Studies demonstrate that community structure plays an important role in information spreading recently. In this paper, we investigate the impact of multi-community structure on information diffusion with linear threshold model. We utilize extended GN network that contains four communities and analyze dynamic behaviors of information that spreads on it. And we discover the optimal multi-community network modularity for information diffusion based on the social reinforcement. Results show that, within the appropriate range, multi-community structure will facilitate information diffusion instead of hindering it, which accords with the results derived from two-community network.

Acoustic system for communication in pipelines

DOEpatents

Martin, II, Louis Peter; Cooper, John F [Oakland, CA

2008-09-09

A system for communication in a pipe, or pipeline, or network of pipes containing a fluid. The system includes an encoding and transmitting sub-system connected to the pipe, or pipeline, or network of pipes that transmits a signal in the frequency range of 3-100 kHz into the pipe, or pipeline, or network of pipes containing a fluid, and a receiver and processor sub-system connected to the pipe, or pipeline, or network of pipes containing a fluid that receives said signal and uses said signal for a desired application.

Cross-Linker Unbinding and Self-Similarity in Bundled Cytoskeletal Networks

NASA Astrophysics Data System (ADS)

Lieleg, O.; Bausch, A. R.

2007-10-01

The macromechanical properties of purely bundled in vitro actin networks are not only determined by the micromechanical properties of individual bundles but also by molecular unbinding events of the actin-binding protein (ABP) fascin. Under high mechanical load the network elasticity depends on the forced unbinding of individual ABPs in a rate dependent manner. Cross-linker unbinding in combination with the structural self-similarity of the network enables the introduction of a concentration-time superposition principle—broadening the mechanically accessible frequency range over 8 orders of magnitude.

Deep Recurrent Neural Networks for Human Activity Recognition

PubMed Central

Murad, Abdulmajid

2017-01-01

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs. PMID:29113103

Deep Recurrent Neural Networks for Human Activity Recognition.

PubMed

Murad, Abdulmajid; Pyun, Jae-Young

2017-11-06

Adopting deep learning methods for human activity recognition has been effective in extracting discriminative features from raw input sequences acquired from body-worn sensors. Although human movements are encoded in a sequence of successive samples in time, typical machine learning methods perform recognition tasks without exploiting the temporal correlations between input data samples. Convolutional neural networks (CNNs) address this issue by using convolutions across a one-dimensional temporal sequence to capture dependencies among input data. However, the size of convolutional kernels restricts the captured range of dependencies between data samples. As a result, typical models are unadaptable to a wide range of activity-recognition configurations and require fixed-length input windows. In this paper, we propose the use of deep recurrent neural networks (DRNNs) for building recognition models that are capable of capturing long-range dependencies in variable-length input sequences. We present unidirectional, bidirectional, and cascaded architectures based on long short-term memory (LSTM) DRNNs and evaluate their effectiveness on miscellaneous benchmark datasets. Experimental results show that our proposed models outperform methods employing conventional machine learning, such as support vector machine (SVM) and k-nearest neighbors (KNN). Additionally, the proposed models yield better performance than other deep learning techniques, such as deep believe networks (DBNs) and CNNs.

A novel topology control approach to maintain the node degree in dynamic wireless sensor networks.

PubMed

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-03-07

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power.

Electricity storage: Friend or foe of the networks?

NASA Astrophysics Data System (ADS)

Jamasb, Tooraj

2017-06-01

As storage technology progresses it offers a range of solutions and services to users and the electricity industry. A new study explores whether or not this will eventually lead to self-sufficient consumers and spell the end of the networks as we know them.

Security in Wireless Sensor Networks Employing MACGSP6

ERIC Educational Resources Information Center

Nitipaichit, Yuttasart

2010-01-01

Wireless Sensor Networks (WSNs) have unique characteristics which constrain them; including small energy stores, limited computation, and short range communication capability. Most traditional security algorithms use cryptographic primitives such as Public-key cryptography and are not optimized for energy usage. Employing these algorithms for the…

Conceptual Developments in Schema Theory.

ERIC Educational Resources Information Center

Bigenho, Frederick W., Jr.

The conceptual development of schema theory, the way an individual organizes knowledge, is discussed, reviewing a range of perspectives regarding schema. Schema has been defined as the interfacing of incoming information with prior knowledge, clustered in networks. These networks comprise a superordinate concept and supporting information. The…

Three-Dimensional Superhydrophobic Nanowire Networks for Enhancing Condensation Heat Transfer

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

Yang, Ronggui; Wen, Rongfu; Xu, Shanshan

Spontaneous droplet jumping on nanostructured surfaces can potentially enhance condensation heat transfer by accelerating droplet removal. However, uncontrolled nucleation in the micro-defects of nanostructured superhydrophobic surfaces could lead to the formation of large pinned droplets, which greatly degrades the performance. Here, we experimentally demonstrate for the first time stable and efficient jumping droplet condensation on a superhydrophobic surface with three-dimensional (3D) copper nanowire networks. Due to the formation of interconnections among nanowires, the micro-defects are eliminated while the spacing between nanowires is reduced, which results in the formation of highly mobile droplets. By preventing flooding on 3D nanowire networks, wemore » experimentally demonstrate a 100% higher heat flux compared with that on the state-of-the-art hydrophobic surface over a wide range of subcooling (up to 28 K). The remarkable water repellency of 3D nanowire networks can be applied to a broad range of water-harvesting and phase-change heat transfer applications.« less

EMERGEncy ID NET: Review of a 20-Year Multisite Emergency Department Emerging Infections Research Network

PubMed Central

Santibanez, Scott; Fischer, Leah S; Krishnadasan, Anusha; Sederdahl, Bethany; Merlin, Toby; Moran, Gregory J; Talan, David A; Mower, William; Sullivan, Matthew; Abrahamian, Fredrick M; Ong, Sam; Gross, Eric; Salhi, Bisan; Heilpern, Katherine; Hess, Jeremy; Karras, David; Biros, Michelle; Dunbar, Lala; Takhar, Sukhjit; Pollack, Charles; Runge, Jeffrey; Cheney, Paul; Rothrock, Stephen; O’Brian, John; Citron, Diane; Goldstein, Ellie; Finegold, Sydney; Nakase, Janet; Newdow, Michael; Merchant, Guy; Pathmarajah, Kavitha; Gonzalez, Eva; Mulrow, Mary; Bussman, Silas; Kalugdnan, Vernon; Peterson, Stephen; Pitts, Seth; Narayan, Kamil; Rubin, Ada; Kemble, Laurie; Beckham, Danielle; Neal, Niccole; Yagapen, Annick; Von Hofen, Carol; Hatala, Kathleen; Fuentes, Shelley; Sibley, Debbi; Colucci, Ashley; Hernandez, Jackeline; Cruse, Hope; Usher, Sarah; Hendrickson, Audrey; Dehnkamp, Kimberly; Zeglin, Britney; Jambaulikar, Guruprasad; Gorwitz, Rachel; Limbago, Brandi; Kuehnert, Matthew; Jarvis, William; Slutsker, Larry; Arvay, Melissa; Conn, Laura

2017-01-01

Abstract As providers of frontline clinical care for patients with acute and potentially life-threatening infections, emergency departments (EDs) have the priorities of saving lives and providing care quickly and efficiently. Although these facilities see a diversity of patients 24 hours per day and can collect prospective data in real time, their ability to conduct timely research on infectious syndromes is not well recognized. EMERGEncy ID NET is a national network that demonstrates that EDs can also collect data and conduct research in real time. This network collaborates with the Centers for Disease Control and Prevention (CDC) and other partners to study and address a wide range of infectious diseases and clinical syndromes. In this paper, we review selected highlights of EMERGEncy ID NET’s history from 1995 to 2017. We focus on the establishment of this multisite research network and the network’s collaborative research on a wide range of ED clinical topics. PMID:29670931

A Bayesian network to predict vulnerability to sea-level rise: data report

USGS Publications Warehouse

Gutierrez, Benjamin T.; Plant, Nathaniel G.; Thieler, E. Robert

2011-01-01

During the 21st century, sea-level rise is projected to have a wide range of effects on coastal environments, development, and infrastructure. Consequently, there has been an increased focus on developing modeling or other analytical approaches to evaluate potential impacts to inform coastal management. This report provides the data that were used to develop and evaluate the performance of a Bayesian network designed to predict long-term shoreline change due to sea-level rise. The data include local rates of relative sea-level rise, wave height, tide range, geomorphic classification, coastal slope, and shoreline-change rate compiled as part of the U.S. Geological Survey Coastal Vulnerability Index for the U.S. Atlantic coast. In this project, the Bayesian network is used to define relationships among driving forces, geologic constraints, and coastal responses. Using this information, the Bayesian network is used to make probabilistic predictions of shoreline change in response to different future sea-level-rise scenarios.

Brief communication: Possible explanation of the values of Hack's drainage basin, river length scaling exponent

NASA Astrophysics Data System (ADS)

Hunt, Allen G.

2016-04-01

Percolation theory can be used to find water flow paths of least resistance. Application of percolation theory to drainage networks allows identification of the range of exponent values that describe the tortuosity of rivers in real river networks, which is then used to generate the observed scaling between drainage basin area and channel length, a relationship known as Hack's law. Such a theoretical basis for Hack's law may allow interpretation of the range of exponent values based on an assessment of the heterogeneity of the substrate.

Explanation of the values of Hack's drainage basin, river length scaling exponent

NASA Astrophysics Data System (ADS)

Hunt, A. G.

2015-08-01

Percolation theory can be used to find water flow paths of least resistance. The application of percolation theory to drainage networks allows identification of the range of exponent values that describe the tortuosity of rivers in real river networks, which is then used to generate the observed scaling between drainage basin area and channel length, a relationship known as Hack's law. Such a theoretical basis for Hack's law allows interpretation of the range of exponent values based on an assessment of the heterogeneity of the substrate.

Micro-Computer Network Architecture for Range Instrumentation Applications - Volume 1

DTIC Science & Technology

1991-12-18

AD-A247 836 MTI-R89-006-28 Micro-Computer Network Architecture for Range Instrumentation Applications Volume 1 Mitchell R. Belzer DTIC Yong M . Cho... M . Belzer, Y. Cho, J. Han 13a. TYPE OF REPORT 13b. TIME COVERED 14. DATE OF REPORT (Year, Month, Day) 15. PAGE COUNT Final Technical FROM 09SeDQ89...SpecialI I , 1 . . r l n l m s n u m mt l ~ i m I n : t l l l Contents page cover page ....................................................... 1 Report

Quantitative analysis of volatile organic compounds using ion mobility spectra and cascade correlation neural networks

NASA Technical Reports Server (NTRS)

Harrington, Peter DEB.; Zheng, Peng

1995-01-01

Ion Mobility Spectrometry (IMS) is a powerful technique for trace organic analysis in the gas phase. Quantitative measurements are difficult, because IMS has a limited linear range. Factors that may affect the instrument response are pressure, temperature, and humidity. Nonlinear calibration methods, such as neural networks, may be ideally suited for IMS. Neural networks have the capability of modeling complex systems. Many neural networks suffer from long training times and overfitting. Cascade correlation neural networks train at very fast rates. They also build their own topology, that is a number of layers and number of units in each layer. By controlling the decay parameter in training neural networks, reproducible and general models may be obtained.

Optimal synchronization in space

NASA Astrophysics Data System (ADS)

Brede, Markus

2010-02-01

In this Rapid Communication we investigate spatially constrained networks that realize optimal synchronization properties. After arguing that spatial constraints can be imposed by limiting the amount of “wire” available to connect nodes distributed in space, we use numerical optimization methods to construct networks that realize different trade offs between optimal synchronization and spatial constraints. Over a large range of parameters such optimal networks are found to have a link length distribution characterized by power-law tails P(l)∝l-α , with exponents α increasing as the networks become more constrained in space. It is also shown that the optimal networks, which constitute a particular type of small world network, are characterized by the presence of nodes of distinctly larger than average degree around which long-distance links are centered.

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.

Uniting functional network topology and oscillations in the fronto-parietal single unit network of behaving primates.

PubMed

Dann, Benjamin; Michaels, Jonathan A; Schaffelhofer, Stefan; Scherberger, Hansjörg

2016-08-15

The functional communication of neurons in cortical networks underlies higher cognitive processes. Yet, little is known about the organization of the single neuron network or its relationship to the synchronization processes that are essential for its formation. Here, we show that the functional single neuron network of three fronto-parietal areas during active behavior of macaque monkeys is highly complex. The network was closely connected (small-world) and consisted of functional modules spanning these areas. Surprisingly, the importance of different neurons to the network was highly heterogeneous with a small number of neurons contributing strongly to the network function (hubs), which were in turn strongly inter-connected (rich-club). Examination of the network synchronization revealed that the identified rich-club consisted of neurons that were synchronized in the beta or low frequency range, whereas other neurons were mostly non-oscillatory synchronized. Therefore, oscillatory synchrony may be a central communication mechanism for highly organized functional spiking networks.

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.

The Human Immunodeficiency Virus Endemic: Maintaining Disease Transmission in At-Risk Urban Areas.

PubMed

Rothenberg, Richard B; Dai, Dajun; Adams, Mary Anne; Heath, John Wesley

2017-02-01

A study of network relationships, geographic contiguity, and risk behavior was designed to test the hypothesis that all 3 are required to maintain endemicity of human immunodeficiency virus (HIV) in at-risk urban communities. Specifically, a highly interactive network, close geographic proximity, and compound risk (multiple high-risk activities with multiple partners) would be required. We enrolled 927 participants from two contiguous geographic areas in Atlanta, GA: a higher-risk area and lower-risk area, as measured by history of HIV reporting. We began by enrolling 30 "seeds" (15 in each area) who were comparable in their demographic and behavioral characteristics, and constructed 30 networks using a chain-link design. We assessed each individual's geographic range; measured the network characteristics of those in the higher and lower-risk areas; and measured compound risk as the presence of two or more (of 6) major risks for HIV. Among participants in the higher-risk area, the frequency of compound risk was 15%, compared with 5% in the lower-risk area. Geographic cohesion in the higher-risk group was substantially higher than that in the lower-risk group, based on comparison of geographic distance and social distance, and on the extent of overlap of personal geographic range. The networks in the 2 areas were similar: both areas show highly interactive networks with similar degree distributions, and most measures of network attributes were virtually the same. Our original hypothesis was supported in part. The higher and lower-risk groups differed appreciably with regard to risk and geographic cohesion, but were substantially the same with regard to network properties. These results suggest that a "minimum" network configuration may be required for maintenance of endemic transmission, but a particular prevalence level may be determined by factors related to risk, geography, and possibly other factors.

Network inference from multimodal data: A review of approaches from infectious disease transmission.

PubMed

Ray, Bisakha; Ghedin, Elodie; Chunara, Rumi

2016-12-01

Networks inference problems are commonly found in multiple biomedical subfields such as genomics, metagenomics, neuroscience, and epidemiology. Networks are useful for representing a wide range of complex interactions ranging from those between molecular biomarkers, neurons, and microbial communities, to those found in human or animal populations. Recent technological advances have resulted in an increasing amount of healthcare data in multiple modalities, increasing the preponderance of network inference problems. Multi-domain data can now be used to improve the robustness and reliability of recovered networks from unimodal data. For infectious diseases in particular, there is a body of knowledge that has been focused on combining multiple pieces of linked information. Combining or analyzing disparate modalities in concert has demonstrated greater insight into disease transmission than could be obtained from any single modality in isolation. This has been particularly helpful in understanding incidence and transmission at early stages of infections that have pandemic potential. Novel pieces of linked information in the form of spatial, temporal, and other covariates including high-throughput sequence data, clinical visits, social network information, pharmaceutical prescriptions, and clinical symptoms (reported as free-text data) also encourage further investigation of these methods. The purpose of this review is to provide an in-depth analysis of multimodal infectious disease transmission network inference methods with a specific focus on Bayesian inference. We focus on analytical Bayesian inference-based methods as this enables recovering multiple parameters simultaneously, for example, not just the disease transmission network, but also parameters of epidemic dynamics. Our review studies their assumptions, key inference parameters and limitations, and ultimately provides insights about improving future network inference methods in multiple applications. Copyright © 2016 Elsevier Inc. All rights reserved.

The Anthropogenic/Lightning Effects Around Houston: The Houston Environmental Aerosol Thunderstorm (HEAT) Project - 2005

NASA Astrophysics Data System (ADS)

Orville, R. E.

2004-12-01

A major field program will occur in summer 2005 to determine the sources and causes for the enhanced cloud-to-ground lightning over Houston, Texas. This program will be in association with simultaneous experiments supported by the Environmental Protection Agency (EPA) and the Texas Commission on Environmental Quality (TCEQ), formally the Texas Natural Resource Conservation Commission (TNRCC). Recent studies covering the period 1989-2002 document a 60 percent increase of cloud-to-ground lightning in the Houston area as compared to surrounding background values, which is second in flash density only to the Tampa Bay, Florida area. We suggest that the elevated flash densities could result from several factors, including 1) the convergence due to the urban heat island effect and complex sea breeze (thermal hypothesis), and 2) the increasing levels of air pollution from anthropogenic sources producing numerous small cloud droplets and thereby suppressing mean droplet size (aerosol hypothesis). The latter effect would enable more cloud water to reach the mixed phase region where it is involved in the formation of precipitation and the separation of electric charge, leading to an enhancement of lightning. The primary goals of HEAT are to examine the effects of (1) pollution, (2) the urban heat island, and (3) the complex coastline on storms and lightning characteristics in the Houston area. The transport of air pollutants by Houston thunderstorms will be investigated. In particular, the relative amounts of lightning-produced and convectively transported NOx into the upper troposphere will be determined, and a comparison of the different NOx sources in the urban area of Houston will be developed. The HEAT project is based on the observation that there is an enhancement in cloud-to-ground (CG) lightning. Total lightning (intracloud (IC) and CG) will be measured using a lightning mapping system (LDAR II) to observe if there is an enhancement in intracloud lightning as well.

High altitude organic gold: The production network for Ophiocordyceps sinensis from far-western Nepal.

PubMed

Pouliot, Mariève; Pyakurel, Dipesh; Smith-Hall, Carsten

2018-05-23

Ophiocordyceps sinensis (Berk.) G.H.Sung, J.M.Sung, Hywel-Jones & Spatafora, a high altitude Himalayan fungus-caterpillar product found in alpine meadows in China, Bhutan, Nepal, and India, has been used in the Traditional Chinese Medicine system for over 2000 years. Heightened demand in China over the past 15 years, coupled with limited production, has led to a price hike and increased economic importance of harvests to rural households throughout the species' range. There is, however, limited knowledge on the actors and profit distribution in the O. sinensis production network, especially from the distribution areas on the southern flanks of the Himalayas. Filling in this knowledge gap is essential to the identification and design of public interventions. To describe and quantify the O. sinensis production network originating from Darchula District in far-western Nepal. Data was collected, for fiscal year 2014-15, in spring and summer 2016 using standardized collector (n=56) and trader (n=45) questionnaires in Darchula District, and central wholesaler (n=9) questionnaires in cities of Nepal. All questionnaires contained quantitative and qualitative components focusing on key elements of the production network, i.e. value creation, enhancement, and capture; and network and territorial embeddedness. Trade is sustained and significant even at the margins of the distributional range, with 384.1 kg of O. sinensis harvested in and traded from Darchula District in 2014-15, having a collector value of approximately USD 4.7 million and constituting the dominant household-level source of income for collectors. The functioning production network is characterised by conflicts in relation to value creation, a high share of value capture by collectors, limited value enhancement, and a high degree of network and territorial embeddedness. O. sinensis income is of major economic importance for rural households at the margin of its distribution range in Nepal. Production networks operated by informal actors establishing trust-based relationships allow responses to cross-border market signals, enabling the flow of rural and remote environmental resources to urban centres of demand. There is scope for public interventions, e.g. to determine the drivers of demand. Copyright © 2018 Elsevier B.V. All rights reserved.

International co-operative program on assessment and monitoring of air pollution effects on forests: The Sierra Ancha Experimental Forest, Arizona

Treesearch

Boris Poff; Daniel G. Neary

2008-01-01

At the end of the 2007 Fiscal Year, the Experimental Forests and Ranges (EFR) Synthesis Network Committee awarded funds to 18 sites to establish a strategic ICP Level II (described below) synthesis network in the United States. Eleven Experimental Forest were selected to be included in the network, as well as seven Long Term Ecological Research (LTER) sites. This will...

Using algebra for massively parallel processor design and utilization

NASA Technical Reports Server (NTRS)

Campbell, Lowell; Fellows, Michael R.

1990-01-01

This paper summarizes the author's advances in the design of dense processor networks. Within is reported a collection of recent constructions of dense symmetric networks that provide the largest know values for the number of nodes that can be placed in a network of a given degree and diameter. The constructions are in the range of current potential engineering significance and are based on groups of automorphisms of finite-dimensional vector spaces.

Climate Controls on Tree Growth in the Western Mediterranean

NASA Technical Reports Server (NTRS)

Touchan, Ramzi; Anchukaitis, Kevin J.; Meko, David M.; Kerchouche, Dalila; Slimani, Said; Ilmen, Rachid; Hasnaoui, Fouad; Guibal, Frederic; Canarerim Hesys Hykui; Sanchez-Salguero, Raul;

Achieving large dynamic range control of gene expression with a compact RNA transcription–translation regulator

PubMed Central

2017-01-01

Abstract RNA transcriptional regulators are emerging as versatile components for genetic network construction. However, these regulators suffer from incomplete repression in their OFF state, making their dynamic range less than that of their protein counterparts. This incomplete repression causes expression leak, which impedes the construction of larger synthetic regulatory networks as leak propagation can interfere with desired network function. To address this, we demonstrate how naturally derived antisense RNA-mediated transcriptional regulators can be configured to regulate both transcription and translation in a single compact RNA mechanism that functions in Escherichia coli. Using in vivo gene expression assays, we show that a combination of transcriptional termination and ribosome binding site sequestration increases repression from 85% to 98%, or activation from 10-fold to over 900-fold, in response to cognate antisense RNAs. We also show that orthogonal repressive versions of this mechanism can be created through engineering minimal antisense RNAs. Finally, to demonstrate the utility of this mechanism, we use it to reduce network leak in an RNA-only cascade. We anticipate these regulators will find broad use as synthetic biology moves beyond parts engineering to the design and construction of more sophisticated regulatory networks. PMID:28387839

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks.

PubMed

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-09-25

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude.

Network analysis of a regional fishery: Implications for management of natural resources, and recruitment and retention of anglers

USGS Publications Warehouse

Martin, Dustin R.; Shizuka, Daizaburo; Chizinski, Christopher J.; Pope, Kevin L.

2017-01-01

Angler groups and water-body types interact to create a complex social-ecological system. Network analysis could inform detailed mechanistic models on, and provide managers better information about, basic patterns of fishing activity. Differences in behavior and reservoir selection among angler groups in a regional fishery, the Salt Valley fishery in southeastern Nebraska, USA, were assessed using a combination of cluster and network analyses. The four angler groups assessed ranged from less active, unskilled anglers (group One) to highly active, very skilled anglers (group Four). Reservoir use patterns and the resulting network communities of these four angler groups differed; the number of reservoir communities for these groups ranged from two to three and appeared to be driven by reservoir location (group One), reservoir size and its associated attributes (groups Two and Four), or an interaction between reservoir size and location (group Three). Network analysis is a useful tool to describe differences in participation among angler groups within a regional fishery, and provides new insights about possible recruitment of anglers. For example, group One anglers fished reservoirs closer to home and had a greater probability of dropping out if local reservoir access were restricted.

A Cluster-Based Dual-Adaptive Topology Control Approach in Wireless Sensor Networks

PubMed Central

Gui, Jinsong; Zhou, Kai; Xiong, Naixue

2016-01-01

Multi-Input Multi-Output (MIMO) can improve wireless network performance. Sensors are usually single-antenna devices due to the high hardware complexity and cost, so several sensors are used to form virtual MIMO array, which is a desirable approach to efficiently take advantage of MIMO gains. Also, in large Wireless Sensor Networks (WSNs), clustering can improve the network scalability, which is an effective topology control approach. The existing virtual MIMO-based clustering schemes do not either fully explore the benefits of MIMO or adaptively determine the clustering ranges. Also, clustering mechanism needs to be further improved to enhance the cluster structure life. In this paper, we propose an improved clustering scheme for virtual MIMO-based topology construction (ICV-MIMO), which can determine adaptively not only the inter-cluster transmission modes but also the clustering ranges. Through the rational division of cluster head function and the optimization of cluster head selection criteria and information exchange process, the ICV-MIMO scheme effectively reduces the network energy consumption and improves the lifetime of the cluster structure when compared with the existing typical virtual MIMO-based scheme. Moreover, the message overhead and time complexity are still in the same order of magnitude. PMID:27681731

The Multi-Scale Network Landscape of Collaboration.

PubMed

Bae, Arram; Park, Doheum; Ahn, Yong-Yeol; Park, Juyong

2016-01-01

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena--which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists.

Modulation of network pacemaker neurons by oxygen at the anaerobic threshold.

PubMed

Hill, Andrew A V; Simmers, John; Meyrand, Pierre; Massabuau, Jean-Charles

2012-07-01

Previous in vitro and in vivo studies showed that the frequency of rhythmic pyloric network activity in the lobster is modulated directly by oxygen partial pressure (PO(2)). We have extended these results by (1) increasing the period of exposure to low PO(2) and by (2) testing the sensitivity of the pyloric network to changes in PO(2) that are within the narrow range normally experienced by the lobster (1 to 6 kPa). We found that the pyloric network rhythm was indeed altered by changes in PO(2) within the range typically observed in vivo. Furthermore, a previous study showed that the lateral pyloric constrictor motor neuron (LP) contributes to the O(2) sensitivity of the pyloric network. Here, we expanded on this idea by testing the hypothesis that pyloric pacemaker neurons also contribute to pyloric O(2) sensitivity. A 2-h exposure to 1 kPa PO(2), which was twice the period used previously, decreased the frequency of an isolated group of pacemaker neurons, suggesting that changes in the rhythmogenic properties of these cells contribute to pyloric O(2) sensitivity during long-term near-anaerobic (anaerobic threshold, 0.7-1.2 kPa) conditions.

Evolution and selection of river networks: Statics, dynamics, and complexity

PubMed Central

Rinaldo, Andrea; Rigon, Riccardo; Banavar, Jayanth R.; Maritan, Amos; Rodriguez-Iturbe, Ignacio

2014-01-01

Moving from the exact result that drainage network configurations minimizing total energy dissipation are stationary solutions of the general equation describing landscape evolution, we review the static properties and the dynamic origins of the scale-invariant structure of optimal river patterns. Optimal channel networks (OCNs) are feasible optimal configurations of a spanning network mimicking landscape evolution and network selection through imperfect searches for dynamically accessible states. OCNs are spanning loopless configurations, however, only under precise physical requirements that arise under the constraints imposed by river dynamics—every spanning tree is exactly a local minimum of total energy dissipation. It is remarkable that dynamically accessible configurations, the local optima, stabilize into diverse metastable forms that are nevertheless characterized by universal statistical features. Such universal features explain very well the statistics of, and the linkages among, the scaling features measured for fluvial landforms across a broad range of scales regardless of geology, exposed lithology, vegetation, or climate, and differ significantly from those of the ground state, known exactly. Results are provided on the emergence of criticality through adaptative evolution and on the yet-unexplored range of applications of the OCN concept. PMID:24550264

Transfer-Efficient Face Routing Using the Planar Graphs of Neighbors in High Density WSNs

PubMed Central

Kim, Sang-Ha

2017-01-01

Face routing has been adopted in wireless sensor networks (WSNs) where topological changes occur frequently or maintaining full network information is difficult. For message forwarding in networks, a planar graph is used to prevent looping, and because long edges are removed by planarization and the resulting planar graph is composed of short edges, and messages are forwarded along multiple nodes connected by them even though they can be forwarded directly. To solve this, face routing using information on all nodes within 2-hop range was adopted to forward messages directly to the farthest node within radio range. However, as the density of the nodes increases, network performance plunges because message transfer nodes receive and process increased node information. To deal with this problem, we propose a new face routing using the planar graphs of neighboring nodes to improve transfer efficiency. It forwards a message directly to the farthest neighbor and reduces loads and processing time by distributing network graph construction and planarization to the neighbors. It also decreases the amount of location information to be transmitted by sending information on the planar graph nodes rather than on all neighboring nodes. Simulation results show that it significantly improves transfer efficiency. PMID:29053623

Modeling constitutive behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot compression using artificial neural network

NASA Astrophysics Data System (ADS)

Mandal, Sumantra

2006-11-01

ABSTRACT In this paper, an artificial neural network (ANN) model has been suggested to predict the constitutive flow behavior of a 15Cr-15Ni-2.2Mo-Ti modified austenitic stainless steel under hot deformation. Hot compression tests in the temperature range 850°C- 1250°C and strain rate range 10-3-102 s-1 were carried out. These tests provided the required data for training the neural network and for subsequent testing. The inputs of the neural network are strain, log strain rate and temperature while flow stress is obtained as output. A three layer feed-forward network with ten neurons in a single hidden layer and back-propagation learning algorithm has been employed. A very good correlation between experimental and predicted result has been obtained. The effect of temperature and strain rate on flow behavior has been simulated employing the ANN model. The results have been found to be consistent with the metallurgical trend. Finally, a monte carlo analiysis has been carried out to find out the noise sensitivity of the developed model.

The Multi-Scale Network Landscape of Collaboration

PubMed Central

Ahn, Yong-Yeol; Park, Juyong

2016-01-01

Propelled by the increasing availability of large-scale high-quality data, advanced data modeling and analysis techniques are enabling many novel and significant scientific understanding of a wide range of complex social, natural, and technological systems. These developments also provide opportunities for studying cultural systems and phenomena—which can be said to refer to all products of human creativity and way of life. An important characteristic of a cultural product is that it does not exist in isolation from others, but forms an intricate web of connections on many levels. In the creation and dissemination of cultural products and artworks in particular, collaboration and communication of ideas play an essential role, which can be captured in the heterogeneous network of the creators and practitioners of art. In this paper we propose novel methods to analyze and uncover meaningful patterns from such a network using the network of western classical musicians constructed from a large-scale comprehensive Compact Disc recordings data. We characterize the complex patterns in the network landscape of collaboration between musicians across multiple scales ranging from the macroscopic to the mesoscopic and microscopic that represent the diversity of cultural styles and the individuality of the artists. PMID:26990088

A comparative study of Sm networks in Al-10 at.%Sm glass and associated crystalline phases

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

Lv, Xiaobao; Ye, Zhuo; Sun, Yang

Here, the Al–Sm system is selected as a model system to study the transition process from liquid and amorphous to crystalline states. In recent work, we have shown that, in addition to long-range translational periodicity, crystal structures display well-defined short-range local atomic packing motifs that transcends liquid, amorphous and crystalline states. In this paper, we investigate the longer range spatial packing of these short-range motifs by studying the interconnections of Sm–Sm networks in different amorphous and crystalline samples obtained from molecular dynamics simulations. In our analysis, we concentrate on Sm–Sm distances in the range ~5.0–7.2 Å, corresponding to Sm atomsmore » in the second and third shells of Sm-centred clusters. We discover a number of empirical rules characterising the evolution of Sm networks from the liquid and amorphous states to associated metastable crystalline phases experimentally observed in the initial stages of devitrification of different amorphous samples. As direct simulation of glass formation is difficult because of the vast difference between experimental quench rates and what is achievable on the computer, we hope these rules will be helpful in building a better picture of structural evolution during glass formation as well as a more detailed description of phase selection and growth during devitrification.« less

A comparative study of Sm networks in Al-10 at.%Sm glass and associated crystalline phases

DOE PAGES

Lv, Xiaobao; Ye, Zhuo; Sun, Yang; ...

2018-04-03

Here, the Al–Sm system is selected as a model system to study the transition process from liquid and amorphous to crystalline states. In recent work, we have shown that, in addition to long-range translational periodicity, crystal structures display well-defined short-range local atomic packing motifs that transcends liquid, amorphous and crystalline states. In this paper, we investigate the longer range spatial packing of these short-range motifs by studying the interconnections of Sm–Sm networks in different amorphous and crystalline samples obtained from molecular dynamics simulations. In our analysis, we concentrate on Sm–Sm distances in the range ~5.0–7.2 Å, corresponding to Sm atomsmore » in the second and third shells of Sm-centred clusters. We discover a number of empirical rules characterising the evolution of Sm networks from the liquid and amorphous states to associated metastable crystalline phases experimentally observed in the initial stages of devitrification of different amorphous samples. As direct simulation of glass formation is difficult because of the vast difference between experimental quench rates and what is achievable on the computer, we hope these rules will be helpful in building a better picture of structural evolution during glass formation as well as a more detailed description of phase selection and growth during devitrification.« less

Inferring global network properties from egocentric data with applications to epidemics.

PubMed

Britton, Tom; Trapman, Pieter

2015-03-01

Social networks are often only partly observed, and it is sometimes desirable to infer global properties of the network from 'egocentric' data. In the current paper, we study different types of egocentric data, and show which global network properties are consistent with data. Two global network properties are considered: the size of the largest connected component (the giant) and the size of an epidemic outbreak taking place on the network. The main conclusion is that, in most cases, egocentric data allow for a large range of possible sizes of the giant and the outbreak, implying that egocentric data carry very little information about these global properties. The asymptotic size of the giant and the outbreak is also characterized, assuming the network is selected uniformly among networks with prescribed egocentric data. © The Authors 2013. Published by Oxford University Press on behalf of the Institute of Mathematics and its Applications. All rights reserved.

Topological properties of robust biological and computational networks

PubMed Central

Navlakha, Saket; He, Xin; Faloutsos, Christos; Bar-Joseph, Ziv

2014-01-01

Network robustness is an important principle in biology and engineering. Previous studies of global networks have identified both redundancy and sparseness as topological properties used by robust networks. By focusing on molecular subnetworks, or modules, we show that module topology is tightly linked to the level of environmental variability (noise) the module expects to encounter. Modules internal to the cell that are less exposed to environmental noise are more connected and less robust than external modules. A similar design principle is used by several other biological networks. We propose a simple change to the evolutionary gene duplication model which gives rise to the rich range of module topologies observed within real networks. We apply these observations to evaluate and design communication networks that are specifically optimized for noisy or malicious environments. Combined, joint analysis of biological and computational networks leads to novel algorithms and insights benefiting both fields. PMID:24789562

Read You Loud and Clear! The Story of NASA's Spaceflight Tracking and Data Network

NASA Technical Reports Server (NTRS)

Tsiao, Sunny

2008-01-01

A historical account is provided of NASA's Spaceflight Tracking and Data Network (STDN), starting with its formation in the late 1950s to what it is today in the first decade of the 21st century. It traces the roots of the tracking network from its beginnings at the White Sands Missile Range in New Mexico to the Tracking and Data Relay Satellite System space-based constellation of today. The story spans the early days of satellite tracking using the Minitrack Network, through the expansion of the Satellite Tracking and Data Acquisition Network and the Manned Space Flight Network, and finally, to the Space and Ground networks of today. These accounts tell how international goodwill and foreign cooperation were crucial to the operation of the network and why the space agency chose to build the STDN as it did.

Network structure from rich but noisy data

NASA Astrophysics Data System (ADS)

Newman, M. E. J.

2018-06-01

Driven by growing interest across the sciences, a large number of empirical studies have been conducted in recent years of the structure of networks ranging from the Internet and the World Wide Web to biological networks and social networks. The data produced by these experiments are often rich and multimodal, yet at the same time they may contain substantial measurement error1-7. Accurate analysis and understanding of networked systems requires a way of estimating the true structure of networks from such rich but noisy data8-15. Here we describe a technique that allows us to make optimal estimates of network structure from complex data in arbitrary formats, including cases where there may be measurements of many different types, repeated observations, contradictory observations, annotations or metadata, or missing data. We give example applications to two different social networks, one derived from face-to-face interactions and one from self-reported friendships.

Using Social Network Measures in Wildlife Disease Ecology, Epidemiology, and Management

PubMed Central

Silk, Matthew J.; Croft, Darren P.; Delahay, Richard J.; Hodgson, David J.; Boots, Mike; Weber, Nicola; McDonald, Robbie A.

2017-01-01

Abstract Contact networks, behavioral interactions, and shared use of space can all have important implications for the spread of disease in animals. Social networks enable the quantification of complex patterns of interactions; therefore, network analysis is becoming increasingly widespread in the study of infectious disease in animals, including wildlife. We present an introductory guide to using social-network-analytical approaches in wildlife disease ecology, epidemiology, and management. We focus on providing detailed practical guidance for the use of basic descriptive network measures by suggesting the research questions to which each technique is best suited and detailing the software available for each. We also discuss how using network approaches can be used beyond the study of social contacts and across a range of spatial and temporal scales. Finally, we integrate these approaches to examine how network analysis can be used to inform the implementation and monitoring of effective disease management strategies. PMID:28596616

Response to defects in multipartite and bipartite entanglement of isotropic quantum spin networks

NASA Astrophysics Data System (ADS)

Roy, Sudipto Singha; Dhar, Himadri Shekhar; Rakshit, Debraj; SenDe, Aditi; Sen, Ujjwal

2018-05-01

Quantum networks are an integral component in performing efficient computation and communication tasks that are not accessible using classical systems. A key aspect in designing an effective and scalable quantum network is generating entanglement between its nodes, which is robust against defects in the network. We consider an isotropic quantum network of spin-1/2 particles with a finite fraction of defects, where the corresponding wave function of the network is rotationally invariant under the action of local unitaries. By using quantum information-theoretic concepts like strong subadditivity of von Neumann entropy and approximate quantum telecloning, we prove analytically that in the presence of defects, caused by loss of a finite fraction of spins, the network, composed of a fixed numbers of lattice sites, sustains genuine multisite entanglement and at the same time may exhibit finite moderate-range bipartite entanglement, in contrast to the network with no defects.

Modeling fluctuations in default-mode brain network using a spiking neural network.

PubMed

Yamanishi, Teruya; Liu, Jian-Qin; Nishimura, Haruhiko

2012-08-01

Recently, numerous attempts have been made to understand the dynamic behavior of complex brain systems using neural network models. The fluctuations in blood-oxygen-level-dependent (BOLD) brain signals at less than 0.1 Hz have been observed by functional magnetic resonance imaging (fMRI) for subjects in a resting state. This phenomenon is referred to as a "default-mode brain network." In this study, we model the default-mode brain network by functionally connecting neural communities composed of spiking neurons in a complex network. Through computational simulations of the model, including transmission delays and complex connectivity, the network dynamics of the neural system and its behavior are discussed. The results show that the power spectrum of the modeled fluctuations in the neuron firing patterns is consistent with the default-mode brain network's BOLD signals when transmission delays, a characteristic property of the brain, have finite values in a given range.

Online Community Detection for Large Complex Networks

PubMed Central

Pan, Gang; Zhang, Wangsheng; Wu, Zhaohui; Li, Shijian

2014-01-01

Complex networks describe a wide range of systems in nature and society. To understand complex networks, it is crucial to investigate their community structure. In this paper, we develop an online community detection algorithm with linear time complexity for large complex networks. Our algorithm processes a network edge by edge in the order that the network is fed to the algorithm. If a new edge is added, it just updates the existing community structure in constant time, and does not need to re-compute the whole network. Therefore, it can efficiently process large networks in real time. Our algorithm optimizes expected modularity instead of modularity at each step to avoid poor performance. The experiments are carried out using 11 public data sets, and are measured by two criteria, modularity and NMI (Normalized Mutual Information). The results show that our algorithm's running time is less than the commonly used Louvain algorithm while it gives competitive performance. PMID:25061683

Rutgers University Subcontract B611610 Final Report

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

Soundarajan, Sucheta; Eliassi-Rad, Tina; Gallagher, Brian

Given an incomplete (i.e., partially-observed) network, which nodes should we actively probe in order to achieve the highest accuracy for a given network feature? For example, consider a cyber-network administrator who observes only a portion of the network at time t and wants to accurately identify the most important (e.g., highest PageRank) nodes in the complete network. She has a limited budget for probing the network. Of all the nodes she has observed, which should she probe in order to most accurately identify the important nodes? We propose a novel and scalable algorithm, MaxOutProbe, and evaluate it w.r.t. four networkmore » features (largest connected component, PageRank, core-periphery, and community detection), five network sampling strategies, and seven network datasets from different domains. Across a range of conditions, MaxOutProbe demonstrates consistently high performance relative to several baseline strategies« less

Passenger flow analysis of Beijing urban rail transit network using fractal approach

NASA Astrophysics Data System (ADS)

Li, Xiaohong; Chen, Peiwen; Chen, Feng; Wang, Zijia

2018-04-01

To quantify the spatiotemporal distribution of passenger flow and the characteristics of an urban rail transit network, we introduce four radius fractal dimensions and two branch fractal dimensions by combining a fractal approach with passenger flow assignment model. These fractal dimensions can numerically describe the complexity of passenger flow in the urban rail transit network and its change characteristics. Based on it, we establish a fractal quantification method to measure the fractal characteristics of passenger follow in the rail transit network. Finally, we validate the reasonability of our proposed method by using the actual data of Beijing subway network. It has been shown that our proposed method can effectively measure the scale-free range of the urban rail transit network, network development and the fractal characteristics of time-varying passenger flow, which further provides a reference for network planning and analysis of passenger flow.

Community Landscapes: An Integrative Approach to Determine Overlapping Network Module Hierarchy, Identify Key Nodes and Predict Network Dynamics

PubMed Central

Kovács, István A.; Palotai, Robin; Szalay, Máté S.; Csermely, Peter

2010-01-01

Background Network communities help the functional organization and evolution of complex networks. However, the development of a method, which is both fast and accurate, provides modular overlaps and partitions of a heterogeneous network, has proven to be rather difficult. Methodology/Principal Findings Here we introduce the novel concept of ModuLand, an integrative method family determining overlapping network modules as hills of an influence function-based, centrality-type community landscape, and including several widely used modularization methods as special cases. As various adaptations of the method family, we developed several algorithms, which provide an efficient analysis of weighted and directed networks, and (1) determine pervasively overlapping modules with high resolution; (2) uncover a detailed hierarchical network structure allowing an efficient, zoom-in analysis of large networks; (3) allow the determination of key network nodes and (4) help to predict network dynamics. Conclusions/Significance The concept opens a wide range of possibilities to develop new approaches and applications including network routing, classification, comparison and prediction. PMID:20824084

Unified functional network and nonlinear time series analysis for complex systems science: The pyunicorn package

NASA Astrophysics Data System (ADS)

Donges, Jonathan F.; Heitzig, Jobst; Beronov, Boyan; Wiedermann, Marc; Runge, Jakob; Feng, Qing Yi; Tupikina, Liubov; Stolbova, Veronika; Donner, Reik V.; Marwan, Norbert; Dijkstra, Henk A.; Kurths, Jürgen

2015-11-01

We introduce the pyunicorn (Pythonic unified complex network and recurrence analysis toolbox) open source software package for applying and combining modern methods of data analysis and modeling from complex network theory and nonlinear time series analysis. pyunicorn is a fully object-oriented and easily parallelizable package written in the language Python. It allows for the construction of functional networks such as climate networks in climatology or functional brain networks in neuroscience representing the structure of statistical interrelationships in large data sets of time series and, subsequently, investigating this structure using advanced methods of complex network theory such as measures and models for spatial networks, networks of interacting networks, node-weighted statistics, or network surrogates. Additionally, pyunicorn provides insights into the nonlinear dynamics of complex systems as recorded in uni- and multivariate time series from a non-traditional perspective by means of recurrence quantification analysis, recurrence networks, visibility graphs, and construction of surrogate time series. The range of possible applications of the library is outlined, drawing on several examples mainly from the field of climatology.

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

Self-Consistent Scheme for Spike-Train Power Spectra in Heterogeneous Sparse Networks.

PubMed

Pena, Rodrigo F O; Vellmer, Sebastian; Bernardi, Davide; Roque, Antonio C; Lindner, Benjamin

2018-01-01

Recurrent networks of spiking neurons can be in an asynchronous state characterized by low or absent cross-correlations and spike statistics which resemble those of cortical neurons. Although spatial correlations are negligible in this state, neurons can show pronounced temporal correlations in their spike trains that can be quantified by the autocorrelation function or the spike-train power spectrum. Depending on cellular and network parameters, correlations display diverse patterns (ranging from simple refractory-period effects and stochastic oscillations to slow fluctuations) and it is generally not well-understood how these dependencies come about. Previous work has explored how the single-cell correlations in a homogeneous network (excitatory and inhibitory integrate-and-fire neurons with nearly balanced mean recurrent input) can be determined numerically from an iterative single-neuron simulation. Such a scheme is based on the fact that every neuron is driven by the network noise (i.e., the input currents from all its presynaptic partners) but also contributes to the network noise, leading to a self-consistency condition for the input and output spectra. Here we first extend this scheme to homogeneous networks with strong recurrent inhibition and a synaptic filter, in which instabilities of the previous scheme are avoided by an averaging procedure. We then extend the scheme to heterogeneous networks in which (i) different neural subpopulations (e.g., excitatory and inhibitory neurons) have different cellular or connectivity parameters; (ii) the number and strength of the input connections are random (Erdős-Rényi topology) and thus different among neurons. In all heterogeneous cases, neurons are lumped in different classes each of which is represented by a single neuron in the iterative scheme; in addition, we make a Gaussian approximation of the input current to the neuron. These approximations seem to be justified over a broad range of parameters as indicated by comparison with simulation results of large recurrent networks. Our method can help to elucidate how network heterogeneity shapes the asynchronous state in recurrent neural networks.

A highly crystalline single Au wire network as a high temperature transparent heater

NASA Astrophysics Data System (ADS)

Rao, K. D. M.; Kulkarni, Giridhar U.

2014-05-01

A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. A transparent conductor which can generate high temperatures finds important applications in optoelectronics. In this article, a wire network made of Au on quartz is shown to serve as an effective high temperature transparent heater. The heater has been fabricated by depositing Au onto a cracked sacrificial template. The highly interconnected Au wire network thus formed exhibited a transmittance of ~87% in a wide spectral range with a sheet resistance of 5.4 Ω □-1. By passing current through the network, it could be joule heated to ~600 °C within a few seconds. The extraordinary thermal performance and stability owe much to the seamless junctions present in the wire network. Furthermore, the wire network gets self-annealed through joule heating as seen from its increased crystallinity. Interestingly, both transmittance and sheet resistance improved following annealing to 92% and 3.2 Ω □-1, respectively. Electronic supplementary information (ESI) available: Optical micrographs, EDAX, XRD, SEM and TEM images of Au metal wires. See DOI: 10.1039/c4nr00869c

Access to Accredited Cancer Hospitals Within Federal Exchange Plans Under the Affordable Care Act

PubMed Central

Liao, Kai-Ping; Krause, Trudy M.; Giordano, Sharon H.

2017-01-01

Purpose The Affordable Care Act expanded access to health insurance in the United States, but concerns have arisen about access to specialized cancer care within narrow provider networks. To characterize the scope and potential impact of this problem, we assessed rates of inclusion of Commission on Cancer (CoC) –accredited hospitals and National Cancer Institute (NCI) –designated cancer centers within federal exchange networks. Methods We downloaded publicly available machine-readable network data and public use files for individual federal exchange plans from the Centers for Medicare and Medicaid Services for the 2016 enrollment year. We linked this information to National Provider Identifier data, identified a set of distinct provider networks, and assessed the rates of inclusion of CoC-accredited hospitals and NCI-designated centers. We measured variation in these rates according to geography, plan type, and metal level. Results Of 4,058 unique individual plans, network data were available for 3,637 (90%); hospital information was available for 3,531 (87%). Provider lists for these plans reduced into 295 unique networks for analysis. Ninety-five percent of networks included at least one CoC-accredited hospital, but just 41% of networks included NCI-designated centers. States and counties each varied substantially in the proportion of networks listed that included NCI-designated centers (range, 0% to 100%). The proportion of networks that included NCI-designated centers also varied by plan type (range, 31% for health maintenance organizations to 49% for preferred provider organizations; P = .04) but not by metal level. Conclusion A large majority of federal exchange networks contain CoC-accredited hospitals, but most do not contain NCI-designated cancer centers. These results will inform policy regarding access to cancer care, and they reinforce the importance of promoting access to clinical trials and specialized care through community sites. PMID:28068172

Primary health care service delivery networks for the prevention and management of type 2 diabetes: using social network methods to describe interorganisational collaboration in a rural setting.

PubMed

McDonald, Julie; Jayasuriya, Rohan; Harris, Mark Fort

2011-01-01

Adults with type 2 diabetes or with behavioural risk factors require comprehensive and well coordinated responses from a range of health care providers who often work in different organisational settings. This study examines three types of collaborative links between organisations involved in a rural setting. Social network methods were employed using survey data on three types of links, and data was collected from a purposive sample of 17 organisations representing the major provider types. The analysis included a mix of unconfirmed and confirmed links, and network measures. General practices were the most influential provider group in initiating referrals, and they referred to the broadest range of organisations in the network. Team care arrangements formed a small part of the general practice referral network. They were used more for access to private sector allied health care providers and less for sharing care with public sector health services. Involvement in joint programs/activities was limited to public and non-government sector services, with no participation from the private sector. The patterns of interactions suggest that informal referral networks provide access to services and coordination of care for individual patients with diabetes. Two population subgroups would benefit from more proactive approaches to ensure equitable access to services and coordination of care across organisational boundaries: people with more complex health care needs and people at risk of developing diabetes.

Spatially dynamic recurrent information flow across long-range dorsal motor network encodes selective motor goals.

PubMed

Yoo, Peter E; Hagan, Maureen A; John, Sam E; Opie, Nicholas L; Ordidge, Roger J; O'Brien, Terence J; Oxley, Thomas J; Moffat, Bradford A; Wong, Yan T

2018-06-01

Performing voluntary movements involves many regions of the brain, but it is unknown how they work together to plan and execute specific movements. We recorded high-resolution ultra-high-field blood-oxygen-level-dependent signal during a cued ankle-dorsiflexion task. The spatiotemporal dynamics and the patterns of task-relevant information flow across the dorsal motor network were investigated. We show that task-relevant information appears and decays earlier in the higher order areas of the dorsal motor network then in the primary motor cortex. Furthermore, the results show that task-relevant information is encoded in general initially, and then selective goals are subsequently encoded in specifics subregions across the network. Importantly, the patterns of recurrent information flow across the network vary across different subregions depending on the goal. Recurrent information flow was observed across all higher order areas of the dorsal motor network in the subregions encoding for the current goal. In contrast, only the top-down information flow from the supplementary motor cortex to the frontoparietal regions, with weakened recurrent information flow between the frontoparietal regions and bottom-up information flow from the frontoparietal regions to the supplementary cortex were observed in the subregions encoding for the opposing goal. We conclude that selective motor goal encoding and execution rely on goal-dependent differences in subregional recurrent information flow patterns across the long-range dorsal motor network areas that exhibit graded functional specialization. © 2018 Wiley Periodicals, Inc.

Weak and strong publics: drawing on Nancy Fraser to explore parental participation in neonatal networks.

PubMed

Gibson, Andrew J; Lewando-Hundt, Gillian; Blaxter, Loraine

2014-02-01

We draw on the work of Nancy Fraser, and in particular her concepts of weak and strong publics, to analyze the process of parental involvement in managed neonatal network boards. Public involvement has moved beyond the individual level to include greater involvement of both patients and the public in governance. However, there is relatively little literature that explores the nature and outcomes of long-term patient involvement initiatives or has attempted to theorize, particularly at the level of corporate decision making, the process of patient and public involvement. A repeated survey of all neonatal network managers in England was carried out in 2006-07 to capture developments and changes in parental representation over this time period. This elicited information about the current status of parent representation on neonatal network boards. Four networks were also selected as case studies. This involved interviews with key members of each network board, interviews with parent representatives, observation of meetings and access to board minutes. Data collected show that a wide range of approaches to involving parents has been adopted. These range from decisions not to involve parents at this level to relatively well-developed systems designed to link parent representatives on network boards to parents in neonatal units. Despite these variations, we suggest that parental participation within neonatal services remains an example of a weak public because the parent representatives had limited participation with little influence on decision making. © 2011 John Wiley & Sons Ltd.

Machine learning topological states

NASA Astrophysics Data System (ADS)

Deng, Dong-Ling; Li, Xiaopeng; Das Sarma, S.

2017-11-01

Artificial neural networks and machine learning have now reached a new era after several decades of improvement where applications are to explode in many fields of science, industry, and technology. Here, we use artificial neural networks to study an intriguing phenomenon in quantum physics—the topological phases of matter. We find that certain topological states, either symmetry-protected or with intrinsic topological order, can be represented with classical artificial neural networks. This is demonstrated by using three concrete spin systems, the one-dimensional (1D) symmetry-protected topological cluster state and the 2D and 3D toric code states with intrinsic topological orders. For all three cases, we show rigorously that the topological ground states can be represented by short-range neural networks in an exact and efficient fashion—the required number of hidden neurons is as small as the number of physical spins and the number of parameters scales only linearly with the system size. For the 2D toric-code model, we find that the proposed short-range neural networks can describe the excited states with Abelian anyons and their nontrivial mutual statistics as well. In addition, by using reinforcement learning we show that neural networks are capable of finding the topological ground states of nonintegrable Hamiltonians with strong interactions and studying their topological phase transitions. Our results demonstrate explicitly the exceptional power of neural networks in describing topological quantum states, and at the same time provide valuable guidance to machine learning of topological phases in generic lattice models.

Optimization of the graph model of the water conduit network, based on the approach of search space reducing

NASA Astrophysics Data System (ADS)

Korovin, Iakov S.; Tkachenko, Maxim G.

2018-03-01

In this paper we present a heuristic approach, improving the efficiency of methods, used for creation of efficient architecture of water distribution networks. The essence of the approach is a procedure of search space reduction the by limiting the range of available pipe diameters that can be used for each edge of the network graph. In order to proceed the reduction, two opposite boundary scenarios for the distribution of flows are analysed, after which the resulting range is further narrowed by applying a flow rate limitation for each edge of the network. The first boundary scenario provides the most uniform distribution of the flow in the network, the opposite scenario created the net with the highest possible flow level. The parameters of both distributions are calculated by optimizing systems of quadratic functions in a confined space, which can be effectively performed with small time costs. This approach was used to modify the genetic algorithm (GA). The proposed GA provides a variable number of variants of each gene, according to the number of diameters in list, taking into account flow restrictions. The proposed approach was implemented to the evaluation of a well-known test network - the Hanoi water distribution network [1], the results of research were compared with a classical GA with an unlimited search space. On the test data, the proposed trip significantly reduced the search space and provided faster and more obvious convergence in comparison with the classical version of GA.

Betweenness centrality and its applications from modeling traffic flows to network community detection

NASA Astrophysics Data System (ADS)

Ren, Yihui

As real-world complex networks are heterogeneous structures, not all their components such as nodes, edges and subgraphs carry the same role or importance in the functions performed by the networks: some elements are more critical than others. Understanding the roles of the components of a network is crucial for understanding the behavior of the network as a whole. One the most basic function of networks is transport; transport of vehicles/people, information, materials, forces, etc., and these quantities are transported along edges between source and destination nodes. For this reason, network path-based importance measures, also called centralities, play a crucial role in the understanding of the transport functions of the network and the network's structural and dynamical behavior in general. In this thesis we study the notion of betweenness centrality, which measures the fraction of lowest-cost (or shortest) paths running through a network component, in particular through a node or an edge. High betweenness centrality nodes/edges are those that will be frequently used by the entities transported through the network and thus they play a key role in the overall transport properties of the network. In the first part of the thesis we present a first-principles based method for traffic prediction using a cost-based generalization of the radiation model (emission/absorbtion model) for human mobility, coupled with a cost-minimizing algorithm for efficient distribution of the mobility fluxes through the network. Using US census and highway traffic data, we show that traffic can efficiently and accurately be computed from a range-limited, network betweenness type calculation. The model based on travel time costs captures the log-normal distribution of the traffic and attains a high Pearson correlation coefficient (0.75) when compared with real traffic. We then focus on studying the extent of changes in traffic flows in the wake of a localized damage or alteration to the network and we demonstrate that the changes can propagate globally, affecting traffic several hundreds of miles away. Because of its principled nature, this method can inform many applications related to human mobility driven flows in spatial networks, ranging from transportation, through urban planning to mitigation of the effects of catastrophic events. In the second part of the thesis we focus on network deconstruction and community detection problems, both intensely studied topics in network science, using a weighted betweenness centrality approach. We present an algorithm that solves both problems efficiently and accurately and demonstrate that on both benchmark networks and data networks.

Systemic risk on different interbank network topologies

NASA Astrophysics Data System (ADS)

Lenzu, Simone; Tedeschi, Gabriele

2012-09-01

In this paper we develop an interbank market with heterogeneous financial institutions that enter into lending agreements on different network structures. Credit relationships (links) evolve endogenously via a fitness mechanism based on agents' performance. By changing the agent's trust on its neighbor's performance, interbank linkages self-organize themselves into very different network architectures, ranging from random to scale-free topologies. We study which network architecture can make the financial system more resilient to random attacks and how systemic risk spreads over the network. To perturb the system, we generate a random attack via a liquidity shock. The hit bank is not automatically eliminated, but its failure is endogenously driven by its incapacity to raise liquidity in the interbank network. Our analysis shows that a random financial network can be more resilient than a scale free one in case of agents' heterogeneity.

Coherence analysis of a class of weighted networks

NASA Astrophysics Data System (ADS)

Dai, Meifeng; He, Jiaojiao; Zong, Yue; Ju, Tingting; Sun, Yu; Su, Weiyi

2018-04-01

This paper investigates consensus dynamics in a dynamical system with additive stochastic disturbances that is characterized as network coherence by using the Laplacian spectrum. We introduce a class of weighted networks based on a complete graph and investigate the first- and second-order network coherence quantifying as the sum and square sum of reciprocals of all nonzero Laplacian eigenvalues. First, the recursive relationship of its eigenvalues at two successive generations of Laplacian matrix is deduced. Then, we compute the sum and square sum of reciprocal of all nonzero Laplacian eigenvalues. The obtained results show that the scalings of first- and second-order coherence with network size obey four and five laws, respectively, along with the range of the weight factor. Finally, it indicates that the scalings of our studied networks are smaller than other studied networks when 1/√{d }

Cascade-based attacks on complex networks

NASA Astrophysics Data System (ADS)

Motter, Adilson E.; Lai, Ying-Cheng

2002-12-01

We live in a modern world supported by large, complex networks. Examples range from financial markets to communication and transportation systems. In many realistic situations the flow of physical quantities in the network, as characterized by the loads on nodes, is important. We show that for such networks where loads can redistribute among the nodes, intentional attacks can lead to a cascade of overload failures, which can in turn cause the entire or a substantial part of the network to collapse. This is relevant for real-world networks that possess a highly heterogeneous distribution of loads, such as the Internet and power grids. We demonstrate that the heterogeneity of these networks makes them particularly vulnerable to attacks in that a large-scale cascade may be triggered by disabling a single key node. This brings obvious concerns on the security of such systems.

Using the structure of social networks to map inter-agency relationships in public health services.

PubMed

West, Robert M; House, Allan O; Keen, Justin; Ward, Vicky L

2015-11-01

This article investigates network governance in the context of health and wellbeing services in England, focussing on relationships between managers in a range of services. There are three aims, namely to investigate, (i) the configurations of networks, (ii) the stability of network relationships over time and, (iii) the balance between formal and informal ties that underpin inter-agency relationships. Latent position cluster network models were used to characterise relationships. Managers were asked two questions, both designed to characterise informal relationships. The resulting networks differed substantially from one another in membership. Managers described networks of relationships that spanned organisational boundaries, and that changed substantially over time. The findings suggest that inter-agency co-ordination depends more on informal than on formal relationships. Copyright © 2015 Elsevier Ltd. All rights reserved.

Wide range operation of regenerative optical parametric wavelength converter using ASE-degraded 43-Gb/s RZ-DPSK signals.

PubMed

Gao, Mingyi; Kurumida, Junya; Namiki, Shu

2011-11-07

For sustainable growth of the Internet, wavelength-tunable optical regeneration is the key to scaling up high energy-efficiency dynamic optical path networks while keeping the flexibility of the network. Wavelength-tunable optical parametric regenerator (T-OPR) based on the gain saturation effect of parametric amplification in a highly nonlinear fiber is promising for noise reduction in phase-shift keying signals. In this paper, we experimentally evaluated the T-OPR performance for ASE-degraded 43-Gb/s RZ-DPSK signals over a 20-nm input wavelength range between 1527 nm and 1547 nm. As a result, we achieved improved power penalty performance for the regenerated idler with a proper pump power range.

Multi-attribute integrated measurement of node importance in complex networks.

PubMed

Wang, Shibo; Zhao, Jinlou

2015-11-01

The measure of node importance in complex networks is very important to the research of networks stability and robustness; it also can ensure the security of the whole network. Most researchers have used a single indicator to measure the networks node importance, so that the obtained measurement results only reflect certain aspects of the networks with a loss of information. Meanwhile, because of the difference of networks topology, the nodes' importance should be described by combining the character of the networks topology. Most of the existing evaluation algorithms cannot completely reflect the circumstances of complex networks, so this paper takes into account the degree of centrality, the relative closeness centrality, clustering coefficient, and topology potential and raises an integrated measuring method to measure the nodes' importance. This method can reflect nodes' internal and outside attributes and eliminate the influence of network structure on the node importance. The experiments of karate network and dolphin network show that networks topology structure integrated measure has smaller range of metrical result than a single indicator and more universal. Experiments show that attacking the North American power grid and the Internet network with the method has a faster convergence speed than other methods.

Microwave analog fiber-optic link for use in the deep space network

NASA Technical Reports Server (NTRS)

Logan, R. T., Jr.; Lutes, G. F.; Maleki, L.

1990-01-01

A novel fiber-optic system with dynamic range of up to 150 dB-Hz for transmission of microwave analog signals is described. The design, analysis, and laboratory evaluations of this system are reported, and potential applications in the NASA/JPL Deep Space Network are discussed.

Computational Models for Belief Revision, Group Decision-Making and Cultural Shifts

DTIC Science & Technology

2010-10-25

34social" networks; the green numbers are pseudo-trees or artificial (non-social) constructions. The dashed blue line indicates the range of Erdos- Renyi ...non-social networks such as Erdos- Renyi random graphs or the more passive non-cognitive spreading of disease or information flow, As mentioned

Word Processing for Technical Writers and Teachers.

ERIC Educational Resources Information Center

Mullins, Carolyn J.; West, Thomas W.

This discussion of the computing network and word processing facilities available to professionals on the Indiana University campuses identifies the word and text processing needs of technical writers and faculty, describes the current computing network, and outlines both long- and short-range objectives, policies, and plans for meeting these…

More than Good Intentions: Building a Network of Collaboratives.

ERIC Educational Resources Information Center

Bailey, Adrienne, Y.

1986-01-01

College Board's national network of school-college collaborative projects to increase the number of high school students prepared to attend college is described: (1) College Board's role; (2) sample conferences on pertinent issues; (3) range of support activities provided by College Board; and (4) lessons learned about both local collaboratives…

Investigating Hastily-Formed Collaborative Networks

DTIC Science & Technology

2007-03-01

support a minimum range of 250 meters since this is the minimum required length of a fire hose . • Jurisdiction Area Network (JAN): This is the main...but typical braided one-half inch polypropylene rope weighs less than two pounds per one hundred 4-19 feet and has tensile strengths greater than two

Wyoming: Open Range for Library Technology.

ERIC Educational Resources Information Center

Maul, Helen Meadors

1996-01-01

Describes the development of library technology and the need for telecommunications in a state with a lack of population density. Topics include the state library's role; shared library resources and library networks; government information; the Wyoming State Home Page on the World Wide Web; Ariel software; network coordinating; and central…

Qualitative Network Analysis Tools for the Configurative Articulation of Cultural Value and Impact from Research

ERIC Educational Resources Information Center

Oancea, Alis; Florez Petour, Teresa; Atkinson, Jeanette

2017-01-01

This article introduces a methodological approach for articulating and communicating the impact and value of research: qualitative network analysis using collaborative configuration tracing and visualization. The approach was proposed initially in Oancea ("Interpretations and Practices of Research Impact across the Range of Disciplines…

VARIATION IN JUVENILE COHO SALMON SUMMER ABUNDANCE: HIERARCHICAL ANALYSIS OF HABITAT EFFECTS

EPA Science Inventory

Varying habitat conditions found across a stream network during the summer months may limit the abundance of salmonids such as coho (Oncorhynchus kisutch). We examined the abundance of juvenile coho salmon across a stream network in an Oregon coast range basin from 2002 through ...

Nutrient Uptake and Outcome network (NUOnet): Connecting a Wide Range of Natural Resource Conservation Networks

USDA-ARS?s Scientific Manuscript database

Nutrient application and its uptake by crops are essential to increasing agricultural production, which is essential to feed a growing world population. Efficiency in management of nutrients could be increased with conservation practices that reduce nutrient losses to the environment and promote con...

Child Rights Information Network Newsletter, 2000-2002.

ERIC Educational Resources Information Center

Khan, Andrea, Ed.; Greenwood, Laura, Ed.

These five newsletter issues communicate activities of the Child Rights Information Network (CRIN) and report on information resources and world-wide activities concerning children and child rights. The March 2000 issue focuses on children's right to education, assessing the matter form a range of differing perspectives, at international and…

Using Auditory Steady State Responses to Outline the Functional Connectivity in the Tinnitus Brain

PubMed Central

Schlee, Winfried; Weisz, Nathan; Bertrand, Olivier; Hartmann, Thomas; Elbert, Thomas

2008-01-01

Background Tinnitus is an auditory phantom perception that is most likely generated in the central nervous system. Most of the tinnitus research has concentrated on the auditory system. However, it was suggested recently that also non-auditory structures are involved in a global network that encodes subjective tinnitus. We tested this assumption using auditory steady state responses to entrain the tinnitus network and investigated long-range functional connectivity across various non-auditory brain regions. Methods and Findings Using whole-head magnetoencephalography we investigated cortical connectivity by means of phase synchronization in tinnitus subjects and healthy controls. We found evidence for a deviating pattern of long-range functional connectivity in tinnitus that was strongly correlated with individual ratings of the tinnitus percept. Phase couplings between the anterior cingulum and the right frontal lobe and phase couplings between the anterior cingulum and the right parietal lobe showed significant condition x group interactions and were correlated with the individual tinnitus distress ratings only in the tinnitus condition and not in the control conditions. Conclusions To the best of our knowledge this is the first study that demonstrates existence of a global tinnitus network of long-range cortical connections outside the central auditory system. This result extends the current knowledge of how tinnitus is generated in the brain. We propose that this global extend of the tinnitus network is crucial for the continuos perception of the tinnitus tone and a therapeutical intervention that is able to change this network should result in relief of tinnitus. PMID:19005566

Fetal functional imaging portrays heterogeneous development of emerging human brain networks

PubMed Central

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M.; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26–29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity. PMID:25374531

Fetal functional imaging portrays heterogeneous development of emerging human brain networks.

PubMed

Jakab, András; Schwartz, Ernst; Kasprian, Gregor; Gruber, Gerlinde M; Prayer, Daniela; Schöpf, Veronika; Langs, Georg

2014-01-01

The functional connectivity architecture of the adult human brain enables complex cognitive processes, and exhibits a remarkably complex structure shared across individuals. We are only beginning to understand its heterogeneous structure, ranging from a strongly hierarchical organization in sensorimotor areas to widely distributed networks in areas such as the parieto-frontal cortex. Our study relied on the functional magnetic resonance imaging (fMRI) data of 32 fetuses with no detectable morphological abnormalities. After adapting functional magnetic resonance acquisition, motion correction, and nuisance signal reduction procedures of resting-state functional data analysis to fetuses, we extracted neural activity information for major cortical and subcortical structures. Resting fMRI networks were observed for increasing regional functional connectivity from 21st to 38th gestational weeks (GWs) with a network-based statistical inference approach. The overall connectivity network, short range, and interhemispheric connections showed sigmoid expansion curve peaking at the 26-29 GW. In contrast, long-range connections exhibited linear increase with no periods of peaking development. Region-specific increase of functional signal synchrony followed a sequence of occipital (peak: 24.8 GW), temporal (peak: 26 GW), frontal (peak: 26.4 GW), and parietal expansion (peak: 27.5 GW). We successfully adapted functional neuroimaging and image post-processing approaches to correlate macroscopical scale activations in the fetal brain with gestational age. This in vivo study reflects the fact that the mid-fetal period hosts events that cause the architecture of the brain circuitry to mature, which presumably manifests in increasing strength of intra- and interhemispheric functional macro connectivity.

A growing social network model in geographical space

NASA Astrophysics Data System (ADS)

Antonioni, Alberto; Tomassini, Marco

2017-09-01

In this work we propose a new model for the generation of social networks that includes their often ignored spatial aspects. The model is a growing one and links are created either taking space into account, or disregarding space and only considering the degree of target nodes. These two effects can be mixed linearly in arbitrary proportions through a parameter. We numerically show that for a given range of the combination parameter, and for given mean degree, the generated network class shares many important statistical features with those observed in actual social networks, including the spatial dependence of connections. Moreover, we show that the model provides a good qualitative fit to some measured social networks.

Inter-computer communication architecture for a mixed redundancy distributed system

NASA Technical Reports Server (NTRS)

Lala, Jaynarayan H.; Adams, Stuart J.

1987-01-01

The triply redundant intercomputer network for the Advanced Information Processing System (AIPS), an architecture developed to serve as the core avionics system for a broad range of aerospace vehicles, is discussed. The AIPS intercomputer network provides a high-speed, Byzantine-fault-resilient communication service between processing sites, even in the presence of arbitrary failures of simplex and duplex processing sites on the IC network. The IC network contention poll has evolved from the Laning Poll. An analysis of the failure modes and effects and a simulation of the AIPS contention poll, demonstrate the robustness of the system.

Maintaining relationships is critical in network's success.

PubMed

Huerta, Timothy

2006-01-01

As the authors of the lead paper recognize, networks have become an increasingly popular form of organizing, both in the delivery of public services and within political arenas. A network is an arrangement of individuals and/or organizations that are linked through connections that range from informal relationships to formally agreed protocols. Networks have proved useful in addressing complex and intractable problems that require a holistic approach to identifying and implementing long-term solutions. They succeed in situations where hierarchies and "silo-based" systems have failed, and are particularly valuable in facilitating the transfer of resources and knowledge across sectoral and organizational boundaries.

Topological Edge Floppy Modes in Disordered Fiber Networks

NASA Astrophysics Data System (ADS)

Zhou, Di; Zhang, Leyou; Mao, Xiaoming

2018-02-01

Disordered fiber networks are ubiquitous in a broad range of natural (e.g., cytoskeleton) and manmade (e.g., aerogels) materials. In this Letter, we discuss the emergence of topological floppy edge modes in two-dimensional fiber networks as a result of deformation or active driving. It is known that a network of straight fibers exhibits bulk floppy modes which only bend the fibers without stretching them. We find that, interestingly, with a perturbation in geometry, these bulk modes evolve into edge modes. We introduce a topological index for these edge modes and discuss their implications in biology.

Small-World Network Spectra in Mean-Field Theory

NASA Astrophysics Data System (ADS)

Grabow, Carsten; Grosskinsky, Stefan; Timme, Marc

2012-05-01

Collective dynamics on small-world networks emerge in a broad range of systems with their spectra characterizing fundamental asymptotic features. Here we derive analytic mean-field predictions for the spectra of small-world models that systematically interpolate between regular and random topologies by varying their randomness. These theoretical predictions agree well with the actual spectra (obtained by numerical diagonalization) for undirected and directed networks and from fully regular to strongly random topologies. These results may provide analytical insights to empirically found features of dynamics on small-world networks from various research fields, including biology, physics, engineering, and social science.

Development of a monitoring network for lightning stokes accompanying the eruptions of the Northern group of volcanoes on Kamchatka peninsula

NASA Astrophysics Data System (ADS)

Mochalov, V. A.; Firstov, P. P.; Cherneva, N. V.; Sannikov, D. V.; Akbashev, R. R.; Uvarov, V. N.; Shevtsov, B. M.; Druzhin, G. I.; Mochalova, A. V.

2017-11-01

In the region of the Northern group of volcanoes in Kamchatka peninsula, a distributed network is being planned to monitor the VLF range electromagnetic radiation and to locate the lightning strokes. It will allow the researchers to register weaker electromagnetic pulses from lightning strokes in comparison to the World Wide Lightning Location Network. The hardware-software complex of the network under construction is presented. The capabilities of the available and the developing hardware and software to investigate natural phenomena associated with lightning activity are described.

First Lessons From The Biarritz Trial Network [1

NASA Astrophysics Data System (ADS)

Touyarot, P.; Marc, B.; de Panafieu, A.

1986-07-01

Opened for commercial operation in 1984, the trial optical fiber network at Biarritz in south-west France gives 1,500 subscribers access to a whole range of broadband services - videophony, audiovisual databases, TV and stereo sound program distribution, and an on-line TV program library - in addition to conventional narrow-band services like telephony and videotex. The Biarritz network is an outstanding technology and engineering testbed. It is also a sociological testing ground for new services, unique in the world, with results of particular relevance to the interactive cable TV and visual communications networks of the future.

Quantitative angle-insensitive flow measurement using relative standard deviation OCT.

PubMed

Zhu, Jiang; Zhang, Buyun; Qi, Li; Wang, Ling; Yang, Qiang; Zhu, Zhuqing; Huo, Tiancheng; Chen, Zhongping

2017-10-30

Incorporating different data processing methods, optical coherence tomography (OCT) has the ability for high-resolution angiography and quantitative flow velocity measurements. However, OCT angiography cannot provide quantitative information of flow velocities, and the velocity measurement based on Doppler OCT requires the determination of Doppler angles, which is a challenge in a complex vascular network. In this study, we report on a relative standard deviation OCT (RSD-OCT) method which provides both vascular network mapping and quantitative information for flow velocities within a wide range of Doppler angles. The RSD values are angle-insensitive within a wide range of angles, and a nearly linear relationship was found between the RSD values and the flow velocities. The RSD-OCT measurement in a rat cortex shows that it can quantify the blood flow velocities as well as map the vascular network in vivo .

Quantitative angle-insensitive flow measurement using relative standard deviation OCT

NASA Astrophysics Data System (ADS)

Zhu, Jiang; Zhang, Buyun; Qi, Li; Wang, Ling; Yang, Qiang; Zhu, Zhuqing; Huo, Tiancheng; Chen, Zhongping

2017-10-01

Incorporating different data processing methods, optical coherence tomography (OCT) has the ability for high-resolution angiography and quantitative flow velocity measurements. However, OCT angiography cannot provide quantitative information of flow velocities, and the velocity measurement based on Doppler OCT requires the determination of Doppler angles, which is a challenge in a complex vascular network. In this study, we report on a relative standard deviation OCT (RSD-OCT) method which provides both vascular network mapping and quantitative information for flow velocities within a wide range of Doppler angles. The RSD values are angle-insensitive within a wide range of angles, and a nearly linear relationship was found between the RSD values and the flow velocities. The RSD-OCT measurement in a rat cortex shows that it can quantify the blood flow velocities as well as map the vascular network in vivo.

Network marketing on a small-world network

NASA Astrophysics Data System (ADS)

Kim, Beom Jun; Jun, Tackseung; Kim, Jeong-Yoo; Choi, M. Y.

2006-02-01

We investigate a dynamic model of network marketing in a small-world network structure artificially constructed similarly to the Watts-Strogatz network model. Different from the traditional marketing, consumers can also play the role of the manufacturer's selling agents in network marketing, which is stimulated by the referral fee the manufacturer offers. As the wiring probability α is increased from zero to unity, the network changes from the one-dimensional regular directed network to the star network where all but one player are connected to one consumer. The price p of the product and the referral fee r are used as free parameters to maximize the profit of the manufacturer. It is observed that at α=0 the maximized profit is constant independent of the network size N while at α≠0, it increases linearly with N. This is in parallel to the small-world transition. It is also revealed that while the optimal value of p stays at an almost constant level in a broad range of α, that of r is sensitive to a change in the network structure. The consumer surplus is also studied and discussed.

Understanding the influence of all nodes in a network

PubMed Central

Lawyer, Glenn

2015-01-01

Centrality measures such as the degree, k-shell, or eigenvalue centrality can identify a network's most influential nodes, but are rarely usefully accurate in quantifying the spreading power of the vast majority of nodes which are not highly influential. The spreading power of all network nodes is better explained by considering, from a continuous-time epidemiological perspective, the distribution of the force of infection each node generates. The resulting metric, the expected force, accurately quantifies node spreading power under all primary epidemiological models across a wide range of archetypical human contact networks. When node power is low, influence is a function of neighbor degree. As power increases, a node's own degree becomes more important. The strength of this relationship is modulated by network structure, being more pronounced in narrow, dense networks typical of social networking and weakening in broader, looser association networks such as the Internet. The expected force can be computed independently for individual nodes, making it applicable for networks whose adjacency matrix is dynamic, not well specified, or overwhelmingly large. PMID:25727453

Federated queries of clinical data repositories: Scaling to a national network.

PubMed

Weber, Griffin M

2015-06-01

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

Single-hidden-layer feed-forward quantum neural network based on Grover learning.

PubMed

Liu, Cheng-Yi; Chen, Chein; Chang, Ching-Ter; Shih, Lun-Min

2013-09-01

In this paper, a novel single-hidden-layer feed-forward quantum neural network model is proposed based on some concepts and principles in the quantum theory. By combining the quantum mechanism with the feed-forward neural network, we defined quantum hidden neurons and connected quantum weights, and used them as the fundamental information processing unit in a single-hidden-layer feed-forward neural network. The quantum neurons make a wide range of nonlinear functions serve as the activation functions in the hidden layer of the network, and the Grover searching algorithm outstands the optimal parameter setting iteratively and thus makes very efficient neural network learning possible. The quantum neuron and weights, along with a Grover searching algorithm based learning, result in a novel and efficient neural network characteristic of reduced network, high efficient training and prospect application in future. Some simulations are taken to investigate the performance of the proposed quantum network and the result show that it can achieve accurate learning. Copyright © 2013 Elsevier Ltd. All rights reserved.

The Edge of Stability: Response Times and Delta Oscillations in Balanced Networks

PubMed Central

Gillary, Grant; Niebur, Ernst

2016-01-01

The standard architecture of neocortex is a network with excitation and inhibition in closely maintained balance. These networks respond fast and with high precision to their inputs and they allow selective amplification of patterned signals. The stability of such networks is known to depend on balancing the strengths of positive and negative feedback. We here show that a second condition is required for stability which depends on the relative strengths and time courses of fast (AMPA) and slow (NMDA) currents in the excitatory projections. This condition also determines the response time of the network. We show that networks which respond quickly to an input are necessarily close to an oscillatory instability which resonates in the delta range. This instability explains the existence of neocortical delta oscillations and the emergence of absence epilepsy. Although cortical delta oscillations are a network-level phenomenon, we show that in non-pathological networks, individual neurons receive sufficient information to keep the network in the fast-response regime without sliding into the instability. PMID:27689361

Cluster Synchronization of Diffusively Coupled Nonlinear Systems: A Contraction-Based Approach

NASA Astrophysics Data System (ADS)

Aminzare, Zahra; Dey, Biswadip; Davison, Elizabeth N.; Leonard, Naomi Ehrich

2018-04-01

Finding the conditions that foster synchronization in networked nonlinear systems is critical to understanding a wide range of biological and mechanical systems. However, the conditions proved in the literature for synchronization in nonlinear systems with linear coupling, such as has been used to model neuronal networks, are in general not strict enough to accurately determine the system behavior. We leverage contraction theory to derive new sufficient conditions for cluster synchronization in terms of the network structure, for a network where the intrinsic nonlinear dynamics of each node may differ. Our result requires that network connections satisfy a cluster-input-equivalence condition, and we explore the influence of this requirement on network dynamics. For application to networks of nodes with FitzHugh-Nagumo dynamics, we show that our new sufficient condition is tighter than those found in previous analyses that used smooth or nonsmooth Lyapunov functions. Improving the analytical conditions for when cluster synchronization will occur based on network configuration is a significant step toward facilitating understanding and control of complex networked systems.

Constructing regional climate networks in the Amazonia during recent drought events.

PubMed

Guo, Heng; Ramos, Antônio M T; Macau, Elbert E N; Zou, Yong; Guan, Shuguang

2017-01-01

Climate networks are powerful approaches to disclose tele-connections in climate systems and to predict severe climate events. Here we construct regional climate networks from precipitation data in the Amazonian region and focus on network properties under the recent drought events in 2005 and 2010. Both the networks of the entire Amazon region and the extreme networks resulted from locations severely affected by drought events suggest that network characteristics show slight difference between the two drought events. Based on network degrees of extreme drought events and that without drought conditions, we identify regions of interest that are correlated to longer expected drought period length. Moreover, we show that the spatial correlation length to the regions of interest decayed much faster in 2010 than in 2005, which is because of the dual roles played by both the Pacific and Atlantic oceans. The results suggest that hub nodes in the regional climate network of Amazonia have fewer long-range connections when more severe drought conditions appeared in 2010 than that in 2005.

Security and Efficiency Concerns With Distributed Collaborative Networking Environments

DTIC Science & Technology

2003-09-01

have the ability to access Web communications services of the WebEx MediaTone Network from a single login. [24] WebEx provides a range of secure...Web. WebEx services enable secure data, voice and video communications through the browser and are supported by the WebEx MediaTone Network, a global...designed to host large-scale, structured events and conferences, featuring a Q&A Manager that allows multiple moderators to handle questions while

Development of mini VSAT system

NASA Astrophysics Data System (ADS)

Lu, Shyue-Ching; Chiu, Wu-Jhy; Lin, Hen-Dao; Shih, Mu-Piao

1992-03-01

This paper presents the mini VSAT (very small aperture terminal) system, which is a low cost networking system providing economical alternatives to the business world's datacom needs. The system is designed to achieve the highest possible performance/price ratio for private VSAT networks that range from a few tens of remote terminals to large networks of several thousands remote terminals. The paper describes the system architecture, major features, hardware and software structure, access protocol and performance of the developed system.

Compressed glassy carbon: An ultrastrong and elastic interpenetrating graphene network

PubMed Central

Hu, Meng; He, Julong; Zhao, Zhisheng; Strobel, Timothy A.; Hu, Wentao; Yu, Dongli; Sun, Hao; Liu, Lingyu; Li, Zihe; Ma, Mengdong; Kono, Yoshio; Shu, Jinfu; Mao, Ho-kwang; Fei, Yingwei; Shen, Guoyin; Wang, Yanbin; Juhl, Stephen J.; Huang, Jian Yu; Liu, Zhongyuan; Xu, Bo; Tian, Yongjun

2017-01-01

Carbon’s unique ability to have both sp2 and sp3 bonding states gives rise to a range of physical attributes, including excellent mechanical and electrical properties. We show that a series of lightweight, ultrastrong, hard, elastic, and conductive carbons are recovered after compressing sp2-hybridized glassy carbon at various temperatures. Compression induces the local buckling of graphene sheets through sp3 nodes to form interpenetrating graphene networks with long-range disorder and short-range order on the nanometer scale. The compressed glassy carbons have extraordinary specific compressive strengths—more than two times that of commonly used ceramics—and simultaneously exhibit robust elastic recovery in response to local deformations. This type of carbon is an optimal ultralight, ultrastrong material for a wide range of multifunctional applications, and the synthesis methodology demonstrates potential to access entirely new metastable materials with exceptional properties. PMID:28630918

CSRQ: Communication-Efficient Secure Range Queries in Two-Tiered Sensor Networks

PubMed Central

Dai, Hua; Ye, Qingqun; Yang, Geng; Xu, Jia; He, Ruiliang

2016-01-01

In recent years, we have seen many applications of secure query in two-tiered wireless sensor networks. Storage nodes are responsible for storing data from nearby sensor nodes and answering queries from Sink. It is critical to protect data security from a compromised storage node. In this paper, the Communication-efficient Secure Range Query (CSRQ)—a privacy and integrity preserving range query protocol—is proposed to prevent attackers from gaining information of both data collected by sensor nodes and queries issued by Sink. To preserve privacy and integrity, in addition to employing the encoding mechanisms, a novel data structure called encrypted constraint chain is proposed, which embeds the information of integrity verification. Sink can use this encrypted constraint chain to verify the query result. The performance evaluation shows that CSRQ has lower communication cost than the current range query protocols. PMID:26907293

Probable LAGEOS contributions to a worldwide geodynamics control network

NASA Technical Reports Server (NTRS)

Bender, P. L.; Goad, C. C.

1979-01-01

The paper describes simulations performed on the contributions which LAGEOS laser ranging data can make to the establishment of a worldwide geodynamics control network. A distribution of 10 fixed ranging stations was assumed for most of the calculations, and a single 7-day arc was used, measurements assumed to be made every 10 minutes in order to avoid artificial reductions in the uncertainties due to oversampling. Computer simulations were carried out in which the coordinates of the stations and improvements in the gravity field coefficients were solved for simultaneously. It is suggested that good accuracy for station coordinates can be expected, even with the present gravity field model uncertainties, if sufficient measurement accuracy is achieved at a reasonable distribution of stations. Further, it is found that even 2-cm range measurement errors would be likely to be the main source of station coordinate errors in retrospective analyses of LAGEOS ranging results five or six years from now.

Mining for recurrent long-range interactions in RNA structures reveals embedded hierarchies in network families.

PubMed

Reinharz, Vladimir; Soulé, Antoine; Westhof, Eric; Waldispühl, Jérôme; Denise, Alain

2018-05-04

The wealth of the combinatorics of nucleotide base pairs enables RNA molecules to assemble into sophisticated interaction networks, which are used to create complex 3D substructures. These interaction networks are essential to shape the 3D architecture of the molecule, and also to provide the key elements to carry molecular functions such as protein or ligand binding. They are made of organised sets of long-range tertiary interactions which connect distinct secondary structure elements in 3D structures. Here, we present a de novo data-driven approach to extract automatically from large data sets of full RNA 3D structures the recurrent interaction networks (RINs). Our methodology enables us for the first time to detect the interaction networks connecting distinct components of the RNA structure, highlighting their diversity and conservation through non-related functional RNAs. We use a graphical model to perform pairwise comparisons of all RNA structures available and to extract RINs and modules. Our analysis yields a complete catalog of RNA 3D structures available in the Protein Data Bank and reveals the intricate hierarchical organization of the RNA interaction networks and modules. We assembled our results in an online database (http://carnaval.lri.fr) which will be regularly updated. Within the site, a tool allows users with a novel RNA structure to detect automatically whether the novel structure contains previously observed RINs.

Development of large-scale functional brain networks in children.

PubMed

Supekar, Kaustubh; Musen, Mark; Menon, Vinod

2009-07-01

The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7-9 y) and 22 young-adults (ages 19-22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar "small-world" organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism.

Influence of Bulk PDMS Network Properties on Water Wettability

NASA Astrophysics Data System (ADS)

Melillo, Matthew; Walker, Edwin; Klein, Zoe; Efimenko, Kirill; Genzer, Jan

Poly(dimethylsiloxane) (PDMS) is one of the most common elastomers, with applications ranging from sealants and marine antifouling coatings to absorbents for water treatment. Fundamental understanding of how liquids spread on the surface of and absorb into PDMS networks is of critical importance for the design and use of another application - medical devices. We have systematically studied the effects of polymer molecular weight, loading of tetra-functional crosslinker, and end-group chemical functionality on the mechanical and surface properties of end-linked PDMS networks. Wettability was investigated through the sessile drop technique, wherein a DI water droplet was placed on the bulk network surface and droplet volume, shape, surface area, and contact angle were monitored as a function of time. Various silicone substrates ranging from incredibly soft and flexible materials (E' 50 kPa) to highly rigid networks (E' 5 MPa) were tested. The dynamic behavior of the droplet on the surfaces demonstrated equilibration times between the droplet and surface on the order of 5 minutes. Similar trends were observed for the commercial PDMS material, Sylgard-184. Our results have provided new evidence for the strong influence that substrate modulus and molecular network structure have on the wettability of PDMS elastomers. These findings will aid in the design and implementation of efficient, accurate, and safe PDMS-based medical devices and microfluidic materials that involve aqueous media.

Development of Large-Scale Functional Brain Networks in Children

PubMed Central

Supekar, Kaustubh; Musen, Mark; Menon, Vinod

2009-01-01

The ontogeny of large-scale functional organization of the human brain is not well understood. Here we use network analysis of intrinsic functional connectivity to characterize the organization of brain networks in 23 children (ages 7–9 y) and 22 young-adults (ages 19–22 y). Comparison of network properties, including path-length, clustering-coefficient, hierarchy, and regional connectivity, revealed that although children and young-adults' brains have similar “small-world” organization at the global level, they differ significantly in hierarchical organization and interregional connectivity. We found that subcortical areas were more strongly connected with primary sensory, association, and paralimbic areas in children, whereas young-adults showed stronger cortico-cortical connectivity between paralimbic, limbic, and association areas. Further, combined analysis of functional connectivity with wiring distance measures derived from white-matter fiber tracking revealed that the development of large-scale brain networks is characterized by weakening of short-range functional connectivity and strengthening of long-range functional connectivity. Importantly, our findings show that the dynamic process of over-connectivity followed by pruning, which rewires connectivity at the neuronal level, also operates at the systems level, helping to reconfigure and rebalance subcortical and paralimbic connectivity in the developing brain. Our study demonstrates the usefulness of network analysis of brain connectivity to elucidate key principles underlying functional brain maturation, paving the way for novel studies of disrupted brain connectivity in neurodevelopmental disorders such as autism. PMID:19621066

Character recognition from trajectory by recurrent spiking neural networks.

PubMed

Jiangrong Shen; Kang Lin; Yueming Wang; Gang Pan

2017-07-01

Spiking neural networks are biologically plausible and power-efficient on neuromorphic hardware, while recurrent neural networks have been proven to be efficient on time series data. However, how to use the recurrent property to improve the performance of spiking neural networks is still a problem. This paper proposes a recurrent spiking neural network for character recognition using trajectories. In the network, a new encoding method is designed, in which varying time ranges of input streams are used in different recurrent layers. This is able to improve the generalization ability of our model compared with general encoding methods. The experiments are conducted on four groups of the character data set from University of Edinburgh. The results show that our method can achieve a higher average recognition accuracy than existing methods.

Modeling propagation of infrasound signals observed by a dense seismic network.

PubMed

Chunchuzov, I; Kulichkov, S; Popov, O; Hedlin, M

2014-01-01

The long-range propagation of infrasound from a surface explosion with an explosive yield of about 17.6 t TNT that occurred on June 16, 2008 at the Utah Test and Training Range (UTTR) in the western United States is simulated using an atmospheric model that includes fine-scale layered structure of the wind velocity and temperature fields. Synthetic signal parameters (waveforms, amplitudes, and travel times) are calculated using parabolic equation and ray-tracing methods for a number of ranges between 100 and 800 km from the source. The simulation shows the evolution of several branches of stratospheric and thermospheric signals with increasing range from the source. Infrasound signals calculated using a G2S (ground-to-space) atmospheric model perturbed by small-scale layered wind velocity and temperature fluctuations are shown to agree well with recordings made by the dense High Lava Plains seismic network located at an azimuth of 300° from UTTR. The waveforms of calculated infrasound arrivals are compared with those of seismic recordings. This study illustrates the utility of dense seismic networks for mapping an infrasound field with high spatial resolution. The parabolic equation calculations capture both the effect of scattering of infrasound into geometric acoustic shadow zones and significant temporal broadening of the arrivals.

Development and Implementation of Joint Programs in Laser Ranging and Other Space Geodetic Techniques

NASA Technical Reports Server (NTRS)

Pearlman, Michael R.; Carter, David (Technical Monitor)

2004-01-01

This progress report discusses the status and progress made in joint international programs including: 1) WEGENER; 2) Arabian Peninsula program; 3) Asia-Pacific Space Geodynamics (APSG) program; 4) the Fourteenth International Workshop on Laser Ranging; 5) the International Laser Ranging Service; and 6) current support for the NASA network.

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks.

PubMed

Ma, Junjie; Meng, Fansheng; Zhou, Yuexi; Wang, Yeyao; Shi, Ping

2018-02-16

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths.

Distributed Water Pollution Source Localization with Mobile UV-Visible Spectrometer Probes in Wireless Sensor Networks

PubMed Central

Zhou, Yuexi; Wang, Yeyao; Shi, Ping

2018-01-01

Pollution accidents that occur in surface waters, especially in drinking water source areas, greatly threaten the urban water supply system. During water pollution source localization, there are complicated pollutant spreading conditions and pollutant concentrations vary in a wide range. This paper provides a scalable total solution, investigating a distributed localization method in wireless sensor networks equipped with mobile ultraviolet-visible (UV-visible) spectrometer probes. A wireless sensor network is defined for water quality monitoring, where unmanned surface vehicles and buoys serve as mobile and stationary nodes, respectively. Both types of nodes carry UV-visible spectrometer probes to acquire in-situ multiple water quality parameter measurements, in which a self-adaptive optical path mechanism is designed to flexibly adjust the measurement range. A novel distributed algorithm, called Dual-PSO, is proposed to search for the water pollution source, where one particle swarm optimization (PSO) procedure computes the water quality multi-parameter measurements on each node, utilizing UV-visible absorption spectra, and another one finds the global solution of the pollution source position, regarding mobile nodes as particles. Besides, this algorithm uses entropy to dynamically recognize the most sensitive parameter during searching. Experimental results demonstrate that online multi-parameter monitoring of a drinking water source area with a wide dynamic range is achieved by this wireless sensor network and water pollution sources are localized efficiently with low-cost mobile node paths. PMID:29462929

High performance interconnection between high data rate networks

NASA Technical Reports Server (NTRS)

Foudriat, E. C.; Maly, K.; Overstreet, C. M.; Zhang, L.; Sun, W.

1992-01-01

The bridge/gateway system needed to interconnect a wide range of computer networks to support a wide range of user quality-of-service requirements is discussed. The bridge/gateway must handle a wide range of message types including synchronous and asynchronous traffic, large, bursty messages, short, self-contained messages, time critical messages, etc. It is shown that messages can be classified into three basic classes, synchronous and large and small asynchronous messages. The first two require call setup so that packet identification, buffer handling, etc. can be supported in the bridge/gateway. Identification enables resequences in packet size. The third class is for messages which do not require call setup. Resequencing hardware based to handle two types of resequencing problems is presented. The first is for a virtual parallel circuit which can scramble channel bytes. The second system is effective in handling both synchronous and asynchronous traffic between networks with highly differing packet sizes and data rates. The two other major needs for the bridge/gateway are congestion and error control. A dynamic, lossless congestion control scheme which can easily support effective error correction is presented. Results indicate that the congestion control scheme provides close to optimal capacity under congested conditions. Under conditions where error may develop due to intervening networks which are not lossless, intermediate error recovery and correction takes 1/3 less time than equivalent end-to-end error correction under similar conditions.

Exemplar-Based Image and Video Stylization Using Fully Convolutional Semantic Features.

PubMed

Zhu, Feida; Yan, Zhicheng; Bu, Jiajun; Yu, Yizhou

2017-05-10

Color and tone stylization in images and videos strives to enhance unique themes with artistic color and tone adjustments. It has a broad range of applications from professional image postprocessing to photo sharing over social networks. Mainstream photo enhancement softwares, such as Adobe Lightroom and Instagram, provide users with predefined styles, which are often hand-crafted through a trial-and-error process. Such photo adjustment tools lack a semantic understanding of image contents and the resulting global color transform limits the range of artistic styles it can represent. On the other hand, stylistic enhancement needs to apply distinct adjustments to various semantic regions. Such an ability enables a broader range of visual styles. In this paper, we first propose a novel deep learning architecture for exemplar-based image stylization, which learns local enhancement styles from image pairs. Our deep learning architecture consists of fully convolutional networks (FCNs) for automatic semantics-aware feature extraction and fully connected neural layers for adjustment prediction. Image stylization can be efficiently accomplished with a single forward pass through our deep network. To extend our deep network from image stylization to video stylization, we exploit temporal superpixels (TSPs) to facilitate the transfer of artistic styles from image exemplars to videos. Experiments on a number of datasets for image stylization as well as a diverse set of video clips demonstrate the effectiveness of our deep learning architecture.

A Novel Topology Control Approach to Maintain the Node Degree in Dynamic Wireless Sensor Networks

PubMed Central

Huang, Yuanjiang; Martínez, José-Fernán; Díaz, Vicente Hernández; Sendra, Juana

2014-01-01

Topology control is an important technique to improve the connectivity and the reliability of Wireless Sensor Networks (WSNs) by means of adjusting the communication range of wireless sensor nodes. In this paper, a novel Fuzzy-logic Topology Control (FTC) is proposed to achieve any desired average node degree by adaptively changing communication range, thus improving the network connectivity, which is the main target of FTC. FTC is a fully localized control algorithm, and does not rely on location information of neighbors. Instead of designing membership functions and if-then rules for fuzzy-logic controller, FTC is constructed from the training data set to facilitate the design process. FTC is proved to be accurate, stable and has short settling time. In order to compare it with other representative localized algorithms (NONE, FLSS, k-Neighbor and LTRT), FTC is evaluated through extensive simulations. The simulation results show that: firstly, similar to k-Neighbor algorithm, FTC is the best to achieve the desired average node degree as node density varies; secondly, FTC is comparable to FLSS and k-Neighbor in terms of energy-efficiency, but is better than LTRT and NONE; thirdly, FTC has the lowest average maximum communication range than other algorithms, which indicates that the most energy-consuming node in the network consumes the lowest power. PMID:24608008

Range estimates of whale signals recorded by triplets of hydrophones.

NASA Astrophysics Data System (ADS)

Le Bras, R. J.; Nielsen, P.

2017-12-01

The International Monitoring System of the Comprehensive Nuclear-Test-Ban Treaty Organization includes a hydroacoustic network as one of the monitoring technologies. The underwater part of this network includes six stations and is now complete with the recent installation of the HA04 station located in the Southern Ocean island of Crozet (France). A large number of calls emanating from marine mammals are recorded by the hydrophones, and we present examples where the animals are sufficiently close that a range estimate can be attempted. We also present examples of scattered arrivals and related interpretations.

RF-Plasma Source Commissioning in Indian Negative Ion Facility

NASA Astrophysics Data System (ADS)

Singh, M. J.; Bandyopadhyay, M.; Bansal, G.; Gahlaut, A.; Soni, J.; Kumar, Sunil; Pandya, K.; Parmar, K. G.; Sonara, J.; Yadava, Ratnakar; Chakraborty, A. K.; Kraus, W.; Heinemann, B.; Riedl, R.; Obermayer, S.; Martens, C.; Franzen, P.; Fantz, U.

2011-09-01

The Indian program of the RF based negative ion source has started off with the commissioning of ROBIN, the inductively coupled RF based negative ion source facility under establishment at Institute for Plasma research (IPR), India. The facility is being developed under a technology transfer agreement with IPP Garching. It consists of a single RF driver based beam source (BATMAN replica) coupled to a 100 kW, 1 MHz RF generator with a self excited oscillator, through a matching network, for plasma production and ion extraction and acceleration. The delivery of the RF generator and the RF plasma source without the accelerator, has enabled initiation of plasma production experiments. The recent experimental campaign has established the matching circuit parameters that result in plasma production with density in the range of 0.5-1×1018/m3, at operational gas pressures ranging between 0.4-1 Pa. Various configurations of the matching network have been experimented upon to obtain a stable operation of the set up for RF powers ranging between 25-85 kW and pulse lengths ranging between 4-20 s. It has been observed that the range of the parameters of the matching circuit, over which the frequency of the power supply is stable, is narrow and further experiments with increased number of turns in the coil are in the pipeline to see if the range can be widened. In this paper, the description of the experimental system and the commissioning data related to the optimisation of the various parameters of the matching network, to obtain stable plasma of required density, are presented and discussed.

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity

PubMed Central

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R.; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows. PMID:23970852

Stochastic cycle selection in active flow networks.

PubMed

Woodhouse, Francis G; Forrow, Aden; Fawcett, Joanna B; Dunkel, Jörn

2016-07-19

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models.

Stochastic cycle selection in active flow networks

NASA Astrophysics Data System (ADS)

Woodhouse, Francis; Forrow, Aden; Fawcett, Joanna; Dunkel, Jorn

2016-11-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such non-equilibrium networks. By connecting concepts from lattice field theory, graph theory and transition rate theory, we show how topology controls dynamics in a generic model for actively driven flow on a network. Through theoretical and numerical analysis we identify symmetry-based rules to classify and predict the selection statistics of complex flow cycles from the network topology. Our conceptual framework is applicable to a broad class of biological and non-biological far-from-equilibrium networks, including actively controlled information flows, and establishes a new correspondence between active flow networks and generalized ice-type models.

Locating multiple diffusion sources in time varying networks from sparse observations.

PubMed

Hu, Zhao-Long; Shen, Zhesi; Cao, Shinan; Podobnik, Boris; Yang, Huijie; Wang, Wen-Xu; Lai, Ying-Cheng

2018-02-08

Data based source localization in complex networks has a broad range of applications. Despite recent progress, locating multiple diffusion sources in time varying networks remains to be an outstanding problem. Bridging structural observability and sparse signal reconstruction theories, we develop a general framework to locate diffusion sources in time varying networks based solely on sparse data from a small set of messenger nodes. A general finding is that large degree nodes produce more valuable information than small degree nodes, a result that contrasts that for static networks. Choosing large degree nodes as the messengers, we find that sparse observations from a few such nodes are often sufficient for any number of diffusion sources to be located for a variety of model and empirical networks. Counterintuitively, sources in more rapidly varying networks can be identified more readily with fewer required messenger nodes.

Long-term optical stimulation of channelrhodopsin-expressing neurons to study network plasticity.

PubMed

Lignani, Gabriele; Ferrea, Enrico; Difato, Francesco; Amarù, Jessica; Ferroni, Eleonora; Lugarà, Eleonora; Espinoza, Stefano; Gainetdinov, Raul R; Baldelli, Pietro; Benfenati, Fabio

2013-01-01

Neuronal plasticity produces changes in excitability, synaptic transmission, and network architecture in response to external stimuli. Network adaptation to environmental conditions takes place in time scales ranging from few seconds to days, and modulates the entire network dynamics. To study the network response to defined long-term experimental protocols, we setup a system that combines optical and electrophysiological tools embedded in a cell incubator. Primary hippocampal neurons transduced with lentiviruses expressing channelrhodopsin-2/H134R were subjected to various photostimulation protocols in a time window in the order of days. To monitor the effects of light-induced gating of network activity, stimulated transduced neurons were simultaneously recorded using multi-electrode arrays (MEAs). The developed experimental model allows discerning short-term, long-lasting, and adaptive plasticity responses of the same neuronal network to distinct stimulation frequencies applied over different temporal windows.

Topological properties of a self-assembled electrical network via ab initio calculation

NASA Astrophysics Data System (ADS)

Stephenson, C.; Lyon, D.; Hübler, A.

2017-02-01

Interacting electrical conductors self-assemble to form tree like networks in the presence of applied voltages or currents. Experiments have shown that the degree distribution of the steady state networks are identical over a wide range of network sizes. In this work we develop a new model of the self-assembly process starting from the underlying physical interaction between conductors. In agreement with experimental results we find that for steady state networks, our model predicts that the fraction of endpoints is a constant of 0.252, and the fraction of branch points is 0.237. We find that our model predicts that these scaling properties also hold for the network during the approach to the steady state as well. In addition, we also reproduce the experimental distribution of nodes with a given Strahler number for all steady state networks studied.

Fast Construction of Near Parsimonious Hybridization Networks for Multiple Phylogenetic Trees.

PubMed

Mirzaei, Sajad; Wu, Yufeng

2016-01-01

Hybridization networks represent plausible evolutionary histories of species that are affected by reticulate evolutionary processes. An established computational problem on hybridization networks is constructing the most parsimonious hybridization network such that each of the given phylogenetic trees (called gene trees) is "displayed" in the network. There have been several previous approaches, including an exact method and several heuristics, for this NP-hard problem. However, the exact method is only applicable to a limited range of data, and heuristic methods can be less accurate and also slow sometimes. In this paper, we develop a new algorithm for constructing near parsimonious networks for multiple binary gene trees. This method is more efficient for large numbers of gene trees than previous heuristics. This new method also produces more parsimonious results on many simulated datasets as well as a real biological dataset than a previous method. We also show that our method produces topologically more accurate networks for many datasets.

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.

A systematic approach to infer biological relevance and biases of gene network structures.

PubMed

Antonov, Alexey V; Tetko, Igor V; Mewes, Hans W

2006-01-10

The development of high-throughput technologies has generated the need for bioinformatics approaches to assess the biological relevance of gene networks. Although several tools have been proposed for analysing the enrichment of functional categories in a set of genes, none of them is suitable for evaluating the biological relevance of the gene network. We propose a procedure and develop a web-based resource (BIOREL) to estimate the functional bias (biological relevance) of any given genetic network by integrating different sources of biological information. The weights of the edges in the network may be either binary or continuous. These essential features make our web tool unique among many similar services. BIOREL provides standardized estimations of the network biases extracted from independent data. By the analyses of real data we demonstrate that the potential application of BIOREL ranges from various benchmarking purposes to systematic analysis of the network biology.

Deformable complex network for refining low-resolution X-ray structures

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

Zhang, Chong; Wang, Qinghua; Ma, Jianpeng, E-mail: jpma@bcm.edu

2015-10-27

A new refinement algorithm called the deformable complex network that combines a novel angular network-based restraint with a deformable elastic network model in the target function has been developed to aid in structural refinement in macromolecular X-ray crystallography. In macromolecular X-ray crystallography, building more accurate atomic models based on lower resolution experimental diffraction data remains a great challenge. Previous studies have used a deformable elastic network (DEN) model to aid in low-resolution structural refinement. In this study, the development of a new refinement algorithm called the deformable complex network (DCN) is reported that combines a novel angular network-based restraint withmore » the DEN model in the target function. Testing of DCN on a wide range of low-resolution structures demonstrated that it constantly leads to significantly improved structural models as judged by multiple refinement criteria, thus representing a new effective refinement tool for low-resolution structural determination.« less

Mixture models with entropy regularization for community detection in networks

NASA Astrophysics Data System (ADS)

Chang, Zhenhai; Yin, Xianjun; Jia, Caiyan; Wang, Xiaoyang

2018-04-01

Community detection is a key exploratory tool in network analysis and has received much attention in recent years. NMM (Newman's mixture model) is one of the best models for exploring a range of network structures including community structure, bipartite and core-periphery structures, etc. However, NMM needs to know the number of communities in advance. Therefore, in this study, we have proposed an entropy regularized mixture model (called EMM), which is capable of inferring the number of communities and identifying network structure contained in a network, simultaneously. In the model, by minimizing the entropy of mixing coefficients of NMM using EM (expectation-maximization) solution, the small clusters contained little information can be discarded step by step. The empirical study on both synthetic networks and real networks has shown that the proposed model EMM is superior to the state-of-the-art methods.

Polynomial algebra of discrete models in systems biology.

PubMed

Veliz-Cuba, Alan; Jarrah, Abdul Salam; Laubenbacher, Reinhard

2010-07-01

An increasing number of discrete mathematical models are being published in Systems Biology, ranging from Boolean network models to logical models and Petri nets. They are used to model a variety of biochemical networks, such as metabolic networks, gene regulatory networks and signal transduction networks. There is increasing evidence that such models can capture key dynamic features of biological networks and can be used successfully for hypothesis generation. This article provides a unified framework that can aid the mathematical analysis of Boolean network models, logical models and Petri nets. They can be represented as polynomial dynamical systems, which allows the use of a variety of mathematical tools from computer algebra for their analysis. Algorithms are presented for the translation into polynomial dynamical systems. Examples are given of how polynomial algebra can be used for the model analysis. alanavc@vt.edu Supplementary data are available at Bioinformatics online.

Multimedia Information Networks in Social Media

NASA Astrophysics Data System (ADS)

Cao, Liangliang; Qi, Guojun; Tsai, Shen-Fu; Tsai, Min-Hsuan; Pozo, Andrey Del; Huang, Thomas S.; Zhang, Xuemei; Lim, Suk Hwan

The popularity of personal digital cameras and online photo/video sharing community has lead to an explosion of multimedia information. Unlike traditional multimedia data, many new multimedia datasets are organized in a structural way, incorporating rich information such as semantic ontology, social interaction, community media, geographical maps, in addition to the multimedia contents by themselves. Studies of such structured multimedia data have resulted in a new research area, which is referred to as Multimedia Information Networks. Multimedia information networks are closely related to social networks, but especially focus on understanding the topics and semantics of the multimedia files in the context of network structure. This chapter reviews different categories of recent systems related to multimedia information networks, summarizes the popular inference methods used in recent works, and discusses the applications related to multimedia information networks. We also discuss a wide range of topics including public datasets, related industrial systems, and potential future research directions in this field.

Social Network Assessments and Interventions for Health Behavior Change: A Critical Review.

PubMed

Latkin, Carl A; Knowlton, Amy R

2015-01-01

Social networks provide a powerful approach for health behavior change. This article documents how social network interventions have been successfully used for a range of health behaviors, including HIV risk practices, smoking, exercise, dieting, family planning, bullying, and mental health. We review the literature that suggests the relationship between health behaviors and social network attributes demonstrates a high degree of specificity. The article then examines hypothesized social influence mechanisms including social norms, modeling, and social rewards and the factors of social identity and social rewards that can be employed to sustain social network interventions. Areas of future research avenues are highlighted, including the need to examine and to adjust analytically for contamination and social diffusion, social influence versus differential affiliation, and network change. Use and integration of mhealth and face-to-face networks for promoting health behavior change are also critical research areas.

Successful strategies for competing networks

NASA Astrophysics Data System (ADS)

Aguirre, J.; Papo, D.; Buldú, J. M.

2013-04-01

Competitive interactions represent one of the driving forces behind evolution and natural selection in biological and sociological systems. For example, animals in an ecosystem may vie for food or mates; in a market economy, firms may compete over the same group of customers; sensory stimuli may compete for limited neural resources to enter the focus of attention. Here, we derive rules based on the spectral properties of the network governing the competitive interactions between groups of agents organized in networks. In the scenario studied here the winner of the competition, and the time needed to prevail, essentially depend on the way a given network connects to its competitors and on its internal structure. Our results allow assessment of the extent to which real networks optimize the outcome of their interaction, but also provide strategies through which competing networks can improve on their situation. The proposed approach is applicable to a wide range of systems that can be modelled as networks.

Stochastic cycle selection in active flow networks

PubMed Central

Woodhouse, Francis G.; Forrow, Aden; Fawcett, Joanna B.; Dunkel, Jörn

2016-01-01

Active biological flow networks pervade nature and span a wide range of scales, from arterial blood vessels and bronchial mucus transport in humans to bacterial flow through porous media or plasmodial shuttle streaming in slime molds. Despite their ubiquity, little is known about the self-organization principles that govern flow statistics in such nonequilibrium networks. Here we connect concepts from lattice field theory, graph theory, and transition rate theory to understand how topology controls dynamics in a generic model for actively driven flow on a network. Our combined theoretical and numerical analysis identifies symmetry-based rules that make it possible to classify and predict the selection statistics of complex flow cycles from the network topology. The conceptual framework developed here is applicable to a broad class of biological and nonbiological far-from-equilibrium networks, including actively controlled information flows, and establishes a correspondence between active flow networks and generalized ice-type models. PMID:27382186

On investigating social dynamics in tactical opportunistic mobile networks

NASA Astrophysics Data System (ADS)

Gao, Wei; Li, Yong

2014-06-01

The efficiency of military mobile network operations at the tactical edge is challenging due to the practical Disconnected, Intermittent, and Limited (DIL) environments at the tactical edge which make it hard to maintain persistent end-to-end wireless network connectivity. Opportunistic mobile networks are hence devised to depict such tactical networking scenarios. Social relations among warfighters in tactical opportunistic mobile networks are implicitly represented by their opportunistic contacts via short-range radios, but were inappropriately considered as stationary over time by the conventional wisdom. In this paper, we develop analytical models to probabilistically investigate the temporal dynamics of this social relationship, which is critical to efficient mobile communication in the battlespace. We propose to formulate such dynamics by developing various sociological metrics, including centrality and community, with respect to the opportunistic mobile network contexts. These metrics investigate social dynamics based on the experimentally validated skewness of users' transient contact distributions over time.

Tennessee long-range transportation plan : modal needs

DOT National Transportation Integrated Search

2005-12-01

This report documents one of several major steps in the long-range planning process. This report examines each component of the states transportation network to identify the long-term needs of the transportation modes to 2030. The determination of...

Primary health care teams and the patient perspective: a social network analysis.

PubMed

Cheong, Lynn H M; Armour, Carol L; Bosnic-Anticevich, Sinthia Z

2013-01-01

Multidisciplinary care (MDC) has been proposed as a potential strategy to address the rising challenges of modern health issues. However, it remains unclear as to how patients' health connections may impact on multidisciplinary processes and outcomes. This research aims to gain a deeper understanding of patients' potential role in MDC: i) describe patients' health networks, ii) compare different care groups, iii) gain an understanding of the nature and extent of their interactions, and iv) identify the role of pharmacists within patient networks. In-depth, semi-structured interviews were conducted with asthma patients from Sydney, Australia. Participants were recruited from a range of standard asthma health care access points (community group) and a specialized multidisciplinary asthma clinic (clinic group). Quantitative social network analysis provided structural insight into asthma networks while qualitative social network analysis assisted in interpretation of network data. A total of 47 interviews were conducted (26 community group participants and 21 clinic group participants). Although participants' asthma networks consisted of a range of health care professionals (HCPs), these did not reflect or encourage MDC. Not only did participants favor minimal interaction with any HCP, they preferred sole-charge care and were found to strongly rely on lay individuals such as family and friends. While general practitioners and respiratory specialists were participants' principal choice of HCP, community pharmacists were less regarded. Limited opportunities were presented for HCPs to collaborate, particularly pharmacists. As patients' choices of HCPs may strongly influence collaborative processes and outcomes, this research highlights the need to consider patient perspectives in the development of MDC models in primary care. Copyright © 2013 Elsevier Inc. All rights reserved.

Long-range functional interactions of anterior insula and medial frontal cortex are differently modulated by visuospatial and inductive reasoning tasks.

PubMed

Ebisch, Sjoerd J H; Mantini, Dante; Romanelli, Roberta; Tommasi, Marco; Perrucci, Mauro G; Romani, Gian Luca; Colom, Roberto; Saggino, Aristide

2013-09-01

The brain is organized into functionally specific networks as characterized by intrinsic functional relationships within discrete sets of brain regions. However, it is poorly understood whether such functional networks are dynamically organized according to specific task-states. The anterior insular cortex (aIC)-dorsal anterior cingulate cortex (dACC)/medial frontal cortex (mFC) network has been proposed to play a central role in human cognitive abilities. The present functional magnetic resonance imaging (fMRI) study aimed at testing whether functional interactions of the aIC-dACC/mFC network in terms of temporally correlated patterns of neural activity across brain regions are dynamically modulated by transitory, ongoing task demands. For this purpose, functional interactions of the aIC-dACC/mFC network are compared during two distinguishable fluid reasoning tasks, Visualization and Induction. The results show an increased functional coupling of bilateral aIC with visual cortices in the occipital lobe during the Visualization task, whereas coupling of mFC with right anterior frontal cortex was enhanced during the Induction task. These task-specific modulations of functional interactions likely reflect ability related neural processing. Furthermore, functional connectivity strength between right aIC and right dACC/mFC reliably predicts general task performance. The findings suggest that the analysis of long-range functional interactions may provide complementary information about brain-behavior relationships. On the basis of our results, it is proposed that the aIC-dACC/mFC network contributes to the integration of task-common and task-specific information based on its within-network as well as its between-network dynamic functional interactions. Copyright © 2013 Elsevier Inc. All rights reserved.

78 FR 43262 - Use of Wireless Mobile Data Devices as Transponders for the Commercial Motor Vehicle Information...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-07-19

... Data Devices as Transponders for the Commercial Motor Vehicle Information Systems and Networks (CVISN...; announcement of policy. SUMMARY: FMCSA announces that Commercial Mobile Radio Services (CMRS) network devices... information between the driver and the inspection site as the dedicated short-range communication (DSRC...