estimation network eden: Topics by Science.gov

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Hydrologic Record Extension of Water-Level Data in the Everglades Depth Estimation Network (EDEN) Using Artificial Neural Network Models, 2000-2006

USGS Publications Warehouse

Conrads, Paul; Roehl, Edwin A.

2007-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface models designed to provide scientists, engineers, and water-resource managers with current (2000-present) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystem Science provides support for EDEN and the goal of providing quality assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the water-surface models, 25 real-time water-level gaging stations were added to the network of 253 established water-level gaging stations. To incorporate the data from the newly added stations to the 7-year EDEN database in the greater Everglades, the short-term water-level records (generally less than 1 year) needed to be simulated back in time (hindcasted) to be concurrent with data from the established gaging stations in the database. A three-step modeling approach using artificial neural network models was used to estimate the water levels at the new stations. The artificial neural network models used static variables that represent the gaging station location and percent vegetation in addition to dynamic variables that represent water-level data from the established EDEN gaging stations. The final step of the modeling approach was to simulate the computed error of the initial estimate to increase the accuracy of the final water-level estimate. The three-step modeling approach for estimating water levels at the new EDEN gaging stations produced satisfactory results. The coefficients of determination (R2) for 21 of the 25 estimates were greater than 0.95, and all of the estimates (25 of 25) were greater than 0.82. The model estimates showed good agreement with the measured data. For some new EDEN stations with limited measured data, the record extension (hindcasts) included periods beyond the range of the data used to train the artificial neural network models. The comparison of the hindcasts with long-term water-level data proximal to the new EDEN gaging stations indicated that the water-level estimates were reasonable. The percent model error (root mean square error divided by the range of the measured data) was less than 6 percent, and for the majority of stations (20 of 25), the percent model error was less than 1 percent.
Spatially continuous interpolation of water stage and water depths using the Everglades depth estimation network (EDEN)

USGS Publications Warehouse

Pearlstine, Leonard; Higer, Aaron; Palaseanu, Monica; Fujisaki, Ikuko; Mazzotti, Frank

2007-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (2000-present), online water-stage and water-depth information for the entire freshwater portion of the Greater Everglades. Continuous daily spatial interpolations of the EDEN network stage data are presented on a 400-square-meter grid spacing. EDEN offers a consistent and documented dataset that can be used by scientists and managers to (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan (CERP) The target users are biologists and ecologists examining trophic level responses to hydrodynamic changes in the Everglades.
The Everglades Depth Estimation Network (EDEN) for Support of Ecological and Biological Assessments

USGS Publications Warehouse

Telis, Pamela A.

2006-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring, ground-elevation modeling, and water-surface modeling that provides scientists and managers with current (1999-present), online water-depth information for the entire freshwater portion of the Greater Everglades. Presented on a 400-square-meter grid spacing, EDEN offers a consistent and documented dataset that can be used by scientists and managers to (1) guide large-scale field operations, (2) integrate hydrologic and ecological responses, and (3) support biological and ecological assessments that measure ecosystem responses to the implementation of the Comprehensive Everglades Restoration Plan.
Everglades Depth Estimation Network (EDEN) Applications: Tools to View, Extract, Plot, and Manipulate EDEN Data

USGS Publications Warehouse

Telis, Pamela A.; Henkel, Heather

2009-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated system of real-time water-level monitoring, ground-elevation data, and water-surface elevation modeling to provide scientists and water managers with current on-line water-depth information for the entire freshwater part of the greater Everglades. To assist users in applying the EDEN data to their particular needs, a series of five EDEN tools, or applications (EDENapps), were developed. Using EDEN's tools, scientists can view the EDEN datasets of daily water-level and ground elevations, compute and view daily water depth and hydroperiod surfaces, extract data for user-specified locations, plot transects of water level, and animate water-level transects over time. Also, users can retrieve data from the EDEN datasets for analysis and display in other analysis software programs. As scientists and managers attempt to restore the natural volume, timing, and distribution of sheetflow in the wetlands, such information is invaluable. Information analyzed and presented with these tools is used to advise policy makers, planners, and decision makers of the potential effects of water management and restoration scenarios on the natural resources of the Everglades.
The Everglades Depth Estimation Network (EDEN) surface-water model, version 2

USGS Publications Warehouse

Telis, Pamela A.; Xie, Zhixiao; Liu, Zhongwei; Li, Yingru; Conrads, Paul

2015-01-01

Three applications of the EDEN-modeled water surfaces and other EDEN datasets are presented in the report to show how scientists and resource managers are using EDEN datasets to analyze biological and ecological responses to hydrologic changes in the Everglades. The biological responses of two important Everglades species, alligators and wading birds, to changes in hydrology are described. The effects of hydrology on fire dynamics in the Everglades are also discussed.
Initial Everglades Depth Estimation Network (EDEN) Digital Elevation Model Research and Development

USGS Publications Warehouse

Jones, John W.; Price, Susan D.

2007-01-01

Introduction The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan (Telis, 2006). To produce historic and near-real time maps of water depths, the EDEN requires a system-wide digital elevation model (DEM) of the ground surface. Accurate Everglades wetland ground surface elevation data were non-existent before the U.S. Geological Survey (USGS) undertook the collection of highly accurate surface elevations at the regional scale. These form the foundation for EDEN DEM development. This development process is iterative as additional high accuracy elevation data (HAED) are collected, water surfacing algorithms improve, and additional ground-based ancillary data become available. Models are tested using withheld HAED and independently measured water depth data, and by using DEM data in EDEN adaptive management applications. Here the collection of HAED is briefly described before the approach to DEM development and the current EDEN DEM are detailed. Finally future research directions for continued model development, testing, and refinement are provided.
Estimation of Missing Water-Level Data for the Everglades Depth Estimation Network (EDEN)

USGS Publications Warehouse

Conrads, Paul; Petkewich, Matthew D.

2009-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level gaging stations, ground-elevation models, and water-surface elevation models designed to provide scientists, engineers, and water-resource managers with current (2000-2009) water-depth information for the entire freshwater portion of the greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for EDEN and their goal of providing quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. To increase the accuracy of the daily water-surface elevation model, water-level estimation equations were developed to fill missing data. To minimize the occurrences of no estimation of data due to missing data for an input station, a minimum of three linear regression equations were developed for each station using different input stations. Of the 726 water-level estimation equations developed to fill missing data at 239 stations, more than 60 percent of the equations have coefficients of determination greater than 0.90, and 92 percent have an coefficient of determination greater than 0.70.
Everglades Depth Estimation Network (EDEN)—A decade of serving hydrologic information to scientists and resource managers

USGS Publications Warehouse

Patino, Eduardo; Conrads, Paul; Swain, Eric; Beerens, James M.

2017-10-30

IntroductionThe Everglades Depth Estimation Network (EDEN) provides scientists and resource managers with regional maps of daily water levels and depths in the freshwater part of the Greater Everglades landscape. The EDEN domain includes all or parts of five Water Conservation Areas, Big Cypress National Preserve, Pennsuco Wetlands, and Everglades National Park. Daily water-level maps are interpolated from water-level data at monitoring gages, and depth is estimated by using a digital elevation model of the land surface. Online datasets provide time series of daily water levels at gages and rainfall and evapotranspiration data (https://sofia.usgs.gov/eden/). These datasets are used by scientists and resource managers to guide large-scale field operations, describe hydrologic changes, and support biological and ecological assessments that measure ecosystem response to the implementation of the Comprehensive Everglades Restoration Plan. EDEN water-level data have been used in a variety of biological and ecological studies including (1) the health of American alligators as a function of water depth, (2) the variability of post-fire landscape dynamics in relation to water depth, (3) the habitat quality for wading birds with dynamic habitat selection, and (4) an evaluation of the habitat of the Cape Sable seaside sparrow.
User’s manual for the Automated Data Assurance and Management application developed for quality control of Everglades Depth Estimation Network water-level data

USGS Publications Warehouse

Petkewich, Matthew D.; Daamen, Ruby C.; Roehl, Edwin A.; Conrads, Paul

2016-09-29

The generation of Everglades Depth Estimation Network (EDEN) daily water-level and water-depth maps is dependent on high quality real-time data from over 240 water-level stations. To increase the accuracy of the daily water-surface maps, the Automated Data Assurance and Management (ADAM) tool was created by the U.S. Geological Survey as part of Greater Everglades Priority Ecosystems Science. The ADAM tool is used to provide accurate quality-assurance review of the real-time data from the EDEN network and allows estimation or replacement of missing or erroneous data. This user’s manual describes how to install and operate the ADAM software. File structure and operation of the ADAM software is explained using examples.
Conceptual Design of the Everglades Depth Estimation Network (EDEN) Grid

USGS Publications Warehouse

Jones, John W.; Price, Susan D.

2007-01-01

INTRODUCTION The Everglades Depth Estimation Network (EDEN) offers a consistent and documented dataset that can be used to guide large-scale field operations, to integrate hydrologic and ecological responses, and to support biological and ecological assessments that measure ecosystem responses to the Comprehensive Everglades Restoration Plan (Telis, 2006). Ground elevation data for the greater Everglades and the digital ground elevation models derived from them form the foundation for all EDEN water depth and associated ecologic/hydrologic modeling (Jones, 2004, Jones and Price, 2007). To use EDEN water depth and duration information most effectively, it is important to be able to view and manipulate information on elevation data quality and other land cover and habitat characteristics across the Everglades region. These requirements led to the development of the geographic data layer described in this techniques and methods report. Relying on extensive experience in GIS data development, distribution, and analysis, a great deal of forethought went into the design of the geographic data layer used to index elevation and other surface characteristics for the Greater Everglades region. To allow for simplicity of design and use, the EDEN area was broken into a large number of equal-sized rectangles ('Cells') that in total are referred to here as the 'grid'. Some characteristics of this grid, such as the size of its cells, its origin, the area of Florida it is designed to represent, and individual grid cell identifiers, could not be changed once the grid database was developed. Therefore, these characteristics were selected to design as robust a grid as possible and to ensure the grid's long-term utility. It is desirable to include all pertinent information known about elevation and elevation data collection as grid attributes. Also, it is very important to allow for efficient grid post-processing, sub-setting, analysis, and distribution. This document details the conceptual design of the EDEN grid spatial parameters and cell attribute-table content.
Development of inferential sensors for real-time quality control of water-level data for the Everglades Depth Estimation Network

USGS Publications Warehouse

Daamen, Ruby C.; Edwin A. Roehl, Jr.; Conrads, Paul

2010-01-01

A technology often used for industrial applications is “inferential sensor.” Rather than installing a redundant sensor to measure a process, such as an additional waterlevel gage, an inferential sensor, or virtual sensor, is developed that estimates the processes measured by the physical sensor. The advantage of an inferential sensor is that it provides a redundant signal to the sensor in the field but without exposure to environmental threats. In the event that a gage does malfunction, the inferential sensor provides an estimate for the period of missing data. The inferential sensor also can be used in the quality assurance and quality control of the data. Inferential sensors for gages in the EDEN network are currently (2010) under development. The inferential sensors will be automated so that the real-time EDEN data will continuously be compared to the inferential sensor signal and digital reports of the status of the real-time data will be sent periodically to the appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.
Extension Disaster Education Network (EDEN): Preparing Families for Disaster

ERIC Educational Resources Information Center

Washburn, Carolyn; Saunders, Kristine

2010-01-01

According to the American Red Cross (n.d.), less than half of Americans have an emergency preparedness plan in place. Therefore, it is critical that the Cooperative Extension System takes a role in encouraging the development of family preparedness plans. The Extension Disaster Education Network (EDEN) has developed a family and consumer sciences…
Using inferential sensors for quality control of Everglades Depth Estimation Network water-level data

USGS Publications Warehouse

Petkewich, Matthew D.; Daamen, Ruby C.; Roehl, Edwin A.; Conrads, Paul

2016-09-29

The Everglades Depth Estimation Network (EDEN), with over 240 real-time gaging stations, provides hydrologic data for freshwater and tidal areas of the Everglades. These data are used to generate daily water-level and water-depth maps of the Everglades that are used to assess biotic responses to hydrologic change resulting from the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. The generation of EDEN daily water-level and water-depth maps is dependent on high quality real-time data from water-level stations. Real-time data are automatically checked for outliers by assigning minimum and maximum thresholds for each station. Small errors in the real-time data, such as gradual drift of malfunctioning pressure transducers, are more difficult to immediately identify with visual inspection of time-series plots and may only be identified during on-site inspections of the stations. Correcting these small errors in the data often is time consuming and water-level data may not be finalized for several months. To provide daily water-level and water-depth maps on a near real-time basis, EDEN needed an automated process to identify errors in water-level data and to provide estimates for missing or erroneous water-level data.The Automated Data Assurance and Management (ADAM) software uses inferential sensor technology often used in industrial applications. Rather than installing a redundant sensor to measure a process, such as an additional water-level station, inferential sensors, or virtual sensors, were developed for each station that make accurate estimates of the process measured by the hard sensor (water-level gaging station). The inferential sensors in the ADAM software are empirical models that use inputs from one or more proximal stations. The advantage of ADAM is that it provides a redundant signal to the sensor in the field without the environmental threats associated with field conditions at stations (flood or hurricane, for example). In the event that a station does malfunction, ADAM provides an accurate estimate for the period of missing data. The ADAM software also is used in the quality assurance and quality control of the data. The virtual signals are compared to the real-time data, and if the difference between the two signals exceeds a certain tolerance, corrective action to the data and (or) the gaging station can be taken. The ADAM software is automated so that, each morning, the real-time EDEN data are compared to the inferential sensor signals and digital reports highlighting potential erroneous real-time data are generated for appropriate support personnel. The development and application of inferential sensors is easily transferable to other real-time hydrologic monitoring networks.
Around the Globe.

ERIC Educational Resources Information Center

Wheeler, Steve

2001-01-01

Presents a report by Rigmor Sterner (Lulea University of Technology) on the 10th annual European Distance Education Network (EDEN) conference (June 10-13, 2001, Stockholm, Sweden). EDEN is a non-governmental educational association whose aim is to foster the development of open and distance learning and distance education. (AEF)
Efficient wetland surface water detection and monitoring via Landsat: Comparison with in situ data from the Everglades Depth Estimation Network

USGS Publications Warehouse

Jones, John W.

2015-01-01

The U.S. Geological Survey is developing new Landsat science products. One, named Dynamic Surface Water Extent (DSWE), is focused on the representation of ground surface inundation as detected in cloud-/shadow-/snow-free pixels for scenes collected over the U.S. and its territories. Characterization of DSWE uncertainty to facilitate its appropriate use in science and resource management is a primary objective. A unique evaluation dataset developed from data made publicly available through the Everglades Depth Estimation Network (EDEN) was used to evaluate one candidate DSWE algorithm that is relatively simple, requires no scene-based calibration data, and is intended to detect inundation in the presence of marshland vegetation. A conceptual model of expected algorithm performance in vegetated wetland environments was postulated, tested and revised. Agreement scores were calculated at the level of scenes and vegetation communities, vegetation index classes, water depths, and individual EDEN gage sites for a variety of temporal aggregations. Landsat Archive cloud cover attribution errors were documented. Cloud cover had some effect on model performance. Error rates increased with vegetation cover. Relatively low error rates for locations of little/no vegetation were unexpectedly dominated by omission errors due to variable substrates and mixed pixel effects. Examined discrepancies between satellite and in situ modeled inundation demonstrated the utility of such comparisons for EDEN database improvement. Importantly, there seems no trend or bias in candidate algorithm performance as a function of time or general hydrologic conditions, an important finding for long-term monitoring. The developed database and knowledge gained from this analysis will be used for improved evaluation of candidate DSWE algorithms as well as other measurements made on Everglades surface inundation, surface water heights and vegetation using radar, lidar and hyperspectral instruments. Although no other sites have such an extensive in situ network or long-term records, the broader applicability of this and other candidate DSWE algorithms is being evaluated in other wetlands using this work as a guide. Continued interaction among DSWE producers and potential users will help determine whether the measured accuracies are adequate for practical utility in resource management.
Landscape unit based digital elevation model development for the freshwater wetlands within the Arthur C. Marshall Loxahatchee National Wildlife Refuge, Southeastern Florida

USGS Publications Warehouse

Xie, Zhixiao; Liu, Zhongwei; Jones, John W.; Higer, Aaron L.; Telis, Pamela A.

2011-01-01

The hydrologic regime is a critical limiting factor in the delicate ecosystem of the greater Everglades freshwater wetlands in south Florida that has been severely altered by management activities in the past several decades. "Getting the water right" is regarded as the key to successful restoration of this unique wetland ecosystem. An essential component to represent and model its hydrologic regime, specifically water depth, is an accurate ground Digital Elevation Model (DEM). The Everglades Depth Estimation Network (EDEN) supplies important hydrologic data, and its products (including a ground DEM) have been well received by scientists and resource managers involved in Everglades restoration. This study improves the EDEN DEMs of the Loxahatchee National Wildlife Refuge, also known as Water Conservation Area 1 (WCA1), by adopting a landscape unit (LU) based interpolation approach. The study first filtered the input elevation data based on newly available vegetation data, and then created a separate geostatistical model (universal kriging) for each LU. The resultant DEMs have encouraging cross-validation and validation results, especially since the validation is based on an independent elevation dataset (derived by subtracting water depth measurements from EDEN water surface elevations). The DEM product of this study will directly benefit hydrologic and ecological studies as well as restoration efforts. The study will also be valuable for a broad range of wetland studies.
Hydrologic record extension of water-level data in the Everglades Depth Estimation Network (EDEN), 1991-99

USGS Publications Warehouse

Conrads, Paul; Petkewich, Matthew D.; O'Reilly, Andrew M.; Telis, Pamela A.

2015-01-01

To hindcast and fill data records, 214 empirical models were developed—189 are linear regression models and 25 are artificial neural network models. The coefficient of determination (R2) for 163 of the models is greater than 0.80 and the median percent model error (root mean square error divided by the range of the measured data) is 5 percent. To evaluate the performance of the hindcast models as a group, contour maps of modeled water-level surfaces at 2-centimeter (cm) intervals were generated using the hindcasted data. The 2-cm contour maps were examined for selected days to verify that water surfaces from the EDEN model are consistent with the input data. The biweekly 2-cm contour maps did show a higher number of issues during days in 1990 as compared to days after 1990. May 1990 had the lowest water levels in the Everglades of the 21-year dataset used for the hindcasting study. To hindcast these record low conditions in 1990, many of the hindcast models would require large extrapolations beyond the range of the predictive quality of the models. For these reasons, it was decided to limit the hindcasted data to the period January 1, 1991, to December 31, 1999. Overall, the hindcasted and gap-filled data are assumed to provide reasonable estimates of station-specific water-level data for an extended historical period to inform research and natural resource management in the Everglades.
Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks.

PubMed

Dâmaso, Antônio; Rosa, Nelson; Maciel, Paulo

2017-11-05

Power consumption is a primary interest in Wireless Sensor Networks (WSNs), and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way.
Estimation of missing water-level data for the Everglades Depth Estimation Network (EDEN), 2013 update

USGS Publications Warehouse

Petkewich, Matthew D.; Conrads, Paul

2013-01-01

The Everglades Depth Estimation Network is an integrated network of real-time water-level gaging stations, a ground-elevation model, and a water-surface elevation model designed to provide scientists, engineers, and water-resource managers with water-level and water-depth information (1991-2013) for the entire freshwater portion of the Greater Everglades. The U.S. Geological Survey Greater Everglades Priority Ecosystems Science provides support for the Everglades Depth Estimation Network in order for the Network to provide quality-assured monitoring data for the U.S. Army Corps of Engineers Comprehensive Everglades Restoration Plan. In a previous study, water-level estimation equations were developed to fill in missing data to increase the accuracy of the daily water-surface elevation model. During this study, those equations were updated because of the addition and removal of water-level gaging stations, the consistent use of water-level data relative to the North American Vertical Datum of 1988, and availability of recent data (March 1, 2006, to September 30, 2011). Up to three linear regression equations were developed for each station by using three different input stations to minimize the occurrences of missing data for an input station. Of the 667 water-level estimation equations developed to fill missing data at 223 stations, more than 72 percent of the equations have coefficients of determination greater than 0.90, and 97 percent have coefficients of determination greater than 0.70.
Integrated Evaluation of Reliability and Power Consumption of Wireless Sensor Networks

PubMed Central

Dâmaso, Antônio; Maciel, Paulo

2017-01-01

Power consumption is a primary interest in Wireless Sensor Networks (WSNs), and a large number of strategies have been proposed to evaluate it. However, those approaches usually neither consider reliability issues nor the power consumption of applications executing in the network. A central concern is the lack of consolidated solutions that enable us to evaluate the power consumption of applications and the network stack also considering their reliabilities. To solve this problem, we introduce a fully automatic solution to design power consumption aware WSN applications and communication protocols. The solution presented in this paper comprises a methodology to evaluate the power consumption based on the integration of formal models, a set of power consumption and reliability models, a sensitivity analysis strategy to select WSN configurations and a toolbox named EDEN to fully support the proposed methodology. This solution allows accurately estimating the power consumption of WSN applications and the network stack in an automated way. PMID:29113078

Dimension improvement in Dhar's refutation of the Eden conjecture

NASA Astrophysics Data System (ADS)

Bertrand, Quentin; Pertinand, Jules

2018-03-01

We consider the Eden model on the d-dimensional hypercubical unoriented lattice, for large d. Initially, every lattice point is healthy, except the origin which is infected. Then, each infected lattice point contaminates any of its neighbours with rate 1. The Eden model is equivalent to first passage percolation, with exponential passage times on edges. The Eden conjecture states that the limit shape of the Eden model is a Euclidean ball. By pushing the computations of Dhar [5] a little further with modern computers and efficient implementation we obtain improved bounds for the speed of infection. This shows that the Eden conjecture does not hold in dimension superior to 22 (the lowest known dimension was 35).
Organic Techniques for Protecting Virtual Private Network (VPN) Services from Access Link Flooding Attacks

DTIC Science & Technology

2002-01-01

Submitted to ICN 2002 Organic Techniques for Protecting Virtual Private Network (VPN) Services from Access Link Flooding Attacks1 Ranga S. Ramanujan ...using these techniques is also described. Contact author: Dr. Ranga S. Ramanujan Architecture Technology Corporation 9971 Valley View Road Eden Prairie...OF ABSTRACT 18. NUMBER OF PAGES 15 19a. NAME OF RESPONSIBLE PERSON a. REPORT unclassified b. ABSTRACT unclassified c . THIS PAGE unclassified
Estimation of water surface elevations for the Everglades, Florida

USGS Publications Warehouse

Palaseanu, Monica; Pearlstine, Leonard

2008-01-01

The Everglades Depth Estimation Network (EDEN) is an integrated network of real-time water-level monitoring gages and modeling methods that provides scientists and managers with current (2000–present) online water surface and water depth information for the freshwater domain of the Greater Everglades. This integrated system presents data on a 400-m square grid to assist in (1) large-scale field operations; (2) integration of hydrologic and ecologic responses; (3) supporting biological and ecological assessment of the implementation of the Comprehensive Everglades Restoration Plan (CERP); and (4) assessing trophic-level responses to hydrodynamic changes in the Everglades.This paper investigates the radial basis function multiquadric method of interpolation to obtain a continuous freshwater surface across the entire Everglades using radio-transmitted data from a network of water-level gages managed by the US Geological Survey (USGS), the South Florida Water Management District (SFWMD), and the Everglades National Park (ENP). Since the hydrological connection is interrupted by canals and levees across the study area, boundary conditions were simulated by linearly interpolating along those features and integrating the results together with the data from marsh stations to obtain a continuous water surface through multiquadric interpolation. The absolute cross-validation errors greater than 5 cm correlate well with the local outliers and the minimum distance between the closest stations within 2000-m radius, but seem to be independent of vegetation or season.
School Boundary Debate Divides Minnesota Suburb

ERIC Educational Resources Information Center

Samuels, Christina A.

2011-01-01

The author discusses how an assignment plan intended to keep schools socioeconomically balanced spurs a bitter debate in suburban Eden Prairie. The boundary debate in the 9,700-student Eden Prairie, Minnesota, district has been bruising. Eden Prairie adopted new school attendance boundaries this year based on socioeconomic balance, ensuring for…
The complexity of classical music networks

NASA Astrophysics Data System (ADS)

Rolla, Vitor; Kestenberg, Juliano; Velho, Luiz

2018-02-01

Previous works suggest that musical networks often present the scale-free and the small-world properties. From a musician's perspective, the most important aspect missing in those studies was harmony. In addition to that, the previous works made use of outdated statistical methods. Traditionally, least-squares linear regression is utilised to fit a power law to a given data set. However, according to Clauset et al. such a traditional method can produce inaccurate estimates for the power law exponent. In this paper, we present an analysis of musical networks which considers the existence of chords (an essential element of harmony). Here we show that only 52.5% of music in our database presents the scale-free property, while 62.5% of those pieces present the small-world property. Previous works argue that music is highly scale-free; consequently, it sounds appealing and coherent. In contrast, our results show that not all pieces of music present the scale-free and the small-world properties. In summary, this research is focused on the relationship between musical notes (Do, Re, Mi, Fa, Sol, La, Si, and their sharps) and accompaniment in classical music compositions. More information about this research project is available at https://eden.dei.uc.pt/~vitorgr/MS.html.
Talking about Plants and People

ERIC Educational Resources Information Center

Peacock, Alan

2004-01-01

The Eden Project in Cornwall sets out to educate about the interdependence of plants and people. Its tropical and Mediterranean biomes are housed in the largest "greenhouses" in the world, which serve as a backdrop to plants that grow in the temperate zones of the world, grown in Eden's outdoor landscape. Eden has aroused worldwide…
"Don't Forget Your Leech Socks"! Children's Learning during an Eden Education Officer's Workshop

ERIC Educational Resources Information Center

Bowker, Rob; Jasper, Andy

2007-01-01

This study looked at 30 primary aged children between 10 and 11 years old who were visiting the Eden Project, Cornwall and participating in workshops led and designed by the Eden Education Officers. The study attempted to directly test the effects of the Education Officers' workshops on children's learning. Personal meaning mapping, a…
Eden Institute: Using Health Games for ASD Student and Staff Development.

PubMed

Ferguson, Moderator Bill; McCool, Participants Thomas; Gasdia, Dominique; Sharp, Tim; Breeman, Lisa; Parikh, Nish; Taub, Bob; Finkler, Nina

2013-02-01

Eden Autism Services is a leading-edge resource for children and adults suffering from more severe effects of autism spectrum disorder (ASD). The strategic use of games in the development of students, staff, teachers, parents, friends, and employers has advanced the quality of life of Eden's students and, consequently, their relationships, productivity, and happiness.
Children's Perceptions of Plants Following Their Visit to the Eden Project

ERIC Educational Resources Information Center

Bowker, Rob

2004-01-01

The study described is part of a larger research programme designed to investigate primary aged children's learning during a visit to the Eden Project. Children from eight primary schools were interviewed approximately four weeks after a one-day, teacher-led visit to the Eden Project (EP) in Cornwall. Their responses revealed that the children…
Feasibility Study of Economics and Performance of Solar Photovoltaics at the VAG Mine Site in Eden and Lowell, Vermont. A Study Prepared in Partnership with the Environmental Protection Agency for the RE-Powering America's Land Initiative: Siting Renewable Energy on Potentially Contaminated Land and Mine Sites

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

Simon, J.; Mosey, G.

The U.S. Environmental Protection Agency (EPA), in accordance with the RE-Powering America's Land initiative, selected the Vermont Asbestos Group (VAG) Mine site in Eden, Vermont, and Lowell, Vermont, for a feasibility study of renewable energy production. The National Renewable Energy Laboratory (NREL) provided technical assistance for this project. The purpose of this report is to assess the site for a possible photovoltaic (PV) system installation and estimate the cost, performance, and site impacts of different PV options. In addition, the report recommends financing options that could assist in the implementation of a PV system at the site.
Clinical study in genital herpes: natural Gene-Eden-VIR/Novirin versus acyclovir, valacyclovir, and famciclovir.

PubMed

Polansky, Hanan; Javaherian, Adrian; Itzkovitz, Edan

2016-01-01

This paper reports the results of a clinical study that tested the effect of suppressive treatment with the botanical product Gene-Eden-VIR/Novirin on the number of genital herpes outbreaks. The results in this study were compared to those published in clinical studies of acyclovir, valacyclovir, and famciclovir. The framework was a retrospective chart review. The population included 139 participants. The treatment was one to four capsules of Gene-Eden-VIR/Novirin per day. The duration of treatment was 2-48 months. The study included three controls recommended by the US Food and Drug Administration (FDA): baseline, no treatment, and dose response. The treatment decreased the number of outbreaks per year in 90.8% of the participants. The treatment also decreased the mean number of outbreaks per year from 7.27 and 5.5 in the control groups to 2.39 (P<0.0001 and P<0.001, respectively). The treated participants reported no adverse experiences. Out of the 15 tests that compared Gene-Eden-VIR/Novirin to the three drugs, Gene-Eden-VIR/Novirin had superior efficacy in eight tests, inferior efficacy in three tests, and comparable efficacy in four tests. Gene-Eden-VIR/Novirin also had superior safety. The clinical study showed that the natural Gene-Eden-VIR/Novirin decreases the number of genital herpes outbreaks without any side effects. The study also showed that the clinical effects reported in this study are mostly better than those reported in the reviewed studies of acyclovir, valacyclovir, and famciclovir.
The source provenance of an obsidian Eden point from Sierra County, New Mexico

DOE PAGES

Dolan, Sean Gregory; Berryman, Judy; Shackley, M. Steven

2016-01-02

Eden projectile points associated with the Cody complex are underrepresented in the late Paleoindian record of the American Southwest. EDXRF analysis of an obsidian Eden point from a site in Sierra County, New Mexico demonstrates this artifact is from the Cerro del Medio (Valles Rhyolite) source in the Jemez Mountains. Lastly, we contextualize our results by examining variability in obsidian procurement practices beyond the Cody heartland in southcentral New Mexico.
Packing Fraction of a Two-dimensional Eden Model with Random-Sized Particles

NASA Astrophysics Data System (ADS)

Kobayashi, Naoki; Yamazaki, Hiroshi

2018-01-01

We have performed a numerical simulation of a two-dimensional Eden model with random-size particles. In the present model, the particle radii are generated from a Gaussian distribution with mean μ and standard deviation σ. First, we have examined the bulk packing fraction for the Eden cluster and investigated the effects of the standard deviation and the total number of particles NT. We show that the bulk packing fraction depends on the number of particles and the standard deviation. In particular, for the dependence on the standard deviation, we have determined the asymptotic value of the bulk packing fraction in the limit of the dimensionless standard deviation. This value is larger than the packing fraction obtained in a previous study of the Eden model with uniform-size particles. Secondly, we have investigated the packing fraction of the entire Eden cluster including the effect of the interface fluctuation. We find that the entire packing fraction depends on the number of particles while it is independent of the standard deviation, in contrast to the bulk packing fraction. In a similar way to the bulk packing fraction, we have obtained the asymptotic value of the entire packing fraction in the limit NT → ∞. The obtained value of the entire packing fraction is smaller than that of the bulk value. This fact suggests that the interface fluctuation of the Eden cluster influences the packing fraction.
South Bay Salt Pond Restoration Project: Planning Phase at Southern Eden Landing

EPA Pesticide Factsheets

This project will complete the design and permits to restore 1,300 acres of tidal wetlands, provide 3.5 miles shoreline protection, and accelerate wetlands restoration at the Eden Landing Ecological Reserve.
EDEN: evolutionary dynamics within environments

PubMed Central

Münch, Philipp C.; Stecher, Bärbel; McHardy, Alice C.

2017-01-01

Abstract Summary Metagenomics revolutionized the field of microbial ecology, giving access to Gb-sized datasets of microbial communities under natural conditions. This enables fine-grained analyses of the functions of community members, studies of their association with phenotypes and environments, as well as of their microevolution and adaptation to changing environmental conditions. However, phylogenetic methods for studying adaptation and evolutionary dynamics are not able to cope with big data. EDEN is the first software for the rapid detection of protein families and regions under positive selection, as well as their associated biological processes, from meta- and pangenome data. It provides an interactive result visualization for detailed comparative analyses. Availability and implementation EDEN is available as a Docker installation under the GPL 3.0 license, allowing its use on common operating systems, at http://www.github.com/hzi-bifo/eden. Contact alice.mchardy@helmholtz-hzi.de Supplementary information Supplementary data are available at Bioinformatics online. PMID:28637301
Impacts of Cropland Changes on Water Balance, Sediment and Nutrient Transport in Eden River, UK

NASA Astrophysics Data System (ADS)

Huang, Yumei; Quinn, Paul; Liang, Qiuhua; Adams, Russell

2017-04-01

Water is the key to food and human life. Farming is the main part of economic and society in Eden, with approximately 2000 farms which covers 95% of under crops. However, with the growth of farming practice and global climate changes, Eden has presented great challenges and bringing uncertainty in the water quality caused by the agricultural diffuse pollution. This expected to reduce negative impacts of the water diffuse pollution from agriculture in Eden. Therefore, there is a high need to ensure effective water resource management to enhance water quality, to address the flow pathways and sediment transport in different farming practice and cropland changes. Hence we need to understand nutrient and the hydrological flow pathways from soil to Hillslope to channel. The aim of this research is to evaluate the impacts of different cropland changes on water balance, sediment and nutrient transport. By using the hydrological models Soil and Water Assessment Tool (SWAT) and the Catchment Runoff Attenuation Flux Tool (CRAFT), it can show the sediment and nutrient export from the load for each flow pathways (overland flow, soil water flow and ground water flow). We will show results from a small research catchment (10km2) area to the whole of Eden (800km2) at a daily time step.
Bioregenerative Life Support System Research as part of the DLR EDEN Initiative

NASA Astrophysics Data System (ADS)

Bamsey, Matthew; Schubert, Daniel; Zabel, Paul; Poulet, Lucie; Zeidler, Conrad

In 2011, the DLR Institute of Space Systems launched a research initiative called EDEN - Evolution and Design of Environmentally-closed Nutrition-Sources. The research initiative focuses on bioregenerative life support systems, especially greenhouse modules, and technologies for future crewed vehicles. The EDEN initiative comprises several projects with respect to space research, ground testing and spin-offs. In 2014, EDEN’s new laboratory officially opened. This new biological cleanroom laboratory comprises several plant growth chambers incorporating a number of novel controlled environment agriculture technologies. This laboratory will be the nucleus for a variety of plant cultivation experiments within closed environments. The utilized technologies are being advanced using the pull of space technology and include such items as stacked growth systems, PAR-specific LEDs, intracanopy lighting, aeroponic nutrient delivery systems and ion-selective nutrient sensors. The driver of maximizing biomass output per unit volume and energy has much application in future bioregenerative life support systems but can also provide benefit terrestrially. The EDEN laboratory also includes several specially constructed chambers for advancing models addressing the interaction between bioregenerative and physical-chemical life support systems. The EDEN team is presently developing designs for containerized greenhouse modules. One module is planned for deployment to the German Antarctic Station, Neumayer III. The shipping container based system will provide supplementation to the overwintering crew’s diet, provide psychological benefit while at the same time advancing the technology and operational readiness of harsh environment plant production systems. In addition to hardware development, the EDEN team has participated in several early phase designs such as for the ESA Greenhouse Module for Space System and for large-scale vertical farming. These studies often utilize the Institute of Space Systems Concurrent Engineering Facility.
Clinical study of Gene-Eden-VIR/Novirin in genital herpes: suppressive treatment safely decreases the duration of outbreaks in both severe and mild cases.

PubMed

Polansky, Hanan; Itzkovitz, Edan; Javaherian, Adrian

2016-12-01

We conducted a clinical study that tested the effect of suppressive treatment with the botanical product Gene-Eden-VIR/Novirin on genital herpes. Our previous paper showed that the treatment decreased the number of genital herpes outbreaks without any side effects. It also showed that the clinical effects of Gene-Eden-VIR/Novirin are mostly better than those reported in the studies that tested acyclovir, valacyclovir, and famciclovir. The current paper reports the effect of suppressive treatment with Gene-Eden-VIR/Novirin on the duration of outbreaks, in severe and mild genital herpes cases. The framework was a retrospective chart review. The population included 137 participants. The treatment was 1-4 capsules per day. The duration of treatment was 2-48 months. The study included three controls: baseline, no-treatment, and dose-response. The treatment decreased the duration of outbreaks in 87 % of participants and decreased the mean duration of outbreaks from 8.77 days and 6.7 days in the control groups to 2.87 days in the treatment group (P < 0.001, both groups). All participants reported no adverse experiences. This paper shows that suppressive treatment with Gene-Eden-VIR/Novirin decreased the duration of genital herpes outbreaks, in both severe and mild cases, without any side effects. Based on the results reported in this and our previous paper, we recommend suppressive treatment with Gene-Eden-VIR/Novirin as a natural alternative to both suppressive and episodic treatments with current drugs, in both severe and mild genital herpes cases. Trial registration ClinicalTrials.gov NCT02715752 Registered 17 March 2016 Retrospectively Registered.
Using the "Eden Express" to Teach Introductory Psychology.

ERIC Educational Resources Information Center

Gorman, Michael E.

1984-01-01

Students read Mark Vonnegut's "The Eden Express," an autobiographical account of a young man's schizophrenic breakdown, and wrote papers comparing how different perspectives, e.g., the biomedical and behavioral, would describe the cause and cure of Vonnegut's schizophrenia. Students liked the book and the assignment. (RM)
Advertising and Irreversible Opinion Spreading in Complex Social Networks

NASA Astrophysics Data System (ADS)

Candia, Julián

Irreversible opinion spreading phenomena are studied on small-world and scale-free networks by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. In this model, the opinion of an individual is affected by those of their acquaintances, but opinion changes (analogous to spin flips in an Ising-like model) are not allowed. We focus on the influence of advertising, which is represented by external magnetic fields. The interplay and competition between temperature and fields lead to order-disorder transitions, which are found to also depend on the link density and the topology of the complex network substrate. The effects of advertising campaigns with variable duration, as well as the best cost-effective strategies to achieve consensus within different scenarios, are also discussed.

Is Emile in the Garden of Eden? Western Ideologies of Nature

ERIC Educational Resources Information Center

Tulloch, Lynley

2015-01-01

This paper will explore ideologies of nature including the "Garden of Eden" and "wildernesses." It locates these ideologies as morphing to accommodate the later trajectory of the Enlightenment Project and its endorsement of modern Western scientific and technological principles. Beginning with the premise that nature is…
Scholarly Transitions: Finding Eden in the Academic Periphery?

ERIC Educational Resources Information Center

Mathews-Aydinli, Julie

2009-01-01

How do international doctoral students in the "west" make the decision to return home when their studies are completed? Upon return, what types of re-adaptation problems do they face? Are they able to fully engage with the international academic community--or do they suffer from a form of Geertzian "exile-from-Eden" syndrome?…
Irreversible opinion spreading on scale-free networks

NASA Astrophysics Data System (ADS)

Candia, Julián

2007-02-01

We study the dynamical and critical behavior of a model for irreversible opinion spreading on Barabási-Albert (BA) scale-free networks by performing extensive Monte Carlo simulations. The opinion spreading within an inhomogeneous society is investigated by means of the magnetic Eden model, a nonequilibrium kinetic model for the growth of binary mixtures in contact with a thermal bath. The deposition dynamics, which is studied as a function of the degree of the occupied sites, shows evidence for the leading role played by hubs in the growth process. Systems of finite size grow either ordered or disordered, depending on the temperature. By means of standard finite-size scaling procedures, the effective order-disorder phase transitions are found to persist in the thermodynamic limit. This critical behavior, however, is absent in related equilibrium spin systems such as the Ising model on BA scale-free networks, which in the thermodynamic limit only displays a ferromagnetic phase. The dependence of these results on the degree exponent is also discussed for the case of uncorrelated scale-free networks.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Dolan, Sean Gregory; Berryman, Judy; Shackley, M. Steven

Eden projectile points associated with the Cody complex are underrepresented in the late Paleoindian record of the American Southwest. EDXRF analysis of an obsidian Eden point from a site in Sierra County, New Mexico demonstrates this artifact is from the Cerro del Medio (Valles Rhyolite) source in the Jemez Mountains. Lastly, we contextualize our results by examining variability in obsidian procurement practices beyond the Cody heartland in southcentral New Mexico.
DOE Office of Scientific and Technical Information (OSTI.GOV)

Steed, Chad Allen

EDENx is a multivariate data visualization tool that allows interactive user driven analysis of large-scale data sets with high dimensionality. EDENx builds on our earlier system, called EDEN to enable analysis of more dimensions and larger scale data sets. EDENx provides an initial overview of summary statistics for each variable in the data set under investigation. EDENx allows the user to interact with graphical summary plots of the data to investigate subsets and their statistical associations. These plots include histograms, binned scatterplots, binned parallel coordinate plots, timeline plots, and graphical correlation indicators. From the EDENx interface, a user can selectmore » a subsample of interest and launch a more detailed data visualization via the EDEN system. EDENx is best suited for high-level, aggregate analysis tasks while EDEN is more appropriate for detail data investigations.« less
Eisenhower’s Pursuit of Strategy: The Importance of Understanding the Influence of Leadership Styles on Strategic Decision Makers

DTIC Science & Technology

2013-05-23

change. Eden liked Eisenhower as a soldier but Eden, ever the archetype imperialist, did not like having to defer to the General and America.65 In...to use atomic power to serve the usages of peace, we take into full account our great and growing number of nuclear weapons and the most effective
One year outcomes in patients with acute lung injury randomised to initial trophic or full enteral feeding: prospective follow-up of EDEN randomised trial.

PubMed

Needham, Dale M; Dinglas, Victor D; Bienvenu, O Joseph; Colantuoni, Elizabeth; Wozniak, Amy W; Rice, Todd W; Hopkins, Ramona O

2013-03-19

To evaluate the effect of initial low energy permissive underfeeding ("trophic feeding") versus full energy enteral feeding ("full feeding") on physical function and secondary outcomes in patients with acute lung injury. Prospective longitudinal follow-up evaluation of the NHLBI ARDS Clinical Trials Network's EDEN trial 41hospitals in the United States. 525 patients with acute lung injury. Randomised assignment to trophic or full feeding for up to six days; thereafter, all patients still receiving mechanical ventilation received full feeding. Blinded assessment of the age and sex adjusted physical function domain of the SF-36 instrument at 12 months after acute lung injury. Secondary outcome measures included survival; physical, psychological, and cognitive functioning; quality of life; and employment status at six and 12 months. After acute lung injury, patients had substantial physical, psychological, and cognitive impairments, reduced quality of life, and impaired return to work. Initial trophic versus full feeding did not affect mean SF-36 physical function at 12 months (55 (SD 33) v 55 (31), P=0.54), survival to 12 months (65% v 63%, P=0.63), or nearly all of the secondary outcomes. In survivors of acute lung injury, there was no difference in physical function, survival, or multiple secondary outcomes at 6 and 12 month follow-up after initial trophic or full enteral feeding. NCT No 00719446.
Complex-time singularity and locality estimates for quantum lattice systems

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

Bouch, Gabriel

2015-12-15

We present and prove a well-known locality bound for the complex-time dynamics of a general class of one-dimensional quantum spin systems. Then we discuss how one might hope to extend this same procedure to higher dimensions using ideas related to the Eden growth process and lattice trees. Finally, we demonstrate with a specific family of lattice trees in the plane why this approach breaks down in dimensions greater than one and prove that there exist interactions for which the complex-time dynamics blows-up in finite imaginary time. .
Evaluating the electronic tuberculosis register surveillance system in Eden District, Western Cape, South Africa, 2015.

PubMed

Mlotshwa, Mandla; Smit, Sandra; Williams, Seymour; Reddy, Carl; Medina-Marino, Andrew

2017-01-01

Tuberculosis (TB) surveillance data are crucial to the effectiveness of National TB Control Programs. In South Africa, few surveillance system evaluations have been undertaken to provide a rigorous assessment of the platform from which the national and district health systems draws data to inform programs and policies. Evaluate the attributes of Eden District's TB surveillance system, Western Cape Province, South Africa. Data quality, sensitivity and positive predictive value were assessed using secondary data from 40,033 TB cases entered in Eden District's ETR.Net from 2007 to 2013, and 79 purposively selected TB Blue Cards (TBCs), a medical patient file and source document for data entered into ETR.Net. Simplicity, flexibility, acceptability, stability and usefulness of the ETR.Net were assessed qualitatively through interviews with TB nurses, information health officers, sub-district and district coordinators involved in the TB surveillance. TB surveillance system stakeholders report that Eden District's ETR.Net system was simple, acceptable, flexible and stable, and achieves its objective of informing TB control program, policies and activities. Data were less complete in the ETR.Net (66-100%) than in the TBCs (76-100%), and concordant for most variables except pre-treatment smear results, antiretroviral therapy (ART) and treatment outcome. The sensitivity of recorded variables in ETR.Net was 98% for gender, 97% for patient category, 93% for ART, 92% for treatment outcome and 90% for pre-treatment smear grading. Our results reveal that the system provides useful information to guide TB control program activities in Eden District. However, urgent attention is needed to address gaps in clinical recording on the TBC and data capturing into the ETR.Net system. We recommend continuous training and support of TB personnel involved with TB care, management and surveillance on TB data recording into the TBCs and ETR.Net as well as the implementation of a well-structured quality control and assurance system.
Anthony Eden’s (Lord Avon) Biliary Tract Saga

PubMed Central

Braasch, John W.

2003-01-01

Anthony Eden (Lord Avon) was the youngest foreign secretary in Great Britain’s history. He subsequently became Prime Minister, succeeding Winston Churchill. Eden had the misfortune to have, during cholecystectomy, a biliary tract injury which required four subsequent biliary tract operations. He was subject to recurrent fevers and postoperative disability at important times in his career and during international crises. This report details the operative procedures used and his clinical status at crucial times in national and international affairs. PMID:14578742
Frequency-dependent selection at rough expanding fronts

NASA Astrophysics Data System (ADS)

Kuhr, Jan-Timm; Stark, Holger

2015-10-01

Microbial colonies are experimental model systems for studying the colonization of new territory by biological species through range expansion. We study a generalization of the two-species Eden model, which incorporates local frequency-dependent selection, in order to analyze how social interactions between two species influence surface roughness of growing microbial colonies. The model includes several classical scenarios from game theory. We then concentrate on an expanding public goods game, where either cooperators or defectors take over the front depending on the system parameters. We analyze in detail the critical behavior of the nonequilibrium phase transition between global cooperation and defection and thereby identify a new universality class of phase transitions dealing with absorbing states. At the transition, the number of boundaries separating sectors decays with a novel power law in time and their superdiffusive motion crosses over from Eden scaling to a nearly ballistic regime. In parallel, the width of the front initially obeys Eden roughening and, at later times, passes over to selective roughening.
The correlation of coal beds in Squaw Basin and part of Eden Ridge, T. 33 S., R. 11 W., W. M., southwestern Oregon

USGS Publications Warehouse

Wayland, Russell Gibson

1965-01-01

A conflict in correlation of coal beds dating from 1914 is reexamined-with the aid of new. core hole data, photogeologic interpretation, a broader understanding of the stratigraphy, and brief field studies. It is concluded that the known coal beds in Squaw Basin area of limited lateral extent and are older than those exposed at Eden Ridge. Similar coal beds may be found in other rocks of the Tyee Formation in this area. More core drilling could be justified.
Mediation and modification of genetic susceptibility to obesity by eating behaviors.

PubMed

de Lauzon-Guillain, Blandine; Clifton, Emma Ad; Day, Felix R; Clément, Karine; Brage, Soren; Forouhi, Nita G; Griffin, Simon J; Koudou, Yves Akoli; Pelloux, Véronique; Wareham, Nicholas J; Charles, Marie-Aline; Heude, Barbara; Ong, Ken K

2017-10-01

Background: Many genetic variants show highly robust associations with body mass index (BMI). However, the mechanisms through which genetic susceptibility to obesity operates are not well understood. Potentially modifiable mechanisms, including eating behaviors, are of particular interest to public health. Objective: Here we explore whether eating behaviors mediate or modify genetic susceptibility to obesity. Design: Genetic risk scores for BMI (BMI-GRSs) were calculated for 3515 and 2154 adults in the Fenland and EDEN (Etude des déterminants pré et postnatals de la santé et du développement de l'enfant) population-based cohort studies, respectively. The eating behaviors-emotional eating, uncontrolled eating, and cognitive restraint-were measured through the use of a validated questionnaire. The mediating effect of each eating behavior on the association between the BMI-GRS and measured BMI was assessed by using the Sobel test. In addition, we tested for interactions between each eating behavior and the BMI-GRS on BMI. Results: The association between the BMI-GRS and BMI was mediated by both emotional eating (EDEN: P- Sobel = 0.01; Fenland: P- Sobel = 0.02) and uncontrolled eating (EDEN: P- Sobel = 0.04; Fenland: P -Sobel = 0.0006) in both sexes combined. Cognitive restraint did not mediate this association ( P -Sobel > 0.10), except among EDEN women ( P -Sobel = 0.0009). Cognitive restraint modified the relation between the BMI-GRS and BMI among men (EDEN: P -interaction = 0.0001; Fenland: P -interaction = 0.04) and Fenland women ( P -interaction = 0.0004). By tertiles of cognitive restraint, the association between the BMI-GRS and BMI was strongest in the lowest tertile of cognitive restraint, and weakest in the highest tertile. Conclusions: Genetic susceptibility to obesity was partially mediated by the "appetitive" eating behavior traits (uncontrolled and emotional eating) and, in 3 of the 4 population groups studied, was modified by cognitive restraint. High levels of cognitive control over eating appear to attenuate the genetic susceptibility to obesity. Future research into interventions designed to support restraint may help to protect genetically susceptible individuals from weight gain. © 2017 American Society for Nutrition.
Postorogenic carbonatites at Eden Lake, Trans-Hudson Orogen (northern Manitoba, Canada): Geological setting, mineralogy and geochemistry

NASA Astrophysics Data System (ADS)

Chakhmouradian, A. R.; Mumin, A. H.; Demény, A.; Elliott, B.

2008-07-01

The Eden Lake pluton in the Trans-Hudson Orogen is the first known occurrence of carbonatites in Manitoba. The pluton is largely made up of modally and geochemically diverse syenitic rocks derived from postorogenic magma(s) of shoshonitic affinity. Their diversity can be accounted for by a combination of crystal fractionation and fluid release in the final evolutionary stage (crystallization of quartz alkali-feldspar syenite). At Eden Lake, carbonatites, represented predominantly by coarse-grained massive to foliated sövite, occur as branching veins and lenticular bodies up to 4 m in thickness showing crosscutting relations with respect to all of the syenitic units. The host rocks are intensely fenitized at the contact, and there is also abundant mineralogical and textural evidence for assimilation of silicate material by carbonatitic magma through wallrock reaction and xenolith fragmentation and digestion. The bulk of the carbonatites are composed of (in order of crystallization): Sr-REE-rich fluorapatite, aegirine-augite, and coarse calcite crystals surrounded by fine-grained calcite (on average, ˜ 90 vol.% of the rock). Noteworthy accessory constituents are celestine, bastnäsite-(Ce) (both as primary inclusions in calcite), Nb-Zr-rich titanite, low-Hf zircon, allanite-(Ce) and andradite. The calcite is chemically uniform (Sr-rich, Mg-Mn-Fe-poor and low in 13C), but shows clear evidence of ductile deformation and syndeformational cataclasis. Geochemically, the carbonatites are enriched in Sr, Ba, light rare-earth elements, Th and U, but depleted in high-field-strength elements (particularly, Ti, Nb and Ta). The stable-isotope composition of coarse- and fine-grained calcite from the carbonatites and interstitial calcite from syenites is remarkably uniform: ca. - 8.16 ± 0.27‰ δ13C (PDB) and + 8.04 ± 0.19‰ δ18O (SMOW). The available textural and geochemical evidence indicates that the Eden Lake carbonatites are not consanguineous with the associated syenites and may have been derived from a Nb-Ti-retentive and 13C-depleted source such as the subducted crustal material underlying the Eden Lake deformation corridor.
The role of fearless dominance in differentiating psychopathy from antisocial personality disorder: comment on Marcus, Fulton, and Edens.

PubMed

Patrick, Christopher J; Venables, Noah C; Drislane, Laura E

2013-01-01

Comments on the original article by Marcus et al. (see record 2011-23134-001). Based on their meta-analytic review of the correlates of the two factors of the Psychopathic Personality Inventory (PPI), Fearless Dominance (FD) and Self-Centered Impulsivity (SCI), Marcus, Fulton, and Edens (this issue, pp. 70-79) raise important questions about the role of FD in diagnostic conceptualizations of psychopathy. In considering their findings, general limitations of metaanalyses (e.g., Ioannidis & Lau, 1999) should be borne in mind, along with specific limitations of their analysis. These limitations are discussed here.
The Influence of temporal sampling regime on the WFD classification of catchments within the Eden Demonstration Test Catchment Project

NASA Astrophysics Data System (ADS)

Jonczyk, Jennine; Haygarth, Phil; Quinn, Paul; Reaney, Sim

2014-05-01

A high temporal resolution data set from the Eden Demonstration Test Catchment (DTC) project is used to investigate the processes causing pollution and the influence of temporal sampling regime on the WFD classification of three catchments. This data highlights WFD standards may not be fit for purpose. The Eden DTC project is part of a UK government-funded project designed to provide robust evidence regarding how diffuse pollution can be cost-effectively controlled to improve and maintain water quality in rural river catchments. The impact of multiple water quality parameters on ecosystems and sustainable food production are being studied at the catchment scale. Three focus catchments approximately 10 km2 each, have been selected to represent the different farming practices and geophysical characteristics across the Eden catchment, Northern England. A field experimental programme has been designed to monitor the dynamics of agricultural diffuse pollution at multiple scales using state of the art sensors providing continuous real time data. The data set, which includes Total Phosphorus and Total Reactive Phosphorus, Nitrate, Ammonium, pH, Conductivity, Turbidity and Chlorophyll a reveals the frequency and duration of nutrient concentration target exceedance which arises from the prevalence of storm events of increasing magnitude. This data set is sub-sampled at different time intervals to explore how different sampling regimes affects our understanding of nutrient dynamics and the ramification of the different regimes to WFD chemical status. This presentation seeks to identify an optimum temporal resolution of data for effective catchment management and to question the usefulness of the WFD status metric for determining health of a system. Criteria based on high frequency short duration events needs to be accounted for.
Corrigendum.

PubMed

2017-09-01

Baïz, N., Chastang, J., Ibanez, G., & Annesi-Maesano I. (2016). Prenatal exposure to selenium may protect against wheezing in children by the age of 3. Immunity, Inflammation and Disease, 5 (1), 37-44. DOI: 10.1002/iid3.138. In the article "Prenatal exposure to selenium may protect against wheezing in children by the age of 3", it has been noted that the 'EDEN Mother-Child Cohort Study Group was omitted in error. The correct author list for the article is shown below. Nour Baïz 1 , Julie Chastang 1,2 , Gladys Ibanez 1,2 , Isabella Annesi-Maesano 1 and the EDEN Mother-Child Cohort Study Group 3 . 3 Members of the EDEN Mother-Child Cohort Study Group includes the following members: I. Annesi-Maesano, J. Y. Bernard, J. Botton, M.A. Charles, P. Dargent-Molina, B. de Lauzon-Guillain, P. Ducimetière, M. de Agostini, B. Foliguet, A. Forhan, X. Fritel, A. Germa, V. Goua, R. Hankard, B. Heude, M. Kaminski, B. Larroque, N. Lelong, J. Lepeule, G. Magnin, L. Marchand, C. Nabet, F. Pierre, R. Slama, M.J. Saurel-Cubizolles, M. Schweitzer, O. Thiebaugeorges. We apologize for any inconvenience caused. © 2017 The Authors. Immunity, Inflammation and Disease Published by John Wiley & Sons Ltd.
Perinatal risk factors and social withdrawal behaviour.

PubMed

Guedeney, Antoine; Marchand-Martin, Laetitia; Cote, Sylvana J; Larroque, Béatrice

2012-04-01

The objectives of the study were (1) to assess prevalence of social withdrawal behaviour in infants aged 12 months included in the French Perinatal Risk Factor Study Eden; (2) To study the correlation between relational withdrawal and several perinatal and parental factors assessed in the EDEN study. A longitudinal study using the ADBB scale was conducted within the Eden Cohort in the year 2008. 1,586 infants were included in the study. Fourteen percent of the children who had an ADBB assessment had a score at 5 and over on the ADBB, a scale designed to assess social withdrawal behaviour at age 0-24 months. Social withdrawal at 12 months was associated with low birth weight, low gestational age and with intra uterine growth retardation. Social withdrawal was independently associated with several maternal and paternal risk factors. The level of social withdrawal behaviour increased with a score of maternal difficulties. This study on a large longitudinally followed volunteer sample demonstrate a clear association of social withdrawal behaviour at age one with low birth weight and preterm birth, possibly mediated by parental vulnerabilities. Social withdrawal behaviour seems to be an important alarm signal to detect early on particularly in premature and small for date babies. © Springer-Verlag 2012
Peripheral Faulting of Eden Patera: Potential Evidence in Support of a New Volcanic Construct on Mars

NASA Astrophysics Data System (ADS)

Harlow, J.

2016-12-01

Arabia Terra's (AT) pock-marked topography in the expansive upland region of Mars Northern Hemisphere has been assumed to be the result of impact crater bombardment. However, examination of several craters by researchers revealed morphologies inconsistent with neighboring craters of similar size and age. These 'craters' share features with terrestrial super-eruption calderas, and are considered a new volcanic construct on Mars called `plains-style' caldera complexes. Eden Patera (EP), located on the northern boundary of AT is a reference type for these calderas. EP lacks well-preserved impact crater morphologies, including a decreasing depth to diameter ratio. Conversely, Eden shares geomorphological attributes with terrestrial caldera complexes such as Valles Caldera (New Mexico): arcuate caldera walls, concentric fracturing/faulting, flat-topped benches, irregular geometric circumferences, etc. This study focuses on peripheral fractures surrounding EP to provide further evidence of calderas within the AT region. Scaled balloon experiments mimicking terrestrial caldera analogs have showcased fracturing/faulting patterns and relationships of caldera systems. These experiments show: 1) radial fracturing (perpendicular to caldera rim) upon inflation, 2) concentric faulting (parallel to sub-parallel to caldera rim) during evacuation, and 3) intersecting radial and concentric peripheral faulting from resurgence. Utilizing Mars Reconnaissance Orbiter Context Camera (CTX) imagery, peripheral fracturing is analyzed using GIS to study variations in peripheral fracture geometries relative to the caldera rim. Visually, concentric fractures dominate within 20 km, radial fractures prevail between 20 and 50 km, followed by gradation into randomly oriented and highly angular intersections in the fretted terrain region. Rose diagrams of orientation relative to north expose uniformly oriented mean regional stresses, but do not illuminate localized caldera stresses. Further examination of orientation relative to caldera rim reveals expected orientations of ±30° on rose diagrams, taking into account the geometric nature of concentric faulting. These results establish a quantitative geometric system to differentiate localized from regional faulting surrounding Eden Patera.
EDEN: a payload dedicated to neurovestibular research for Neurolab

NASA Technical Reports Server (NTRS)

Bellossi, F.; Clement, G.; Cohen, B.; Cork, M.

1998-01-01

The European Space Agency contributes to the Neurolab mission through the delivery of the ESA Developed Elements for Neurolab (EDEN). Those elements include one set supporting the Autonomic Nervous System experiment and one set supporting the Neurovestibular (so-called ATLAS) experiment. This second set is called the Visual and Vestibular Investigation System (VVIS). This paper describes the main characteristics of the VVIS and its various subsystems. The scientific objectives and operational constraints of the ATLAS experiment to be carried out with this equipment during Neurolab are presented to underline the correspondence between the VVIS design and the scientific requirements. Further scientific and technical perspectives for the VVIS, particularly within the scope of the International Space station, are also proposed.

Presidential Green Chemistry Challenge: 2001 Small Business Award

EPA Pesticide Factsheets

Presidential Green Chemistry Challenge 2001 award winner, EDEN Bioscience, discovered and commercialized harpins: nontoxic, naturally occurring, biodegradable proteins that activate a plant's defense and growth mechanisms.
Alchemy in eden: entrepreneurialism, branding, and food marketing in the United States, 1880–1920.

PubMed

Lonier, Terri

2010-01-01

Through an investigation into the origins of American food marketing, this dissertation reveals how branding—specifically, the centennial brands Quaker Oats, Coca-Cola, and Crisco—came to underpin much of today's market-driven economy. In a manner akin to alchemy, the entrepreneurs behind these three firms recognized the inherent value of an agricultural Eden, then found ways to convert common, low-cost agricultural goods—oats, sugar, and cottonseed oil—into appealing, high-revenue branded food products. In the process, these ventures devised new demand-driven business models that exploited technology and communications advances, enabling them to tap a nascent consumer culture. Their pioneering efforts generated unprecedented profits, laid the foundation for iconic billion-dollar brands, and fundamentally changed how Americans make daily food choices.
27 CFR 4.91 - List of approved names.

Code of Federal Regulations, 2014 CFR

2014-04-01

... Early Burgundy Early Muscat Edelweiss Eden Ehrenfelser Ellen Scott Elvira Emerald Riesling Erbaluce... Kerner Kay Gray Kleinberger La Crescent LaCrosse Lagrein Lake Emerald Lambrusco Landal Landot noir Lenoir...
Local recharge processes in glacial and alluvial deposits of a temperate catchment

NASA Astrophysics Data System (ADS)

Fragalà, Federico A.; Parkin, Geoff

2010-07-01

SummaryThis study demonstrates that the composition and structure of Quaternary deposits and topography significantly influence rates of recharge and distribution of diffuse agricultural pollution at the hillslope scale. Analyses were made of vertical profiles of naturally-occurring chloride and nitrate, and artificially introduced bromide, in unsaturated and saturated sections of borehole cores of glacial till and alluvium under different land uses in the Upper Eden valley (UK). Estimates of local potential recharge were made based on chloride mass balance and nitrate peak methods. Persistent chloride bulges below the root zone were observed, and are interpreted to result from filtration processes at lithological boundaries. Changes in the shape of chloride profiles downslope, corroborated by nitrate profiles, indicate the roles of surface or near-surface runoff and runon, and the existence of lateral subsurface flows at depth. These findings have implications for estimation of recharge rates through unsaturated zones in Quaternary deposits, and the interpretation of potential 'hot-spots' of diffuse agrochemicals, particularly nitrates, moving through Quaternary deposits into groundwater.
Extended Eden model reproduces growth of an acellular slime mold.

PubMed

Wagner, G; Halvorsrud, R; Meakin, P

1999-11-01

A stochastic growth model was used to simulate the growth of the acellular slime mold Physarum polycephalum on substrates where the nutrients were confined in separate drops. Growth of Physarum on such substrates was previously studied experimentally and found to produce a range of different growth patterns [Phys. Rev. E 57, 941 (1998)]. The model represented the aging of cluster sites and differed from the original Eden model in that the occupation probability of perimeter sites depended on the time of occupation of adjacent cluster sites. This feature led to a bias in the selection of growth directions. A moderate degree of persistence was found to be crucial to reproduce the biological growth patterns under various conditions. Persistence in growth combined quick propagation in heterogeneous environments with a high probability of locating sources of nutrients.
Extended Eden model reproduces growth of an acellular slime mold

NASA Astrophysics Data System (ADS)

Wagner, Geri; Halvorsrud, Ragnhild; Meakin, Paul

1999-11-01

A stochastic growth model was used to simulate the growth of the acellular slime mold Physarum polycephalum on substrates where the nutrients were confined in separate drops. Growth of Physarum on such substrates was previously studied experimentally and found to produce a range of different growth patterns [Phys. Rev. E 57, 941 (1998)]. The model represented the aging of cluster sites and differed from the original Eden model in that the occupation probability of perimeter sites depended on the time of occupation of adjacent cluster sites. This feature led to a bias in the selection of growth directions. A moderate degree of persistence was found to be crucial to reproduce the biological growth patterns under various conditions. Persistence in growth combined quick propagation in heterogeneous environments with a high probability of locating sources of nutrients.
The Garden of Eden: Implications for cardiovascular disease prevention.

PubMed

Jenkins, D J; Jenkins, A L; Kendall, C W; Vuksan, V; Vidgen, E

2000-09-01

Creationists and evolutionists acknowledge that the human diet has passed through at least four phases. The original plant food-based diet; a second phase of increasing meat consumption; a third phase of agricultural dependence on starchy foods; and, finally, the supermarket high-saturated fat, low-fibre phase with minimal energy expenditure. Our aim is to define the value of the original or 'Garden of Eden' diet and to speculate on which components should be retained in the modern supermarket diet. The original plant-based diet would have been high in vegetable proteins, plant sterols, dietary fibre and antioxidants, and low in saturated fats with no trans fatty acids. This diet would increase fecal cholesterol losses from the body as bile acids and neutral sterols, while providing little stimulus to cholesterol synthesis. To replace the bile acid losses we would have adapted to a relatively high capacity for cholesterol synthesis. Now, in the high-saturated fat, low-fibre supermarket age, this may be a disadvantage and predisposes consumers to high serum cholesterol and increased risk of cardiovascular disease. We believe part of the solution is a return to the plant-based 'Garden of Eden' diet combined with physical activity. A lipid-lowering portfolio containing vegetable proteins, especially soy, plant sterols and high fibre intakes combined with low saturated and trans fatty acids and cholesterol, would go a long way to reducing serum lipids and coronary heart disease risk seen in the modern Western diet.
Assessment of trace element impacts on agricultural use of water from the Dan River following the Eden coal ash release.

PubMed

Hesterberg, Dean; Polizzotto, Matthew L; Crozier, Carl; Austin, Robert E

2016-04-01

Catastrophic events require rapid, scientifically sound decision making to mitigate impacts on human welfare and the environment. The objective of this study was to analyze potential impacts of coal ash-derived trace elements on agriculture following a 35,000-tonne release of coal ash into the Dan River at the Duke Energy Steam Station in Eden, North Carolina. We performed scenario calculations to assess the potential for excessive trace element loading to soils via irrigation and flooding with Dan River water, uptake of trace elements by crops, and livestock consumption of trace elements via drinking water. Concentrations of 13 trace elements measured in Dan River water samples within 4 km of the release site declined sharply after the release and were equivalent within 5 d to measurements taken upriver. Mass-balance calculations based on estimates of soil trace-element concentrations and the nominal river water concentrations indicated that irrigation or flooding with 25 cm of Dan River water would increase soil concentrations of all trace elements by less than 0.5%. Calculations of potential increases of trace elements in corn grain and silage, fescue, and tobacco leaves suggested that As, Cr, Se, Sr, and V were elements of most concern. Concentrations of trace elements measured in river water following the ash release never exceeded adopted standards for livestock drinking water. Based on our analyses, we present guidelines for safe usage of Dan River water to diminish negative impacts of trace elements on soils and crop production. In general, the approach we describe here may serve as a basis for rapid assessment of environmental and agricultural risks associated with any similar types of releases that arise in the future. © 2015 SETAC.
Self-affine fractal growth front of Aspergillus oryzae

NASA Astrophysics Data System (ADS)

Matsuura, Shu; Miyazima, Sasuke

1992-12-01

Aspergillus oryzae have been grown in various environmental conditions and analyzed from the viewpoint of self-affinity. The growth behavior can be described by the Eden model in favorable conditions, and by DLA in unfavorable conditions.
Shaking Eden: Voyages, Bodies and Change in the Social Construction of South American Skies

NASA Astrophysics Data System (ADS)

López, Alejandro Martín

2015-05-01

South America presents a clear example of the importance of displacements and exchanges in shaping human societies. Nevertheless, the academic works, following the ideas of the first European visitors, have tended to see it as an undisturbed Eden in a `state of nature.´ For too long, South American societies were thought of as small units without history, isolated from each other. The opposition to the excesses of diffusionism helped to reinforce that image. However, in recent years this static and `naturaĺ representation has collapsed. New works from the most varied perspectives show us a changing and interconnected South America, where the notions of body, person and territory are complex social constructions and not the expression of an 'unmediated' experience of the world. We discuss the implications of these new perspectives of thinking on South America for the study of ways of perceiving and representing the sky in this region.
INFINITY ribbon-cutting

NASA Image and Video Library

2012-04-11

Clare Johnston, 10, and Eden Landis, 3, stare in wonder at the moon rock on display at the INFINITY at NASA Stennis Space Center visitor center and museum. The children toured INFINITY exhibits during ribbon-cutting activities for the facility April 11, 2012.
Evaluation and Development of Radiation Countermeasures at AFRRI

DTIC Science & Technology

2005-01-01

epigallocatechin gallate ( EGCG ), benzyl styryl sulfones, CpG oligonucleotides, a superoxide dismutase mi- metic, statins, dipeptidyl peptidase inhibitors, and...candidate (Hollis-Eden Pharmaceuticals) • Soy isoflavones • Vitamin E-related compounds (Yasoo Health) • Phenylacetate, phenylbutyrate, EGCG (late
All-Arthroscopic Revision Eden-Hybinette Procedure for Failed Instability Surgery: Technique and Preliminary Results.

PubMed

Giannakos, Antonios; Vezeridis, Peter S; Schwartz, Daniel G; Jany, Richard; Lafosse, Laurent

2017-01-01

To describe the technique of an all-arthroscopic Eden-Hybinette procedure in the revision setting for treatment of a failed instability procedure, particularly after failed Latarjet, as well as to present preliminary results of this technique. Between 2007 and 2011, 18 shoulders with persistent instability after failed instability surgery were treated with an arthroscopic Eden-Hybinette technique using an autologous bicortical iliac crest bone graft. Of 18 patients, 12 (9 men, 3 women) were available for follow-up. The average follow-up was 28.8 months (range, 15 to 60 months). A Latarjet procedure was performed as an index surgery in 10 patients (83%). Two patients (17%) had a prior arthroscopic Bankart repair. Eight patients (67%) obtained a good or excellent result, whereas 4 patients (33%) reported a fair or poor result. Seven patients (58%) returned to sport activities. A positive apprehension test persisted in 5 patients (42%), including 2 patients (17%) with recurrent subluxations. The Rowe score increased from 30.00 to 78.33 points (P < .0001). The Walch-Duplay score increased from 11.67 to 76.67 points (P < .0001). The Western Ontario Shoulder Instability Index score showed a good result of 28.71% (603 points). The average anterior flexion was 176° (range, 150° to 180°), and the average external rotation was 66° (range, 0° to 90°). Two patients (16.67%) showed a progression of glenohumeral osteoarthritic changes, with each patient increasing by one stage in the Samilson-Prieto classification. All 4 patients (33%) with a fair or poor result had a nonunion identified on postoperative computed tomography scan. An all-arthroscopic Eden-Hybinette procedure in the revision setting for failed instability surgery, although technically demanding, is a safe, effective, and reproducible technique. Although the learning curve is considerable, this procedure offers all the advantages of arthroscopic surgery and allows reconstruction of glenoid defects and restoration of shoulder stability in this challenging patient population. In our hands, this procedure yields good or excellent results in 67% of patients. Successful outcome is correlated with bony healing of the iliac crest graft to the glenoid. Level IV, therapeutic case series. Copyright © 2016 Arthroscopy Association of North America. Published by Elsevier Inc. All rights reserved.
The Lyapunov dimension and its estimation via the Leonov method

NASA Astrophysics Data System (ADS)

Kuznetsov, N. V.

2016-06-01

Along with widely used numerical methods for estimating and computing the Lyapunov dimension there is an effective analytical approach, proposed by G.A. Leonov in 1991. The Leonov method is based on the direct Lyapunov method with special Lyapunov-like functions. The advantage of the method is that it allows one to estimate the Lyapunov dimension of invariant sets without localization of the set in the phase space and, in many cases, to get effectively an exact Lyapunov dimension formula. In this work the invariance of the Lyapunov dimension with respect to diffeomorphisms and its connection with the Leonov method are discussed. For discrete-time dynamical systems an analog of Leonov method is suggested. In a simple but rigorous way, here it is presented the connection between the Leonov method and the key related works: Kaplan and Yorke (the concept of the Lyapunov dimension, 1979), Douady and Oesterlé (upper bounds of the Hausdorff dimension via the Lyapunov dimension of maps, 1980), Constantin, Eden, Foiaş, and Temam (upper bounds of the Hausdorff dimension via the Lyapunov exponents and Lyapunov dimension of dynamical systems, 1985-90), and the numerical calculation of the Lyapunov exponents and dimension.
Implicit timing activates the left inferior parietal cortex.

PubMed

Wiener, Martin; Turkeltaub, Peter E; Coslett, H Branch

2010-11-01

Coull and Nobre (2008) suggested that tasks that employ temporal cues might be divided on the basis of whether these cues are explicitly or implicitly processed. Furthermore, they suggested that implicit timing preferentially engages the left cerebral hemisphere. We tested this hypothesis by conducting a quantitative meta-analysis of eleven neuroimaging studies of implicit timing using the activation-likelihood estimation (ALE) algorithm (Turkeltaub, Eden, Jones, & Zeffiro, 2002). Our analysis revealed a single but robust cluster of activation-likelihood in the left inferior parietal cortex (supramarginal gyrus). This result is in accord with the hypothesis that the left hemisphere subserves implicit timing mechanisms. Furthermore, in conjunction with a previously reported meta-analysis of explicit timing tasks, our data support the claim that implicit and explicit timing are supported by at least partially distinct neural structures. Copyright © 2010 Elsevier Ltd. All rights reserved.
Narrative Family Therapy.

ERIC Educational Resources Information Center

Walsh, William M.; Keenan, Robert

1997-01-01

States that narrative family therapy is informed by social constructionism and postmodern worldviews, and is a relatively significant departure from mainstream psychotherapy. Discusses the use of narrative family therapy. Uses the story of Adam and Eve in the Garden of Eden as an example. (MKA)
The River EdenDTC Project: A National Demonstration Test Catchment

NASA Astrophysics Data System (ADS)

Benskin, C.; Surridge, B.; Deasy, C.; Woods, C.; Rimmer, D.; Lees, E.; Owens, G.; Jonczyk, J.; Quinton, J.; Wilkinson, M.; Perks, M.; Quinn, P.; Barker, P.; Haygarth, P.; Burke, S.; Reaney, S.; Watson, N.

2012-04-01

Our environment is a complex system of interactions between natural process and anthropogenic activities that disrupt them. It is crucial to manage the balance for continued food production whilst maintaining the quality of the environment. The challenges we face include managing the impact of agricultural land use on aquatic quality and biodiversity as an integral system, rather than as separate issues. In order to do this, it is critical to understand how the different components are linked - how does land use affect our water courses and ground water, and their associated ecosystems, and how can the impact of agricultural land use on these systems be minimised? Regulating farm nutrient management through measures that minimise sources, their exposure to mobilisation, and reduce drainage pathways to water courses are all fundamental to the UK's approach to meeting the Water Framework Directive objective of achieving 'good ecological status' in all surface and groundwater bodies by 2015. The EdenDTC project is part of a 5-year national Demonstration Test Catchments (DTC) environmental scheme, aiming to understand the above issues through combining scientific research with local knowledge and experience from multiple stakeholders. The DTC project is a 5-year initiative by Defra, Welsh Assembly Government and the Environment Agency, which encompasses a research platform covering three distinct river catchments: the Eden in Cumbria; the Wensum in Norfolk; and the Avon in Hampshire. Within the EdenDTC, the impact and effects of multiple diffuse pollutants on ecosystems and sustainable food production are being studied on a river catchment scale. Three 10 km2 focus catchments, selected to represent the different farming practices and geologies observed across the Eden, have been instrumented to record the dynamics of agricultural diffuse pollution at multiple scales. Within each focus catchment, two sub-catchments were selected: one control and one mitigation, in which a number of existing and novel mitigation measures will be tested. A number of on-farm measures, aimed at reducing agricultural diffuse pollution, will be evaluated by monitoring their effect on water quality and associated biodiversity. In order to achieve this, state of the art hydro-meteorological logging systems have been installed. The outlets of the focus catchments each have a 'high-tech' multi-parameter station that will provide data for total P, soluble reactive P, nitrate, ammonium, temperature, conductivity, dissolved oxygen, turbidity, pH and flow. At the sub-catchment scale are 10 sub-stations, which provide a record of turbidity and water level. All are continuously sampling at 15 minute intervals and are telemetered. The goal is to give an abundance of high quality, multi-scale continuous data provided in real time. Additional storm sampling is being performed at all stations using automatic water samplers, and monthly spot samples are also analysed for each site. The information gathered at these different scales is hoped to improve the effectiveness/efficiency of schemes such as the England Catchment Sensitive Farming Delivery Initiative (ECSFDI). It is also hoped that many of the mitigation features will be multipurpose, having positive effects on flooding, carbon sequestration, habitat creation and biodiversity.
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings

PubMed Central

Lin, Tiger W.; Das, Anup; Krishnan, Giri P.; Bazhenov, Maxim; Sejnowski, Terrence J.

2017-01-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005; Pillow et al., 2008), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals. PMID:28777719
Differential Covariance: A New Class of Methods to Estimate Sparse Connectivity from Neural Recordings.

PubMed

Lin, Tiger W; Das, Anup; Krishnan, Giri P; Bazhenov, Maxim; Sejnowski, Terrence J

2017-10-01

With our ability to record more neurons simultaneously, making sense of these data is a challenge. Functional connectivity is one popular way to study the relationship of multiple neural signals. Correlation-based methods are a set of currently well-used techniques for functional connectivity estimation. However, due to explaining away and unobserved common inputs (Stevenson, Rebesco, Miller, & Körding, 2008 ), they produce spurious connections. The general linear model (GLM), which models spike trains as Poisson processes (Okatan, Wilson, & Brown, 2005 ; Truccolo, Eden, Fellows, Donoghue, & Brown, 2005 ; Pillow et al., 2008 ), avoids these confounds. We develop here a new class of methods by using differential signals based on simulated intracellular voltage recordings. It is equivalent to a regularized AR(2) model. We also expand the method to simulated local field potential recordings and calcium imaging. In all of our simulated data, the differential covariance-based methods achieved performance better than or similar to the GLM method and required fewer data samples. This new class of methods provides alternative ways to analyze neural signals.
NOVA Fall 2000 Teacher's Guide.

ERIC Educational Resources Information Center

Ransick, Kristina; Rosene, Dale; Sammons, Fran Lyons; Sammons, James

This teacher's guide complements six programs that aired on the Public Broadcasting System (PBS) in the fall of 2000. Programs include: (1) "Lincoln's Secret Weapon"; (2) "Hitler's Lost Sub"; (3) "Runaway Universe"; (4) "Garden of Eden"; (5) "Dying to Be Thin"; and (6) "Japan's Secret…

Eden Revisited. Art across the Curriculum.

ERIC Educational Resources Information Center

Sartorius, Tara Cady

2000-01-01

Provides information on china painting, focusing on Kurt Weiser, who paints on ceramics using china paints. Discusses his techniques and describes his work titled "Woman with Mongoose." Includes accompanying projects for art history, visual arts, language arts, natural science, and science or mathematics. (CMK)
Expert Maintenance Advisor Development for Navy Shipboard Systems

DTIC Science & Technology

1994-01-01

Estoril (EDEN) Chair: Xavier Alaman, Instituto de Ingenieria del Conocimiento, SPAIN "A Model of Handling Uncertainty in Expert Systems," 01 Zhao...for Supervisory Process Control," Xavier Alaman, Instituto de Ingenieria del Conocimiento, SPAIN - (L) INTEGRATED KNOWLEDGE BASED SYSTEMS IN POWER
Beyond the basics. Effects of the Eden Alternative model on quality of life issues.

PubMed

Bergman-Evans, Brenda

2004-06-01

In Life Worth Living, Thomas (1996) proposed that in long-term care facilities for elderly individuals, loneliness, helplessness, and boredom are out of control and are steadily decaying the residents' spirits, adversely affecting quality of life. While Thomas' contention appeals to common sense, no empirical evidence is offered in its support. The purpose of this quasi-experimental study was to assess the impact of implementation of the Eden Alternative model on levels of loneliness, boredom, and helplessness of older residents of a long-term care facility. The model was introduced into the experimental facility on May 1, 1998. The final sample for the experimental group included 21 cognitively intact older adults from a state veterans home (13 men, 8 women, mean age = 76.1). The final control group was composed of 13 residents in a private long-term care facility (11 women, 2 men, mean age = 85.7). A Background Data Sheet, the Geriatric Depression Scale (includes yes or no questions related to helplessness and boredom), and the UCLA Loneliness Scale (Version 3) were administered by an interviewer at baseline and 1-year post-implementation of the Eden Alternative model. Data analysis from the post-implementation phase revealed significant differences between the groups on levels of boredom (z = -2.6, p = .01) and helplessness (z = -2.2, p = .03). Lower levels of distress were found in the experimental group on both boredom and helplessness, but not loneliness. Findings suggest health care professionals and researchers have an opportunity to take a leading role in impacting services related to quality-of-life issues for this important, but often overlooked, population.
Cross-Cultural Inquiry in Science

ERIC Educational Resources Information Center

Spires, Hiller A.; Himes, Marie; Wang, Lisa

2016-01-01

This compilation of articles describes three projects aimed at offering students authentic opportunities to develop global competencies. The first article describes Out of Eden Learn, an initiative from Project Zero at Harvard Graduate School of Education. The project engages students in learning journeys that follow Pulitzer Prize-winning…
76 FR 63657 - Certain Flat Panel Display Devices, and Products Containing the Same; Notice of Commission...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-10-13

... the same by reason of infringement of various claims of United States Patent Nos. 6,281,955; 7,697,093... Mobility LLC of Atlanta, Georgia; Best Buy Stores, L.P. of Richfield, Minnesota; BestBuy.com , LLC of Eden...
Heidegger, Education and the "Cult of the Authentic"

ERIC Educational Resources Information Center

Trubody, Ben

2015-01-01

Within educational philosophies that utilise the Heideggerian idea of "authenticity" there can be distinguished at least two readings that correspond with the categories of "weak" and "strong" utopianism. "Strong-utopianism" is the nostalgia for some lost Edenic paradise to be restored at some future time.…
From Pen Pals to Global Citizens

ERIC Educational Resources Information Center

Kirshner, Jean; Tzib, Eli; Tzib, Zilpa; Fry, Sara

2016-01-01

This compilation of articles describes three projects aimed at offering students authentic opportunities to develop global competencies. The first article describes Out of Eden Learn, an initiative from Project Zero at Harvard Graduate School of Education. The project engages students in learning journeys that follow Pulitzer Prize-winning…
A Learning Journey around the World

ERIC Educational Resources Information Center

Duraisingh, Liz Dawes

2016-01-01

This compilation of articles describes three projects aimed at offering students authentic opportunities to develop global competencies. The first article describes Out of Eden Learn, an initiative from Project Zero at Harvard Graduate School of Education. The project engages students in learning journeys that follow Pulitzer Prize-winning…
Hazardous Waste Cleanup: Veolia ES Technical Solutions, L.L.C. in Flanders, New Jersey

EPA Pesticide Factsheets

Veolia Environmental Services occupies approximately six acres on Eden Lane in Flanders, New Jersey. The facility is located in a light industrial area that is generally surrounded by wooded areas and farms. Veolia began operations in 1989 on land that was
75 FR 21577 - Rogue River-Siskiyou National Forest, Powers Ranger District, Coos County, OR; Eden Ridge Timber...

Federal Register 2010, 2011, 2012, 2013, 2014

2010-04-26

... on soil, slope and hydrological concerns. New system road construction, reconstruction of... natural succession processes. The residual trees would have less competition for sunlight, water and soil... designed to: Minimize soil impacts (erosion, compaction and/or displacement); Minimize damage to residual...
Canaanites in a Promised Land: The American Indian and the Providential Theory of Empire.

ERIC Educational Resources Information Center

Cave, Alfred E.

1988-01-01

Reviews sixteenth-and seventeenth-century writings by Rastell, More, Eden, Hakluyt, Peckham, Gray, Symonds, Johnson, Strachey, Purchas, Winthrop, and Cotton justifying English occupation of Indian lands through the Biblical Canaan analogy and the secular "vacant land" (vacuum domicilium) principle. Notes dissent by Crashaw, Williams, and…
Pluralism Lost: Sustainability's Unfortunate Fall

ERIC Educational Resources Information Center

Wimberley, Edward T.

2010-01-01

"Paradise Lost" explores the themes of human frailty, failure, and redemption following humanity's "original sin," eating of the tree of knowledge in the Garden of Eden. This original sin resulted in human beings being banished from an earthly paradise and compelled to wander eternally a world fraught with danger, despair,…
78 FR 28636 - Notice of Determinations Regarding Eligibility To Apply for Worker Adjustment Assistance

Federal Register 2010, 2011, 2012, 2013, 2014

2013-05-15

... Welding & Machine Co., Brookville, PA...... February 20, 2012. Spherion Staffing Service. 82,487A Miller Welding & Machine Co., Brookville, PA...... February 20, 2012. Spherion Staffing Service. Negative... Analytics Process. 82,249P UnitedHealth Group, Optum, Inc., Eden Prairie, MN.... Credit Balance Field...
Acremonium camptosporum isolated as an endophyte of Bursera simaruba from Yucatan Peninsula, Mexico

USDA-ARS?s Scientific Manuscript database

This paper draws on morphological and molecular analyses to determinate the systematic position of an interesting endophytic fungus isolated from the leaves of Bursera simaruba, a tree of semideciduous dry tropical forest at El Eden Ecological Reserve. The cultured strain develops the characteristic...
Allelochemical effects of volatile compounds and organic extracts from Muscodor yucatanensis, a tropical endophytic fungus from Bursera simaruba

USDA-ARS?s Scientific Manuscript database

Muscodor yucatanensis, a recently described endophytic fungus, was isolated from the leaves of Bursera simaruba (Burseraceae) growing in the dry, semideciduous tropical forest of the Ecological Reserve El Eden, Quintana Roo, Mexico. In the present study we tested in vitro the mixture of volatile org...
Beyond the Garden of Eden: Deep Teacher Professional Development

ERIC Educational Resources Information Center

Samuel, M.

2009-01-01

Becoming a professional teacher is falsely understood to be a simple process: usually consisting of a transference of skills to execute classroom pedagogy or classroom management. This article begins by exploring the many forces which influence the curriculum of teacher education in higher education, signaling the complexity of the practice of…
Democracy in Schools: Are Educators Ready for Teacher Leadership?

ERIC Educational Resources Information Center

de Villiers, Elsabé; Pretorius, S. G.

2011-01-01

The aim of this research was to determine educators' perceptions of and readiness for teacher leadership. A total of 283 educators in the Eden and Central Karoo Education District in the Western Cape participated in the study. The participants included district officials, principals, and members of school management teams, as well as veteran,…
Towards a Theoretical Basis for Programs of Student Behavior.

ERIC Educational Resources Information Center

Howick, William H.

The historical background, principles, and practices of two major theories concerning student behavior are described. Theory A is religiously based and can be traced back to the biblical "Garden of Eden." It views human nature as fundamentally evil, the school as a means of both controlling and overcoming the child's innate propensities to…
ASDIR-II. Volume 3. Reference Documentation

DTIC Science & Technology

1975-01-01

Output> is the file where line images for the printer are to be written. FoUowing is a glossary of the parameters that can be varied via the name...47, 1967, p. 3096. 45. D. D. Eden, R. B. Lindsay, ahd H. Zink , "Acoustic Attenuation and Relaxation Phenomena in Stream at High Temperature and
Social Withdrawal Behaviour at One Year of Age Is Associated with Delays in Reaching Language Milestones in the EDEN Mother-Child Cohort Study.

PubMed

Guedeney, Antoine; Forhan, Anne; Larroque, Beatrice; de Agostini, Maria; Pingault, Jean-Baptiste; Heude, Barbara

2016-01-01

The aim of the study was to examine the relationship between social withdrawal behaviour at one year and motor and language milestones. One-year old children from the EDEN French population-based birth cohort study (Study on the pre- and postnatal determinants of the child's development and prospective health Birth Cohort Study) were included. Social withdrawal at one year was assessed by trained midwives using the Alarm Distress BaBy (ADBB) scale. Midwives concurrently examined infants' motor and language milestones. Parents reported on child's psychomotor and language milestones, during the interview with the midwife. After adjusting for potential confounding factors, social withdrawal behaviour was significantly associated with concurrent delays in motor and language milestones assessed by the midwife or the parents. Higher scores on social withdrawal behaviour as assessed with the ADBB were associated with delays in reaching language milestones, and to a lesser extent with lower motor ability scores. Taking the contribution of social withdrawal behaviour into account may help understand the unfolding of developmental difficulties in children.

Temporal and spatial distributions of sediment mercury at salt pond wetland restoration sites, San Francisco Bay, CA, USA

USGS Publications Warehouse

Miles, A.K.; Ricca, M.A.

2010-01-01

Decommissioned agricultural salt ponds within south San Francisco Bay, California, are in the process of being converted to habitat for the benefit of wildlife as well as water management needs and recreation. Little is known of baseline levels of contaminants in these ponds, particularly mercury (Hg), which has a well established legacy in the Bay. In this study we described spatial and short-term temporal variations in sediment Hg species concentrations within and among the Alviso and Eden Landing salt ponds in the southern region of San Francisco Bay. We determined total Hg (Hgt) and methylmercury (MeHg) in the top 5 cm of sediment of most ponds in order to establish baseline conditions prior to restoration, sediment Hgt concentrations in a subset of these ponds after commencement of restoration, and variation in MeHg concentrations relative to sediment Hgt, pH, and total Fe concentrations and water depth and salinity in the subset of Alviso ponds. Inter-pond differences were greatest within the Alviso pond complex, where sediment Hgt concentrations averaged (arithmetic mean) 0.74 ??g/g pre and 1.03 ??g/g post-restoration activity compared to 0.11 ??g/g pre and post at Eden Landing ponds. Sediment Hgt levels at Alviso were fairly stable temporally and spatially, whereas MeHg levels were variable relative to restoration activities across time and space. Mean (arithmetic) sediment MeHg concentrations increased (2.58 to 3.03 ng/g) in Alviso and decreased (2.20 to 1.03 ng/g) in Eden Landing restoration ponds during the study. Differences in MeHg levels were related to water depth and pH, but these relationships were not consistent between years or among ponds and were viewed with caution. Factors affecting MeHg levels in these ponds (and in general) are highly complex and require in-depth study to understand.
The Extension Service and Rural/Frontier Disaster Planning, Response, and Recovery

ERIC Educational Resources Information Center

Eighmy, Myron A.; Hall, Thomas E.; Sahr, Eunice

2012-01-01

The purpose of the study reported here was to (a) determine the role of Extension in disaster response, (b) identify the information needs, and (c) disseminate education and training modules through the EDEN. Extension staff should know their county's emergency plan and the role identified for Extension. Extension staff should attend local…
Retreat to Eden: Time and the Cosmos in Monet's Images of Giverny.

ERIC Educational Resources Information Center

Call, Michael

1997-01-01

Attempts to explain Claude Monet's obsession with water lilies in the last 25 years of his life through an interdisciplinary approach combining ideas from the history of landscape design, social history, and anthropology. Offers Monet's gardens at Giverny (France), his series paintings, and the railroad as some possible influences on his painting.…
University Communities and the Next American Upgrade

ERIC Educational Resources Information Center

Levin, Blair

2012-01-01

Knowledge is humanity's first and final frontier. From the Edenic exodus to flights beyond earth, mythic narratives reveal that going where no one has gone before to learn what no one has known before drives people like no other quest. That quest, for many millennium largely driven by spiritual needs, has become core to economic and social…
Children's Perceptions and Learning about Tropical Rainforests: An Analysis of Their Drawings

ERIC Educational Resources Information Center

Bowker, Rob

2007-01-01

This study analysed 9 to 11 year old children's drawings of tropical rainforests immediately before and after a visit to the Humid Tropics Biome at the Eden Project, Cornwall, UK. A theoretical framework derived from considerations of informal learning and constructivism was used as a basis to develop a methodology to interpret the children's…
So you want to meet Elvis?

PubMed

Pearce, Lynne

2016-01-13

Eden House care home in County Durham, part of Helen McArdle Care, was rated ‘outstanding’ by the Care Quality Commission after one of its new unannounced inspections. Residents have a nail bar and hair salon and are served restaurant-style meals at the home. Set amid landscaped gardens, it prides itself on meeting the expectations of its 53 residents.
The Bundian Way: Mapping with stories

Treesearch

John Blay

2015-01-01

The Bundian Way is a shared history pathway that connects the highest part of the Australian continent and the south-eastern coast via an ancient Aboriginal route that brought together the people of the greater region. The Eden Local Aboriginal Land Council has long worked towards its use for educational/ tourism purposes and recognition for heritage protection. In...
Breaking down Literature Boxes while Traveling with the Little Prince

ERIC Educational Resources Information Center

Steadman, Sharilyn C.

2012-01-01

Teach "The Little Prince" to senior English students? Senior Advanced Placement English students? What could these people who had analyzed "A Portrait of the Artist as a Young Man," wrestled with "The Sound and the Fury," dissected "Heart of Darkness," and deconstructed "East of Eden" possibly find of value in a "children's book"? The tendency to…
To Set One's Heart in a Violent World

ERIC Educational Resources Information Center

Tran, Mai-Anh Le

2015-01-01

Mai-Anh Le Tran is Associate Professor of Christian Education at Eden Theological Seminary in St. Louis, Missouri, and is president of the Religious Education Association. Here she begins with a saying attributed to Hindu mystic and guru Sri Ramakrishna (Love 2007, xii; cf. Cousineau 2003), "Religion is like a cow. It kicks but it gives milk,…
Darwin's Doodles

ERIC Educational Resources Information Center

Kirkman, Robbie

2016-01-01

The Eden Project, an educational charity based in Cornwall, is home to the largest rainforest in captivity and is a unique and awe-inspiring destination. It is one thing to talk about the idea of adaptation to environment but quite another to actually go into the rainforest and use your senses to explore up close living examples of ingenious plant…
77 FR 2508 - Rogue River-Siskiyou National Forest, Powers Ranger District, Coos County, OR; Eden Ridge Timber...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-01-18

... Probable Sale Quantity. The planning area is located approximately four (4) air miles southeast of the city... for sunlight, and would also reduce competition for water and soil nutrients when compared to... Ridge planning area is located approximately four (4) air miles southeast of the city of Powers, Oregon...
JPRS Report, Near East and South Asia.

DTIC Science & Technology

1991-10-31

the rise in the cost cussion on the conflict-resolution potential of the solu- of the nuclear station’s construction was among ... the world, has only two options for an agreed solution: Spain in the Middle Ages]," the lost Garden of Eden, an a vertical or a horizontal division...position willingness to wallow in reality and experience its dull- of
How do you keep the music playing?

PubMed

Baumann, Steven L

2008-10-01

The classical Chinese philosophy of Confucius is here reconsidered in light of the current challenge of sustaining loving relationships not only in words but in actions, and providing a life worth living for frail older adults. The Ox Mountain Parable of Meng Tzu (Mencius) is described and linked to the nursing home reform movement known as "The Eden Alternative." Implications for nursing are considered.
Culture Change in Long Term Care Services: Eden-Greenhouse-Aging in the Community

ERIC Educational Resources Information Center

Brune, Kendall

2011-01-01

To discuss the relationship between residents and the management team, we must first review the transition from a medical model to a social model of care that is sweeping across America. Long-term care (LTC) management models were developed for a very autocratic and hierarchical style of management based in the 1960s. Those facilities were built…
Learning in Eden: The Philosophy of Outdoor Education of the SCOPE Outdoor Learning Laboratories Program.

ERIC Educational Resources Information Center

O'Grady, Jerome

Designed as a guide in inservice preparation and as a reference in planning and conducting outdoor lessons, this statement of philosophy will help teachers who participate in the Outdoor Learning Laboratories program understand the aims and methods of outdoor education. To educate children is, of course, the ultimate purpose of outdoor education.…
"Greenbelt or Gutter": Youth "Place-Based" Performance and the Myth of the Suburban/Urban Divide

ERIC Educational Resources Information Center

Wessels, Anne

2014-01-01

A youth-created "place-based" performance set in the grounds of their suburban school challenged the myth of the suburban/urban divide that pits the edenic suburb against the dirty and crime-ridden city. Depicting the power relations of a failed utopia, these youth provoked the researcher to embark on further inquiry, analysing other…
Zinc Methionine Supplementation Impacts Gene and Protein Expression in Calf-fed Holstein Steers with Miniaml Impact on Feedlot Performance

USDA-ARS?s Scientific Manuscript database

Calf-fed Holstein steers were supplemented with a zinc (Zn) methionine supplement (ZnMet; ZINPRO®; Zinpro Corporation, Eden Prairie, MN) for 115±5 days prior to harvest along with zilpaterol hydrochloride (ZH; Zilmax®; Merck Animal Health, Summit, NJ) for the last 20 days with a 3 day withdrawal to ...
Eden-Hybinette and Pectoralis Major Transfer for Recurrent Shoulder Instability Due to Failed Latarjet and Chronic Subscapularis Rupture.

PubMed

Li, Xinning; Cusano, Antonio; Eichinger, Josef

2017-01-01

Shoulder dislocations are a common injury, with anterior shoulder dislocation among male patients being the most common presentation. A patient with recurrent shoulder instability, anterior-superior escape, and chronic subscapularis tendon rupture following multiple shoulder stabilization surgeries presents the surgeon with a complex and challenging case. This report describes a 40-year-old man with an extensive left shoulder history that included a failed Latarjet procedure, an irreparable, chronic subscapularis tear with grade 4 Goutallier fatty infiltration, and associated anterior-superior escape. Given his marked dysfunction, weakness, pain, and recurrent instability in the absence of glenohumeral arthritis, he underwent an open Eden-Hybinette procedure (iliac crest autograft), a pectoralis major transfer, and an anterior capsule repair. The patient returned to his previous work activities without limitations. To the authors' knowledge, this is the first report describing a combination of anterior glenoid bone grafting with a full pectoralis major muscle transfer for a patient with chronic subscapularis rupture and anterior-superior escape after a failed Latarjet procedure with minimum glenoid bone loss. Furthermore, the authors provide a biomechanical rationale for the reconstruction used for this problem. [Orthopedics. 2017; 40(1):e182-e187.]. Copyright 2016, SLACK Incorporated.
The Emergence of Predators in Early Life: There was No Garden of Eden

PubMed Central

de Nooijer, Silvester; Holland, Barbara R.; Penny, David

2009-01-01

Background Eukaryote cells are suggested to arise somewhere between 0.85∼2.7 billion years ago. However, in the present world of unicellular organisms, cells that derive their food and metabolic energy from larger cells engulfing smaller cells (phagocytosis) are almost exclusively eukaryotic. Combining these propositions, that eukaryotes were the first phagocytotic predators and that they arose only 0.85∼2.7 billion years ago, leads to an unexpected prediction of a long period (∼1–3 billion years) with no phagocytotes – a veritable Garden of Eden. Methodology We test whether such a long period is reasonable by simulating a population of very simple unicellular organisms - given only basic physical, biological and ecological principles. Under a wide range of initial conditions, cellular specialization occurs early in evolution; we find a range of cell types from small specialized primary producers to larger opportunistic or specialized predators. Conclusions Both strategies, specialized smaller cells and phagocytotic larger cells are apparently fundamental biological strategies that are expected to arise early in cellular evolution. Such early predators could have been ‘prokaryotes’, but if the earliest cells on the eukaryote lineage were predators then this explains most of their characteristic features. PMID:19492046
Social Withdrawal Behaviour at One Year of Age Is Associated with Delays in Reaching Language Milestones in the EDEN Mother-Child Cohort Study

PubMed Central

Guedeney, Antoine; Forhan, Anne; de Agostini, Maria; Pingault, Jean-Baptiste; Heude, Barbara

2016-01-01

Objective The aim of the study was to examine the relationship between social withdrawal behaviour at one year and motor and language milestones. Materials and Methods One-year old children from the EDEN French population-based birth cohort study (Study on the pre- and postnatal determinants of the child’s development and prospective health Birth Cohort Study) were included. Social withdrawal at one year was assessed by trained midwives using the Alarm Distress BaBy (ADBB) scale. Midwives concurrently examined infants’ motor and language milestones. Parents reported on child’s psychomotor and language milestones, during the interview with the midwife. Results After adjusting for potential confounding factors, social withdrawal behaviour was significantly associated with concurrent delays in motor and language milestones assessed by the midwife or the parents. Discussion Higher scores on social withdrawal behaviour as assessed with the ADBB were associated with delays in reaching language milestones, and to a lesser extent with lower motor ability scores. Taking the contribution of social withdrawal behaviour into account may help understand the unfolding of developmental difficulties in children. PMID:27391482

[Evaluation of the Global Research Architecture Regarding Diabetic Retinopathy].

PubMed

Schöffel, N; Wahrlich, N; Groneberg, D A; Bundschuh, M; Ohlendorf, D; Bendels, M H K

2017-02-01

Aims and Scope: Diabetic retinopathy (DR) is of major scientific and socioeconomic interest in most of the industrialized countries due to increasing prevalence. This interest is reflected by a marked increase in the number of publications since the 1990 s. It is therefore difficult for a single author to obtain an overview of the topic. Material and Methods: The total number of published items on DR was determined in the Web of Science database. All bibliometric data were collected for the period 1900 to 2008 (search term:"diabet* retinopath*" and "diabet* macul*"). A number of different scientometric methods were applied in accordance with the NewQIS protocol, e.g. state of the art visualisation techniques such as density equalising maps and network diagrams. Results: A total of 15,624 publications were identified. The U. S. A. leads in the overall number of publications (4,689). The most productive and the most prolific institutions, authors and publications are all in the U. S. A. The University of Wisconsin (i.e. Ronald Klein and his wife Barbara Eden Kobrin Klein) have established an international network with a large number of institutions publishing important work. Nevertheless, many other important institutions can be identified, e.g. the Joslin Diabetes Center, which has published many articles on VEGF. Conclusion: The results reveal that the scientific interest on the topic DR is continuously increasing. International collaboration is of growing importance in this field. Georg Thieme Verlag KG Stuttgart · New York.
Maternal nutritional determinants of colostrum fatty acids in the EDEN mother-child cohort.

PubMed

Armand, Martine; Bernard, Jonathan Y; Forhan, Anne; Heude, Barbara; Charles, Marie-Aline

2017-10-21

Programming of infant development and later health may depend on early-milk polyunsaturated fatty acids (PUFA) contents, that are very variable between women for reasons not well elucidated. Indeed, a high n-6/n-3 PUFA in milk was associated with higher adiposity, arterial pressure and lower psychomotor scores in childhood. We aimed to explore the respective contribution of several maternal and perinatal factors to the variability of linoleic (LA), α-linolenic (ALA), arachidonic (AA), and docosahexaenoic (DHA) acid levels in early milk. Fatty acids of 934 colostrum samples from the EDEN mother-child cohort were analyzed by gas chromatography. The dietary intakes during the last trimester of pregnancy were estimated using a quantitative food frequency questionnaire. Relationship between milk PUFA and dietary fatty acids, and other maternal or pregnancy variables were analyzed by multiple linear regression. The means (±SD) of colostrum LA, ALA, AA and DHA levels were, respectively, 9.85 ± 1.85, 0.65 ± 0.22, 0.86 ± 0.16, and 0.64 ± 0.19% of total fatty acids. Obese mothers colostrum contained the highest level of LA and AA and the lowest level of ALA and DHA. Colostrum LA, AA and DHA levels were higher in primiparous women. Mother's age was positively associated with colostrum AA and DHA. Dietary n-6 PUFA were associated with higher LA and lower DHA levels in colostrum, while dietary n-3 PUFA were related to higher LA and lower AA levels. Contrary to what was observed for DHA, AA level in colostrum was not related to its dietary intake. High dietary AA/DHA and total n-6/n-3 ratios were critical for the content of DHA in colostrum lipids. Our study brings new insights in the understanding of the main maternal factors involved in PUFA levels variability in early milk. These data are important to consider for dietary counseling for women prior to and during pregnancy. Copyright © 2017 Elsevier Ltd and European Society for Clinical Nutrition and Metabolism. All rights reserved.
Initial trophic vs full enteral feeding in patients with acute lung injury: the EDEN randomized trial.

PubMed

Rice, Todd W; Wheeler, Arthur P; Thompson, B Taylor; Steingrub, Jay; Hite, R Duncan; Moss, Marc; Morris, Alan; Dong, Ning; Rock, Peter

2012-02-22

The amount of enteral nutrition patients with acute lung injury need is unknown. To determine if initial lower-volume trophic enteral feeding would increase ventilator-free days and decrease gastrointestinal intolerances compared with initial full enteral feeding. The EDEN study, a randomized, open-label, multicenter trial conducted from January 2, 2008, through April 12, 2011. Participants were 1000 adults within 48 hours of developing acute lung injury requiring mechanical ventilation whose physicians intended to start enteral nutrition at 44 hospitals in the National Heart, Lung, and Blood Institute ARDS Clinical Trials Network. Participants were randomized to receive either trophic or full enteral feeding for the first 6 days. After day 6, the care of all patients who were still receiving mechanical ventilation was managed according to the full feeding protocol. Ventilator-free days to study day 28. Baseline characteristics were similar between the trophic-feeding (n = 508) and full-feeding (n = 492) groups. The full-feeding group received more enteral calories for the first 6 days, about 1300 kcal/d compared with 400 kcal/d (P < .001). Initial trophic feeding did not increase the number of ventilator-free days (14.9 [95% CI, 13.9 to 15.8] vs 15.0 [95% CI, 14.1 to 15.9]; difference, -0.1 [95% CI, -1.4 to 1.2]; P = .89) or reduce 60-day mortality (23.2% [95% CI, 19.6% to 26.9%] vs 22.2% [95% CI, 18.5% to 25.8%]; difference, 1.0% [95% CI, -4.1% to 6.3%]; P = .77) compared with full feeding. There were no differences in infectious complications between the groups. Despite receiving more prokinetic agents, the full-feeding group experienced more vomiting (2.2% vs 1.7% of patient feeding days; P = .05), elevated gastric residual volumes (4.9% vs 2.2% of feeding days; P < .001), and constipation (3.1% vs 2.1% of feeding days; P = .003). Mean plasma glucose values and average hourly insulin administration were both higher in the full-feeding group over the first 6 days. In patients with acute lung injury, compared with full enteral feeding, a strategy of initial trophic enteral feeding for up to 6 days did not improve ventilator-free days, 60-day mortality, or infectious complications but was associated with less gastrointestinal intolerance. clinicaltrials.gov Identifiers: NCT00609180 and NCT00883948.
The Every Student Succeeds Act: What It Means for Schools, Systems, and States

ERIC Educational Resources Information Center

Hess, Frederick M., Ed.; Eden, Max, Ed.

2017-01-01

Frederick M. Hess and Max Eden bring together a cross-section of respected academics and journalists to examine key aspects of the Every Student Succeeds Act (ESSA). The Chapters appear on the table of contents as follows: (1) From ESEA to NCLB: The Growth of the Federal Role and the Shift to Accountability (Patrick McGuinn); (2) From NCLB to…
Hamas and Israel: Conflicting Strategies of Group-Based Politics

DTIC Science & Technology

2008-12-01

Brotherhood’s survival, as a result of the Egyptian government’s severe suppression of the Brethren. Even when the Brethren were released from Egyptian jails...founders of HAMAS developed a wing for militant action, thus breaking with the Palestinian, Egyptian , and Jordanian Muslim Brotherhood’s more...Eden supposed the Egyptian population would overthrow President Jamal abd al-Nasir. 13 Ironically, the attacks cemented Nasir’s popularity and
Assassination: A Military View.

DTIC Science & Technology

1987-03-23

Assassination: A Military View Individual Essay S. PERFORMING ORG. REPORT NUMBER 7. AUTI4OR(e) S. CONTRACT OR GRANT NUMGER(e) COL Charles K. Eden S...CLASSIFICATION OF THIS PAGE(hen Data Entered) USAWC MILITARY STUDIES PROGRAM PAPER ASSASSINATION: A MILITARY VILW An Individual Essay hceess ’. :’r by...Military View FORMAT: Individual Essay DATE: 23 March 1987 PAGES: 17 CLASSIFICATION: Unclassified -Assassination is a topic with which most Americans
New Class of Excimer-Pumped Atomic Lasers (XPALS)

DTIC Science & Technology

2017-01-27

quantum efficiency greater thnn one, has been demonstrated. We believe this laser to represent a breakthrough in laser technology because the system...navy.mil Prepared by J. G. Eden and A. E. Mironov Laboratory For Optical Physics and Engineering Department of Electrical and Computer Engineering...viability of an atomic laser having a quantum efficiency greater than one. We believe this laser to represent a breakthrough in laser technology
Designing a Training Tool for Imaging Mental Models

DTIC Science & Technology

1990-11-01

Removing Aspects of Mental Models ................. 72 The History Function ............................................................ 75 In C...Category’ Dialog 73 A-63 ’Remove Entity’ Dialog 74 A-64 ’Remove Unk’ Dialog 75 A-65 ’ History ’ from ’Special’ Menu 76 A-66 Viewing the * History ’ Cluster...religious (the Garden of Eden), 4 computational (the Macintosh computer), botanic (trees), aesthetic (the Beatles ’ record company), scientific (Isaac
Comparison of Key West and Persian Gulf Seawaters

DTIC Science & Technology

2007-07-18

D. A. Eden, R. A. Davis, J. E. McElhiney, C. Di lorio, "The effects of desulphated seawater injection on microbiological hydrogen sulphide generation... Mercury Hg (5.0 x 10-6) 5.6 x 102 Gallium Ga (3.0 x 10-4) 9.0 x 103 Bismuth al (1.0 x 10-4) - Niobium Nb (ɝ x 10-5) - Thallium Ti 6.0 x 10- - Gold Au
Neurolab - A Space Shuttle Mission Dedicated to Neuroscience Research

NASA Technical Reports Server (NTRS)

1997-01-01

Session JA5 includes short reports concerning: (1) NASA/NIH Neurolab Collaborations; (2) Neurolab Mission: An Example of International Cooperation; (3) Neurolab: An Overview of the Planned Scientific Investigations; (4) EDEN: A Payload for NEUROLAB, dedicated to Neuro Vestibular Research; (5) Neurolab Experiments on the Role of Visual Cues in Microgravity Spatial Orientation; and (6) The Role of Space in the Exploration of the Mammalian Vestibular System.
The U.S. Military’s Reliance on Bottled Water During Military Operations

DTIC Science & Technology

2011-06-17

15 Tony Perry, "Afghan Dam a Monument to US Challenges," Daily Press Newspaper, (September 7...Effects on Operations. Santa Monica, CA: Arroyo Center, RAND Corporation, 2005. Peltz, Eric, Marc L Robbins , Kenneth J Girardini, Rick Eden, John M...Defense Technical Information Center, 2005. Perry, Tony . "Afghan Dam a Monument to US Challenges." Daily Press Newspaper, September 07, 2010. Rogers
Net-Centric Sustainment and Operational Reach on the Modern Battlefield

DTIC Science & Technology

2012-05-17

Halliday, Marc L. Robbins , and Kenneth J. Girardini. "Sustainment of Army Forces in Operation Iraqi Freedom: Battlefield Logistics and Effects on Operations... Robbins , Kenneth J. Girardini, Rick Eden, John M. Halliday, and Jeffrey Angers. "Operation Iraqi Freedom: Major Findings and Recommendations...Timothy P. Williams, Tony R. Sherrill, Amy R. McGrath, Morris G. Hayes, Antoniette C. McGrady, and John M. Sheckler. "Logistics Command and Control
Maternal weight prior and during pregnancy and offspring's BMI and adiposity at 5-6 years in the EDEN mother-child cohort.

PubMed

Jacota, M; Forhan, A; Saldanha-Gomes, C; Charles, M A; Heude, B

2017-08-01

Beyond pre-pregnancy BMI, maternal weight change before and during pregnancy may also affect offspring adiposity. To investigate the relationship between maternal weight history before and during pregnancy with children's adiposity at 5-6 years. In 1069 mother-child dyads from the EDEN Cohort, we examined by linear regression the associations of children's BMI, fat mass and abdominal adiposity at 5-6 years with maternal pre-pregnancy BMI, pre-pregnancy average yearly weight change from age 20 and gestational weight gain. The shapes of relationships were investigated using splines and polynomial functions were tested. Children's BMI and adiposity parameters were positively associated with maternal pre-pregnancy BMI, but these relationships were mainly seen in thin mothers, with no substantial variation for maternal BMI ranging from 22 to 35 kg/m 2 . Gestational weight gain was positively associated with children's BMI Z-score, but again more so in thin mothers. We found no association with pre-pregnancy weight change. Before the adiposity rebound, maternal pre-pregnancy thinness explains most of the relationship with children's BMI. The relationship may emerge at older ages in children of overweight and obese mothers, and this latency may be an obstacle to early prevention. © 2016 World Obesity Federation.
Effect of mechanical damage and wound healing on the viscoelastic properties of stems of flax cultivars (Linum usitatissimum L. cv. Eden and cv. Drakkar).

PubMed

Paul-Victor, Cloé; Dalle Vacche, Sara; Sordo, Federica; Fink, Siegfried; Speck, Thomas; Michaud, Véronique; Speck, Olga

2017-01-01

As plant fibres are increasingly used in technical textiles and their composites, underlying principles of wound healing in living plant fibres are relevant to product quality, and provide inspiration for biomimetic healing in synthetic materials. In this work, two Linum usitatissimum cultivars differing in their stem mechanical properties, cv. Eden (stems resistant to lodging) and cv. Drakkar (with more flexible stems), were grown without wound or with stems previously wounded with a cut parallel or transversal to the stem. To investigate wound healing efficiency, growth traits, stem biomechanics with Dynamic Mechanical Analysis and anatomy were analysed after 25-day recovery. Longitudinal incisions formed open wounds while transversal incisions generated stem growth restoring the whole cross-section but not the original stem organisation. In the case of transversal wound healing, all the bast fibre bundles in the perturbed area became lignified and pulled apart by parenchyma cells growth. Both Linum cultivars showed a healing efficiency from 79% to 95% with higher scores for transversal healing. Morphological and anatomical modifications of Linum were related to mechanical properties and healing ability. Alongside with an increased understanding of wound healing in plants, our results highlight their possible impact on textile quality and fibre yield.
Effect of mechanical damage and wound healing on the viscoelastic properties of stems of flax cultivars (Linum usitatissimum L. cv. Eden and cv. Drakkar)

PubMed Central

Paul-Victor, Cloé; Dalle Vacche, Sara; Sordo, Federica; Fink, Siegfried; Speck, Thomas; Michaud, Véronique

2017-01-01

As plant fibres are increasingly used in technical textiles and their composites, underlying principles of wound healing in living plant fibres are relevant to product quality, and provide inspiration for biomimetic healing in synthetic materials. In this work, two Linum usitatissimum cultivars differing in their stem mechanical properties, cv. Eden (stems resistant to lodging) and cv. Drakkar (with more flexible stems), were grown without wound or with stems previously wounded with a cut parallel or transversal to the stem. To investigate wound healing efficiency, growth traits, stem biomechanics with Dynamic Mechanical Analysis and anatomy were analysed after 25-day recovery. Longitudinal incisions formed open wounds while transversal incisions generated stem growth restoring the whole cross-section but not the original stem organisation. In the case of transversal wound healing, all the bast fibre bundles in the perturbed area became lignified and pulled apart by parenchyma cells growth. Both Linum cultivars showed a healing efficiency from 79% to 95% with higher scores for transversal healing. Morphological and anatomical modifications of Linum were related to mechanical properties and healing ability. Alongside with an increased understanding of wound healing in plants, our results highlight their possible impact on textile quality and fibre yield. PMID:28982196
The Use of Spatial Analysis to Estimate the Prevalence of Canine Leishmaniasis in Greece and Cyprus to Predict Its Future Variation and Relate It to Human Disease

PubMed Central

Sifaki-Pistola, Dimitra; Ntais, Pantelis; Christodoulou, Vasiliki; Mazeris, Apostolos; Antoniou, Maria

2014-01-01

Climatic, environmental, and demographic changes favor the emergence of neglected vector-borne diseases like leishmaniasis, which is spreading through dogs, the principle host of the protozoan Leishmania infantum. Surveillance of the disease in dogs is important, because the number of infected animals in an area determines the local risk of human infection. However, dog epidemiological studies are costly. Our aim was to evaluate the Emerging Diseases in a Changing European Environment (EDEN) veterinary questionnaire as a cost-effective tool in providing reliable, spatially explicit indicators of canine leishmaniasis prevalence. For this purpose, the data from the questionnaire were compared with data from two epidemiological studies on leishmaniasis carried out in Greece and Cyprus at the same time using statistical methods and spatial statistics. Although the questionnaire data cannot provide a quantitative measure of leishmaniasis in an area, it indicates the dynamic of the disease; information is obtained in a short period of time at low cost. PMID:24957543
Defense Small Business Innovation Research Program (SBIR) Abstracts of Phase I Awards 1984.

DTIC Science & Technology

1985-04-16

PROTECTION OF SATELLITES FROM DIRECTED ENERGY WEAPONS, IS THE UTILIZATION OF HEAT PIPES WITHIN A SHIELD STRUCTURE. HEAT PIPES COULD BE DESIGNED TO...780 EDEN ROAD LANCASTER, PA 17601 ROBERT M. SHAUBACK TITLE: ANALYSIS AND PERFORMNCE EVALUATION OF HEAT PIPES WITH MULTIPLE HEAT SOURCES TOPIC: 97... PIPES CAPABLE OF ACCEPTING HEAT FROM MULTIPLE HEAT SOURCES. THERE IS NO THOROUGH ANALYTICAL OR EXPERIMENTAL BASIS FOR THE DESIGN OF HEAT PIPES OF
The Indian Hill Petroglyph Site, 14EW1, Kanopolis Lake: Development of Alternative Mitigation Plans

DTIC Science & Technology

1980-01-01

flood control in the Smoky Hill River basin in 1948. The dam is approxi- mately 33 miles southwest of Salina, Kansas. The lake stores 61,400 acre feet...The Plainview, Midland, Milnesand, and Meserve are of the former type; the Scottsbluff, Eden, Cody, Angostura (or Frederick), and Agate Basin ...circular to irregular elliptical shallow basins , post molds, central firepits, and refuse pits (Wedel 1959: 552). Diagnostic artifacts recovered
The DASH Project: An Overview

DTIC Science & Technology

1988-02-29

by memory copyin g will degrade system performance on shared-memory multiprocessors. Virtual memor y (VM) remapping, as opposed to memory copying...Bershad, G.D. Giuseppe Facchetti, Kevin Fall, G . Scott Graham, Ellen Nelson , P. Venkat Rangan, Bruno Sartirana, Shin-Yuan Tzou, Raj Vaswani, and Robert...Remote Execution in NEST", IEEE Trans. on Software Eng. 13, 8 (August 1987), 905-912. 3. G . T. Almes, A. P. Black, E. Lazowska and J. Noe, "The Eden
Cortical-Cortical Interactions and Sensory Information Processing in Autism

DTIC Science & Technology

2011-04-01

Titan T-1 FDM (StrataSys, Inc., Eden Prairie, MN). All housing and mechanism components and assemblies were solid m odeled prior to fabri cation...tissue that is suspected to be diseased or injured. While this method is invasive, it is nevertheless quite effective as a means of putting one piece...been afflicted by trauma, disease and/or injury in a similar manner. For this reason, we hypothesized that we could develop novel means to “non

Archeological and Historic Cultural Resources Inventory for a Proposed Flood Control Project at Grafton, Walsh County, North Dakota.

DTIC Science & Technology

1983-10-01

types such as the Alberta, Plainview, Scotts Aluff, Eden Valley and Hell Gap ( Plano Complex) . A private collector from Sheyenne, North Dakota--on the...Grafton) (Michlovic 1979). An apparently early type point of the Plano Complex (Alberta point) was found net: the Manitoba community of Manitou (Pettipas...with the DL-S Burial Complex include miniature, smooth mortuary vessels, sometimes decorated with incised thunderbird designs and/or raised lizzards or
Relationship between early language skills and the development of inattention/hyperactivity symptoms during the preschool period: Results of the EDEN mother-child cohort.

PubMed

Peyre, Hugo; Galera, Cedric; van der Waerden, Judith; Hoertel, Nicolas; Bernard, Jonathan Y; Melchior, Maria; Ramus, Franck

2016-11-08

This study aims to examine bidirectional relationships between children's language skills and Inattention/Hyperactivity (IH) symptoms during preschool. Children (N = 1459) from the EDEN mother-child cohort were assessed at ages 3 and 5.5 years. Language skills were evaluated using the WPPSI-III, NEPSY and ELOLA batteries. Children's behavior, including IH symptoms, was assessed using the parent-rated Strengths & Difficulties Questionnaire (SDQ). Using a Structural Equation Modeling (SEM) approach, we examined the relationship between language skills and IH symptoms, as well as potential mediating processes. SEM analyses indicated a small negative effect of language skills at 3 years on ADHD symptoms at 5.5 years after adjusting for IH symptoms at 3 years (β =-0.12, SE = 0.04, p-value = 0.002). Interpersonal difficulties did not mediate the relationship between early language skills and later IH symptoms, nor was this association reduced after adjusting for a broad range of pre- and postnatal environmental factors and performance IQ. Among different language skills, receptive syntax at 3 years was most strongly related to IH symptoms at 5.5 years. Poor language skills at age 3 may predict IH symptoms when a child enters primary school. Implications for the understanding and the prevention of the co-occurrence of language disorders and ADHD are discussed.
Endobronchial Photoacoustic Microscopy for Staging of Lung Cancer

DTIC Science & Technology

2015-06-01

polydimethylsiloxane ( PDMS ) membrane, which is bio-compatible and can make a seamless contact with the trachea wall. When the tissues receive the pulsed...illustrated in figures 1 (a)-1(e). The mold can be printed by a 3-D printer (Objet Eden 260V). Liquid-phase PDMS is poured into the mold, and after 40...minutes baking at 80 degree Celsius the PDMS can be hardened. Then it is peeled off from the mold and bonded with another piece of PDMS membrane. The
Charge-Dependent Atomic-Scale Structures of High-Index and (110) Gold Electrode Surfaces as Revealed by Scanning Tunneling Microscopy

DTIC Science & Technology

1994-02-01

known gold atomic diameter of 2.89 A. Within a given domain, featuring adjacent terrace strings separated by monoatomic steps, the measured unit-cell...to utilize high-index gold faces in exploring the influence of monoatomic steps and related structural features on surface electrochemical phenomena...110) Gold Electrode Surfaces D1 T IC as Revealed by Scanning Tunneling Microscopy FLECTE MAR 10 19941 by E Xiaoping Gao, Gregory J. Edens, Antoinette
Interaction of Jet Fuel Hydrocarbon Components with Red Blood Cells and Hemoglobin

DTIC Science & Technology

2014-06-24

Directorate (RHDJ), Wright-Patterson AFB, OH. The authors would like to thank Maj. Paul Eden, Nicole Schaeublin, Christin Grabinski, Dr. Jeff Gearhart...We would also like to thank LtCol. Norman Fox (Laboratory Flight Commander), Mrs. Nersa Loh (Supervisor, Transfusion Services), and Mr. Dan Fischer ...Approximately 7.8 mg of hemoglobin sample was concentrated into a total volume of 5 mL of Fischer PBS pH 7.5 buffer using an Amicon Centrifugal Filter Unit
Tandem Reduction/Cyclization of O-Nitrophenyl Propargyl Alcohols-A Novel Synthesis of 2- & 2,4-Disubstituted Quinolines and Application to the Synthesis of Streptonigrin

DTIC Science & Technology

2008-01-01

surrogate” acrolein, which was synthesized from vinyl acetate (Scheme 11, upper equation )31. This mild method was an improvement over their previous work...11, lower equation ). Both cyclizations were facile with N,S-acetals bearing strongly activating groups on aniline. The resulting 2-methylthio...kinetic studies of the MS rearrangement.144 Using a combination of Hammett analysis of substituent effects and solvent isotope effects, Edens concluded
Reynolds Number Effects on Thrust Coefficients and PIV for Flapping Wing Micro Air Vehicles

DTIC Science & Technology

2012-03-09

flapper and smallest gear attached to the drive shaft from the motor. Lastly, passive rotation stops were designed and printed using the Objet Eden 500V...for different flapping mechanisms are also compared to a rotating propeller with the goal of elucidating the design trade space between rotorcraft and...flapping wings at Reynolds numbers less than 100,000. One flapping-wing mechanism was designed to incorporate a coupled 4-bar planar and 4-bar
Archeological and Historic Cultural Resources Inventory for a Proposed Flood Control Project at Grafton, Walsh County, North Dakota.

DTIC Science & Technology

1983-10-01

possibly Midland (Folsom Complex) , and a var iet- f point types such as the Alberta, Plainview, Scotts Bluff, Eden Valley anj Hell Gap ( Plano Complex). A...Red River Valley near Glyndon, Minnesota (south and slightly east of Grafton) (Michlovic 1979). An apparently early type point of the Plano Complex... incised thunderbird designs and/or raised lizzards or salamanders; welk shell (marine snail) masks/gorgets; "cigar holder-shaped" tubular pipes; and
Archeological Investigations at the Cow-Killer Site, 140S347, Melvern Lake, Kansas, 1974-1975.

DTIC Science & Technology

1982-09-01

represented in the Plains region by several cultural groups including the Llano and Plano complexes (Cald- well and Henning 1978:118). It should be noted...variety of distinctive projectile points of the Llano and Plano complexes, including such types as * Scottsbluff, Eden, Plainview, and the fluted...the site which show evidence of grinding and incising (Plate 11, F). These are all made from a fairly soft stone that appears to be limonite with
[Population estimates and conservation of felids (Carnivora: Felidae) in Northern Quintana Roo, Mexico].

PubMed

Ávila-Nájera, Dulce María; Chávez, Cuauhtémoc; Lazcano-Barrero, Marco A; Pérez-Elizalde, Sergio; Alcántara-Carbajal, José Luis

2015-09-01

Wildlife density estimates provide an idea of the current state of populations, and in some cases, reflect the conservation status of ecosystems, essential aspects for effective management actions. In Mexico, several regions have been identified as high priority areas for the conservation of species that have some level of risk, like the Yucatan Peninsula (YP), where the country has the largest population of jaguars. However, little is known about the current status of threatened and endangered felids, which coexist in the Northeastern portion of the Peninsula. Our objective was to estimate the wild cats' density population over time at El Eden Ecological Reserve (EEER) and its surrounding areas. Camera trap surveys over four years (2008, 2010, 2011 and 2012) were conducted, and data were obtained with the use of capture-recapture models for closed populations (CAPTURE + MMDM or 1/2 MMDM), and the spatially explicit capture-recapture model (SPACECAP). The species studied were jaguar (Panthera onca), puma (Puma concolor), ocelot (Leopardus pardalis), jaguarundi (Puma yaguaroundi) and margay (Leopardus wiedii). Capture frequency was obtained for all five species and the density for three (individuals/100km2). The density estimated with The Mean Maximum Distance Moved (MMDM), CAPTURE, ranged from 1.2 to 2.6 for jaguars, from 1.7 to 4.3 for pumas and from 1.4 to 13.8 for ocelots. The density estimates in SPACECAP ranged from 0.7 to 3.6 for jaguars, from 1.8 to 5.2 for pumas and 2.1 to 5.1 for ocelots. Spatially explicit capture recapture (SECR) methods in SPACECAP were less likely to overestimate densities, making it a useful tool in the planning and decision making process for the conservation of these species. The Northeastern portion of the Yucatan Peninsula maintains high populations of cats, the EEER and its surrounding areas are valuable sites for the conservation of this group of predators. Rev. Biol.
Using protein-protein interactions for refining gene networks estimated from microarray data by Bayesian networks.

PubMed

Nariai, N; Kim, S; Imoto, S; Miyano, S

2004-01-01

We propose a statistical method to estimate gene networks from DNA microarray data and protein-protein interactions. Because physical interactions between proteins or multiprotein complexes are likely to regulate biological processes, using only mRNA expression data is not sufficient for estimating a gene network accurately. Our method adds knowledge about protein-protein interactions to the estimation method of gene networks under a Bayesian statistical framework. In the estimated gene network, a protein complex is modeled as a virtual node based on principal component analysis. We show the effectiveness of the proposed method through the analysis of Saccharomyces cerevisiae cell cycle data. The proposed method improves the accuracy of the estimated gene networks, and successfully identifies some biological facts.
Real-time hydraulic interval state estimation for water transport networks: a case study

NASA Astrophysics Data System (ADS)

Vrachimis, Stelios G.; Eliades, Demetrios G.; Polycarpou, Marios M.

2018-03-01

Hydraulic state estimation in water distribution networks is the task of estimating water flows and pressures in the pipes and nodes of the network based on some sensor measurements. This requires a model of the network as well as knowledge of demand outflow and tank water levels. Due to modeling and measurement uncertainty, standard state estimation may result in inaccurate hydraulic estimates without any measure of the estimation error. This paper describes a methodology for generating hydraulic state bounding estimates based on interval bounds on the parametric and measurement uncertainties. The estimation error bounds provided by this method can be applied to determine the existence of unaccounted-for water in water distribution networks. As a case study, the method is applied to a modified transport network in Cyprus, using actual data in real time.
Unmanned aerial vehicles for the assessment and monitoring of environmental contamination: An example from coal ash spills.

PubMed

Messinger, Max; Silman, Miles

2016-11-01

Unmanned aerial vehicles (UAVs) offer new opportunities to monitor pollution and provide valuable information to support remediation. Their low-cost, ease of use, and rapid deployment capability make them ideal for environmental emergency response. Here we present a UAV-based study of the third largest coal ash spill in the United States. Coal ash from coal combustion is a toxic industrial waste material present worldwide. Typically stored in settling ponds in close proximity to waterways, coal ash poses significant risk to the environment and drinking water supplies from both chronic contamination of surface and ground water and catastrophic pond failure. We sought to provide an independent estimate of the volume of coal ash and contaminated water lost during the rupture of the primary coal ash pond at the Dan River Steam Station in Eden, NC, USA and to demonstrate the feasibility of using UAVs to rapidly respond to and measure the volume of spills from ponds or containers that are open to the air. Using structure-from-motion (SfM) imagery analysis techniques, we reconstructed the 3D structure of the pond bottom after the spill, used historical imagery to estimate the pre-spill waterline, and calculated the volume of material lost. We estimated a loss of 66,245 ± 5678 m 3 of ash and contaminated water. The technique used here allows rapid response to environmental emergencies and quantification of their impacts at low cost, and these capabilities will make UAVs a central tool in environmental planning, monitoring, and disaster response. Copyright © 2016 Elsevier Ltd. All rights reserved.
Predictors of persistent maternal depression trajectories in early childhood: results from the EDEN mother-child cohort study in France.

PubMed

van der Waerden, J; Galéra, C; Saurel-Cubizolles, M-J; Sutter-Dallay, A-L; Melchior, M

2015-07-01

Maternal depression in the pre- and postpartum period may set women on a course of chronic depressive symptoms. Little is known about predictors of persistently elevated depressive symptoms in mothers from pregnancy onwards. The aims of this study are to determine maternal depression trajectories from pregnancy to the child's fifth birthday and identify associated risk factors. Mothers (N = 1807) from the EDEN mother-child birth cohort study based in France (2003-2011) were followed from 24-28 weeks of pregnancy to their child's fifth birthday. Maternal depression trajectories were determined with a semi-parametric group-based modelling strategy. Sociodemographic, psychosocial and psychiatric predictors were explored for their association with trajectory class membership. Five trajectories of maternal symptoms of depression from pregnancy onwards were identified: no symptoms (60.2%); persistent intermediate-level depressive symptoms (25.2%); persistent high depressive symptoms (5.0%); high symptoms in pregnancy only (4.7%); high symptoms in the child's preschool period only (4.9%). Socio-demographic predictors associated with persistent depression were non-French origin; psychosocial predictors were childhood adversities, life events during pregnancy and work overinvestment; psychiatric predictors were previous mental health problems, psychological help, and high anxiety during pregnancy. Persistent depression in mothers of young children is associated to several risk factors present prior to or during pregnancy, notably anxiety. These characteristics precede depression trajectories and offer a possible entry point to enhance mother's mental health and reduce its burden on children.
A New Zealand outbreak of norovirus gastroenteritis linked to the consumption of imported raw Korean oysters.

PubMed

Simmons, Greg; Garbutt, Claire; Hewitt, Joanne; Greening, Gail

2007-10-26

To investigate an outbreak of gastroenteritis that occurred following an international rugby test at Eden Park (Auckland, New Zealand) on 17 June 2006. 387 patrons were interviewed. Cases were defined as those from one of four hospitality areas who consumed food or beverage at Eden Park on the evening of 17 June 2006 and subsequently suffered from diarrhoea or vomiting; or, stomach cramps and nausea. A case-control study was conducted and food and beverage items associated with illness were identified. Clinical specimens were requested from patrons and food handlers, and leftover foods were analysed for pathogens. A food safety assessment was conducted at the implicated catering premises. A total of 115 cases were identified. Attack rates varied between the four hospitality areas from 8% to 47%. Predominant symptoms among cases included nausea, vomiting, diarrhoea, stomach cramps, fever, and chills. The consumption of several foods was associated with an increased risk of illness but the strongest was for raw oysters consumed in Hospitality Area 1 (Risk Ratio 11.9; 95%CI 3.9-36.1; p<0.00001), attack rate 65%. Norovirus (genogroups I and II) was detected in samples provided by four of the cases and three unopened packets of implicated batches of imported Korean Pacific oysters (Crassostrea gigas) linked to the outbreak. This outbreak resulted from consumption of raw imported Korean oysters contaminated by norovirus. Labelling recommending cooking prior to consumption failed to prevent the outbreak.
Factors influencing specialist outreach and support services to rural populations in the Eden and Central Karoo districts of the Western Cape.

PubMed

Schoevers, Johan; Jenkins, Louis

2015-04-21

Access to health care often depends on where one lives. Rural populations have significantly poorer health outcomes than their urban counterparts. Specialist outreach to rural communities is one way of improving access to care. A multifaceted style of outreach improves access and health outcomes, whilst a shifted outpatients style only improves access. In principle, stakeholders agree that specialist outreach and support (O&S) to rural populations is necessary. In practice, however, factors influence whether or not O&S reaches its goals, affecting sustainability.Aim and setting: Our aim was to better understand factors associated with the success or failure of specialist O&S to rural populations in the Eden and Central Karoo districts in the Western Cape. An anonymous parallel three-stage Delphi process was followed to obtain consensus in a specialist and district hospital panel. Twenty eight specialist and 31 district hospital experts were invited, with response rates of 60.7%-71.4% and 58.1%-74.2% respectively across the three rounds. Relationships, communication and planning were found to be factors feeding into a service delivery versus capacity building tension, which affects the efficiency of O&S. The success of the O&S programme is dependent on a site-specific model that is acceptable to both the outreaching specialists and the hosting district hospital. Good communication, constructive feedback and improved planning may improve relationships and efficiency, which might lead to a more sustainable and mutually beneficial O&S system.
Social withdrawal at 1 year is associated with emotional and behavioural problems at 3 and 5 years: the Eden mother-child cohort study.

PubMed

Guedeney, Antoine; Pingault, Jean-Baptiste; Thorr, Antoine; Larroque, Beatrice

2014-12-01

The objective of the study was to examine how social withdrawal in infants aged 12 months predicted emotional and behavioural problems at ages 3 and 5 years. The sample included 1,586 infants from the French Eden Mother-Child Cohort Study who had a measure of social withdrawal with the Alarm Distress BaBy scale at age 1 year; among these children, emotional and behavioural difficulties were rated by mothers using the Strength and Difficulty Questionnaire (SDQ) at 3 years for 1,257 (79 %) children and at 5 years for 1,123 (72 %) children. Social withdrawal behaviour at age 1 year was significantly associated with the SDQ behavioural disorder scale at 3 years, independently of a host of familial and child temperament confounders. The association with the relational disorder, prosocial and total difficulty scales was close to significance at 3 years after taking into account familial and temperament confounders. Social withdrawal significantly predicted the three aforementioned scales when measured at 5 years. No significant predictivity of the emotional scale and hyperactivity scale was detected at any age. This study made with a large longitudinal sample confirms the negative effects on development of social withdrawal behaviour, shedding light on the unfolding of behavioural disorders and relational difficulties in children; this calls for early detection of sustained social withdrawal behaviour, as it seems to hamper emotional development.
Network Analysis on Attitudes: A Brief Tutorial.

PubMed

Dalege, Jonas; Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L J

2017-07-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs.
Network Analysis on Attitudes

PubMed Central

Borsboom, Denny; van Harreveld, Frenk; van der Maas, Han L. J.

2017-01-01

In this article, we provide a brief tutorial on the estimation, analysis, and simulation on attitude networks using the programming language R. We first discuss what a network is and subsequently show how one can estimate a regularized network on typical attitude data. For this, we use open-access data on the attitudes toward Barack Obama during the 2012 American presidential election. Second, we show how one can calculate standard network measures such as community structure, centrality, and connectivity on this estimated attitude network. Third, we show how one can simulate from an estimated attitude network to derive predictions from attitude networks. By this, we highlight that network theory provides a framework for both testing and developing formalized hypotheses on attitudes and related core social psychological constructs. PMID:28919944
Evolution of forensic odontology: An overview

PubMed Central

Balachander, N.; Babu, N. Aravindha; Jimson, Sudha; Priyadharsini, C.; Masthan, K. M. K.

2015-01-01

Forensic dentistry or forensic odontology admits dentists’ participation or identification of the victim and assisting legal and criminal issues. It refers to the proper handling, examination, identification and evaluation of dental evidence. This article summarizes the evolution of forensic odontology that started right from Garden of Eden to the modern scenario in identification of the gang rape case which happened in the state capital. Forensic dentistry plays a significant role in identifying the victims of crime, deceased individuals through the examination of anatomical structures, dental appliances and dental restorations. PMID:26015703

A False Sense of Collective Security: How German Intervention in the Spanish Civil War Thwarted British Multilateral Efforts during the Phony Peace

DTIC Science & Technology

2017-06-09

Chicago Tribune on September 29, 1936, in a story entitled “Rebel Airmen Bomb Madrid; Begin New Push” reported that “bombardment of Madrid from the air...1,000 prisoners of war (Republicans) or ordering the aerial bombing of civilian sectors of the city, with up to 2,000 more killed (Nationalists). Eden...turned out to have little if any military value. While Guernica captured the world’s attention, at least four other such bombings took place the
Introducing Systems Approaches

NASA Astrophysics Data System (ADS)

Reynolds, Martin; Holwell, Sue

Systems Approaches to Managing Change brings together five systems approaches to managing complex issues, each having a proven track record of over 25 years. The five approaches are: System Dynamics (SD) developed originally in the late 1950s by Jay Forrester Viable Systems Model (VSM) developed originally in the late 1960s by Stafford Beer Strategic Options Development and Analysis (SODA: with cognitive mapping) developed originally in the 1970s by Colin Eden Soft Systems Methodology (SSM) developed originally in the 1970s by Peter Checkland Critical Systems Heuristics (CSH) developed originally in the late 1970s by Werner Ulrich
The Shock and Vibration Bulletin. Part 1. Welcome, Keynote Address Invited Papers MIL-STD-810D MIL-STD-810D Panel Session

DTIC Science & Technology

1984-06-01

computer. The testing purposes is both expensive and time failure criterion is basically the effective comsuming , making it more difficult to obtain... behavior of a structure in terms of do critical review on a science because a its normal modes. The fundamental *science is something that ia fact... behavior expressed in some simple sort of rules of living in the Garden of Eden; they characteristic, and a deflected shape of each could eat from any
Children and violence.

PubMed

Renshaw, Domeena C

2006-01-01

In this "information era" it can no longer be said that children are being raised in Eden or in a gentle environment of kindness and love. However rural their home, children will undoubtedly see depictions of violence on television, in the movies, or in newspapers, or hear about it on the radio or while at school or on classroom computers. All children require safety education in order to learn how to protect themselves from harm at home, at school, or in the neighborhood. This article outlines how violence may impact today's children who seek medical care.
Acute respiratory distress syndrome without identifiable risk factors: A secondary analysis of the ARDS network trials.

PubMed

Harrington, John S; Schenck, Edward J; Oromendia, Clara; Choi, Augustine M K; Siempos, Ilias I

2018-06-02

We examined whether patients with acute respiratory distress syndrome (ARDS) lacking risk factors are enrolled in therapeutic trials and assessed their clinical characteristics and outcomes. We performed a secondary analysis of patient-level data pooled from the ARMA, ALVEOLI, FACTT, ALTA and EDEN ARDSNet randomized controlled trials obtained from the Biologic Specimen and Data Repository Information Coordinating Center of the National Heart, Lung and Blood Institute. We compared baseline characteristics and clinical outcomes (before and after adjustment using Poisson regression model) of ARDS patients with versus without risk factors. Of 3733 patients with ARDS, 81 (2.2%) did not have an identifiable risk factor. Patients without risk factors were younger, had lower baseline severity of illness, were more likely to have the ARDS resolve rapidly (i.e., within 24 h) (p < 0.001) and they had more ventilator-free days (median 21; p = 0.003), more intensive care unit-free days (18; p = 0.010), and more non-pulmonary organ failure-free days (24; p < 0.001) than comparators (17, 14 and 18, respectively). Differences persisted after adjustment for potential confounders. Patients with ARDS without identifiable risk factors are enrolled in therapeutic trials and may have better outcomes, including a higher proportion of rapidly resolving ARDS, than those with risk factors. Copyright © 2018. Published by Elsevier Inc.
A Novel Group-Fused Sparse Partial Correlation Method for Simultaneous Estimation of Functional Networks in Group Comparison Studies.

PubMed

Liang, Xiaoyun; Vaughan, David N; Connelly, Alan; Calamante, Fernando

2018-05-01

The conventional way to estimate functional networks is primarily based on Pearson correlation along with classic Fisher Z test. In general, networks are usually calculated at the individual-level and subsequently aggregated to obtain group-level networks. However, such estimated networks are inevitably affected by the inherent large inter-subject variability. A joint graphical model with Stability Selection (JGMSS) method was recently shown to effectively reduce inter-subject variability, mainly caused by confounding variations, by simultaneously estimating individual-level networks from a group. However, its benefits might be compromised when two groups are being compared, given that JGMSS is blinded to other groups when it is applied to estimate networks from a given group. We propose a novel method for robustly estimating networks from two groups by using group-fused multiple graphical-lasso combined with stability selection, named GMGLASS. Specifically, by simultaneously estimating similar within-group networks and between-group difference, it is possible to address inter-subject variability of estimated individual networks inherently related with existing methods such as Fisher Z test, and issues related to JGMSS ignoring between-group information in group comparisons. To evaluate the performance of GMGLASS in terms of a few key network metrics, as well as to compare with JGMSS and Fisher Z test, they are applied to both simulated and in vivo data. As a method aiming for group comparison studies, our study involves two groups for each case, i.e., normal control and patient groups; for in vivo data, we focus on a group of patients with right mesial temporal lobe epilepsy.
Hydrological drivers of wetland vegetational biodiversity patterns within Everglades National Park, Florida

NASA Astrophysics Data System (ADS)

Todd, J.; Pumo, D.; Azaele, S.; Muneepeerakul, R.; Miralles-Wilhelm, F. R.; Rinaldo, A.; Rodriguez-Iturbe, I.

2009-12-01

The influence of hydrological dynamics on vegetational biodiversity and structuring of wetland environments is of growing interest as wetlands are modified by human alteration and the increasing threat from climate change. Hydrology has long been considered a driving force in shaping wetland communities as the frequency of inundation along with the duration and depth of flooding are key determinants of wetland structure. We attempt to link hydrological dynamics with vegetational distribution and species richness across Everglades National Park (ENP) using two publicly available datasets. The first, the Everglades Depth Estimation Network (EDEN),is a water-surface model which determines the median daily measure of water level across a 400mX400m grid over seven years of measurement. The second is a vegetation map and classification system at the 1:15,000 scale which categorizes vegetation within the Everglades into 79 community types. From these data, we have studied the probabilistic structure of the frequency, duration, and depth of hydroperiods. Preliminary results indicate that the percentage of time a location is inundated is a principal structuring variable with individual communities responding differently. For example, sawgrass appears to be more of a generalist community as it is found across a wide range of time inundated percentages while spike rush has a more restricted distribution and favors wetter environments disproportionately more than predicted at random. Further, the diversity of vegetation communities (e.g. a measure of biodiversity) found across a hydrologic variable does not necessarily match the distribution function for that variable on the landscape. For instance, the number of communities does not differ across the percentage of time inundated. Different measures of vegetation biodiversity such as the local number of community types are also studied at different spatial scales with some characteristics, like the slope of the semi-logarithmic relation between rank and occupancy, found to be robust to scale changes. The ENP offers an expansive natural environment in which to study how vegetational dynamics and community composition are affected by hydrologic variables from the small scale (at the individual community level) to the large (biodiversity measures at differing spatial scales).
Low-dimensional recurrent neural network-based Kalman filter for speech enhancement.

PubMed

Xia, Youshen; Wang, Jun

2015-07-01

This paper proposes a new recurrent neural network-based Kalman filter for speech enhancement, based on a noise-constrained least squares estimate. The parameters of speech signal modeled as autoregressive process are first estimated by using the proposed recurrent neural network and the speech signal is then recovered from Kalman filtering. The proposed recurrent neural network is globally asymptomatically stable to the noise-constrained estimate. Because the noise-constrained estimate has a robust performance against non-Gaussian noise, the proposed recurrent neural network-based speech enhancement algorithm can minimize the estimation error of Kalman filter parameters in non-Gaussian noise. Furthermore, having a low-dimensional model feature, the proposed neural network-based speech enhancement algorithm has a much faster speed than two existing recurrent neural networks-based speech enhancement algorithms. Simulation results show that the proposed recurrent neural network-based speech enhancement algorithm can produce a good performance with fast computation and noise reduction. Copyright © 2015 Elsevier Ltd. All rights reserved.
How does the host population's network structure affect the estimation accuracy of epidemic parameters?

NASA Astrophysics Data System (ADS)

Yashima, Kenta; Ito, Kana; Nakamura, Kazuyuki

2013-03-01

When an Infectious disease where to prevail throughout the population, epidemic parameters such as the basic reproduction ratio, initial point of infection etc. are estimated from the time series data of infected population. However, it is unclear how does the structure of host population affects this estimation accuracy. In other words, what kind of city is difficult to estimate its epidemic parameters? To answer this question, epidemic data are simulated by constructing a commuting network with different network structure and running the infection process over this network. From the given time series data for each network structure, we would like to analyzed estimation accuracy of epidemic parameters.
Estimation of the proteomic cancer co-expression sub networks by using association estimators.

PubMed

Erdoğan, Cihat; Kurt, Zeyneb; Diri, Banu

2017-01-01

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

NASA Astrophysics Data System (ADS)

Mastromatteo, Iacopo; Zarinelli, Elia; Marsili, Matteo

2012-03-01

In this paper we estimate the propagation of liquidity shocks through interbank markets when the information about the underlying credit network is incomplete. We show that techniques such as maximum entropy currently used to reconstruct credit networks severely underestimate the risk of contagion by assuming a trivial (fully connected) topology, a type of network structure which can be very different from the one empirically observed. We propose an efficient message-passing algorithm to explore the space of possible network structures and show that a correct estimation of the network degree of connectedness leads to more reliable estimations for systemic risk. Such an algorithm is also able to produce maximally fragile structures, providing a practical upper bound for the risk of contagion when the actual network structure is unknown. We test our algorithm on ensembles of synthetic data encoding some features of real financial networks (sparsity and heterogeneity), finding that more accurate estimations of risk can be achieved. Finally we find that this algorithm can be used to control the amount of information that regulators need to require from banks in order to sufficiently constrain the reconstruction of financial networks.
Breastfeeding duration, social and occupational characteristics of mothers in the French 'EDEN mother-child' cohort.

PubMed

Bonet, Mercedes; Marchand, Laetitia; Kaminski, Monique; Fohran, Anne; Betoko, Aisha; Charles, Marie-Aline; Blondel, Béatrice

2013-05-01

Socio-demographic characteristics of mothers have been associated with exclusive breastfeeding duration, but little is known about the association with maternal full- and part-time employment and return to work in European countries. To study the associations between breastfeeding, any and almost exclusive (infants receiving breast milk as their only milk) breastfeeding, at 4 months of infant's age and the socio-demographic and occupational characteristics of mothers. We used the EDEN mother-child cohort, a prospective study of 2002 singleton pregnant women in two French university hospitals. We selected all mothers (n = 1,339) who were breastfeeding at discharge from the maternity unit. Data on feeding practices were collected at the maternity unit and by postal questionnaires at 4, 8 and 12 months after the birth. Among infants breastfed at discharge, 93% were still receiving any breastfeeding (83% almost exclusive breastfeeding) at the 3rd completed week of life, 78% (63%) at the 1st completed month, and 42% (20%) at the 4th completed month. Time of return to work was a major predictor for stopping breastfeeding: the sooner the mothers returned to work, the less they breastfed their babies at 4 months of infant's age, independently of full-time or part-time employment. The association was stronger for almost exclusive breastfeeding mothers than for any breastfeeding ones. In a society where breastfeeding is not the norm, women may have difficulties combining work and breastfeeding. Specific actions need to be developed and assessed among mothers who return to work and among employers.
Night-waking trajectories and associated factors in French preschoolers from the EDEN birth-cohort.

PubMed

Reynaud, Eve; Forhan, Anne; Heude, Barbara; de Lauzon-Guillain, Blandine; Charles, Marie-Aline; Plancoulaine, Sabine

Night waking in preschoolers has been associated with adverse health outcomes in cross-sectional studies, but has rarely been analyzed in a longitudinal setting. Therefore, little is known about the evolution of night waking in early childhood. The objectives of the present study were: to identify night-waking trajectories in preschoolers, and to examine the risk factors associated with those trajectories. Analyses were based on the French birth-cohort study EDEN, which recruited 2002 pregnant women between 2003 and 2006. Data on a child's night waking at the ages of two, three, and five, six years, and potential confounders, were collected through parental self-reported questionnaires. Night-waking trajectories were computerized using group-based trajectory modeling on 1346 children. Two distinct developmental patterns were identified: the "2-5 rare night-waking" (77% of the children) and the "2-5 common night-waking" pattern. Logistic regressions were performed to identify the factors associated with the trajectories. Risk factors for belonging to the "2-5 common night-waking" trajectory were: exposure to passive smoking at home, daycare in a collective setting, watching television for extended periods, bottle feeding at night, high emotionality, and low shyness. This approach allowed identification of risk factors associated with night waking during a critical age window, and laid the groundwork for identifying children at higher risk of deleterious sleep patterns. Those risk factors were mainly living habits, which indicated that prevention and intervention programs could be highly beneficial in this population. Copyright © 2016 Elsevier B.V. All rights reserved.
Use of seasonal trend decomposition to understand groundwater behaviour in the Permo-Triassic Sandstone aquifer, Eden Valley, UK

NASA Astrophysics Data System (ADS)

Lafare, Antoine E. A.; Peach, Denis W.; Hughes, Andrew G.

2016-02-01

The daily groundwater level (GWL) response in the Permo-Triassic Sandstone aquifers in the Eden Valley, England (UK), has been studied using the seasonal trend decomposition by LOESS (STL) technique. The hydrographs from 18 boreholes in the Permo-Triassic Sandstone were decomposed into three components: seasonality, general trend and remainder. The decomposition was analysed first visually, then using tools involving a variance ratio, time-series hierarchical clustering and correlation analysis. Differences and similarities in decomposition pattern were explained using the physical and hydrogeological information associated with each borehole. The Penrith Sandstone exhibits vertical and horizontal heterogeneity, whereas the more homogeneous St Bees Sandstone groundwater hydrographs characterize a well-identified seasonality; however, exceptions can be identified. A stronger trend component is obtained in the silicified parts of the northern Penrith Sandstone, while the southern Penrith, containing Brockram (breccias) Formation, shows a greater relative variability of the seasonal component. Other boreholes drilled as shallow/deep pairs show differences in responses, revealing the potential vertical heterogeneities within the Penrith Sandstone. The differences in bedrock characteristics between and within the Penrith and St Bees Sandstone formations appear to influence the GWL response. The de-seasonalized and de-trended GWL time series were then used to characterize the response, for example in terms of memory effect (autocorrelation analysis). By applying the STL method, it is possible to analyse GWL hydrographs leading to better conceptual understanding of the groundwater flow. Thus, variation in groundwater response can be used to gain insight into the aquifer physical properties and understand differences in groundwater behaviour.
The effect of tracking network configuration on GPS baseline estimates for the CASA Uno experiment

NASA Technical Reports Server (NTRS)

Wolf, S. Kornreich; Dixon, T. H.; Freymueller, J. T.

1990-01-01

The effect of the tracking network on long (greater than 100 km) GPS baseline estimates was estimated using various subsets of the global tracking network initiated by the first Central and South America (CASA Uno) experiment. It was found that best results could be obtained with a global tacking network consisting of three U.S. stations, two sites in the southwestern Pacific, and two sites in Europe. In comparison with smaller subsets, this global network improved the baseline repeatability, the resolution of carrier phase cycle ambiguities, and formal errors of the orbit estimates.
Network Structure and Biased Variance Estimation in Respondent Driven Sampling

PubMed Central

Verdery, Ashton M.; Mouw, Ted; Bauldry, Shawn; Mucha, Peter J.

2015-01-01

This paper explores bias in the estimation of sampling variance in Respondent Driven Sampling (RDS). Prior methodological work on RDS has focused on its problematic assumptions and the biases and inefficiencies of its estimators of the population mean. Nonetheless, researchers have given only slight attention to the topic of estimating sampling variance in RDS, despite the importance of variance estimation for the construction of confidence intervals and hypothesis tests. In this paper, we show that the estimators of RDS sampling variance rely on a critical assumption that the network is First Order Markov (FOM) with respect to the dependent variable of interest. We demonstrate, through intuitive examples, mathematical generalizations, and computational experiments that current RDS variance estimators will always underestimate the population sampling variance of RDS in empirical networks that do not conform to the FOM assumption. Analysis of 215 observed university and school networks from Facebook and Add Health indicates that the FOM assumption is violated in every empirical network we analyze, and that these violations lead to substantially biased RDS estimators of sampling variance. We propose and test two alternative variance estimators that show some promise for reducing biases, but which also illustrate the limits of estimating sampling variance with only partial information on the underlying population social network. PMID:26679927
MSE-impact of PPP-RTK ZTD estimation strategies

NASA Astrophysics Data System (ADS)

Wang, K.; Khodabandeh, A.; Teunissen, P. J. G.

2018-06-01

In PPP-RTK network processing, the wet component of the zenith tropospheric delay (ZTD) cannot be precisely modelled and thus remains unknown in the observation equations. For small networks, the tropospheric mapping functions of different stations to a given satellite are almost equal to each other, thereby causing a near rank-deficiency between the ZTDs and satellite clocks. The stated near rank-deficiency can be solved by estimating the wet ZTD components relatively to that of the reference receiver, while the wet ZTD component of the reference receiver is constrained to zero. However, by increasing network scale and humidity around the reference receiver, enlarged mismodelled effects could bias the network and the user solutions. To consider both the influences of the noise and the biases, the mean-squared errors (MSEs) of different network and user parameters are studied analytically employing both the ZTD estimation strategies. We conclude that for a certain set of parameters, the difference in their MSE structures using both strategies is only driven by the square of the reference wet ZTD component and the formal variance of its solution. Depending on the network scale and the humidity condition around the reference receiver, the ZTD estimation strategy that delivers more accurate solutions might be different. Simulations are performed to illustrate the conclusions made by analytical studies. We find that estimating the ZTDs relatively in large networks and humid regions (for the reference receiver) could significantly degrade the network ambiguity success rates. Using ambiguity-fixed network-derived PPP-RTK corrections, for networks with an inter-station distance within 100 km, the choices of the ZTD estimation strategy is not crucial for single-epoch ambiguity-fixed user positioning. Using ambiguity-float network corrections, for networks with inter-station distances of 100, 300 and 500 km in humid regions (for the reference receiver), the root-mean-squared errors (RMSEs) of the estimated user coordinates using relative ZTD estimation could be higher than those under the absolute case with differences up to millimetres, centimetres and decimetres, respectively.
Estimation of the proteomic cancer co-expression sub networks by using association estimators

PubMed Central

Kurt, Zeyneb; Diri, Banu

2017-01-01

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

PubMed

Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

2012-01-01

Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods.
Spatial Topography of Individual-Specific Cortical Networks Predicts Human Cognition, Personality, and Emotion.

PubMed

Kong, Ru; Li, Jingwei; Orban, Csaba; Sabuncu, Mert R; Liu, Hesheng; Schaefer, Alexander; Sun, Nanbo; Zuo, Xi-Nian; Holmes, Avram J; Eickhoff, Simon B; Yeo, B T Thomas

2018-06-06

Resting-state functional magnetic resonance imaging (rs-fMRI) offers the opportunity to delineate individual-specific brain networks. A major question is whether individual-specific network topography (i.e., location and spatial arrangement) is behaviorally relevant. Here, we propose a multi-session hierarchical Bayesian model (MS-HBM) for estimating individual-specific cortical networks and investigate whether individual-specific network topography can predict human behavior. The multiple layers of the MS-HBM explicitly differentiate intra-subject (within-subject) from inter-subject (between-subject) network variability. By ignoring intra-subject variability, previous network mappings might confuse intra-subject variability for inter-subject differences. Compared with other approaches, MS-HBM parcellations generalized better to new rs-fMRI and task-fMRI data from the same subjects. More specifically, MS-HBM parcellations estimated from a single rs-fMRI session (10 min) showed comparable generalizability as parcellations estimated by 2 state-of-the-art methods using 5 sessions (50 min). We also showed that behavioral phenotypes across cognition, personality, and emotion could be predicted by individual-specific network topography with modest accuracy, comparable to previous reports predicting phenotypes based on connectivity strength. Network topography estimated by MS-HBM was more effective for behavioral prediction than network size, as well as network topography estimated by other parcellation approaches. Thus, similar to connectivity strength, individual-specific network topography might also serve as a fingerprint of human behavior.

Network structure and travel time perception.

PubMed

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time.
NASA Design Strengthens Welds

NASA Technical Reports Server (NTRS)

2008-01-01

Friction Stir Welding (FSW) is a solid-state joining process-a combination of extruding and forging-ideal for use when the original metal characteristics must remain as unchanged as possible. While exploring methods to improve the use of FSW in manufacturing, engineers at Marshall Space Flight Center created technologies to address the method's shortcomings. MTS Systems Corporation, of Eden Prairie, Minnesota, discovered the NASA-developed technology and then signed a co-exclusive license agreement to commercialize Marshall's design for use in high-strength structural alloys. The resulting process offers the added bonuses of being cost-competitive, efficient, and most importantly, versatile.
Nonlinear calibration for petroleum water content measurement using PSO

NASA Astrophysics Data System (ADS)

Li, Mingbao; Zhang, Jiawei

2008-10-01

A new algorithmic for strapdown inertial navigation system (SINS) state estimation based on neural networks is introduced. In training strategy, the error vector and its delay are introduced. This error vector is made of the position and velocity difference between the estimations of system and the outputs of GPS. After state prediction and state update, the states of the system are estimated. After off-line training, the network can approach the status switching of SINS and after on-line training, the state estimate precision can be improved further by reducing network output errors. Then the network convergence is discussed. In the end, several simulations with different noise are given. The results show that the neural network state estimator has lower noise sensitivity and better noise immunity than Kalman filter.
Discrete-time neural network for fast solving large linear L1 estimation problems and its application to image restoration.

PubMed

Xia, Youshen; Sun, Changyin; Zheng, Wei Xing

2012-05-01

There is growing interest in solving linear L1 estimation problems for sparsity of the solution and robustness against non-Gaussian noise. This paper proposes a discrete-time neural network which can calculate large linear L1 estimation problems fast. The proposed neural network has a fixed computational step length and is proved to be globally convergent to an optimal solution. Then, the proposed neural network is efficiently applied to image restoration. Numerical results show that the proposed neural network is not only efficient in solving degenerate problems resulting from the nonunique solutions of the linear L1 estimation problems but also needs much less computational time than the related algorithms in solving both linear L1 estimation and image restoration problems.
Structured pedigree information for distributed fusion systems

NASA Astrophysics Data System (ADS)

Arambel, Pablo O.

2008-04-01

One of the most critical challenges in distributed data fusion is the avoidance of information double counting (also called "data incest" or "rumor propagation"). This occurs when a node in a network incorporates information into an estimate - e.g. the position of an object - and the estimate is injected into the network. Other nodes fuse this estimate with their own estimates, and continue to propagate estimates through the network. When the first node receives a fused estimate from the network, it does not know if it already contains its own contributions or not. Since the correlation between its own estimate and the estimate received from the network is not known, the node can not fuse the estimates in an optimal way. If it assumes that both estimates are independent from each other, it unknowingly double counts the information that has already being used to obtain the two estimates. This leads to overoptimistic error covariance matrices. If the double-counting is not kept under control, it may lead to serious performance degradation. Double counting can be avoided by propagating uniquely tagged raw measurements; however, that forces each node to process all the measurements and precludes the propagation of derived information. Another approach is to fuse the information using the Covariance Intersection (CI) equations, which maintain consistent estimates irrespective of the cross-correlation among estimates. However, CI does not exploit pedigree information of any kind. In this paper we present an approach that propagates multiple covariance matrices, one for each uncorrelated source in the network. This is a way to compress the pedigree information and avoids the need to propagate raw measurements. The approach uses a generalized version of the Split CI to fuse different estimates with appropriate weights to guarantee the consistency of the estimates.
Network Structure and Travel Time Perception

PubMed Central

Parthasarathi, Pavithra; Levinson, David; Hochmair, Hartwig

2013-01-01

The purpose of this research is to test the systematic variation in the perception of travel time among travelers and relate the variation to the underlying street network structure. Travel survey data from the Twin Cities metropolitan area (which includes the cities of Minneapolis and St. Paul) is used for the analysis. Travelers are classified into two groups based on the ratio of perceived and estimated commute travel time. The measures of network structure are estimated using the street network along the identified commute route. T-test comparisons are conducted to identify statistically significant differences in estimated network measures between the two traveler groups. The combined effect of these estimated network measures on travel time is then analyzed using regression models. The results from the t-test and regression analyses confirm the influence of the underlying network structure on the perception of travel time. PMID:24204932
Why do vulnerability cycles matter in financial networks?

NASA Astrophysics Data System (ADS)

Silva, Thiago Christiano; Tabak, Benjamin Miranda; Guerra, Solange Maria

2017-04-01

We compare two widely employed models that estimate systemic risk: DebtRank and Differential DebtRank. We show that not only network cyclicality but also the average vulnerability of banks are essential concepts that contribute to widening the gap in the systemic risk estimates of both approaches. We find that systemic risk estimates are the same whenever the network has no cycles. However, in case the network presents cyclicality, then we need to inspect the average vulnerability of banks to estimate the underestimation gap. We find that the gap is small regardless of the cyclicality of the network when its average vulnerability is large. In contrast, the observed gap follows a quadratic behavior when the average vulnerability is small or intermediate. We show results using an econometric exercise and draw guidelines both on artificial and real-world financial networks.
One-step estimation of networked population size: Respondent-driven capture-recapture with anonymity.

PubMed

Khan, Bilal; Lee, Hsuan-Wei; Fellows, Ian; Dombrowski, Kirk

2018-01-01

Size estimation is particularly important for populations whose members experience disproportionate health issues or pose elevated health risks to the ambient social structures in which they are embedded. Efforts to derive size estimates are often frustrated when the population is hidden or hard-to-reach in ways that preclude conventional survey strategies, as is the case when social stigma is associated with group membership or when group members are involved in illegal activities. This paper extends prior research on the problem of network population size estimation, building on established survey/sampling methodologies commonly used with hard-to-reach groups. Three novel one-step, network-based population size estimators are presented, for use in the context of uniform random sampling, respondent-driven sampling, and when networks exhibit significant clustering effects. We give provably sufficient conditions for the consistency of these estimators in large configuration networks. Simulation experiments across a wide range of synthetic network topologies validate the performance of the estimators, which also perform well on a real-world location-based social networking data set with significant clustering. Finally, the proposed schemes are extended to allow them to be used in settings where participant anonymity is required. Systematic experiments show favorable tradeoffs between anonymity guarantees and estimator performance. Taken together, we demonstrate that reasonable population size estimates are derived from anonymous respondent driven samples of 250-750 individuals, within ambient populations of 5,000-40,000. The method thus represents a novel and cost-effective means for health planners and those agencies concerned with health and disease surveillance to estimate the size of hidden populations. We discuss limitations and future work in the concluding section.
Inference on network statistics by restricting to the network space: applications to sexual history data.

PubMed

Goyal, Ravi; De Gruttola, Victor

2018-01-30

Analysis of sexual history data intended to describe sexual networks presents many challenges arising from the fact that most surveys collect information on only a very small fraction of the population of interest. In addition, partners are rarely identified and responses are subject to reporting biases. Typically, each network statistic of interest, such as mean number of sexual partners for men or women, is estimated independently of other network statistics. There is, however, a complex relationship among networks statistics; and knowledge of these relationships can aid in addressing concerns mentioned earlier. We develop a novel method that constrains a posterior predictive distribution of a collection of network statistics in order to leverage the relationships among network statistics in making inference about network properties of interest. The method ensures that inference on network properties is compatible with an actual network. Through extensive simulation studies, we also demonstrate that use of this method can improve estimates in settings where there is uncertainty that arises both from sampling and from systematic reporting bias compared with currently available approaches to estimation. To illustrate the method, we apply it to estimate network statistics using data from the Chicago Health and Social Life Survey. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Local Estimators for Spacecraft Formation Flying

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Nabi, Marzieh

2011-01-01

A formation estimation architecture for formation flying builds upon the local information exchange among multiple local estimators. Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are needed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms should rely on a local information-exchange network, relaxing the assumptions on existing algorithms. In this research, it was shown that only local observability is required to design a formation estimator and control law. The approach relies on breaking up the overall information-exchange network into sequence of local subnetworks, and invoking an agreement-type filter to reach consensus among local estimators within each local network. State estimates were obtained by a set of local measurements that were passed through a set of communicating Kalman filters to reach an overall state estimation for the formation. An optimization approach was also presented by means of which diffused estimates over the network can be incorporated in the local estimates obtained by each estimator via local measurements. This approach compares favorably with that obtained by a centralized Kalman filter, which requires complete knowledge of the raw measurement available to each estimator.
SkWwatch: Introducing European Youth to the World of Scientific Research through Interactive Utilisation of a Global Network of Robotic Telescopes

NASA Astrophysics Data System (ADS)

Sotiriou, M.; Vrazopoulos, H.; Ioannou, P.; Sotiriou, S.; Vagenas, E.

2005-12-01

The SkyWatch project is co-fi nanced by the European Community, within the FP6 framework of Science and Society, The SkyWatch consortium is composed by the following partners: Q-PLAN (GR), EDEN - Open Classroom (UK), Astrophysics Research Institute - Liverpool John Moores University (UK), European Physical Society (FR), Ellinogermaniki Agogi (GR), Stockholm University (SE), SCIENCE PROJECTS (UK) and University of Duisburg-Essen (DE). The aim of the SkyWatch project is to build up the number of youngsters involved in a series of science projects to create a virtual community of prospective young researchers promoting scientifi c culture. The project will allow young people to access and use robotic telescopes remotely in real-time, perform observations, analyze data and results and fi nally to develop and suggest solutions to selected research/scientifi c topics, all achieved through an innovative web-based learning environment. The dissemination of the project's activities is also served by a European Science Contest on science topics and projects, a series of popular science distance learning courses (Science Days) for European youth, promotion of concepts and ideas of science of a multidisciplinary nature: astronomy, physics, mathematics, chemistry, etc. The young participants are prompted to organize teams (school classes, groups of students, etc.) and to design, develop and implement projects and activities with the use of robotic telescopes under the guidance and the continuous support of a team of experts.
Developmental milestones at one year for the offspring of mothers with congenital hypothyroidism: a population-based study

PubMed

Léger, Juliane; Forhan, Anne; Dos Santos, Sophie; Larroque, Béatrice; Ecosse, Emmanuel; Charles, Marie-Aline; Heude, Barbara

2018-05-01

Maternal thyroid dysfunction during pregnancy is associated with neurodevelopmental impairment in the offspring. No data are currently available for the offspring of patients treated early for congenital hypothyroidism (CH). The aim of this study was to investigate motor and language milestones at one year of age in a population-based registry of children born to young women with CH. We assessed 110 children born to mothers with CH, and 1367 children from the EDEN French population-based birth cohort study prospectively, at the age of one year, with identical questionnaires. Outcomes were assessed in terms of scores for childhood developmental milestones relating to mobility, motor coordination, communication, motricity and language skills. After adjustment for confounding factors, children born to mothers with CH were found to have a higher risk of poor motor coordination than those of the EDEN cohort (OR: 4.18, 95% CI: 2.52-6.93). No differences were identified for the other four domains investigated. Children born to mothers with gestational diabetes have a higher risk of low motor coordination score than their peers (OR: 2.10, 95% CI: 1.21-3.66). Children born to mothers with TSH ≥ 10 IU/L during the first six months of pregnancy were more likely to have low motricity or communication skills scores than those born to mothers with lower TSH concentrations (56% vs 21% for each score, P < 0.04). Maternal CH may have slight adverse effects on some developmental milestones in the child at one year of age, particularly for children born to mothers with uncontrolled hypothyroidism. However, it remains unclear whether these adverse effects modify subsequent neurodevelopment. © 2018 European Society of Endocrinology.
Temperament in infancy and behavioral and emotional problems at age 5.5: The EDEN mother-child cohort.

PubMed

Abulizi, Xian; Pryor, Laura; Michel, Grégory; Melchior, Maria; van der Waerden, Judith

2017-01-01

Early temperamental characteristics may influence children's developmental pathways and predict future psychopathology. However, the environmental context may also shape or interact with infant temperament and indirectly contribute to increased vulnerability to adverse developmental outcomes. The aim of the present study is to explore the long-term contribution of temperamental traits at twelve months of age to the presence of emotional and behavioral problems later in childhood, and whether this association varies with the child's sex, parental separation, family socioeconomic status and maternal depression. 1184 mother-child pairs from the EDEN mother-child birth cohort study based in France (2003-2011), were followed from 24-28 weeks of pregnancy to the child's fifth birthday. Infant temperament at 12 months was assessed with the Emotionality Activity and Sociability (EAS) questionnaire and behavior at 5.5 years was assessed with the Strengths and Difficulties Questionnaire (SDQ). Emotional temperament in infancy predicts children's overall behavioral scores (β = 1.16, p<0.001), emotional difficulties (β = 0.30, p<0.001), conduct problems (β = 0.51, p<0.001) and symptoms of hyperactivity/inattention (β = 0.31, p = 0.01) at 5.5 years. Infants' active temperament predicts later conduct problems (β = 0.30, p = 0.02), while shyness predicts later emotional problems (β = 0.22, p = 0.04). The association between the child's temperament in infancy and later behavior did not vary with children's own or family characteristics. An emotional temperament in infancy is associated with higher levels of emotional and behavioral difficulties at the age of 5.5 years. Children who show high emotionality early on may require early prevention and intervention efforts to divert possible adverse developmental pathways.
Outcome of triple-tendon transfer, an Eden-Lange variant, to reconstruct trapezius paralysis.

PubMed

Elhassan, Bassem T; Wagner, Eric R

2015-08-01

This study describes the technique and evaluates the outcome of the triple-tendon (T3) transfer, an Eden-Lange variant, to the scapula to stabilize the scapulothoracic articulation in the treatment of symptomatic trapezius paralysis. T3 transfers were performed in 22 patients with a history of persistent trapezius paralysis secondary to spinal accessory nerve injury. The indications for surgery included shoulder pain and weakness and limited range of motion of the shoulder. The T3 transfer included transfer of the levator scapulae to the lateral aspect of the spine of the scapula, the rhomboid minor to the spine of the scapula just medial to the levator scapulae insertion, and the rhomboid major to the medial spine of the scapula, including all muscles bony insertions. At an average follow-up of 35 months, winging was corrected in all patients, with improvement of shoulder asymmetry. All patients had significant improvement of pain (P < .01) and range of motion, including active shoulder abduction that improved from an average of 71° preoperatively to 118° postoperatively (P < .02) and shoulder flexion from an average of 102° to 150° (P < .01). There were also significant improvements in aggregate Constant Shoulder Score (P < .01), subjective shoulder value (P < .01), and Disabilities of the Arm, Shoulder and Hand score (P < .01). All patients were very satisfied with the outcome of surgery. This study shows that the T3 transfer is effective in stabilizing the scapulothoracic articulation and restoring the function of the trapezius, and thus, in improving pain and shoulder function in patients with symptomatic trapezius paralysis. Copyright © 2015 Journal of Shoulder and Elbow Surgery Board of Trustees. Published by Elsevier Inc. All rights reserved.
Maternal tobacco smoking in pregnancy and children's socio-emotional development at age 5: The EDEN mother-child birth cohort study.

PubMed

Melchior, M; Hersi, R; van der Waerden, J; Larroque, B; Saurel-Cubizolles, M-J; Chollet, A; Galéra, C

2015-07-01

There is debate as to whether maternal tobacco use in pregnancy is related to offspring behaviour later on. We tested this association examining multiple aspects of children's behaviour at age 5 and accounting for parental smoking outside of pregnancy, as well as child and family characteristics. Data come from a prospective community based birth cohort study (EDEN; n=1113 families in France followed since pregnancy in 2003-2005 until the child's 5th birthday). Maternal tobacco use in pregnancy was self-reported. Children's socio-emotional development (emotional symptoms, conduct problems, symptoms of hyperactivity/inattention, peer relationship problems, prosocial behaviour) was assessed by mothers using the Strengths and Difficulties Questionnaire (SDQ) at age 5 years. Logistic regression analyses controlled for Inverse Probability Weights (IPW) of maternal tobacco use calculated based on study center, children's characteristics (sex, premature birth, low birth weight, breastfeeding), maternal characteristics (age at the child's birth, psychological difficulties and alcohol use in pregnancy, post-pregnancy depression, and smoking), paternal smoking in and post-pregnancy, parental educational attainment, family income, parental separation, and maternal negative life events. Maternal smoking in pregnancy only predicted children's high symptoms of hyperactivity/inattention (sex and study center-adjusted ORs: maternal smoking in the 1st trimester: 1.95, 95%CI: 1.13-3.38; maternal smoking throughout pregnancy: OR=2.11, 95%CI: 1.36-3.27). In IPW-controlled regression models, only children of mothers who smoked throughout pregnancy had significantly elevated levels of hyperactivity/inattention (OR=2.20, 95%CI: 1.21-4.00). Maternal tobacco smoking in pregnancy may contribute directly or through epigenetic mechanisms to children's symptoms of hyperactivity/inattention. Copyright © 2015 Elsevier Masson SAS. All rights reserved.
Temperament in infancy and behavioral and emotional problems at age 5.5: The EDEN mother-child cohort

PubMed Central

Abulizi, Xian; Pryor, Laura; Michel, Grégory; Melchior, Maria

2017-01-01

Objective Early temperamental characteristics may influence children’s developmental pathways and predict future psychopathology. However, the environmental context may also shape or interact with infant temperament and indirectly contribute to increased vulnerability to adverse developmental outcomes. The aim of the present study is to explore the long-term contribution of temperamental traits at twelve months of age to the presence of emotional and behavioral problems later in childhood, and whether this association varies with the child’s sex, parental separation, family socioeconomic status and maternal depression. Method 1184 mother-child pairs from the EDEN mother-child birth cohort study based in France (2003–2011), were followed from 24–28 weeks of pregnancy to the child’s fifth birthday. Infant temperament at 12 months was assessed with the Emotionality Activity and Sociability (EAS) questionnaire and behavior at 5.5 years was assessed with the Strengths and Difficulties Questionnaire (SDQ). Results Emotional temperament in infancy predicts children’s overall behavioral scores (β = 1.16, p<0.001), emotional difficulties (β = 0.30, p<0.001), conduct problems (β = 0.51, p<0.001) and symptoms of hyperactivity/inattention (β = 0.31, p = 0.01) at 5.5 years. Infants’ active temperament predicts later conduct problems (β = 0.30, p = 0.02), while shyness predicts later emotional problems (β = 0.22, p = 0.04). The association between the child’s temperament in infancy and later behavior did not vary with children’s own or family characteristics. Conclusion An emotional temperament in infancy is associated with higher levels of emotional and behavioral difficulties at the age of 5.5 years. Children who show high emotionality early on may require early prevention and intervention efforts to divert possible adverse developmental pathways. PMID:28199415
Persistent maternal depressive symptoms trajectories influence children's IQ: The EDEN mother-child cohort.

PubMed

van der Waerden, Judith; Bernard, Jonathan Y; De Agostini, Maria; Saurel-Cubizolles, Marie-Josèphe; Peyre, Hugo; Heude, Barbara; Melchior, Maria

2017-02-01

This study assessed the association between timing and course of maternal depression from pregnancy onwards and children's cognitive development at ages 5 to 6. Potential interaction effects with child sex and family socioeconomic status were explored. One thousand thirty-nine mother-child pairs from the French EDEN mother-child birth cohort were followed from 24 to 28 weeks of pregnancy onwards. Based on Center for Epidemiological Studies Depression (CES-D) and Edinburgh Postnatal Depression Scale (EPDS) scores assessed at six timepoints, longitudinal maternal depressive symptom trajectories were calculated with a group-based semiparametric method. Children's cognitive function was assessed at ages 5 to 6 by trained interviewers with the Wechsler Preschool and Primary Scale of Intelligence Third Edition (WPPSI-III), resulting in three composite scores: Verbal IQ (VIQ), Performance IQ (PIQ), and Full-Scale IQ (FSIQ). Five trajectories of maternal symptoms of depression could be distinguished: no symptoms, persistent intermediate-level depressive symptoms, persistent high depressive symptoms, high symptoms in pregnancy only, and high symptoms in the child's preschool period only. Multiple linear regression analyses showed that, compared to children of mothers who were never depressed, children of mothers with persistent high levels of depressive symptoms had reduced VIQ, PIQ, and FSIQ scores. This association was moderated by the child's sex, boys appearing especially vulnerable in case of persistent maternal depression. Chronicity of maternal depression predicts children's cognitive development at school entry age, particularly in boys. As maternal mental health is an early modifiable influence on child development, addressing the treatment needs of depressed mothers may help reduce the associated burden on the next generation. © 2016 Wiley Periodicals, Inc.
Spatio-temporal dynamics in phytobenthos structural properties reveal insights into agricultural catchment dynamics and nutrient fluxes

NASA Astrophysics Data System (ADS)

Reaney, S. M.; Snell, M. A.; Barker, P. A.; Aftab, A.; Barber, N. J.; Benskin, C.; Burke, S.; Cleasby, W.; Haygarth, P.; Jonczyk, J. C.; Owen, G. J.; Perks, M. T.; Quinn, P. F.; Surridge, B.

2016-12-01

Low order streams are spatially extensive, temporally dynamic, systems within the agricultural landscape. This dynamism extends to the aquatic communities within these streams, including the phytobentos, which demonstrates considerable resilience to diffuse anthropogenic nutrient pressures and changing climate dynamics. The phytobenthos community can substantially contribute to the food web, in particular diatoms, which dominate photo-autotrophic assemblages in low order streams. Diatoms are widely used in ecological monitoring because of their high sensitivity to environmental condition, but knowledge is limited on the ecological effects of winter disturbances and variance introduced by multiple and interacting pressures (N, P, sediment), introducing bias in understanding temporal dynamics in benthic diatom communities. Using the environmental time series data from long term monitoring within the River Eden Demonstration Test Catchment programme, we assess the impact of multiple hydro-chemical stressors on phytobenthic community resilience, and synthesize the impact of an extreme winter event. Monthly data from diatom communities collected in the Eden DTC from March 2011 to present show that river flow, strongly coupled to precipitation, is a key driver of these communities. Discharge has a direct effect on communities through scouring, but is also tightly correlated to nutrient delivery, such that 80% of the annual TP load arrives in 10% of the time. Trophic Diatom Index (TDI) values demonstrated considerable resilience by the stability of inter-monthly TDI scores over 5 seasonal cycles against the characterised highly variable hydrological regime. This research demonstrates that well characterised winter disturbances are critical to understanding drivers of aquatic dynamics. This has implications for catchment diffuse pollution policy, farm management and economics, given the climate projections of increases in frequency and intensity of extreme winter events, which may alter instream nutrient fluxes.
To which extent social withdrawal at the age of 1 year is associated with IQ at 5-6 years old? Results of the EDEN mother-child cohort.

PubMed

Guedeney, Antoine; Doukhan, Sarah; Forhan, Anne; Heude, Barbara; Peyre, Hugo

2017-11-01

The present study aims to determine to which extent social withdrawal at 1 year is associated with the child's IQ at the end of the preschool period. Children (N = 1045) from the EDEN mother-child cohort were assessed for social withdrawal behaviours at 1 year by trained midwives using the Alarm Distress BaBy (ADBB) scale. Midwives also examined infants' language and motor development at 1 year. At the age 5-6 years, IQ scores were based on the WPPSI-III. Linear regression models were used to determine the association between IQ and ADBB, adjusted for a broad range of pre- and postnatal environmental factors and for language and motor skills scores at 1 year. After adjusting for environmental factors, children with social withdrawal at 1 years (ADBB ≥5; N = 195) had significantly lower IQ scores at 5-6 years (-2.81 IQ points; p value 0.007) compared to children without social withdrawal (ADBB <5; N = 847). When motor and language skills at 1 year were included in the previous model, no association between social withdrawal and IQ at 5-6 years was found. Being socially withdrawn at 1 year is associated with lower IQ scores at 5-6 years. The potential influence of these developmental aspects on each other (withdrawal behaviour and language/motor skills) may occur early in development. Our results improve our understanding of the outcomes of early social withdrawal behaviour and call for early detection of delay in acquisition of language/motor skills among socially withdrawn young children.
Information Weighted Consensus for Distributed Estimation in Vision Networks

ERIC Educational Resources Information Center

Kamal, Ahmed Tashrif

2013-01-01

Due to their high fault-tolerance, ease of installation and scalability to large networks, distributed algorithms have recently gained immense popularity in the sensor networks community, especially in computer vision. Multi-target tracking in a camera network is one of the fundamental problems in this domain. Distributed estimation algorithms…

A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Astrophysics Data System (ADS)

Guo, T. H.; Musgrave, J.

1992-11-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.
A neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine

NASA Technical Reports Server (NTRS)

Guo, T. H.; Musgrave, J.

1992-01-01

In order to properly utilize the available fuel and oxidizer of a liquid propellant rocket engine, the mixture ratio is closed loop controlled during main stage (65 percent - 109 percent power) operation. However, because of the lack of flight-capable instrumentation for measuring mixture ratio, the value of mixture ratio in the control loop is estimated using available sensor measurements such as the combustion chamber pressure and the volumetric flow, and the temperature and pressure at the exit duct on the low pressure fuel pump. This estimation scheme has two limitations. First, the estimation formula is based on an empirical curve fitting which is accurate only within a narrow operating range. Second, the mixture ratio estimate relies on a few sensor measurements and loss of any of these measurements will make the estimate invalid. In this paper, we propose a neural network-based estimator for the mixture ratio of the Space Shuttle Main Engine. The estimator is an extension of a previously developed neural network based sensor failure detection and recovery algorithm (sensor validation). This neural network uses an auto associative structure which utilizes the redundant information of dissimilar sensors to detect inconsistent measurements. Two approaches have been identified for synthesizing mixture ratio from measurement data using a neural network. The first approach uses an auto associative neural network for sensor validation which is modified to include the mixture ratio as an additional output. The second uses a new network for the mixture ratio estimation in addition to the sensor validation network. Although mixture ratio is not directly measured in flight, it is generally available in simulation and in test bed firing data from facility measurements of fuel and oxidizer volumetric flows. The pros and cons of these two approaches will be discussed in terms of robustness to sensor failures and accuracy of the estimate during typical transients using simulation data.
Population Size Estimation of Men Who Have Sex with Men through the Network Scale-Up Method in Japan

PubMed Central

Ezoe, Satoshi; Morooka, Takeo; Noda, Tatsuya; Sabin, Miriam Lewis; Koike, Soichi

2012-01-01

Background Men who have sex with men (MSM) are one of the groups most at risk for HIV infection in Japan. However, size estimates of MSM populations have not been conducted with sufficient frequency and rigor because of the difficulty, high cost and stigma associated with reaching such populations. This study examined an innovative and simple method for estimating the size of the MSM population in Japan. We combined an internet survey with the network scale-up method, a social network method for estimating the size of hard-to-reach populations, for the first time in Japan. Methods and Findings An internet survey was conducted among 1,500 internet users who registered with a nationwide internet-research agency. The survey participants were asked how many members of particular groups with known population sizes (firepersons, police officers, and military personnel) they knew as acquaintances. The participants were also asked to identify the number of their acquaintances whom they understood to be MSM. Using these survey results with the network scale-up method, the personal network size and MSM population size were estimated. The personal network size was estimated to be 363.5 regardless of the sex of the acquaintances and 174.0 for only male acquaintances. The estimated MSM prevalence among the total male population in Japan was 0.0402% without adjustment, and 2.87% after adjusting for the transmission error of MSM. Conclusions The estimated personal network size and MSM prevalence seen in this study were comparable to those from previous survey results based on the direct-estimation method. Estimating population sizes through combining an internet survey with the network scale-up method appeared to be an effective method from the perspectives of rapidity, simplicity, and low cost as compared with more-conventional methods. PMID:22563366
Detecting Anomalies in Process Control Networks

NASA Astrophysics Data System (ADS)

Rrushi, Julian; Kang, Kyoung-Don

This paper presents the estimation-inspection algorithm, a statistical algorithm for anomaly detection in process control networks. The algorithm determines if the payload of a network packet that is about to be processed by a control system is normal or abnormal based on the effect that the packet will have on a variable stored in control system memory. The estimation part of the algorithm uses logistic regression integrated with maximum likelihood estimation in an inductive machine learning process to estimate a series of statistical parameters; these parameters are used in conjunction with logistic regression formulas to form a probability mass function for each variable stored in control system memory. The inspection part of the algorithm uses the probability mass functions to estimate the normalcy probability of a specific value that a network packet writes to a variable. Experimental results demonstrate that the algorithm is very effective at detecting anomalies in process control networks.
On Estimating End-to-End Network Path Properties

NASA Technical Reports Server (NTRS)

Allman, Mark; Paxson, Vern

1999-01-01

The more information about current network conditions available to a transport protocol, the more efficiently it can use the network to transfer its data. In networks such as the Internet, the transport protocol must often form its own estimates of network properties based on measurements per-formed by the connection endpoints. We consider two basic transport estimation problems: determining the setting of the retransmission timer (RTO) for are reliable protocol, and estimating the bandwidth available to a connection as it begins. We look at both of these problems in the context of TCP, using a large TCP measurement set [Pax97b] for trace-driven simulations. For RTO estimation, we evaluate a number of different algorithms, finding that the performance of the estimators is dominated by their minimum values, and to a lesser extent, the timer granularity, while being virtually unaffected by how often round-trip time measurements are made or the settings of the parameters in the exponentially-weighted moving average estimators commonly used. For bandwidth estimation, we explore techniques previously sketched in the literature [Hoe96, AD98] and find that in practice they perform less well than anticipated. We then develop a receiver-side algorithm that performs significantly better.
Fine-granularity inference and estimations to network traffic for SDN.

PubMed

Jiang, Dingde; Huo, Liuwei; Li, Ya

2018-01-01

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective.
Fine-granularity inference and estimations to network traffic for SDN

PubMed Central

Huo, Liuwei; Li, Ya

2018-01-01

An end-to-end network traffic matrix is significantly helpful for network management and for Software Defined Networks (SDN). However, the end-to-end network traffic matrix's inferences and estimations are a challenging problem. Moreover, attaining the traffic matrix in high-speed networks for SDN is a prohibitive challenge. This paper investigates how to estimate and recover the end-to-end network traffic matrix in fine time granularity from the sampled traffic traces, which is a hard inverse problem. Different from previous methods, the fractal interpolation is used to reconstruct the finer-granularity network traffic. Then, the cubic spline interpolation method is used to obtain the smooth reconstruction values. To attain an accurate the end-to-end network traffic in fine time granularity, we perform a weighted-geometric-average process for two interpolation results that are obtained. The simulation results show that our approaches are feasible and effective. PMID:29718913
Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Rahmani, Amirreza; Mesbahi, Mehran; Fathpour, Nanaz; Hadaegh, Fred Y.

2008-01-01

In this work, we develop an approach to formation estimation by explicitly characterizing formation's system-theoretic attributes in terms of the underlying inter-spacecraft information-exchange network. In particular, we approach the formation observer/estimator design by relaxing the accessibility to the global state information by a centralized observer/estimator- and in turn- providing an analysis and synthesis framework for formation observers/estimators that rely on local measurements. The noveltyof our approach hinges upon the explicit examination of the underlying distributed spacecraft network in the realm of guidance, navigation, and control algorithmic analysis and design. The overarching goal of our general research program, some of whose results are reported in this paper, is the development of distributed spacecraft estimation algorithms that are scalable, modular, and robust to variations inthe topology and link characteristics of the formation information exchange network. In this work, we consider the observability of a spacecraft formation from a single observation node and utilize the agreement protocol as a mechanism for observing formation states from local measurements. Specifically, we show how the symmetry structure of the network, characterized in terms of its automorphism group, directly relates to the observability of the corresponding multi-agent system The ramification of this notion of observability over networks is then explored in the context of distributed formation estimation.
Fiber Orientation Estimation Guided by a Deep Network.

PubMed

Ye, Chuyang; Prince, Jerry L

2017-09-01

Diffusion magnetic resonance imaging (dMRI) is currently the only tool for noninvasively imaging the brain's white matter tracts. The fiber orientation (FO) is a key feature computed from dMRI for tract reconstruction. Because the number of FOs in a voxel is usually small, dictionary-based sparse reconstruction has been used to estimate FOs. However, accurate estimation of complex FO configurations in the presence of noise can still be challenging. In this work we explore the use of a deep network for FO estimation in a dictionary-based framework and propose an algorithm named Fiber Orientation Reconstruction guided by a Deep Network (FORDN). FORDN consists of two steps. First, we use a smaller dictionary encoding coarse basis FOs to represent diffusion signals. To estimate the mixture fractions of the dictionary atoms, a deep network is designed to solve the sparse reconstruction problem. Second, the coarse FOs inform the final FO estimation, where a larger dictionary encoding a dense basis of FOs is used and a weighted ℓ 1 -norm regularized least squares problem is solved to encourage FOs that are consistent with the network output. FORDN was evaluated and compared with state-of-the-art algorithms that estimate FOs using sparse reconstruction on simulated and typical clinical dMRI data. The results demonstrate the benefit of using a deep network for FO estimation.
Integration and Analysis of Neighbor Discovery and Link Quality Estimation in Wireless Sensor Networks

PubMed Central

Radi, Marjan; Dezfouli, Behnam; Abu Bakar, Kamalrulnizam; Abd Razak, Shukor

2014-01-01

Network connectivity and link quality information are the fundamental requirements of wireless sensor network protocols to perform their desired functionality. Most of the existing discovery protocols have only focused on the neighbor discovery problem, while a few number of them provide an integrated neighbor search and link estimation. As these protocols require a careful parameter adjustment before network deployment, they cannot provide scalable and accurate network initialization in large-scale dense wireless sensor networks with random topology. Furthermore, performance of these protocols has not entirely been evaluated yet. In this paper, we perform a comprehensive simulation study on the efficiency of employing adaptive protocols compared to the existing nonadaptive protocols for initializing sensor networks with random topology. In this regard, we propose adaptive network initialization protocols which integrate the initial neighbor discovery with link quality estimation process to initialize large-scale dense wireless sensor networks without requiring any parameter adjustment before network deployment. To the best of our knowledge, this work is the first attempt to provide a detailed simulation study on the performance of integrated neighbor discovery and link quality estimation protocols for initializing sensor networks. This study can help system designers to determine the most appropriate approach for different applications. PMID:24678277
Linear and nonlinear ARMA model parameter estimation using an artificial neural network

NASA Technical Reports Server (NTRS)

Chon, K. H.; Cohen, R. J.

1997-01-01

This paper addresses parametric system identification of linear and nonlinear dynamic systems by analysis of the input and output signals. Specifically, we investigate the relationship between estimation of the system using a feedforward neural network model and estimation of the system by use of linear and nonlinear autoregressive moving-average (ARMA) models. By utilizing a neural network model incorporating a polynomial activation function, we show the equivalence of the artificial neural network to the linear and nonlinear ARMA models. We compare the parameterization of the estimated system using the neural network and ARMA approaches by utilizing data generated by means of computer simulations. Specifically, we show that the parameters of a simulated ARMA system can be obtained from the neural network analysis of the simulated data or by conventional least squares ARMA analysis. The feasibility of applying neural networks with polynomial activation functions to the analysis of experimental data is explored by application to measurements of heart rate (HR) and instantaneous lung volume (ILV) fluctuations.
Deep convolutional neural networks for estimating porous material parameters with ultrasound tomography

NASA Astrophysics Data System (ADS)

Lähivaara, Timo; Kärkkäinen, Leo; Huttunen, Janne M. J.; Hesthaven, Jan S.

2018-02-01

We study the feasibility of data based machine learning applied to ultrasound tomography to estimate water-saturated porous material parameters. In this work, the data to train the neural networks is simulated by solving wave propagation in coupled poroviscoelastic-viscoelastic-acoustic media. As the forward model, we consider a high-order discontinuous Galerkin method while deep convolutional neural networks are used to solve the parameter estimation problem. In the numerical experiment, we estimate the material porosity and tortuosity while the remaining parameters which are of less interest are successfully marginalized in the neural networks-based inversion. Computational examples confirms the feasibility and accuracy of this approach.
Estimation of Heavy Metals Contamination in the Soil of Zaafaraniya City Using the Neural Network

NASA Astrophysics Data System (ADS)

Ghazi, Farah F.

2018-05-01

The aim of this paper is to estimate the heavy metals Contamination in soils which can be used to determine the rate of environmental contamination by using new technique depend on design feedback neural network as an alternative accurate technique. The network simulates to estimate the concentration of Cadmium (Cd), Nickel (Ni), Lead (Pb), Zinc (Zn) and Copper (Cu). Then to show the accuracy and efficiency of suggested design we applied the technique in Al- Zafaraniyah in Baghdad city. The results of this paper show that the suggested networks can be successfully applied to the rapid and accuracy estimation of concentration of heavy metals.
Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions.

PubMed

Potter, Gail E; Smieszek, Timo; Sailer, Kerstin

2015-09-01

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0-5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models.
Modeling workplace contact networks: The effects of organizational structure, architecture, and reporting errors on epidemic predictions

PubMed Central

Potter, Gail E.; Smieszek, Timo; Sailer, Kerstin

2015-01-01

Face-to-face social contacts are potentially important transmission routes for acute respiratory infections, and understanding the contact network can improve our ability to predict, contain, and control epidemics. Although workplaces are important settings for infectious disease transmission, few studies have collected workplace contact data and estimated workplace contact networks. We use contact diaries, architectural distance measures, and institutional structures to estimate social contact networks within a Swiss research institute. Some contact reports were inconsistent, indicating reporting errors. We adjust for this with a latent variable model, jointly estimating the true (unobserved) network of contacts and duration-specific reporting probabilities. We find that contact probability decreases with distance, and that research group membership, role, and shared projects are strongly predictive of contact patterns. Estimated reporting probabilities were low only for 0–5 min contacts. Adjusting for reporting error changed the estimate of the duration distribution, but did not change the estimates of covariate effects and had little effect on epidemic predictions. Our epidemic simulation study indicates that inclusion of network structure based on architectural and organizational structure data can improve the accuracy of epidemic forecasting models. PMID:26634122
Wireless Computing Architecture III

DTIC Science & Technology

2013-09-01

MIMO Multiple-Input and Multiple-Output MIMO /CON MIMO with concurrent hannel access and estimation MU- MIMO Multiuser MIMO OFDM Orthogonal...compressive sensing \\; a design for concurrent channel estimation in scalable multiuser MIMO networking; and novel networking protocols based on machine...Network, Antenna Arrays, UAV networking, Angle of Arrival, Localization MIMO , Access Point, Channel State Information, Compressive Sensing 16
Air pollution during pregnancy and childhood cognitive and psychomotor development: six European birth cohorts.

PubMed

Guxens, Mònica; Garcia-Esteban, Raquel; Giorgis-Allemand, Lise; Forns, Joan; Badaloni, Chiara; Ballester, Ferran; Beelen, Rob; Cesaroni, Giulia; Chatzi, Leda; de Agostini, Maria; de Nazelle, Audrey; Eeftens, Marloes; Fernandez, Mariana F; Fernández-Somoano, Ana; Forastiere, Francesco; Gehring, Ulrike; Ghassabian, Akhgar; Heude, Barbara; Jaddoe, Vincent W V; Klümper, Claudia; Kogevinas, Manolis; Krämer, Ursula; Larroque, Béatrice; Lertxundi, Aitana; Lertxuni, Nerea; Murcia, Mario; Navel, Vladislav; Nieuwenhuijsen, Mark; Porta, Daniela; Ramos, Rosa; Roumeliotaki, Theano; Slama, Rémy; Sørensen, Mette; Stephanou, Euripides G; Sugiri, Dorothea; Tardón, Adonina; Tiemeier, Henning; Tiesler, Carla M T; Verhulst, Frank C; Vrijkotte, Tanja; Wilhelm, Michael; Brunekreef, Bert; Pershagen, Göran; Sunyer, Jordi

2014-09-01

Accumulating evidence from laboratory animal and human studies suggests that air pollution exposure during pregnancy affects cognitive and psychomotor development in childhood. We analyzed data from 6 European population-based birth cohorts-GENERATION R (The Netherlands), DUISBURG (Germany), EDEN (France), GASPII (Italy), RHEA (Greece), and INMA (Spain)-that recruited mother-infant pairs from 1997 to 2008. Air pollution levels-nitrogen oxides (NO2, NOx) in all regions and particulate matter (PM) with diameters of <2.5, <10, and 2.5-10 μm (PM2.5, PM10, and PMcoarse, respectively) and PM2.5 absorbance in a subgroup-at birth addresses were estimated by land-use regression models, based on monitoring campaigns performed primarily between 2008 and 2011. Levels were back-extrapolated to exact pregnancy periods using background monitoring sites. Cognitive and psychomotor development was assessed between 1 and 6 years of age. Adjusted region-specific effect estimates were combined using random-effects meta-analysis. A total of 9482 children were included. Air pollution exposure during pregnancy, particularly NO2, was associated with reduced psychomotor development (global psychomotor development score decreased by 0.68 points [95% confidence interval = -1.25 to -0.11] per increase of 10 μg/m in NO2). Similar trends were observed in most regions. No associations were found between any air pollutant and cognitive development. Air pollution exposure during pregnancy, particularly NO2 (for which motorized traffic is a major source), was associated with delayed psychomotor development during childhood. Due to the widespread nature of air pollution exposure, the public health impact of the small changes observed at an individual level could be considerable.
Multi-temporal Linkages of Net Ecosystem Exchanges (NEE) with the Climatic and Ecohydrologic Drivers in a Florida Everglades Short-hydroperiod Freshwater Marsh

NASA Astrophysics Data System (ADS)

Zaki, M. T.; Abdul-Aziz, O. I.; Ishtiaq, K. S.

2017-12-01

Wetlands are considered one of the most productive and ecologically valuable ecosystems on earth. We investigated the multi-temporal linkages of net ecosystem exchange (NEE) with the relevant climatic and ecohydrological drivers for a Florida Everglades short-hydroperiod freshwater wetland. Hourly NEE observations and the associated driving variables during 2008-12 were collected from the AmeriFlux and EDEN databases, and then averaged for the four temporal scales (1-day, 8-day, 15-day, and 30-day). Pearson correlation and factor analysis were employed to identify the interrelations and grouping patterns among the participatory variables for each time scale. The climatic and ecohydrological linkages of NEE were then reliably estimated using bootstrapped (1000 iterations) partial least squares regressions by resolving multicollinearity. The analytics identified four bio-physical components exhibiting relatively robust interrelations and grouping patterns with NEE across the temporal scales. In general, NEE was most strongly linked with the `radiation-energy (RE)' component, while having a moderate linkage with the `temperature-hydrology (TH)' and `aerodynamic (AD)' components. However, the `ambient atmospheric CO2 (AC)' component was very weakly linked to NEE. Further, RE and TH had a decreasing trend with the increasing time scales (1-30 days). In contrast, the linkages of AD and AC components increased from 1-day to 8-day scales, and then remained relatively invariable at the longer scales of aggregation. The estimated linkages provide insights into the dominant biophysical process components and drivers of ecosystem carbon in the Everglades. The invariant linking pattern and linkages would help to develop low-dimensional models to reliably predict CO2 fluxes from the tidal freshwater wetlands.
Sign: large-scale gene network estimation environment for high performance computing.

PubMed

Tamada, Yoshinori; Shimamura, Teppei; Yamaguchi, Rui; Imoto, Seiya; Nagasaki, Masao; Miyano, Satoru

2011-01-01

Our research group is currently developing software for estimating large-scale gene networks from gene expression data. The software, called SiGN, is specifically designed for the Japanese flagship supercomputer "K computer" which is planned to achieve 10 petaflops in 2012, and other high performance computing environments including Human Genome Center (HGC) supercomputer system. SiGN is a collection of gene network estimation software with three different sub-programs: SiGN-BN, SiGN-SSM and SiGN-L1. In these three programs, five different models are available: static and dynamic nonparametric Bayesian networks, state space models, graphical Gaussian models, and vector autoregressive models. All these models require a huge amount of computational resources for estimating large-scale gene networks and therefore are designed to be able to exploit the speed of 10 petaflops. The software will be available freely for "K computer" and HGC supercomputer system users. The estimated networks can be viewed and analyzed by Cell Illustrator Online and SBiP (Systems Biology integrative Pipeline). The software project web site is available at http://sign.hgc.jp/ .
Maternal mortality following caesarean sections.

PubMed

Sikdar, K; Kundu, S; Mandal, G S

1979-08-01

A study of 26 maternal deaths following 3647 caesarean sections was conducted in Eden Hospital from 1974-1977. During the time period there were 35,544 births and 308 total maternal deaths (8.74/1000). Indications for Caesarean sections included: 1) abnormal presentation; 2) cephalopelvic disproportion; 3) toxemia; 4) prolonged labor; 5) fetal distress; and 6) post-caesarean pregnancies. Highest mortality rates were among cephalopelvic disproportion, toxemia, and prolonged labor patients. 38.4% of the patients died due to septicaemia and peritonitis, but other deaths were due to preclampsia, shock, and hemorrhage. Proper antenatal care may have prevented anemia and preclampsia and treated other pre-existing or superimposed diseases.

Spreading of nonmotile bacteria on a hard agar plate: Comparison between agent-based and stochastic simulations

NASA Astrophysics Data System (ADS)

Rana, Navdeep; Ghosh, Pushpita; Perlekar, Prasad

2017-11-01

We study spreading of a nonmotile bacteria colony on a hard agar plate by using agent-based and continuum models. We show that the spreading dynamics depends on the initial nutrient concentration, the motility, and the inherent demographic noise. Population fluctuations are inherent in an agent-based model, whereas for the continuum model we model them by using a stochastic Langevin equation. We show that the intrinsic population fluctuations coupled with nonlinear diffusivity lead to a transition from a diffusion limited aggregation type of morphology to an Eden-like morphology on decreasing the initial nutrient concentration.
Realizing actual feedback control of complex network

NASA Astrophysics Data System (ADS)

Tu, Chengyi; Cheng, Yuhua

2014-06-01

In this paper, we present the concept of feedbackability and how to identify the Minimum Feedbackability Set of an arbitrary complex directed network. Furthermore, we design an estimator and a feedback controller accessing one MFS to realize actual feedback control, i.e. control the system to our desired state according to the estimated system internal state from the output of estimator. Last but not least, we perform numerical simulations of a small linear time-invariant dynamics network and a real simple food network to verify the theoretical results. The framework presented here could make an arbitrary complex directed network realize actual feedback control and deepen our understanding of complex systems.
Using Neural Networks for Sensor Validation

NASA Technical Reports Server (NTRS)

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

1998-01-01

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

PubMed

Komatsu, Misako; Namikawa, Jun; Chao, Zenas C; Nagasaka, Yasuo; Fujii, Naotaka; Nakamura, Kiyohiko; Tani, Jun

2014-01-01

Many previous studies have proposed methods for quantifying neuronal interactions. However, these methods evaluated the interactions between recorded signals in an isolated network. In this study, we present a novel approach for estimating interactions between observed neuronal signals by theorizing that those signals are observed from only a part of the network that also includes unobserved structures. We propose a variant of the recurrent network model that consists of both observable and unobservable units. The observable units represent recorded neuronal activity, and the unobservable units are introduced to represent activity from unobserved structures in the network. The network structures are characterized by connective weights, i.e., the interaction intensities between individual units, which are estimated from recorded signals. We applied this model to multi-channel brain signals recorded from monkeys, and obtained robust network structures with physiological relevance. Furthermore, the network exhibited common features that portrayed cortical dynamics as inversely correlated interactions between excitatory and inhibitory populations of neurons, which are consistent with the previous view of cortical local circuits. Our results suggest that the novel concept of incorporating an unobserved structure into network estimations has theoretical advantages and could provide insights into brain dynamics beyond what can be directly observed. Copyright © 2014 Elsevier Ireland Ltd and the Japan Neuroscience Society. All rights reserved.
Error estimation of deformable image registration of pulmonary CT scans using convolutional neural networks.

PubMed

Eppenhof, Koen A J; Pluim, Josien P W

2018-04-01

Error estimation in nonlinear medical image registration is a nontrivial problem that is important for validation of registration methods. We propose a supervised method for estimation of registration errors in nonlinear registration of three-dimensional (3-D) images. The method is based on a 3-D convolutional neural network that learns to estimate registration errors from a pair of image patches. By applying the network to patches centered around every voxel, we construct registration error maps. The network is trained using a set of representative images that have been synthetically transformed to construct a set of image pairs with known deformations. The method is evaluated on deformable registrations of inhale-exhale pairs of thoracic CT scans. Using ground truth target registration errors on manually annotated landmarks, we evaluate the method's ability to estimate local registration errors. Estimation of full domain error maps is evaluated using a gold standard approach. The two evaluation approaches show that we can train the network to robustly estimate registration errors in a predetermined range, with subvoxel accuracy. We achieved a root-mean-square deviation of 0.51 mm from gold standard registration errors and of 0.66 mm from ground truth landmark registration errors.
The Correlation Fractal Dimension of Complex Networks

NASA Astrophysics Data System (ADS)

Wang, Xingyuan; Liu, Zhenzhen; Wang, Mogei

2013-05-01

The fractality of complex networks is studied by estimating the correlation dimensions of the networks. Comparing with the previous algorithms of estimating the box dimension, our algorithm achieves a significant reduction in time complexity. For four benchmark cases tested, that is, the Escherichia coli (E. Coli) metabolic network, the Homo sapiens protein interaction network (H. Sapiens PIN), the Saccharomyces cerevisiae protein interaction network (S. Cerevisiae PIN) and the World Wide Web (WWW), experiments are provided to demonstrate the validity of our algorithm.
Representation of the Characteristics of Piezoelectric Fiber Composites with Neural Networks

NASA Astrophysics Data System (ADS)

Yapici, A.; Bickraj, K.; Yenilmez, A.; Li, M.; Tansel, I. N.; Martin, S. A.; Pereira, C. M.; Roth, L. E.

2007-03-01

Ideal sensors for the future should be economical, efficient, highly intelligent, and capable of obtaining their operation power from the environment. The use of piezoelectric fiber composites coupled with a low power microprocessor and backpropagation type neural networks is proposed for the development of a simple sensor to estimate the characteristics of harmonic forces. Three neural networks were used for the estimation of amplitude, gain and variation of the load in the time domain. The average estimation errors of the neural networks were less than 8% in all of the studied cases.
Fuzzy neural network for flow estimation in sewer systems during wet weather.

PubMed

Shen, Jun; Shen, Wei; Chang, Jian; Gong, Ning

2006-02-01

Estimation of the water flow from rainfall intensity during storm events is important in hydrology, sewer system control, and environmental protection. The runoff-producing behavior of a sewer system changes from one storm event to another because rainfall loss depends not only on rainfall intensities, but also on the state of the soil and vegetation, the general condition of the climate, and so on. As such, it would be difficult to obtain a precise flowrate estimation without sufficient a priori knowledge of these factors. To establish a model for flow estimation, one can also use statistical methods, such as the neural network STORMNET, software developed at Lyonnaise des Eaux, France, analyzing the relation between rainfall intensity and flowrate data of the known storm events registered in the past for a given sewer system. In this study, the authors propose a fuzzy neural network to estimate the flowrate from rainfall intensity. The fuzzy neural network combines four STORMNETs and fuzzy deduction to better estimate the flowrates. This study's system for flow estimation can be calibrated automatically by using known storm events; no data regarding the physical characteristics of the drainage basins are required. Compared with the neural network STORMNET, this method reduces the mean square error of the flow estimates by approximately 20%. Experimental results are reported herein.
Estimating the Size of a Large Network and its Communities from a Random Sample

PubMed Central

Chen, Lin; Karbasi, Amin; Crawford, Forrest W.

2017-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = (V, E) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G(W) be the induced subgraph in G of the vertices in W. In addition to G(W), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K, and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios. PMID:28867924
Estimating the Size of a Large Network and its Communities from a Random Sample.

PubMed

Chen, Lin; Karbasi, Amin; Crawford, Forrest W

2016-01-01

Most real-world networks are too large to be measured or studied directly and there is substantial interest in estimating global network properties from smaller sub-samples. One of the most important global properties is the number of vertices/nodes in the network. Estimating the number of vertices in a large network is a major challenge in computer science, epidemiology, demography, and intelligence analysis. In this paper we consider a population random graph G = ( V, E ) from the stochastic block model (SBM) with K communities/blocks. A sample is obtained by randomly choosing a subset W ⊆ V and letting G ( W ) be the induced subgraph in G of the vertices in W . In addition to G ( W ), we observe the total degree of each sampled vertex and its block membership. Given this partial information, we propose an efficient PopULation Size Estimation algorithm, called PULSE, that accurately estimates the size of the whole population as well as the size of each community. To support our theoretical analysis, we perform an exhaustive set of experiments to study the effects of sample size, K , and SBM model parameters on the accuracy of the estimates. The experimental results also demonstrate that PULSE significantly outperforms a widely-used method called the network scale-up estimator in a wide variety of scenarios.
Paule‐Mandel estimators for network meta‐analysis with random inconsistency effects

PubMed Central

Veroniki, Areti Angeliki; Law, Martin; Tricco, Andrea C.; Baker, Rose

2017-01-01

Network meta‐analysis is used to simultaneously compare multiple treatments in a single analysis. However, network meta‐analyses may exhibit inconsistency, where direct and different forms of indirect evidence are not in agreement with each other, even after allowing for between‐study heterogeneity. Models for network meta‐analysis with random inconsistency effects have the dual aim of allowing for inconsistencies and estimating average treatment effects across the whole network. To date, two classical estimation methods for fitting this type of model have been developed: a method of moments that extends DerSimonian and Laird's univariate method and maximum likelihood estimation. However, the Paule and Mandel estimator is another recommended classical estimation method for univariate meta‐analysis. In this paper, we extend the Paule and Mandel method so that it can be used to fit models for network meta‐analysis with random inconsistency effects. We apply all three estimation methods to a variety of examples that have been used previously and we also examine a challenging new dataset that is highly heterogenous. We perform a simulation study based on this new example. We find that the proposed Paule and Mandel method performs satisfactorily and generally better than the previously proposed method of moments because it provides more accurate inferences. Furthermore, the Paule and Mandel method possesses some advantages over likelihood‐based methods because it is both semiparametric and requires no convergence diagnostics. Although restricted maximum likelihood estimation remains the gold standard, the proposed methodology is a fully viable alternative to this and other estimation methods. PMID:28585257
Coarse-Grain Bandwidth Estimation Techniques for Large-Scale Space Network

NASA Technical Reports Server (NTRS)

Cheung, Kar-Ming; Jennings, Esther

2013-01-01

In this paper, we describe a top-down analysis and simulation approach to size the bandwidths of a store-andforward network for a given network topology, a mission traffic scenario, and a set of data types with different latency requirements. We use these techniques to estimate the wide area network (WAN) bandwidths of the ground links for different architecture options of the proposed Integrated Space Communication and Navigation (SCaN) Network.
Scalable population estimates using spatial-stream-network (SSN) models, fish density surveys, and national geospatial database frameworks for streams

Treesearch

Daniel J. Isaak; Jay M. Ver Hoef; Erin E. Peterson; Dona L. Horan; David E. Nagel

2017-01-01

Population size estimates for stream fishes are important for conservation and management, but sampling costs limit the extent of most estimates to small portions of river networks that encompass 100sâ10 000s of linear kilometres. However, the advent of large fish density data sets, spatial-stream-network (SSN) models that benefit from nonindependence among samples,...
Estimating the Size of the Methamphetamine-Using Population in New York City Using Network Sampling Techniques.

PubMed

Dombrowski, Kirk; Khan, Bilal; Wendel, Travis; McLean, Katherine; Misshula, Evan; Curtis, Ric

2012-12-01

As part of a recent study of the dynamics of the retail market for methamphetamine use in New York City, we used network sampling methods to estimate the size of the total networked population. This process involved sampling from respondents' list of co-use contacts, which in turn became the basis for capture-recapture estimation. Recapture sampling was based on links to other respondents derived from demographic and "telefunken" matching procedures-the latter being an anonymized version of telephone number matching. This paper describes the matching process used to discover the links between the solicited contacts and project respondents, the capture-recapture calculation, the estimation of "false matches", and the development of confidence intervals for the final population estimates. A final population of 12,229 was estimated, with a range of 8235 - 23,750. The techniques described here have the special virtue of deriving an estimate for a hidden population while retaining respondent anonymity and the anonymity of network alters, but likely require larger sample size than the 132 persons interviewed to attain acceptable confidence levels for the estimate.
Congestion estimation technique in the optical network unit registration process.

PubMed

Kim, Geunyong; Yoo, Hark; Lee, Dongsoo; Kim, Youngsun; Lim, Hyuk

2016-07-01

We present a congestion estimation technique (CET) to estimate the optical network unit (ONU) registration success ratio for the ONU registration process in passive optical networks. An optical line terminal (OLT) estimates the number of collided ONUs via the proposed scheme during the serial number state. The OLT can obtain congestion level among ONUs to be registered such that this information may be exploited to change the size of a quiet window to decrease the collision probability. We verified the efficiency of the proposed method through simulation and experimental results.
Evolutionary model selection and parameter estimation for protein-protein interaction network based on differential evolution algorithm

PubMed Central

Huang, Lei; Liao, Li; Wu, Cathy H.

2016-01-01

Revealing the underlying evolutionary mechanism plays an important role in understanding protein interaction networks in the cell. While many evolutionary models have been proposed, the problem about applying these models to real network data, especially for differentiating which model can better describe evolutionary process for the observed network urgently remains as a challenge. The traditional way is to use a model with presumed parameters to generate a network, and then evaluate the fitness by summary statistics, which however cannot capture the complete network structures information and estimate parameter distribution. In this work we developed a novel method based on Approximate Bayesian Computation and modified Differential Evolution (ABC-DEP) that is capable of conducting model selection and parameter estimation simultaneously and detecting the underlying evolutionary mechanisms more accurately. We tested our method for its power in differentiating models and estimating parameters on the simulated data and found significant improvement in performance benchmark, as compared with a previous method. We further applied our method to real data of protein interaction networks in human and yeast. Our results show Duplication Attachment model as the predominant evolutionary mechanism for human PPI networks and Scale-Free model as the predominant mechanism for yeast PPI networks. PMID:26357273
Equal Graph Partitioning on Estimated Infection Network as an Effective Epidemic Mitigation Measure

PubMed Central

Hadidjojo, Jeremy; Cheong, Siew Ann

2011-01-01

Controlling severe outbreaks remains the most important problem in infectious disease area. With time, this problem will only become more severe as population density in urban centers grows. Social interactions play a very important role in determining how infectious diseases spread, and organization of people along social lines gives rise to non-spatial networks in which the infections spread. Infection networks are different for diseases with different transmission modes, but are likely to be identical or highly similar for diseases that spread the same way. Hence, infection networks estimated from common infections can be useful to contain epidemics of a more severe disease with the same transmission mode. Here we present a proof-of-concept study demonstrating the effectiveness of epidemic mitigation based on such estimated infection networks. We first generate artificial social networks of different sizes and average degrees, but with roughly the same clustering characteristic. We then start SIR epidemics on these networks, censor the simulated incidences, and use them to reconstruct the infection network. We then efficiently fragment the estimated network by removing the smallest number of nodes identified by a graph partitioning algorithm. Finally, we demonstrate the effectiveness of this targeted strategy, by comparing it against traditional untargeted strategies, in slowing down and reducing the size of advancing epidemics. PMID:21799777
Time concurrency/phase-time synchronization in digital communications networks

NASA Technical Reports Server (NTRS)

Kihara, Masami; Imaoka, Atsushi

1990-01-01

Digital communications networks have the intrinsic capability of time synchronization which makes it possible for networks to supply time signals to some applications and services. A practical estimation method for the time concurrency on terrestrial networks is presented. By using this method, time concurrency capability of the Nippon Telegraph and Telephone Corporation (NTT) digital communications network is estimated to be better than 300 ns rms at an advanced level, and 20 ns rms at final level.
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

DOE PAGES

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

2017-11-15

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

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

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

In Hezaveh et al. (2017) we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data,more » as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single hyperparameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that neural networks can be a fast alternative to Monte Carlo Markov Chains for parameter uncertainty estimation in many practical applications, allowing more than seven orders of magnitude improvement in speed.« less

Estimating the epidemic threshold on networks by deterministic connections

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

Li, Kezan, E-mail: lkzzr@sohu.com; Zhu, Guanghu; Fu, Xinchu

2014-12-15

For many epidemic networks some connections between nodes are treated as deterministic, while the remainder are random and have different connection probabilities. By applying spectral analysis to several constructed models, we find that one can estimate the epidemic thresholds of these networks by investigating information from only the deterministic connections. Nonetheless, in these models, generic nonuniform stochastic connections and heterogeneous community structure are also considered. The estimation of epidemic thresholds is achieved via inequalities with upper and lower bounds, which are found to be in very good agreement with numerical simulations. Since these deterministic connections are easier to detect thanmore » those stochastic connections, this work provides a feasible and effective method to estimate the epidemic thresholds in real epidemic networks.« less
Constructing a Watts-Strogatz network from a small-world network with symmetric degree distribution.

PubMed

Menezes, Mozart B C; Kim, Seokjin; Huang, Rongbing

2017-01-01

Though the small-world phenomenon is widespread in many real networks, it is still challenging to replicate a large network at the full scale for further study on its structure and dynamics when sufficient data are not readily available. We propose a method to construct a Watts-Strogatz network using a sample from a small-world network with symmetric degree distribution. Our method yields an estimated degree distribution which fits closely with that of a Watts-Strogatz network and leads into accurate estimates of network metrics such as clustering coefficient and degree of separation. We observe that the accuracy of our method increases as network size increases.
Optimizing hidden layer node number of BP network to estimate fetal weight

NASA Astrophysics Data System (ADS)

Su, Juan; Zou, Yuanwen; Lin, Jiangli; Wang, Tianfu; Li, Deyu; Xie, Tao

2007-12-01

The ultrasonic estimation of fetal weigh before delivery is of most significance for obstetrical clinic. Estimating fetal weight more accurately is crucial for prenatal care, obstetrical treatment, choosing appropriate delivery methods, monitoring fetal growth and reducing the risk of newborn complications. In this paper, we introduce a method which combines golden section and artificial neural network (ANN) to estimate the fetal weight. The golden section is employed to optimize the hidden layer node number of the back propagation (BP) neural network. The method greatly improves the accuracy of fetal weight estimation, and simultaneously avoids choosing the hidden layer node number with subjective experience. The estimation coincidence rate achieves 74.19%, and the mean absolute error is 185.83g.
Estimation of effective connectivity using multi-layer perceptron artificial neural network.

PubMed

Talebi, Nasibeh; Nasrabadi, Ali Motie; Mohammad-Rezazadeh, Iman

2018-02-01

Studies on interactions between brain regions estimate effective connectivity, (usually) based on the causality inferences made on the basis of temporal precedence. In this study, the causal relationship is modeled by a multi-layer perceptron feed-forward artificial neural network, because of the ANN's ability to generate appropriate input-output mapping and to learn from training examples without the need of detailed knowledge of the underlying system. At any time instant, the past samples of data are placed in the network input, and the subsequent values are predicted at its output. To estimate the strength of interactions, the measure of " Causality coefficient " is defined based on the network structure, the connecting weights and the parameters of hidden layer activation function. Simulation analysis demonstrates that the method, called "CREANN" (Causal Relationship Estimation by Artificial Neural Network), can estimate time-invariant and time-varying effective connectivity in terms of MVAR coefficients. The method shows robustness with respect to noise level of data. Furthermore, the estimations are not significantly influenced by the model order (considered time-lag), and the different initial conditions (initial random weights and parameters of the network). CREANN is also applied to EEG data collected during a memory recognition task. The results implicate that it can show changes in the information flow between brain regions, involving in the episodic memory retrieval process. These convincing results emphasize that CREANN can be used as an appropriate method to estimate the causal relationship among brain signals.
Distributed Estimation, Coding, and Scheduling in Wireless Visual Sensor Networks

ERIC Educational Resources Information Center

Yu, Chao

2013-01-01

In this thesis, we consider estimation, coding, and sensor scheduling for energy efficient operation of wireless visual sensor networks (VSN), which consist of battery-powered wireless sensors with sensing (imaging), computation, and communication capabilities. The competing requirements for applications of these wireless sensor networks (WSN)…
Distributed weighted least-squares estimation with fast convergence for large-scale systems.

PubMed

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods.
Distributed weighted least-squares estimation with fast convergence for large-scale systems☆

PubMed Central

Marelli, Damián Edgardo; Fu, Minyue

2015-01-01

In this paper we study a distributed weighted least-squares estimation problem for a large-scale system consisting of a network of interconnected sub-systems. Each sub-system is concerned with a subset of the unknown parameters and has a measurement linear in the unknown parameters with additive noise. The distributed estimation task is for each sub-system to compute the globally optimal estimate of its own parameters using its own measurement and information shared with the network through neighborhood communication. We first provide a fully distributed iterative algorithm to asymptotically compute the global optimal estimate. The convergence rate of the algorithm will be maximized using a scaling parameter and a preconditioning method. This algorithm works for a general network. For a network without loops, we also provide a different iterative algorithm to compute the global optimal estimate which converges in a finite number of steps. We include numerical experiments to illustrate the performances of the proposed methods. PMID:25641976
H∞ state estimation of stochastic memristor-based neural networks with time-varying delays.

PubMed

Bao, Haibo; Cao, Jinde; Kurths, Jürgen; Alsaedi, Ahmed; Ahmad, Bashir

2018-03-01

This paper addresses the problem of H ∞ state estimation for a class of stochastic memristor-based neural networks with time-varying delays. Under the framework of Filippov solution, the stochastic memristor-based neural networks are transformed into systems with interval parameters. The present paper is the first to investigate the H ∞ state estimation problem for continuous-time Itô-type stochastic memristor-based neural networks. By means of Lyapunov functionals and some stochastic technique, sufficient conditions are derived to ensure that the estimation error system is asymptotically stable in the mean square with a prescribed H ∞ performance. An explicit expression of the state estimator gain is given in terms of linear matrix inequalities (LMIs). Compared with other results, our results reduce control gain and control cost effectively. Finally, numerical simulations are provided to demonstrate the efficiency of the theoretical results. Copyright © 2018 Elsevier Ltd. All rights reserved.
Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

DOE PAGES

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

2017-02-01

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less
Estimation and Fusion for Tracking Over Long-Haul Links Using Artificial Neural Networks

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

Liu, Qiang; Brigham, Katharine; Rao, Nageswara S. V.

In a long-haul sensor network, sensors are remotely deployed over a large geographical area to perform certain tasks, such as tracking and/or monitoring of one or more dynamic targets. A remote fusion center fuses the information provided by these sensors so that a final estimate of certain target characteristics – such as the position – is expected to possess much improved quality. In this paper, we pursue learning-based approaches for estimation and fusion of target states in longhaul sensor networks. In particular, we consider learning based on various implementations of artificial neural networks (ANNs). Finally, the joint effect of (i)more » imperfect communication condition, namely, link-level loss and delay, and (ii) computation constraints, in the form of low-quality sensor estimates, on ANN-based estimation and fusion, is investigated by means of analytical and simulation studies.« less
A Deep Neural Network Model for Rainfall Estimation UsingPolarimetric WSR-88DP Radar Observations

NASA Astrophysics Data System (ADS)

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

2016-12-01

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

NASA Astrophysics Data System (ADS)

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-01

We introduce a general model for a network of quantum sensors, and we use this model to consider the following question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. This immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or nonlinear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.
Adaptive Video Streaming Using Bandwidth Estimation for 3.5G Mobile Network

NASA Astrophysics Data System (ADS)

Nam, Hyeong-Min; Park, Chun-Su; Jung, Seung-Won; Ko, Sung-Jea

Currently deployed mobile networks including High Speed Downlink Packet Access (HSDPA) offer only best-effort Quality of Service (QoS). In wireless best effort networks, the bandwidth variation is a critical problem, especially, for mobile devices with small buffers. This is because the bandwidth variation leads to packet losses caused by buffer overflow as well as picture freezing due to high transmission delay or buffer underflow. In this paper, in order to provide seamless video streaming over HSDPA, we propose an efficient real-time video streaming method that consists of the available bandwidth (AB) estimation for the HSDPA network and the transmission rate control to prevent buffer overflows/underflows. In the proposed method, the client estimates the AB and the estimated AB is fed back to the server through real-time transport control protocol (RTCP) packets. Then, the server adaptively adjusts the transmission rate according to the estimated AB and the buffer state obtained from the RTCP feedback information. Experimental results show that the proposed method achieves seamless video streaming over the HSDPA network providing higher video quality and lower transmission delay.
Functional Brain Networks: Does the Choice of Dependency Estimator and Binarization Method Matter?

NASA Astrophysics Data System (ADS)

Jalili, Mahdi

2016-07-01

The human brain can be modelled as a complex networked structure with brain regions as individual nodes and their anatomical/functional links as edges. Functional brain networks are constructed by first extracting weighted connectivity matrices, and then binarizing them to minimize the noise level. Different methods have been used to estimate the dependency values between the nodes and to obtain a binary network from a weighted connectivity matrix. In this work we study topological properties of EEG-based functional networks in Alzheimer’s Disease (AD). To estimate the connectivity strength between two time series, we use Pearson correlation, coherence, phase order parameter and synchronization likelihood. In order to binarize the weighted connectivity matrices, we use Minimum Spanning Tree (MST), Minimum Connected Component (MCC), uniform threshold and density-preserving methods. We find that the detected AD-related abnormalities highly depend on the methods used for dependency estimation and binarization. Topological properties of networks constructed using coherence method and MCC binarization show more significant differences between AD and healthy subjects than the other methods. These results might explain contradictory results reported in the literature for network properties specific to AD symptoms. The analysis method should be seriously taken into account in the interpretation of network-based analysis of brain signals.
A variational approach to parameter estimation in ordinary differential equations.

PubMed

Kaschek, Daniel; Timmer, Jens

2012-08-14

Ordinary differential equations are widely-used in the field of systems biology and chemical engineering to model chemical reaction networks. Numerous techniques have been developed to estimate parameters like rate constants, initial conditions or steady state concentrations from time-resolved data. In contrast to this countable set of parameters, the estimation of entire courses of network components corresponds to an innumerable set of parameters. The approach presented in this work is able to deal with course estimation for extrinsic system inputs or intrinsic reactants, both not being constrained by the reaction network itself. Our method is based on variational calculus which is carried out analytically to derive an augmented system of differential equations including the unconstrained components as ordinary state variables. Finally, conventional parameter estimation is applied to the augmented system resulting in a combined estimation of courses and parameters. The combined estimation approach takes the uncertainty in input courses correctly into account. This leads to precise parameter estimates and correct confidence intervals. In particular this implies that small motifs of large reaction networks can be analysed independently of the rest. By the use of variational methods, elements from control theory and statistics are combined allowing for future transfer of methods between the two fields.
Manifold absolute pressure estimation using neural network with hybrid training algorithm

PubMed Central

Selamat, Hazlina; Alimin, Ahmad Jais; Haniff, Mohamad Fadzli

2017-01-01

In a modern small gasoline engine fuel injection system, the load of the engine is estimated based on the measurement of the manifold absolute pressure (MAP) sensor, which took place in the intake manifold. This paper present a more economical approach on estimating the MAP by using only the measurements of the throttle position and engine speed, resulting in lower implementation cost. The estimation was done via two-stage multilayer feed-forward neural network by combining Levenberg-Marquardt (LM) algorithm, Bayesian Regularization (BR) algorithm and Particle Swarm Optimization (PSO) algorithm. Based on the results found in 20 runs, the second variant of the hybrid algorithm yields a better network performance than the first variant of hybrid algorithm, LM, LM with BR and PSO by estimating the MAP closely to the simulated MAP values. By using a valid experimental training data, the estimator network that trained with the second variant of the hybrid algorithm showed the best performance among other algorithms when used in an actual retrofit fuel injection system (RFIS). The performance of the estimator was also validated in steady-state and transient condition by showing a closer MAP estimation to the actual value. PMID:29190779
Modelisation of the SECMin molten salts environment

NASA Astrophysics Data System (ADS)

Lucas, M.; Slim, C.; Delpech, S.; di Caprio, D.; Stafiej, J.

2014-06-01

We develop a cellular automata modelisation of SECM experiments to study corrosion in molten salt media for generation IV nuclear reactors. The electrodes used in these experiments are cylindrical glass tips with a coaxial metal wire inside. As the result of simulations we obtain the current approach curves of the electrodes with geometries characterized by several values of the ratios of glass to metal area at the tip. We compare these results with predictions of the known analytic expressions, solutions of partial differential equations for flat uniform geometry of the substrate. We present the results for other, more complicated substrate surface geometries e. g. regular saw modulated surface, surface obtained by Eden model process, ...
Stoking a fierce green fire: A review of Philip Shabecoff's history of the environmental movement

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

Williams, D.

Environmental journalist Philip Shabecoff begins his book on the American environmental movement, A Fierce Green Fire by guiding the reader across the American landscape as it might have looked to a 15th-century European. He creates a verdant land populated with unharried wildlife and noble savages, all living in absolute harmony. Sadly, this paradise is spoiled by villainous Europeans who invade the Edenic garden and, within a few hundred years, transform it into Hell's backyard. This sets the stage for Shabecoff's discussion of those who fought to protect the environment by making the environmental decision-making process more democratic and, therefore, lessmore » destructive.« less
Tracking of time-varying genomic regulatory networks with a LASSO-Kalman smoother

PubMed Central

2014-01-01

It is widely accepted that cellular requirements and environmental conditions dictate the architecture of genetic regulatory networks. Nonetheless, the status quo in regulatory network modeling and analysis assumes an invariant network topology over time. In this paper, we refocus on a dynamic perspective of genetic networks, one that can uncover substantial topological changes in network structure during biological processes such as developmental growth. We propose a novel outlook on the inference of time-varying genetic networks, from a limited number of noisy observations, by formulating the network estimation as a target tracking problem. We overcome the limited number of observations (small n large p problem) by performing tracking in a compressed domain. Assuming linear dynamics, we derive the LASSO-Kalman smoother, which recursively computes the minimum mean-square sparse estimate of the network connectivity at each time point. The LASSO operator, motivated by the sparsity of the genetic regulatory networks, allows simultaneous signal recovery and compression, thereby reducing the amount of required observations. The smoothing improves the estimation by incorporating all observations. We track the time-varying networks during the life cycle of the Drosophila melanogaster. The recovered networks show that few genes are permanent, whereas most are transient, acting only during specific developmental phases of the organism. PMID:24517200
Predicting the evolution of complex networks via similarity dynamics

NASA Astrophysics Data System (ADS)

Wu, Tao; Chen, Leiting; Zhong, Linfeng; Xian, Xingping

2017-01-01

Almost all real-world networks are subject to constant evolution, and plenty of them have been investigated empirically to uncover the underlying evolution mechanism. However, the evolution prediction of dynamic networks still remains a challenging problem. The crux of this matter is to estimate the future network links of dynamic networks. This paper studies the evolution prediction of dynamic networks with link prediction paradigm. To estimate the likelihood of the existence of links more accurate, an effective and robust similarity index is presented by exploiting network structure adaptively. Moreover, most of the existing link prediction methods do not make a clear distinction between future links and missing links. In order to predict the future links, the networks are regarded as dynamic systems in this paper, and a similarity updating method, spatial-temporal position drift model, is developed to simulate the evolutionary dynamics of node similarity. Then the updated similarities are used as input information for the future links' likelihood estimation. Extensive experiments on real-world networks suggest that the proposed similarity index performs better than baseline methods and the position drift model performs well for evolution prediction in real-world evolving networks.

A network-based multi-target computational estimation scheme for anticoagulant activities of compounds.

PubMed

Li, Qian; Li, Xudong; Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-03-22

Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking.
A Network-Based Multi-Target Computational Estimation Scheme for Anticoagulant Activities of Compounds

PubMed Central

Li, Canghai; Chen, Lirong; Song, Jun; Tang, Yalin; Xu, Xiaojie

2011-01-01

Background Traditional virtual screening method pays more attention on predicted binding affinity between drug molecule and target related to a certain disease instead of phenotypic data of drug molecule against disease system, as is often less effective on discovery of the drug which is used to treat many types of complex diseases. Virtual screening against a complex disease by general network estimation has become feasible with the development of network biology and system biology. More effective methods of computational estimation for the whole efficacy of a compound in a complex disease system are needed, given the distinct weightiness of the different target in a biological process and the standpoint that partial inhibition of several targets can be more efficient than the complete inhibition of a single target. Methodology We developed a novel approach by integrating the affinity predictions from multi-target docking studies with biological network efficiency analysis to estimate the anticoagulant activities of compounds. From results of network efficiency calculation for human clotting cascade, factor Xa and thrombin were identified as the two most fragile enzymes, while the catalytic reaction mediated by complex IXa:VIIIa and the formation of the complex VIIIa:IXa were recognized as the two most fragile biological matter in the human clotting cascade system. Furthermore, the method which combined network efficiency with molecular docking scores was applied to estimate the anticoagulant activities of a serial of argatroban intermediates and eight natural products respectively. The better correlation (r = 0.671) between the experimental data and the decrease of the network deficiency suggests that the approach could be a promising computational systems biology tool to aid identification of anticoagulant activities of compounds in drug discovery. Conclusions This article proposes a network-based multi-target computational estimation method for anticoagulant activities of compounds by combining network efficiency analysis with scoring function from molecular docking. PMID:21445339
Assessing network scale-up estimates for groups most at risk of HIV/AIDS: evidence from a multiple-method study of heavy drug users in Curitiba, Brazil.

PubMed

Salganik, Matthew J; Fazito, Dimitri; Bertoni, Neilane; Abdo, Alexandre H; Mello, Maeve B; Bastos, Francisco I

2011-11-15

One of the many challenges hindering the global response to the human immunodeficiency virus (HIV)/acquired immunodeficiency syndrome (AIDS) epidemic is the difficulty of collecting reliable information about the populations most at risk for the disease. Thus, the authors empirically assessed a promising new method for estimating the sizes of most at-risk populations: the network scale-up method. Using 4 different data sources, 2 of which were from other researchers, the authors produced 5 estimates of the number of heavy drug users in Curitiba, Brazil. The authors found that the network scale-up and generalized network scale-up estimators produced estimates 5-10 times higher than estimates made using standard methods (the multiplier method and the direct estimation method using data from 2004 and 2010). Given that equally plausible methods produced such a wide range of results, the authors recommend that additional studies be undertaken to compare estimates based on the scale-up method with those made using other methods. If scale-up-based methods routinely produce higher estimates, this would suggest that scale-up-based methods are inappropriate for populations most at risk of HIV/AIDS or that standard methods may tend to underestimate the sizes of these populations.
Global and system-specific resting-state fMRI fluctuations are uncorrelated: principal component analysis reveals anti-correlated networks.

PubMed

Carbonell, Felix; Bellec, Pierre; Shmuel, Amir

2011-01-01

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

PubMed

Tscheschel, A; Stoyan, D

2003-07-01

The specific Euler number is an important topological characteristic in many applications. It is considered here for the case of random networks, which may appear in microscopy either as primary objects of investigation or as secondary objects describing in an approximate way other structures such as, for example, porous media. For random networks there is a simple and natural estimator of the specific Euler number. For its estimation variance, a simple Poisson approximation is given. It is based on the general exact formula for the estimation variance. In two examples of quite different nature and topology application of the formulas is demonstrated.
Networks consolidation program: Maintenance and Operations (M&O) staffing estimates

NASA Technical Reports Server (NTRS)

Goodwin, J. P.

1981-01-01

The Mark IV-A consolidate deep space and high elliptical Earth orbiter (HEEO) missions tracking and implements centralized control and monitoring at the deep space communications complexes (DSCC). One of the objectives of the network design is to reduce maintenance and operations (M&O) costs. To determine if the system design meets this objective an M&O staffing model for Goldstone was developed which was used to estimate the staffing levels required to support the Mark IV-A configuration. The study was performed for the Goldstone complex and the program office translated these estimates for the overseas complexes to derive the network estimates.
Estimation of Global Network Statistics from Incomplete Data

PubMed Central

Bliss, Catherine A.; Danforth, Christopher M.; Dodds, Peter Sheridan

2014-01-01

Complex networks underlie an enormous variety of social, biological, physical, and virtual systems. A profound complication for the science of complex networks is that in most cases, observing all nodes and all network interactions is impossible. Previous work addressing the impacts of partial network data is surprisingly limited, focuses primarily on missing nodes, and suggests that network statistics derived from subsampled data are not suitable estimators for the same network statistics describing the overall network topology. We generate scaling methods to predict true network statistics, including the degree distribution, from only partial knowledge of nodes, links, or weights. Our methods are transparent and do not assume a known generating process for the network, thus enabling prediction of network statistics for a wide variety of applications. We validate analytical results on four simulated network classes and empirical data sets of various sizes. We perform subsampling experiments by varying proportions of sampled data and demonstrate that our scaling methods can provide very good estimates of true network statistics while acknowledging limits. Lastly, we apply our techniques to a set of rich and evolving large-scale social networks, Twitter reply networks. Based on 100 million tweets, we use our scaling techniques to propose a statistical characterization of the Twitter Interactome from September 2008 to November 2008. Our treatment allows us to find support for Dunbar's hypothesis in detecting an upper threshold for the number of active social contacts that individuals maintain over the course of one week. PMID:25338183
Extended Kalman Filter for Estimation of Parameters in Nonlinear State-Space Models of Biochemical Networks

PubMed Central

Sun, Xiaodian; Jin, Li; Xiong, Momiao

2008-01-01

It is system dynamics that determines the function of cells, tissues and organisms. To develop mathematical models and estimate their parameters are an essential issue for studying dynamic behaviors of biological systems which include metabolic networks, genetic regulatory networks and signal transduction pathways, under perturbation of external stimuli. In general, biological dynamic systems are partially observed. Therefore, a natural way to model dynamic biological systems is to employ nonlinear state-space equations. Although statistical methods for parameter estimation of linear models in biological dynamic systems have been developed intensively in the recent years, the estimation of both states and parameters of nonlinear dynamic systems remains a challenging task. In this report, we apply extended Kalman Filter (EKF) to the estimation of both states and parameters of nonlinear state-space models. To evaluate the performance of the EKF for parameter estimation, we apply the EKF to a simulation dataset and two real datasets: JAK-STAT signal transduction pathway and Ras/Raf/MEK/ERK signaling transduction pathways datasets. The preliminary results show that EKF can accurately estimate the parameters and predict states in nonlinear state-space equations for modeling dynamic biochemical networks. PMID:19018286
Multiparameter Estimation in Networked Quantum Sensors

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

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less
Multiparameter Estimation in Networked Quantum Sensors

DOE PAGES

Proctor, Timothy J.; Knott, Paul A.; Dunningham, Jacob A.

2018-02-21

We introduce a general model for a network of quantum sensors, and we use this model to consider the question: When can entanglement between the sensors, and/or global measurements, enhance the precision with which the network can measure a set of unknown parameters? We rigorously answer this question by presenting precise theorems proving that for a broad class of problems there is, at most, a very limited intrinsic advantage to using entangled states or global measurements. Moreover, for many estimation problems separable states and local measurements are optimal, and can achieve the ultimate quantum limit on the estimation uncertainty. Thismore » immediately implies that there are broad conditions under which simultaneous estimation of multiple parameters cannot outperform individual, independent estimations. Our results apply to any situation in which spatially localized sensors are unitarily encoded with independent parameters, such as when estimating multiple linear or non-linear optical phase shifts in quantum imaging, or when mapping out the spatial profile of an unknown magnetic field. We conclude by showing that entangling the sensors can enhance the estimation precision when the parameters of interest are global properties of the entire network.« less
Sample size and power considerations in network meta-analysis

PubMed Central

2012-01-01

Background Network meta-analysis is becoming increasingly popular for establishing comparative effectiveness among multiple interventions for the same disease. Network meta-analysis inherits all methodological challenges of standard pairwise meta-analysis, but with increased complexity due to the multitude of intervention comparisons. One issue that is now widely recognized in pairwise meta-analysis is the issue of sample size and statistical power. This issue, however, has so far only received little attention in network meta-analysis. To date, no approaches have been proposed for evaluating the adequacy of the sample size, and thus power, in a treatment network. Findings In this article, we develop easy-to-use flexible methods for estimating the ‘effective sample size’ in indirect comparison meta-analysis and network meta-analysis. The effective sample size for a particular treatment comparison can be interpreted as the number of patients in a pairwise meta-analysis that would provide the same degree and strength of evidence as that which is provided in the indirect comparison or network meta-analysis. We further develop methods for retrospectively estimating the statistical power for each comparison in a network meta-analysis. We illustrate the performance of the proposed methods for estimating effective sample size and statistical power using data from a network meta-analysis on interventions for smoking cessation including over 100 trials. Conclusion The proposed methods are easy to use and will be of high value to regulatory agencies and decision makers who must assess the strength of the evidence supporting comparative effectiveness estimates. PMID:22992327
A new class of methods for functional connectivity estimation

NASA Astrophysics Data System (ADS)

Lin, Wutu

Measuring functional connectivity from neural recordings is important in understanding processing in cortical networks. The covariance-based methods are the current golden standard for functional connectivity estimation. However, the link between the pair-wise correlations and the physiological connections inside the neural network is unclear. Therefore, the power of inferring physiological basis from functional connectivity estimation is limited. To build a stronger tie and better understand the relationship between functional connectivity and physiological neural network, we need (1) a realistic model to simulate different types of neural recordings with known ground truth for benchmarking; (2) a new functional connectivity method that produce estimations closely reflecting the physiological basis. In this thesis, (1) I tune a spiking neural network model to match with human sleep EEG data, (2) introduce a new class of methods for estimating connectivity from different kinds of neural signals and provide theory proof for its superiority, (3) apply it to simulated fMRI data as an application.
Nonparametric Density Estimation Based on Self-Organizing Incremental Neural Network for Large Noisy Data.

PubMed

Nakamura, Yoshihiro; Hasegawa, Osamu

2017-01-01

With the ongoing development and expansion of communication networks and sensors, massive amounts of data are continuously generated in real time from real environments. Beforehand, prediction of a distribution underlying such data is difficult; furthermore, the data include substantial amounts of noise. These factors make it difficult to estimate probability densities. To handle these issues and massive amounts of data, we propose a nonparametric density estimator that rapidly learns data online and has high robustness. Our approach is an extension of both kernel density estimation (KDE) and a self-organizing incremental neural network (SOINN); therefore, we call our approach KDESOINN. An SOINN provides a clustering method that learns about the given data as networks of prototype of data; more specifically, an SOINN can learn the distribution underlying the given data. Using this information, KDESOINN estimates the probability density function. The results of our experiments show that KDESOINN outperforms or achieves performance comparable to the current state-of-the-art approaches in terms of robustness, learning time, and accuracy.
Decentralized cooperative TOA/AOA target tracking for hierarchical wireless sensor networks.

PubMed

Chen, Ying-Chih; Wen, Chih-Yu

2012-11-08

This paper proposes a distributed method for cooperative target tracking in hierarchical wireless sensor networks. The concept of leader-based information processing is conducted to achieve object positioning, considering a cluster-based network topology. Random timers and local information are applied to adaptively select a sub-cluster for the localization task. The proposed energy-efficient tracking algorithm allows each sub-cluster member to locally estimate the target position with a Bayesian filtering framework and a neural networking model, and further performs estimation fusion in the leader node with the covariance intersection algorithm. This paper evaluates the merits and trade-offs of the protocol design towards developing more efficient and practical algorithms for object position estimation.
A new similarity measure for link prediction based on local structures in social networks

NASA Astrophysics Data System (ADS)

Aghabozorgi, Farshad; Khayyambashi, Mohammad Reza

2018-07-01

Link prediction is a fundamental problem in social network analysis. There exist a variety of techniques for link prediction which applies the similarity measures to estimate proximity of vertices in the network. Complex networks like social networks contain structural units named network motifs. In this study, a newly developed similarity measure is proposed where these structural units are applied as the source of similarity estimation. This similarity measure is tested through a supervised learning experiment framework, where other similarity measures are compared with this similarity measure. The classification model trained with this similarity measure outperforms others of its kind.
Network modelling methods for FMRI.

PubMed

Smith, Stephen M; Miller, Karla L; Salimi-Khorshidi, Gholamreza; Webster, Matthew; Beckmann, Christian F; Nichols, Thomas E; Ramsey, Joseph D; Woolrich, Mark W

2011-01-15

There is great interest in estimating brain "networks" from FMRI data. This is often attempted by identifying a set of functional "nodes" (e.g., spatial ROIs or ICA maps) and then conducting a connectivity analysis between the nodes, based on the FMRI timeseries associated with the nodes. Analysis methods range from very simple measures that consider just two nodes at a time (e.g., correlation between two nodes' timeseries) to sophisticated approaches that consider all nodes simultaneously and estimate one global network model (e.g., Bayes net models). Many different methods are being used in the literature, but almost none has been carefully validated or compared for use on FMRI timeseries data. In this work we generate rich, realistic simulated FMRI data for a wide range of underlying networks, experimental protocols and problematic confounds in the data, in order to compare different connectivity estimation approaches. Our results show that in general correlation-based approaches can be quite successful, methods based on higher-order statistics are less sensitive, and lag-based approaches perform very poorly. More specifically: there are several methods that can give high sensitivity to network connection detection on good quality FMRI data, in particular, partial correlation, regularised inverse covariance estimation and several Bayes net methods; however, accurate estimation of connection directionality is more difficult to achieve, though Patel's τ can be reasonably successful. With respect to the various confounds added to the data, the most striking result was that the use of functionally inaccurate ROIs (when defining the network nodes and extracting their associated timeseries) is extremely damaging to network estimation; hence, results derived from inappropriate ROI definition (such as via structural atlases) should be regarded with great caution. Copyright © 2010 Elsevier Inc. All rights reserved.
Traffic volume estimation using network interpolation techniques.

DOT National Transportation Integrated Search

2013-12-01

Kriging method is a frequently used interpolation methodology in geography, which enables estimations of unknown values at : certain places with the considerations of distances among locations. When it is used in transportation field, network distanc...
Entomopathogenic fungi from 'El Eden' Ecological Reserve, Quintana Roo, Mexico.

PubMed

Torres-Barragán; Anaya, Ana Luisa; Alatorre, Raquel; Toriello, Conchita

2004-07-01

Entomopathogenic fungi were isolated and identified from insects collected from the tropical forest and an agricultural area at El Eden Ecological Reserve, Quintana Roo, Mexico. These fungi were studied to determine their potential as biological control agents of greenhouse Trialeurodes vaporariorum (Homoptera: Aleyrodidae), and to contribute to the knowledge of biodiversity of this area. No pest insects were observed in the tropical forest. In contrast, all insects collected in the agricultural area were considered important pests by the local farmers, with the whitefly, as the most relevant, plentiful in Cucurbitaceae plants. From approximately 3400 collected insects in three different surveys, different anamorphic Ascomycetes were recovered. One isolate of Aspergillus sp., two of Penicillium sp., three of Paecilomyces marquandii, and three of Verticillium sp. out of 308 insects (2.9%) from three insect orders, Hymenoptera, Diptera and Isoptera in the tropical forest. In contrast, a higher number of fungal isolates were recovered from the agricultural area: three isolates from Aspergillus parasiticus, 100 of Fusarium moniliforme, one of Aschersonia sp., and 246 of Fusarium oxysporum out of 3100 insects (11.3%) from three insect orders, Homoptera, Coleoptera and Lepidoptera. The results of this study show Fusarium moniliforme and F oxysporum as highly virulent to infected insects in the agricultural area, with 100 and 246 isolates respectively, out of 350 infected insects of 3100 studied specimens. Laboratory whitefly nymph bioassays with isolates Ed29a of F. moniliforme, Ed322 of F. oxysporum, and Ed22 of P marquandii showed 96 to 97.5% insect mortality with no significant differences (P < 0.05) among them. F. oxysporum Ed322 produced no mortality when inoculated on tomato, bean, squash and maize seedlings (with and without injuries) compared to the 100% mortality caused by phytopathogenic strains, F. oxysporum f. sp. lycopersici and F. oxysporum f. sp. radicis lycopersici.
Mitigating Agricultural Diffuse Pollution: Learning from The River Eden Demonstration Test Catchment Experiments

NASA Astrophysics Data System (ADS)

Reaney, S. M.; Barker, P. A.; Haygarth, P.; Quinn, P. F.; Aftab, A.; Barber, N.; Burke, S.; Cleasby, W.; Jonczyk, J. C.; Owen, G. J.; Perks, M. T.; Snell, M. A.; Surridge, B.

2016-12-01

Freshwater systems continue to fail to achieve their ecological potential and provide associated ecological services due to poor water quality. A key driver of the failure to achieve good status under the EU Water Framework Directive derives from non-point (diffuse) pollution of sediment, phosphorus and nitrogen from agricultural landscapes. While many mitigation options exist, a framework is lacking which provides a holistic understanding of the impact of mitigation scheme design on catchment function and agronomics. The River Eden Demonstration Test Catchment project (2009-2017) in NW England uses an interdisciplinary approach including catchment hydrology, sediment-nutrient fluxes and farmer attitudes, to understand ecological function and diffuse pollution mitigation feature performance. Water flow (both surface and groundwater) and quality monitoring focused on three ca. 10km2 catchments with N and P measurements every 30 minutes. Ecological status was determined by monthly diatom community analysis and supplemented by macrophyte, macroinvertebrate and fish surveys. Changes in erosion potential and hydrological connectivity were monitored using extensive Landsat images and detailed UAV monitoring. Simulation modelling work utilised hydrological simulation models (CRAFT, CRUM3 and HBV-Light) and SCIMAP based risk mapping. Farmer behaviour and attitudes have been assessed with surveys, interviews and diaries. A suite of mitigation features have been installed including changes to land management - e.g. aeriation, storage features within a `treatment train', riparian fencing and woodland creation. A detailed dataset of the integrated catchment hydrological, water quality and ecological behaviour over multiple years, including a drought period and an extreme rainfall event, highlights the interaction between ecology, hydrological and nutrient dynamics that are driven by sediment and nutrients exported within a small number of high magnitude storm events. Hence these high-resolution processes must be studied in conjunction, rather than in isolation, to understand system dynamics and critically to evaluate effective mitigation schemes.
The dietary n6:n3 fatty acid ratio during pregnancy is inversely associated with child neurodevelopment in the EDEN mother-child cohort.

PubMed

Bernard, Jonathan Y; De Agostini, Maria; Forhan, Anne; de Lauzon-Guillain, Blandine; Charles, Marie-Aline; Heude, Barbara

2013-09-01

Long-chain polyunsaturated fatty acids (LC-PUFAs) of the n6 (ω6) and n3 series are essential for the development of a child's brain. Fetal LC-PUFA exposure as well as infant exposure via breast milk depend on the maternal intake of these LC-PUFAs and of their respective dietary precursors (PUFAs). We aimed to investigate the associations between maternal LC-PUFA and PUFA [(LC)PUFA] dietary intake during pregnancy and child neurodevelopment at ages 2 and 3 y. In 1335 mother-child pairs from the EDEN cohort, we evaluated associations between daily maternal (LC)PUFA intake during the last 3 months of pregnancy with the child's language at age 2 y and with different assessments of development at age 3 y. Associations were investigated separately in breastfed and never-breastfed children. We examined interactions between the ratios of n6 and n3 (LC)PUFA intakes (n6:n3 fatty acid ratio) and duration of breastfeeding. Breastfeeding mothers had a lower n6:n3 fatty acid ratio (8.4 vs. 8.8; P = 0.02). Among never-breastfed children (n = 338), we found negative associations between maternal dietary n6:n3 fatty acid ratios and neurodevelopment, as reflected by the child's language at age 2 y (β ± SE = -2.1 ± 0.7; P = 0.001) and development assessed with the Ages and Stages Questionnaire at age 3 y (-1.5 ± 0.8; P = 0.05). Among mothers with a high n6:n3 fatty acid ratio only, breastfeeding duration was positively associated with language at age 2 y (P-interaction < 0.05). This suggests that the ratio between maternal dietary n6 and n3 (LC)PUFA intake possibly influences the child's brain development during fetal life but not during or by breastfeeding. However, breastfeeding might compensate for prenatal imbalance in maternal dietary n6:n3 fatty acid ratio.

Multidimensionality of the relationship between social status and dietary patterns in early childhood: longitudinal results from the French EDEN mother-child cohort.

PubMed

Camara, Soumaïla; de Lauzon-Guillain, Blandine; Heude, Barbara; Charles, Marie-Aline; Botton, Jérémie; Plancoulaine, Sabine; Forhan, Anne; Saurel-Cubizolles, Marie-Josèphe; Dargent-Molina, Patricia; Lioret, Sandrine

2015-09-24

The association between socioeconomic position and diet in early childhood has mainly been addressed based on maternal education and household income. We aimed to assess the influence of a variety of social factors from different socio-ecological levels (parents, household and child-care) on multi-time point dietary patterns identified from 2 to 5 y. This study included 974 children from the French EDEN mother-child cohort. Two multi-time point dietary patterns were derived in a previous study: they correspond to consistent exposures to either core- or non-core foods across 2, 3 and 5 y and were labelled "Guidelines" and "Processed, fast-foods". The associations of various social factors collected during pregnancy (age, education level) or at 2-y follow-up (mother's single status, occupation, work commitments, household financial disadvantage, presence of older siblings and child-care arrangements) with each of the two dietary patterns, were assessed by multivariable linear regression analysis. The adherence to a diet close to "Guidelines" was positively and independently associated with both maternal and paternal education levels. The adherence to a diet consistently composed of processed and fast-foods was essentially linked with maternal variables (younger age and lower education level), household financial disadvantage, the presence of older sibling (s) and being cared for at home by someone other than the mother. Multiple social factors operating at different levels (parents, household, and child-care) were found to be associated with the diet of young children. Different independent predictors were found for each of the two longitudinal dietary patterns, suggesting distinct pathways of influence. Our findings further suggest that interventions promoting healthier dietary choices for young children should involve both parents and take into account not only household financial disadvantage but also maternal age, family size and options for child-care.
Shape characteristics of equilibrium and non-equilibrium fractal clusters.

PubMed

Mansfield, Marc L; Douglas, Jack F

2013-07-28

It is often difficult in practice to discriminate between equilibrium and non-equilibrium nanoparticle or colloidal-particle clusters that form through aggregation in gas or solution phases. Scattering studies often permit the determination of an apparent fractal dimension, but both equilibrium and non-equilibrium clusters in three dimensions frequently have fractal dimensions near 2, so that it is often not possible to discriminate on the basis of this geometrical property. A survey of the anisotropy of a wide variety of polymeric structures (linear and ring random and self-avoiding random walks, percolation clusters, lattice animals, diffusion-limited aggregates, and Eden clusters) based on the principal components of both the radius of gyration and electric polarizability tensor indicates, perhaps counter-intuitively, that self-similar equilibrium clusters tend to be intrinsically anisotropic at all sizes, while non-equilibrium processes such as diffusion-limited aggregation or Eden growth tend to be isotropic in the large-mass limit, providing a potential means of discriminating these clusters experimentally if anisotropy could be determined along with the fractal dimension. Equilibrium polymer structures, such as flexible polymer chains, are normally self-similar due to the existence of only a single relevant length scale, and are thus anisotropic at all length scales, while non-equilibrium polymer structures that grow irreversibly in time eventually become isotropic if there is no difference in the average growth rates in different directions. There is apparently no proof of these general trends and little theoretical insight into what controls the universal anisotropy in equilibrium polymer structures of various kinds. This is an obvious topic of theoretical investigation, as well as a matter of practical interest. To address this general problem, we consider two experimentally accessible ratios, one between the hydrodynamic and gyration radii, the other between the viscosity and hydrodynamic radii, as potential measures of shape anisotropy. We also find a strong correlation between anisotropy and effective fractal dimension. These observations should provide new practical methods for quantifying the nature of particle clustering in diverse contexts.
Event-Based $H_\\infty $ State Estimation for Time-Varying Stochastic Dynamical Networks With State- and Disturbance-Dependent Noises.

PubMed

Sheng, Li; Wang, Zidong; Zou, Lei; Alsaadi, Fuad E

2017-10-01

In this paper, the event-based finite-horizon H ∞ state estimation problem is investigated for a class of discrete time-varying stochastic dynamical networks with state- and disturbance-dependent noises [also called (x,v) -dependent noises]. An event-triggered scheme is proposed to decrease the frequency of the data transmission between the sensors and the estimator, where the signal is transmitted only when certain conditions are satisfied. The purpose of the problem addressed is to design a time-varying state estimator in order to estimate the network states through available output measurements. By employing the completing-the-square technique and the stochastic analysis approach, sufficient conditions are established to ensure that the error dynamics of the state estimation satisfies a prescribed H ∞ performance constraint over a finite horizon. The desired estimator parameters can be designed via solving coupled backward recursive Riccati difference equations. Finally, a numerical example is exploited to demonstrate the effectiveness of the developed state estimation scheme.
Direct estimations of linear and nonlinear functionals of a quantum state.

PubMed

Ekert, Artur K; Alves, Carolina Moura; Oi, Daniel K L; Horodecki, Michał; Horodecki, Paweł; Kwek, L C

2002-05-27

We present a simple quantum network, based on the controlled-SWAP gate, that can extract certain properties of quantum states without recourse to quantum tomography. It can be used as a basic building block for direct quantum estimations of both linear and nonlinear functionals of any density operator. The network has many potential applications ranging from purity tests and eigenvalue estimations to direct characterization of some properties of quantum channels. Experimental realizations of the proposed network are within the reach of quantum technology that is currently being developed.
Uncertainties in Parameters Estimated with Neural Networks: Application to Strong Gravitational Lensing

NASA Astrophysics Data System (ADS)

Perreault Levasseur, Laurence; Hezaveh, Yashar D.; Wechsler, Risa H.

2017-11-01

In Hezaveh et al. we showed that deep learning can be used for model parameter estimation and trained convolutional neural networks to determine the parameters of strong gravitational-lensing systems. Here we demonstrate a method for obtaining the uncertainties of these parameters. We review the framework of variational inference to obtain approximate posteriors of Bayesian neural networks and apply it to a network trained to estimate the parameters of the Singular Isothermal Ellipsoid plus external shear and total flux magnification. We show that the method can capture the uncertainties due to different levels of noise in the input data, as well as training and architecture-related errors made by the network. To evaluate the accuracy of the resulting uncertainties, we calculate the coverage probabilities of marginalized distributions for each lensing parameter. By tuning a single variational parameter, the dropout rate, we obtain coverage probabilities approximately equal to the confidence levels for which they were calculated, resulting in accurate and precise uncertainty estimates. Our results suggest that the application of approximate Bayesian neural networks to astrophysical modeling problems can be a fast alternative to Monte Carlo Markov Chains, allowing orders of magnitude improvement in speed.
Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Gabrielli, Andrea; Cimini, Giulio; Garlaschelli, Diego; Squartini, Angelo

A typical problem met when studying complex systems is the limited information available on their topology, which hinders our understanding of their structural and dynamical properties. A paramount example is provided by financial networks, whose data are privacy protected. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here we develop a reconstruction method, based on statistical mechanics concepts, that exploits the empirical link density in a highly non-trivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems. Acknoweledgement to ``Growthcom'' ICT - EC project (Grant No: 611272) and ``Crisislab'' Italian Project.
Effective comparative analysis of protein-protein interaction networks by measuring the steady-state network flow using a Markov model.

PubMed

Jeong, Hyundoo; Qian, Xiaoning; Yoon, Byung-Jun

2016-10-06

Comparative analysis of protein-protein interaction (PPI) networks provides an effective means of detecting conserved functional network modules across different species. Such modules typically consist of orthologous proteins with conserved interactions, which can be exploited to computationally predict the modules through network comparison. In this work, we propose a novel probabilistic framework for comparing PPI networks and effectively predicting the correspondence between proteins, represented as network nodes, that belong to conserved functional modules across the given PPI networks. The basic idea is to estimate the steady-state network flow between nodes that belong to different PPI networks based on a Markov random walk model. The random walker is designed to make random moves to adjacent nodes within a PPI network as well as cross-network moves between potential orthologous nodes with high sequence similarity. Based on this Markov random walk model, we estimate the steady-state network flow - or the long-term relative frequency of the transitions that the random walker makes - between nodes in different PPI networks, which can be used as a probabilistic score measuring their potential correspondence. Subsequently, the estimated scores can be used for detecting orthologous proteins in conserved functional modules through network alignment. Through evaluations based on multiple real PPI networks, we demonstrate that the proposed scheme leads to improved alignment results that are biologically more meaningful at reduced computational cost, outperforming the current state-of-the-art algorithms. The source code and datasets can be downloaded from http://www.ece.tamu.edu/~bjyoon/CUFID .
Estimating tree bole volume using artificial neural network models for four species in Turkey.

PubMed

Ozçelik, Ramazan; Diamantopoulou, Maria J; Brooks, John R; Wiant, Harry V

2010-01-01

Tree bole volumes of 89 Scots pine (Pinus sylvestris L.), 96 Brutian pine (Pinus brutia Ten.), 107 Cilicica fir (Abies cilicica Carr.) and 67 Cedar of Lebanon (Cedrus libani A. Rich.) trees were estimated using Artificial Neural Network (ANN) models. Neural networks offer a number of advantages including the ability to implicitly detect complex nonlinear relationships between input and output variables, which is very helpful in tree volume modeling. Two different neural network architectures were used and produced the Back propagation (BPANN) and the Cascade Correlation (CCANN) Artificial Neural Network models. In addition, tree bole volume estimates were compared to other established tree bole volume estimation techniques including the centroid method, taper equations, and existing standard volume tables. An overview of the features of ANNs and traditional methods is presented and the advantages and limitations of each one of them are discussed. For validation purposes, actual volumes were determined by aggregating the volumes of measured short sections (average 1 meter) of the tree bole using Smalian's formula. The results reported in this research suggest that the selected cascade correlation artificial neural network (CCANN) models are reliable for estimating the tree bole volume of the four examined tree species since they gave unbiased results and were superior to almost all methods in terms of error (%) expressed as the mean of the percentage errors. 2009 Elsevier Ltd. All rights reserved.
An Estimate and Score Algorithm for Simultaneous Parameter Estimation and Reconstruction of Incomplete Data on Social Networks

DTIC Science & Technology

2013-01-12

www.security-informatics.com/content/2/1/1 References 1. SM Radilm, C Flint, GE Tita , Spatializing Social Networks: Using Social Network Analysis to...http://www.tandfonline.com/doi/ abs/10.1080/00045600903550428 2. G Tita , S Radil, Spatializing the social networks of gangs to explore patterns of...violence. Journal of Quantitative Criminology. 27, 1–25 (2011) 3. G Tita , JK Riley, G Ridgeway, AF Abrahamse, P Greenwood, Reducing Gun Violence
Estimating the size of hidden populations using respondent-driven sampling data: Case examples from Morocco

PubMed Central

Johnston, Lisa G; McLaughlin, Katherine R; Rhilani, Houssine El; Latifi, Amina; Toufik, Abdalla; Bennani, Aziza; Alami, Kamal; Elomari, Boutaina; Handcock, Mark S

2015-01-01

Background Respondent-driven sampling is used worldwide to estimate the population prevalence of characteristics such as HIV/AIDS and associated risk factors in hard-to-reach populations. Estimating the total size of these populations is of great interest to national and international organizations, however reliable measures of population size often do not exist. Methods Successive Sampling-Population Size Estimation (SS-PSE) along with network size imputation allows population size estimates to be made without relying on separate studies or additional data (as in network scale-up, multiplier and capture-recapture methods), which may be biased. Results Ten population size estimates were calculated for people who inject drugs, female sex workers, men who have sex with other men, and migrants from sub-Sahara Africa in six different cities in Morocco. SS-PSE estimates fell within or very close to the likely values provided by experts and the estimates from previous studies using other methods. Conclusions SS-PSE is an effective method for estimating the size of hard-to-reach populations that leverages important information within respondent-driven sampling studies. The addition of a network size imputation method helps to smooth network sizes allowing for more accurate results. However, caution should be used particularly when there is reason to believe that clustered subgroups may exist within the population of interest or when the sample size is small in relation to the population. PMID:26258908
Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks

PubMed Central

Portela, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon

2016-01-01

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users’ network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders’ or receivers’ identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks. PMID:27809275
Estimation of Anonymous Email Network Characteristics through Statistical Disclosure Attacks.

PubMed

Portela, Javier; García Villalba, Luis Javier; Silva Trujillo, Alejandra Guadalupe; Sandoval Orozco, Ana Lucila; Kim, Tai-Hoon

2016-11-01

Social network analysis aims to obtain relational data from social systems to identify leaders, roles, and communities in order to model profiles or predict a specific behavior in users' network. Preserving anonymity in social networks is a subject of major concern. Anonymity can be compromised by disclosing senders' or receivers' identity, message content, or sender-receiver relationships. Under strongly incomplete information, a statistical disclosure attack is used to estimate the network and node characteristics such as centrality and clustering measures, degree distribution, and small-world-ness. A database of email networks in 29 university faculties is used to study the method. A research on the small-world-ness and Power law characteristics of these email networks is also developed, helping to understand the behavior of small email networks.
Freight Transportation Energy Use : Appendix. Transportation Network Model Output.

DOT National Transportation Integrated Search

1978-07-01

The overall design of the TSC Freight Energy Model is presented. A hierarchical modeling strategy is used, in which detailed modal simulators estimate the performance characteristics of transportation network elements, and the estimates are input to ...
INTERDISCIPLINARY PHYSICS AND RELATED AREAS OF SCIENCE AND TECHNOLOGY: Estimating Topology of Discrete Dynamical Networks

NASA Astrophysics Data System (ADS)

Guo, Shu-Juan; Fu, Xin-Chu

2010-07-01

In this paper, by applying Lasalle's invariance principle and some results about the trace of a matrix, we propose a method for estimating the topological structure of a discrete dynamical network based on the dynamical evolution of the network. The network concerned can be directed or undirected, weighted or unweighted, and the local dynamics of each node can be nonidentical. The connections among the nodes can be all unknown or partially known. Finally, two examples, including a Hénon map and a central network, are illustrated to verify the theoretical results.
High-resolution reflectance spectra of Mars in the 2.3-μm region: evidence for the mineral scapolite

USGS Publications Warehouse

Clark, Roger N.; Swayze, Gregg A.; Singer, Robert B.; Pollack, James B.

1990-01-01

patially resolved reflectance spectra of Mars in the 2.2- to 2.4-μm spectral region were obtained in August 1988 using the NASA 3-m Infrared Telescope Facility. The spectra show weak absorption features due to Martian atmospheric carbon monoxide and a surface mineral. Both CO and the mineral absorptions are composed of overlapping narrow features, but in many locations, such as Hellas, Chryse, Eden, and Moab, the mineral absorptions are quite strong, at least 3 times stronger than at the most absorbing wavelengths of CO near 2.33 μm. Therefore CO complicates the analysis of the surface mineral but does not always overwhelm its signature. Model removal of the Martian atmospheric CO has been performed, and the remaining absorption bands are identified as scapolite. Relatively strong absorptions that match bands in the spectrum of scapolite and have little or no CO absorption interference are seen near 2.41, 2.39, and 2.29 μm. Absorption also occurs at the scapolite bands at 2.36 and 2.33 μm, but the analysis is complicated by uncertainty in the atmospheric CO removal at these wavelengths. Weaker scapolite bands are seen at 2.44 and 2.23 μm where there is virtually no atmospheric interference. The scapolite bands observed on Mars are due to HCO3− and HSO4− ions in the scapolite structure. The bicarbonate and bisulfate contents appear to vary with location: the scapolite in Hellas is more bisulfate-rich relative to that in the Chryse/Moab/Eden area. Other locations contain little (Arabia, Syrtis Major, Hellespontica, and Isidis) or no scapolite (e.g., Margaritifer, Ausonia, and Erythraeum). The calculated abundances are unconstrained because the amounts of HCO3− and HSO4− in the Martian scapolites as well as their grain sizes are not known. If the scapolites contain about 3 wt % of each, near the maximum possible, the scapolite abundances probably range from about 5 wt % scapolite at Eden and Hellas; 3–5% at Chryse, Moab, and Oxia Palus; 2–3% at Arabia, Syrtis Major, and Isidis; to less than 2% at Hellespontica, Syrtis Minor, and Margaritifer, assuming a relatively large grain size of 50–100 μm. If the characteristic grain sizes are smaller or the HCO3− and HSO4− contents are lower, the scapolite abundances required to match the observed band depths would be higher. The mineral bands are apparent in many of the Mars spectra measured, so it appears to be widely but not uniformly distributed. The newly observed fine structure also varies greatly in both depth and spectral detail with location on Mars. Thus there appears to be regional variations in composition. The mineral phases appear to reflect local or regional geology and are not primarily contained in the homogeneous, globally redistributed aeolian dust. Higher spectral resolution Martian spectra in the 2.3-μm region as well as at 3.9 μm are needed to confirm the scapolite identification and to constrain its abundance.
Linear Vector Quantisation and Uniform Circular Arrays based decoupled two-dimensional angle of arrival estimation

NASA Astrophysics Data System (ADS)

Ndaw, Joseph D.; Faye, Andre; Maïga, Amadou S.

2017-05-01

Artificial neural networks (ANN)-based models are efficient ways of source localisation. However very large training sets are needed to precisely estimate two-dimensional Direction of arrival (2D-DOA) with ANN models. In this paper we present a fast artificial neural network approach for 2D-DOA estimation with reduced training sets sizes. We exploit the symmetry properties of Uniform Circular Arrays (UCA) to build two different datasets for elevation and azimuth angles. Linear Vector Quantisation (LVQ) neural networks are then sequentially trained on each dataset to separately estimate elevation and azimuth angles. A multilevel training process is applied to further reduce the training sets sizes.
Network Reconstruction From High-Dimensional Ordinary Differential Equations.

PubMed

Chen, Shizhe; Shojaie, Ali; Witten, Daniela M

2017-01-01

We consider the task of learning a dynamical system from high-dimensional time-course data. For instance, we might wish to estimate a gene regulatory network from gene expression data measured at discrete time points. We model the dynamical system nonparametrically as a system of additive ordinary differential equations. Most existing methods for parameter estimation in ordinary differential equations estimate the derivatives from noisy observations. This is known to be challenging and inefficient. We propose a novel approach that does not involve derivative estimation. We show that the proposed method can consistently recover the true network structure even in high dimensions, and we demonstrate empirical improvement over competing approaches. Supplementary materials for this article are available online.
Optimal Mass Transport for Statistical Estimation, Image Analysis, Information Geometry, and Control

DTIC Science & Technology

2017-01-10

Metric Uncertainty for Spectral Estimation based on Nevanlinna-Pick Interpolation, (with J. Karlsson) Intern. Symp. on the Math . Theory of Networks and...Systems, Melbourne 2012. 22. Geometric tools for the estimation of structured covariances, (with L. Ning, X. Jiang) Intern. Symposium on the Math . Theory...estimation and the reversibility of stochastic processes, (with Y. Chen, J. Karlsson) Proc. Int. Symp. on Math . Theory of Networks and Syst., July
Supervised local error estimation for nonlinear image registration using convolutional neural networks

NASA Astrophysics Data System (ADS)

Eppenhof, Koen A. J.; Pluim, Josien P. W.

2017-02-01

Error estimation in medical image registration is valuable when validating, comparing, or combining registration methods. To validate a nonlinear image registration method, ideally the registration error should be known for the entire image domain. We propose a supervised method for the estimation of a registration error map for nonlinear image registration. The method is based on a convolutional neural network that estimates the norm of the residual deformation from patches around each pixel in two registered images. This norm is interpreted as the registration error, and is defined for every pixel in the image domain. The network is trained using a set of artificially deformed images. Each training example is a pair of images: the original image, and a random deformation of that image. No manually labeled ground truth error is required. At test time, only the two registered images are required as input. We train and validate the network on registrations in a set of 2D digital subtraction angiography sequences, such that errors up to eight pixels can be estimated. We show that for this range of errors the convolutional network is able to learn the registration error in pairs of 2D registered images at subpixel precision. Finally, we present a proof of principle for the extension to 3D registration problems in chest CTs, showing that the method has the potential to estimate errors in 3D registration problems.
Application of Multilayer Feedforward Neural Networks to Precipitation Cell-Top Altitude Estimation

NASA Technical Reports Server (NTRS)

Spina, Michelle S.; Schwartz, Michael J.; Staelin, David H.; Gasiewski, Albin J.

1998-01-01

The use of passive 118-GHz O2 observations of rain cells for precipitation cell-top altitude estimation is demonstrated by using a multilayer feed forward neural network retrieval system. Rain cell observations at 118 GHz were compared with estimates of the cell-top altitude obtained by optical stereoscopy. The observations were made with 2 4 km horizontal spatial resolution by using the Millimeter-wave Temperature Sounder (MTS) scanning spectrometer aboard the NASA ER-2 research aircraft during the Genesis of Atlantic Lows Experiment (GALE) and the COoperative Huntsville Meteorological EXperiment (COHMEX) in 1986. The neural network estimator applied to MTS spectral differences between clouds, and nearby clear air yielded an rms discrepancy of 1.76 km for a combined cumulus, mature, and dissipating cell set and 1.44 km for the cumulus-only set. An improvement in rms discrepancy to 1.36 km was achieved by including additional MTS information on the absolute atmospheric temperature profile. An incremental method for training neural networks was developed that yielded robust results, despite the use of as few as 56 training spectra. Comparison of these results with a nonlinear statistical estimator shows that superior results can be obtained with a neural network retrieval system. Imagery of estimated cell-top altitudes was created from 118-GHz spectral imagery gathered from CAMEX, September through October 1993, and from cyclone Oliver, February 7, 1993.

Statistical inference to advance network models in epidemiology.

PubMed

Welch, David; Bansal, Shweta; Hunter, David R

2011-03-01

Contact networks are playing an increasingly important role in the study of epidemiology. Most of the existing work in this area has focused on considering the effect of underlying network structure on epidemic dynamics by using tools from probability theory and computer simulation. This work has provided much insight on the role that heterogeneity in host contact patterns plays on infectious disease dynamics. Despite the important understanding afforded by the probability and simulation paradigm, this approach does not directly address important questions about the structure of contact networks such as what is the best network model for a particular mode of disease transmission, how parameter values of a given model should be estimated, or how precisely the data allow us to estimate these parameter values. We argue that these questions are best answered within a statistical framework and discuss the role of statistical inference in estimating contact networks from epidemiological data. Copyright © 2011 Elsevier B.V. All rights reserved.
Variable disparity estimation based intermediate view reconstruction in dynamic flow allocation over EPON-based access networks

NASA Astrophysics Data System (ADS)

Bae, Kyung-Hoon; Lee, Jungjoon; Kim, Eun-Soo

2008-06-01

In this paper, a variable disparity estimation (VDE)-based intermediate view reconstruction (IVR) in dynamic flow allocation (DFA) over an Ethernet passive optical network (EPON)-based access network is proposed. In the proposed system, the stereoscopic images are estimated by a variable block-matching algorithm (VBMA), and they are transmitted to the receiver through DFA over EPON. This scheme improves a priority-based access network by converting it to a flow-based access network with a new access mechanism and scheduling algorithm, and then 16-view images are synthesized by the IVR using VDE. Some experimental results indicate that the proposed system improves the peak-signal-to-noise ratio (PSNR) to as high as 4.86 dB and reduces the processing time to 3.52 s. Additionally, the network service provider can provide upper limits of transmission delays by the flow. The modeling and simulation results, including mathematical analyses, from this scheme are also provided.
Output-feedback control of combined sewer networks through receding horizon control with moving horizon estimation

NASA Astrophysics Data System (ADS)

Joseph-Duran, Bernat; Ocampo-Martinez, Carlos; Cembrano, Gabriela

2015-10-01

An output-feedback control strategy for pollution mitigation in combined sewer networks is presented. The proposed strategy provides means to apply model-based predictive control to large-scale sewer networks, in-spite of the lack of measurements at most of the network sewers. In previous works, the authors presented a hybrid linear control-oriented model for sewer networks together with the formulation of Optimal Control Problems (OCP) and State Estimation Problems (SEP). By iteratively solving these problems, preliminary Receding Horizon Control with Moving Horizon Estimation (RHC/MHE) results, based on flow measurements, were also obtained. In this work, the RHC/MHE algorithm has been extended to take into account both flow and water level measurements and the resulting control loop has been extensively simulated to assess the system performance according different measurement availability scenarios and rain events. All simulations have been carried out using a detailed physically based model of a real case-study network as virtual reality.
Evaluating the Real-time and Offline Performance of the Virtual Seismologist Earthquake Early Warning Algorithm

NASA Astrophysics Data System (ADS)

Cua, G.; Fischer, M.; Heaton, T.; Wiemer, S.

2009-04-01

The Virtual Seismologist (VS) algorithm is a Bayesian approach to regional, network-based earthquake early warning (EEW). Bayes' theorem as applied in the VS algorithm states that the most probable source estimates at any given time is a combination of contributions from relatively static prior information that does not change over the timescale of earthquake rupture and a likelihood function that evolves with time to take into account incoming pick and amplitude observations from the on-going earthquake. Potentially useful types of prior information include network topology or station health status, regional hazard maps, earthquake forecasts, and the Gutenberg-Richter magnitude-frequency relationship. The VS codes provide magnitude and location estimates once picks are available at 4 stations; these source estimates are subsequently updated each second. The algorithm predicts the geographical distribution of peak ground acceleration and velocity using the estimated magnitude and location and appropriate ground motion prediction equations; the peak ground motion estimates are also updated each second. Implementation of the VS algorithm in California and Switzerland is funded by the Seismic Early Warning for Europe (SAFER) project. The VS method is one of three EEW algorithms whose real-time performance is being evaluated and tested by the California Integrated Seismic Network (CISN) EEW project. A crucial component of operational EEW algorithms is the ability to distinguish between noise and earthquake-related signals in real-time. We discuss various empirical approaches that allow the VS algorithm to operate in the presence of noise. Real-time operation of the VS codes at the Southern California Seismic Network (SCSN) began in July 2008. On average, the VS algorithm provides initial magnitude, location, origin time, and ground motion distribution estimates within 17 seconds of the earthquake origin time. These initial estimate times are dominated by the time for 4 acceptable picks to be available, and thus are heavily influenced by the station density in a given region; these initial estimate times also include the effects of telemetry delay, which ranges between 6 and 15 seconds at the SCSN, and processing time (~1 second). Other relevant performance statistics include: 95% of initial real-time location estimates are within 20 km of the actual epicenter, 97% of initial real-time magnitude estimates are within one magnitude unit of the network magnitude. Extension of real-time VS operations to networks in Northern California is an on-going effort. In Switzerland, the VS codes have been run on offline waveform data from over 125 earthquakes recorded by the Swiss Digital Seismic Network (SDSN) and the Swiss Strong Motion Network (SSMS). We discuss the performance of the VS algorithm on these datasets in terms of magnitude, location, and ground motion estimation.
Road Network State Estimation Using Random Forest Ensemble Learning

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

Hou, Yi; Edara, Praveen; Chang, Yohan

Network-scale travel time prediction not only enables traffic management centers (TMC) to proactively implement traffic management strategies, but also allows travelers make informed decisions about route choices between various origins and destinations. In this paper, a random forest estimator was proposed to predict travel time in a network. The estimator was trained using two years of historical travel time data for a case study network in St. Louis, Missouri. Both temporal and spatial effects were considered in the modeling process. The random forest models predicted travel times accurately during both congested and uncongested traffic conditions. The computational times for themore » models were low, thus useful for real-time traffic management and traveler information applications.« less
Estimating standard errors in feature network models.

PubMed

Frank, Laurence E; Heiser, Willem J

2007-05-01

Feature network models are graphical structures that represent proximity data in a discrete space while using the same formalism that is the basis of least squares methods employed in multidimensional scaling. Existing methods to derive a network model from empirical data only give the best-fitting network and yield no standard errors for the parameter estimates. The additivity properties of networks make it possible to consider the model as a univariate (multiple) linear regression problem with positivity restrictions on the parameters. In the present study, both theoretical and empirical standard errors are obtained for the constrained regression parameters of a network model with known features. The performance of both types of standard error is evaluated using Monte Carlo techniques.
Global and System-Specific Resting-State fMRI Fluctuations Are Uncorrelated: Principal Component Analysis Reveals Anti-Correlated Networks

PubMed Central

Carbonell, Felix; Bellec, Pierre

2011-01-01

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

PubMed

Hughes, Ashley M; Gregory, Megan E; Joseph, Dana L; Sonesh, Shirley C; Marlow, Shannon L; Lacerenza, Christina N; Benishek, Lauren E; King, Heidi B; Salas, Eduardo

2016-09-01

As the nature of work becomes more complex, teams have become necessary to ensure effective functioning within organizations. The healthcare industry is no exception. As such, the prevalence of training interventions designed to optimize teamwork in this industry has increased substantially over the last 10 years (Weaver, Dy, & Rosen, 2014). Using Kirkpatrick's (1956, 1996) training evaluation framework, we conducted a meta-analytic examination of healthcare team training to quantify its effectiveness and understand the conditions under which it is most successful. Results demonstrate that healthcare team training improves each of Kirkpatrick's criteria (reactions, learning, transfer, results; d = .37 to .89). Second, findings indicate that healthcare team training is largely robust to trainee composition, training strategy, and characteristics of the work environment, with the only exception being the reduced effectiveness of team training programs that involve feedback. As a tertiary goal, we proposed and found empirical support for a sequential model of healthcare team training where team training affects results via learning, which leads to transfer, which increases results. We find support for this sequential model in the healthcare industry (i.e., the current meta-analysis) and in training across all industries (i.e., using meta-analytic estimates from Arthur, Bennett, Edens, & Bell, 2003), suggesting the sequential benefits of training are not unique to medical teams. Ultimately, this meta-analysis supports the expanded use of team training and points toward recommendations for optimizing its effectiveness within healthcare settings. (PsycINFO Database Record (c) 2016 APA, all rights reserved).
Inferring Regulatory Networks by Combining Perturbation Screens and Steady State Gene Expression Profiles

PubMed Central

Michailidis, George

2014-01-01

Reconstructing transcriptional regulatory networks is an important task in functional genomics. Data obtained from experiments that perturb genes by knockouts or RNA interference contain useful information for addressing this reconstruction problem. However, such data can be limited in size and/or are expensive to acquire. On the other hand, observational data of the organism in steady state (e.g., wild-type) are more readily available, but their informational content is inadequate for the task at hand. We develop a computational approach to appropriately utilize both data sources for estimating a regulatory network. The proposed approach is based on a three-step algorithm to estimate the underlying directed but cyclic network, that uses as input both perturbation screens and steady state gene expression data. In the first step, the algorithm determines causal orderings of the genes that are consistent with the perturbation data, by combining an exhaustive search method with a fast heuristic that in turn couples a Monte Carlo technique with a fast search algorithm. In the second step, for each obtained causal ordering, a regulatory network is estimated using a penalized likelihood based method, while in the third step a consensus network is constructed from the highest scored ones. Extensive computational experiments show that the algorithm performs well in reconstructing the underlying network and clearly outperforms competing approaches that rely only on a single data source. Further, it is established that the algorithm produces a consistent estimate of the regulatory network. PMID:24586224
Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models

PubMed Central

Hipp, John R.; Wang, Cheng; Butts, Carter T.; Jose, Rupa; Lakon, Cynthia M.

2015-01-01

Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models. PMID:25745276
Research Note: The consequences of different methods for handling missing network data in Stochastic Actor Based Models.

PubMed

Hipp, John R; Wang, Cheng; Butts, Carter T; Jose, Rupa; Lakon, Cynthia M

2015-05-01

Although stochastic actor based models (e.g., as implemented in the SIENA software program) are growing in popularity as a technique for estimating longitudinal network data, a relatively understudied issue is the consequence of missing network data for longitudinal analysis. We explore this issue in our research note by utilizing data from four schools in an existing dataset (the AddHealth dataset) over three time points, assessing the substantive consequences of using four different strategies for addressing missing network data. The results indicate that whereas some measures in such models are estimated relatively robustly regardless of the strategy chosen for addressing missing network data, some of the substantive conclusions will differ based on the missing data strategy chosen. These results have important implications for this burgeoning applied research area, implying that researchers should more carefully consider how they address missing data when estimating such models.
Fuzzy Neural Network-Based Interacting Multiple Model for Multi-Node Target Tracking Algorithm

PubMed Central

Sun, Baoliang; Jiang, Chunlan; Li, Ming

2016-01-01

An interacting multiple model for multi-node target tracking algorithm was proposed based on a fuzzy neural network (FNN) to solve the multi-node target tracking problem of wireless sensor networks (WSNs). Measured error variance was adaptively adjusted during the multiple model interacting output stage using the difference between the theoretical and estimated values of the measured error covariance matrix. The FNN fusion system was established during multi-node fusion to integrate with the target state estimated data from different nodes and consequently obtain network target state estimation. The feasibility of the algorithm was verified based on a network of nine detection nodes. Experimental results indicated that the proposed algorithm could trace the maneuvering target effectively under sensor failure and unknown system measurement errors. The proposed algorithm exhibited great practicability in the multi-node target tracking of WSNs. PMID:27809271
Robust Learning of High-dimensional Biological Networks with Bayesian Networks

NASA Astrophysics Data System (ADS)

Nägele, Andreas; Dejori, Mathäus; Stetter, Martin

Structure learning of Bayesian networks applied to gene expression data has become a potentially useful method to estimate interactions between genes. However, the NP-hardness of Bayesian network structure learning renders the reconstruction of the full genetic network with thousands of genes unfeasible. Consequently, the maximal network size is usually restricted dramatically to a small set of genes (corresponding with variables in the Bayesian network). Although this feature reduction step makes structure learning computationally tractable, on the downside, the learned structure might be adversely affected due to the introduction of missing genes. Additionally, gene expression data are usually very sparse with respect to the number of samples, i.e., the number of genes is much greater than the number of different observations. Given these problems, learning robust network features from microarray data is a challenging task. This chapter presents several approaches tackling the robustness issue in order to obtain a more reliable estimation of learned network features.
Network meta-analysis, electrical networks and graph theory.

PubMed

Rücker, Gerta

2012-12-01

Network meta-analysis is an active field of research in clinical biostatistics. It aims to combine information from all randomized comparisons among a set of treatments for a given medical condition. We show how graph-theoretical methods can be applied to network meta-analysis. A meta-analytic graph consists of vertices (treatments) and edges (randomized comparisons). We illustrate the correspondence between meta-analytic networks and electrical networks, where variance corresponds to resistance, treatment effects to voltage, and weighted treatment effects to current flows. Based thereon, we then show that graph-theoretical methods that have been routinely applied to electrical networks also work well in network meta-analysis. In more detail, the resulting consistent treatment effects induced in the edges can be estimated via the Moore-Penrose pseudoinverse of the Laplacian matrix. Moreover, the variances of the treatment effects are estimated in analogy to electrical effective resistances. It is shown that this method, being computationally simple, leads to the usual fixed effect model estimate when applied to pairwise meta-analysis and is consistent with published results when applied to network meta-analysis examples from the literature. Moreover, problems of heterogeneity and inconsistency, random effects modeling and including multi-armed trials are addressed. Copyright © 2012 John Wiley & Sons, Ltd. Copyright © 2012 John Wiley & Sons, Ltd.
Earth's oldest (approximately 3.5 Ga) fossils and the 'Early Eden hypothesis': questioning the evidence.

PubMed

Brasier, Martin; Green, Owen; Lindsay, John; Steele, Andrew

2004-02-01

We question the biogenicity of putative bacterial and cyanobacterial 'microfossils' from 3465 Ma Apex cherts of the Warrawoona Group in Western Australia. They are challenged on the basis of integrated multidisciplinary evidence obtained from field and fabric mapping plus new high-resolution research into their context, sedimentology, filament morphology, 'septation' and arrangement. They cannot be distinguished from (and are reinterpreted as) secondary artefacts of amorphous carbon that formed during devitrification of successive generations of carbonaceous hydrothermal dyke vein quartz. Similar structures occur within associated carbonaceous volcanic glass. The null hypothesis of an abiotic or prebiotic origin for such ancient carbonaceous matter is sustained until mutually supporting contextural, morphological and geochemical evidence for a bacterial rather than abiotic origin is forthcoming.
Cokriging of Electromagnetic Induction Soil Electrical Conductivity Measurements and Soil Textural Properties to Demarcate Sub-field Management Zones for Precision Irrigation.

NASA Astrophysics Data System (ADS)

Ding, R.; Cruz, L.; Whitney, J.; Telenko, D.; Oware, E. K.

2017-12-01

There is the growing need for the development of efficient irrigation management practices due to increasing irrigation water scarcity as a result of growing population and changing climate. Soil texture primarily controls the water-holding capacity of soils, which determines the amount of irrigation water that will be available to the plant. However, while there are significant variabilities in the textural properties of the soil across a field, conventional irrigation practices ignore the underlying variability in the soil properties, resulting in over- or under-irrigation. Over-irrigation leaches plant nutrients beyond the root-zone leading to fertilizer, energy, and water wastages with dire environmental consequences. Under-irrigation, in contrast, causes water stress of the plant, thereby reducing plant quality and yield. The goal of this project is to leverage soil textural map of a field to create water management zones (MZs) to guide site-specific precision irrigation. There is increasing application of electromagnetic induction methods to rapidly and inexpensively map spatially continuous soil properties in terms of the apparent electrical conductivity (ECa) of the soil. ECa is a measure of the bulk soil properties, including soil texture, moisture, salinity, and cation exchange capacity, making an ECa map a pseudo-soil map. Data for the project were collected from a farm site at Eden, NY. The objective is to leverage high-resolution ECa map to predict spatially dense soil textural properties from limited measurements of soil texture. Thus, after performing ECa mapping, we conducted particle-size analysis of soil samples to determine the textural properties of soils at selected locations across the field. We cokriged the high-resolution ECa measurements with the sparse soil textural data to estimate a soil texture map for the field. We conducted irrigation experiments at selected locations to calibrate representative water-holding capacities of each estimated soil textural unit. Estimated soil units with similar water-holding characteristics were merged to create sub-field water MZs to guide precision irrigation of each MZ, instructed by each MZ's calibrated water-holding properties.
Boundary conditions estimation on a road network using compressed sensing.

DOT National Transportation Integrated Search

2016-02-01

This report presents a new boundary condition estimation framework for transportation networks in which : the state is modeled by a first order scalar conservation law. Using an equivalent formulation based on a : Hamilton-Jacobi equation, we pose th...
Flood quantile estimation at ungauged sites by Bayesian networks

NASA Astrophysics Data System (ADS)

Mediero, L.; Santillán, D.; Garrote, L.

2012-04-01

Estimating flood quantiles at a site for which no observed measurements are available is essential for water resources planning and management. Ungauged sites have no observations about the magnitude of floods, but some site and basin characteristics are known. The most common technique used is the multiple regression analysis, which relates physical and climatic basin characteristic to flood quantiles. Regression equations are fitted from flood frequency data and basin characteristics at gauged sites. Regression equations are a rigid technique that assumes linear relationships between variables and cannot take the measurement errors into account. In addition, the prediction intervals are estimated in a very simplistic way from the variance of the residuals in the estimated model. Bayesian networks are a probabilistic computational structure taken from the field of Artificial Intelligence, which have been widely and successfully applied to many scientific fields like medicine and informatics, but application to the field of hydrology is recent. Bayesian networks infer the joint probability distribution of several related variables from observations through nodes, which represent random variables, and links, which represent causal dependencies between them. A Bayesian network is more flexible than regression equations, as they capture non-linear relationships between variables. In addition, the probabilistic nature of Bayesian networks allows taking the different sources of estimation uncertainty into account, as they give a probability distribution as result. A homogeneous region in the Tagus Basin was selected as case study. A regression equation was fitted taking the basin area, the annual maximum 24-hour rainfall for a given recurrence interval and the mean height as explanatory variables. Flood quantiles at ungauged sites were estimated by Bayesian networks. Bayesian networks need to be learnt from a huge enough data set. As observational data are reduced, a stochastic generator of synthetic data was developed. Synthetic basin characteristics were randomised, keeping the statistical properties of observed physical and climatic variables in the homogeneous region. The synthetic flood quantiles were stochastically generated taking the regression equation as basis. The learnt Bayesian network was validated by the reliability diagram, the Brier Score and the ROC diagram, which are common measures used in the validation of probabilistic forecasts. Summarising, the flood quantile estimations through Bayesian networks supply information about the prediction uncertainty as a probability distribution function of discharges is given as result. Therefore, the Bayesian network model has application as a decision support for water resources and planning management.
Precision Interval Estimation of the Response Surface by Means of an Integrated Algorithm of Neural Network and Linear Regression

NASA Technical Reports Server (NTRS)

Lo, Ching F.

1999-01-01

The integration of Radial Basis Function Networks and Back Propagation Neural Networks with the Multiple Linear Regression has been accomplished to map nonlinear response surfaces over a wide range of independent variables in the process of the Modem Design of Experiments. The integrated method is capable to estimate the precision intervals including confidence and predicted intervals. The power of the innovative method has been demonstrated by applying to a set of wind tunnel test data in construction of response surface and estimation of precision interval.
Gross domestic product estimation based on electricity utilization by artificial neural network

NASA Astrophysics Data System (ADS)

Stevanović, Mirjana; Vujičić, Slađana; Gajić, Aleksandar M.

2018-01-01

The main goal of the paper was to estimate gross domestic product (GDP) based on electricity estimation by artificial neural network (ANN). The electricity utilization was analyzed based on different sources like renewable, coal and nuclear sources. The ANN network was trained with two training algorithms namely extreme learning method and back-propagation algorithm in order to produce the best prediction results of the GDP. According to the results it can be concluded that the ANN model with extreme learning method could produce the acceptable prediction of the GDP based on the electricity utilization.

Permeability Estimation of Rock Reservoir Based on PCA and Elman Neural Networks

NASA Astrophysics Data System (ADS)

Shi, Ying; Jian, Shaoyong

2018-03-01

an intelligent method which based on fuzzy neural networks with PCA algorithm, is proposed to estimate the permeability of rock reservoir. First, the dimensionality reduction process is utilized for these parameters by principal component analysis method. Further, the mapping relationship between rock slice characteristic parameters and permeability had been found through fuzzy neural networks. The estimation validity and reliability for this method were tested with practical data from Yan’an region in Ordos Basin. The result showed that the average relative errors of permeability estimation for this method is 6.25%, and this method had the better convergence speed and more accuracy than other. Therefore, by using the cheap rock slice related information, the permeability of rock reservoir can be estimated efficiently and accurately, and it is of high reliability, practicability and application prospect.
The Use of Neural Networks in Identifying Error Sources in Satellite-Derived Tropical SST Estimates

PubMed Central

Lee, Yung-Hsiang; Ho, Chung-Ru; Su, Feng-Chun; Kuo, Nan-Jung; Cheng, Yu-Hsin

2011-01-01

An neural network model of data mining is used to identify error sources in satellite-derived tropical sea surface temperature (SST) estimates from thermal infrared sensors onboard the Geostationary Operational Environmental Satellite (GOES). By using the Back Propagation Network (BPN) algorithm, it is found that air temperature, relative humidity, and wind speed variation are the major factors causing the errors of GOES SST products in the tropical Pacific. The accuracy of SST estimates is also improved by the model. The root mean square error (RMSE) for the daily SST estimate is reduced from 0.58 K to 0.38 K and mean absolute percentage error (MAPE) is 1.03%. For the hourly mean SST estimate, its RMSE is also reduced from 0.66 K to 0.44 K and the MAPE is 1.3%. PMID:22164030
Exponentially convergent state estimation for delayed switched recurrent neural networks.

PubMed

Ahn, Choon Ki

2011-11-01

This paper deals with the delay-dependent exponentially convergent state estimation problem for delayed switched neural networks. A set of delay-dependent criteria is derived under which the resulting estimation error system is exponentially stable. It is shown that the gain matrix of the proposed state estimator is characterised in terms of the solution to a set of linear matrix inequalities (LMIs), which can be checked readily by using some standard numerical packages. An illustrative example is given to demonstrate the effectiveness of the proposed state estimator.
Local Spatial Obesity Analysis and Estimation Using Online Social Network Sensors.

PubMed

Sun, Qindong; Wang, Nan; Li, Shancang; Zhou, Hongyi

2018-03-15

Recently, the online social networks (OSNs) have received considerable attentions as a revolutionary platform to offer users massive social interaction among users that enables users to be more involved in their own healthcare. The OSNs have also promoted increasing interests in the generation of analytical, data models in health informatics. This paper aims at developing an obesity identification, analysis, and estimation model, in which each individual user is regarded as an online social network 'sensor' that can provide valuable health information. The OSN-based obesity analytic model requires each sensor node in an OSN to provide associated features, including dietary habit, physical activity, integral/incidental emotions, and self-consciousness. Based on the detailed measurements on the correlation of obesity and proposed features, the OSN obesity analytic model is able to estimate the obesity rate in certain urban areas and the experimental results demonstrate a high success estimation rate. The measurements and estimation experimental findings created by the proposed obesity analytic model show that the online social networks could be used in analyzing the local spatial obesity problems effectively. Copyright © 2018. Published by Elsevier Inc.
Underwater Inherent Optical Properties Estimation Using a Depth Aided Deep Neural Network.

PubMed

Yu, Zhibin; Wang, Yubo; Zheng, Bing; Zheng, Haiyong; Wang, Nan; Gu, Zhaorui

2017-01-01

Underwater inherent optical properties (IOPs) are the fundamental clues to many research fields such as marine optics, marine biology, and underwater vision. Currently, beam transmissometers and optical sensors are considered as the ideal IOPs measuring methods. But these methods are inflexible and expensive to be deployed. To overcome this problem, we aim to develop a novel measuring method using only a single underwater image with the help of deep artificial neural network. The power of artificial neural network has been proved in image processing and computer vision fields with deep learning technology. However, image-based IOPs estimation is a quite different and challenging task. Unlike the traditional applications such as image classification or localization, IOP estimation looks at the transparency of the water between the camera and the target objects to estimate multiple optical properties simultaneously. In this paper, we propose a novel Depth Aided (DA) deep neural network structure for IOPs estimation based on a single RGB image that is even noisy. The imaging depth information is considered as an aided input to help our model make better decision.
Improved PPP Ambiguity Resolution Considering the Stochastic Characteristics of Atmospheric Corrections from Regional Networks

PubMed Central

Li, Yihe; Li, Bofeng; Gao, Yang

2015-01-01

With the increased availability of regional reference networks, Precise Point Positioning (PPP) can achieve fast ambiguity resolution (AR) and precise positioning by assimilating the satellite fractional cycle biases (FCBs) and atmospheric corrections derived from these networks. In such processing, the atmospheric corrections are usually treated as deterministic quantities. This is however unrealistic since the estimated atmospheric corrections obtained from the network data are random and furthermore the interpolated corrections diverge from the realistic corrections. This paper is dedicated to the stochastic modelling of atmospheric corrections and analyzing their effects on the PPP AR efficiency. The random errors of the interpolated corrections are processed as two components: one is from the random errors of estimated corrections at reference stations, while the other arises from the atmospheric delay discrepancies between reference stations and users. The interpolated atmospheric corrections are then applied by users as pseudo-observations with the estimated stochastic model. Two data sets are processed to assess the performance of interpolated corrections with the estimated stochastic models. The results show that when the stochastic characteristics of interpolated corrections are properly taken into account, the successful fix rate reaches 93.3% within 5 min for a medium inter-station distance network and 80.6% within 10 min for a long inter-station distance network. PMID:26633400
Estimating topological properties of weighted networks from limited information.

PubMed

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.
Improved PPP Ambiguity Resolution Considering the Stochastic Characteristics of Atmospheric Corrections from Regional Networks.

PubMed

Li, Yihe; Li, Bofeng; Gao, Yang

2015-11-30

With the increased availability of regional reference networks, Precise Point Positioning (PPP) can achieve fast ambiguity resolution (AR) and precise positioning by assimilating the satellite fractional cycle biases (FCBs) and atmospheric corrections derived from these networks. In such processing, the atmospheric corrections are usually treated as deterministic quantities. This is however unrealistic since the estimated atmospheric corrections obtained from the network data are random and furthermore the interpolated corrections diverge from the realistic corrections. This paper is dedicated to the stochastic modelling of atmospheric corrections and analyzing their effects on the PPP AR efficiency. The random errors of the interpolated corrections are processed as two components: one is from the random errors of estimated corrections at reference stations, while the other arises from the atmospheric delay discrepancies between reference stations and users. The interpolated atmospheric corrections are then applied by users as pseudo-observations with the estimated stochastic model. Two data sets are processed to assess the performance of interpolated corrections with the estimated stochastic models. The results show that when the stochastic characteristics of interpolated corrections are properly taken into account, the successful fix rate reaches 93.3% within 5 min for a medium inter-station distance network and 80.6% within 10 min for a long inter-station distance network.
Estimating topological properties of weighted networks from limited information

NASA Astrophysics Data System (ADS)

Cimini, Giulio; Squartini, Tiziano; Gabrielli, Andrea; Garlaschelli, Diego

2015-10-01

A problem typically encountered when studying complex systems is the limitedness of the information available on their topology, which hinders our understanding of their structure and of the dynamical processes taking place on them. A paramount example is provided by financial networks, whose data are privacy protected: Banks publicly disclose only their aggregate exposure towards other banks, keeping individual exposures towards each single bank secret. Yet, the estimation of systemic risk strongly depends on the detailed structure of the interbank network. The resulting challenge is that of using aggregate information to statistically reconstruct a network and correctly predict its higher-order properties. Standard approaches either generate unrealistically dense networks, or fail to reproduce the observed topology by assigning homogeneous link weights. Here, we develop a reconstruction method, based on statistical mechanics concepts, that makes use of the empirical link density in a highly nontrivial way. Technically, our approach consists in the preliminary estimation of node degrees from empirical node strengths and link density, followed by a maximum-entropy inference based on a combination of empirical strengths and estimated degrees. Our method is successfully tested on the international trade network and the interbank money market, and represents a valuable tool for gaining insights on privacy-protected or partially accessible systems.
Generalizing the Network Scale-Up Method: A New Estimator for the Size of Hidden Populations*

PubMed Central

Feehan, Dennis M.; Salganik, Matthew J.

2018-01-01

The network scale-up method enables researchers to estimate the size of hidden populations, such as drug injectors and sex workers, using sampled social network data. The basic scale-up estimator offers advantages over other size estimation techniques, but it depends on problematic modeling assumptions. We propose a new generalized scale-up estimator that can be used in settings with non-random social mixing and imperfect awareness about membership in the hidden population. Further, the new estimator can be used when data are collected via complex sample designs and from incomplete sampling frames. However, the generalized scale-up estimator also requires data from two samples: one from the frame population and one from the hidden population. In some situations these data from the hidden population can be collected by adding a small number of questions to already planned studies. For other situations, we develop interpretable adjustment factors that can be applied to the basic scale-up estimator. We conclude with practical recommendations for the design and analysis of future studies. PMID:29375167
A mathematical method for verifying the validity of measured information about the flows of energy resources based on the state estimation theory

NASA Astrophysics Data System (ADS)

Pazderin, A. V.; Sof'in, V. V.; Samoylenko, V. O.

2015-11-01

Efforts aimed at improving energy efficiency in all branches of the fuel and energy complex shall be commenced with setting up a high-tech automated system for monitoring and accounting energy resources. Malfunctions and failures in the measurement and information parts of this system may distort commercial measurements of energy resources and lead to financial risks for power supplying organizations. In addition, measurement errors may be connected with intentional distortion of measurements for reducing payment for using energy resources on the consumer's side, which leads to commercial loss of energy resource. The article presents a universal mathematical method for verifying the validity of measurement information in networks for transporting energy resources, such as electricity and heat, petroleum, gas, etc., based on the state estimation theory. The energy resource transportation network is represented by a graph the nodes of which correspond to producers and consumers, and its branches stand for transportation mains (power lines, pipelines, and heat network elements). The main idea of state estimation is connected with obtaining the calculated analogs of energy resources for all available measurements. Unlike "raw" measurements, which contain inaccuracies, the calculated flows of energy resources, called estimates, will fully satisfy the suitability condition for all state equations describing the energy resource transportation network. The state equations written in terms of calculated estimates will be already free from residuals. The difference between a measurement and its calculated analog (estimate) is called in the estimation theory an estimation remainder. The obtained large values of estimation remainders are an indicator of high errors of particular energy resource measurements. By using the presented method it is possible to improve the validity of energy resource measurements, to estimate the transportation network observability, to eliminate the energy resource flows measurement imbalances, and to filter invalid measurements at the data acquisition and processing stage in performing monitoring of an automated energy resource monitoring and accounting system.
DCMDN: Deep Convolutional Mixture Density Network

NASA Astrophysics Data System (ADS)

D'Isanto, Antonio; Polsterer, Kai Lars

2017-09-01

Deep Convolutional Mixture Density Network (DCMDN) estimates probabilistic photometric redshift directly from multi-band imaging data by combining a version of a deep convolutional network with a mixture density network. The estimates are expressed as Gaussian mixture models representing the probability density functions (PDFs) in the redshift space. In addition to the traditional scores, the continuous ranked probability score (CRPS) and the probability integral transform (PIT) are applied as performance criteria. DCMDN is able to predict redshift PDFs independently from the type of source, e.g. galaxies, quasars or stars and renders pre-classification of objects and feature extraction unnecessary; the method is extremely general and allows the solving of any kind of probabilistic regression problems based on imaging data, such as estimating metallicity or star formation rate in galaxies.
A Hybrid Neural Network-Genetic Algorithm Technique for Aircraft Engine Performance Diagnostics

NASA Technical Reports Server (NTRS)

Kobayashi, Takahisa; Simon, Donald L.

2001-01-01

In this paper, a model-based diagnostic method, which utilizes Neural Networks and Genetic Algorithms, is investigated. Neural networks are applied to estimate the engine internal health, and Genetic Algorithms are applied for sensor bias detection and estimation. This hybrid approach takes advantage of the nonlinear estimation capability provided by neural networks while improving the robustness to measurement uncertainty through the application of Genetic Algorithms. The hybrid diagnostic technique also has the ability to rank multiple potential solutions for a given set of anomalous sensor measurements in order to reduce false alarms and missed detections. The performance of the hybrid diagnostic technique is evaluated through some case studies derived from a turbofan engine simulation. The results show this approach is promising for reliable diagnostics of aircraft engines.
Downsizing a long-term precipitation network: Using a quantitative approach to inform difficult decisions.

PubMed

Green, Mark B; Campbell, John L; Yanai, Ruth D; Bailey, Scott W; Bailey, Amey S; Grant, Nicholas; Halm, Ian; Kelsey, Eric P; Rustad, Lindsey E

2018-01-01

The design of a precipitation monitoring network must balance the demand for accurate estimates with the resources needed to build and maintain the network. If there are changes in the objectives of the monitoring or the availability of resources, network designs should be adjusted. At the Hubbard Brook Experimental Forest in New Hampshire, USA, precipitation has been monitored with a network established in 1955 that has grown to 23 gauges distributed across nine small catchments. This high sampling intensity allowed us to simulate reduced sampling schemes and thereby evaluate the effect of decommissioning gauges on the quality of precipitation estimates. We considered all possible scenarios of sampling intensity for the catchments on the south-facing slope (2047 combinations) and the north-facing slope (4095 combinations), from the current scenario with 11 or 12 gauges to only 1 gauge remaining. Gauge scenarios differed by as much as 6.0% from the best estimate (based on all the gauges), depending on the catchment, but 95% of the scenarios gave estimates within 2% of the long-term average annual precipitation. The insensitivity of precipitation estimates and the catchment fluxes that depend on them under many reduced monitoring scenarios allowed us to base our reduction decision on other factors such as technician safety, the time required for monitoring, and co-location with other hydrometeorological measurements (snow, air temperature). At Hubbard Brook, precipitation gauges could be reduced from 23 to 10 with a change of <2% in the long-term precipitation estimates. The decision-making approach illustrated in this case study is applicable to the redesign of monitoring networks when reduction of effort seems warranted.
A new method for constructing networks from binary data

NASA Astrophysics Data System (ADS)

van Borkulo, Claudia D.; Borsboom, Denny; Epskamp, Sacha; Blanken, Tessa F.; Boschloo, Lynn; Schoevers, Robert A.; Waldorp, Lourens J.

2014-08-01

Network analysis is entering fields where network structures are unknown, such as psychology and the educational sciences. A crucial step in the application of network models lies in the assessment of network structure. Current methods either have serious drawbacks or are only suitable for Gaussian data. In the present paper, we present a method for assessing network structures from binary data. Although models for binary data are infamous for their computational intractability, we present a computationally efficient model for estimating network structures. The approach, which is based on Ising models as used in physics, combines logistic regression with model selection based on a Goodness-of-Fit measure to identify relevant relationships between variables that define connections in a network. A validation study shows that this method succeeds in revealing the most relevant features of a network for realistic sample sizes. We apply our proposed method to estimate the network of depression and anxiety symptoms from symptom scores of 1108 subjects. Possible extensions of the model are discussed.
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.
A study on predicting network corrections in PPP-RTK processing

NASA Astrophysics Data System (ADS)

Wang, Kan; Khodabandeh, Amir; Teunissen, Peter

2017-10-01

In PPP-RTK processing, the network corrections including the satellite clocks, the satellite phase biases and the ionospheric delays are provided to the users to enable fast single-receiver integer ambiguity resolution. To solve the rank deficiencies in the undifferenced observation equations, the estimable parameters are formed to generate full-rank design matrix. In this contribution, we firstly discuss the interpretation of the estimable parameters without and with a dynamic satellite clock model incorporated in a Kalman filter during the network processing. The functionality of the dynamic satellite clock model is tested in the PPP-RTK processing. Due to the latency generated by the network processing and data transfer, the network corrections are delayed for the real-time user processing. To bridge the latencies, we discuss and compare two prediction approaches making use of the network corrections without and with the dynamic satellite clock model, respectively. The first prediction approach is based on the polynomial fitting of the estimated network parameters, while the second approach directly follows the dynamic model in the Kalman filter of the network processing and utilises the satellite clock drifts estimated in the network processing. Using 1 Hz data from two networks in Australia, the influences of the two prediction approaches on the user positioning results are analysed and compared for latencies ranging from 3 to 10 s. The accuracy of the positioning results decreases with the increasing latency of the network products. For a latency of 3 s, the RMS of the horizontal and the vertical coordinates (with respect to the ground truth) do not show large differences applying both prediction approaches. For a latency of 10 s, the prediction approach making use of the satellite clock model has generated slightly better positioning results with the differences of the RMS at mm-level. Further advantages and disadvantages of both prediction approaches are also discussed in this contribution.
Regenerating time series from ordinal networks.

PubMed

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.
Regenerating time series from ordinal networks

NASA Astrophysics Data System (ADS)

McCullough, Michael; Sakellariou, Konstantinos; Stemler, Thomas; Small, Michael

2017-03-01

Recently proposed ordinal networks not only afford novel methods of nonlinear time series analysis but also constitute stochastic approximations of the deterministic flow time series from which the network models are constructed. In this paper, we construct ordinal networks from discrete sampled continuous chaotic time series and then regenerate new time series by taking random walks on the ordinal network. We then investigate the extent to which the dynamics of the original time series are encoded in the ordinal networks and retained through the process of regenerating new time series by using several distinct quantitative approaches. First, we use recurrence quantification analysis on traditional recurrence plots and order recurrence plots to compare the temporal structure of the original time series with random walk surrogate time series. Second, we estimate the largest Lyapunov exponent from the original time series and investigate the extent to which this invariant measure can be estimated from the surrogate time series. Finally, estimates of correlation dimension are computed to compare the topological properties of the original and surrogate time series dynamics. Our findings show that ordinal networks constructed from univariate time series data constitute stochastic models which approximate important dynamical properties of the original systems.
Parameter estimation in spiking neural networks: a reverse-engineering approach.

PubMed

Rostro-Gonzalez, H; Cessac, B; Vieville, T

2012-04-01

This paper presents a reverse engineering approach for parameter estimation in spiking neural networks (SNNs). We consider the deterministic evolution of a time-discretized network with spiking neurons, where synaptic transmission has delays, modeled as a neural network of the generalized integrate and fire type. Our approach aims at by-passing the fact that the parameter estimation in SNN results in a non-deterministic polynomial-time hard problem when delays are to be considered. Here, this assumption has been reformulated as a linear programming (LP) problem in order to perform the solution in a polynomial time. Besides, the LP problem formulation makes the fact that the reverse engineering of a neural network can be performed from the observation of the spike times explicit. Furthermore, we point out how the LP adjustment mechanism is local to each neuron and has the same structure as a 'Hebbian' rule. Finally, we present a generalization of this approach to the design of input-output (I/O) transformations as a practical method to 'program' a spiking network, i.e. find a set of parameters allowing us to exactly reproduce the network output, given an input. Numerical verifications and illustrations are provided.

Estimation of Blood Flow Rates in Large Microvascular Networks

PubMed Central

Fry, Brendan C.; Lee, Jack; Smith, Nicolas P.; Secomb, Timothy W.

2012-01-01

Objective Recent methods for imaging microvascular structures provide geometrical data on networks containing thousands of segments. Prediction of functional properties, such as solute transport, requires information on blood flow rates also, but experimental measurement of many individual flows is difficult. Here, a method is presented for estimating flow rates in a microvascular network based on incomplete information on the flows in the boundary segments that feed and drain the network. Methods With incomplete boundary data, the equations governing blood flow form an underdetermined linear system. An algorithm was developed that uses independent information about the distribution of wall shear stresses and pressures in microvessels to resolve this indeterminacy, by minimizing the deviation of pressures and wall shear stresses from target values. Results The algorithm was tested using previously obtained experimental flow data from four microvascular networks in the rat mesentery. With two or three prescribed boundary conditions, predicted flows showed relatively small errors in most segments and fewer than 10% incorrect flow directions on average. Conclusions The proposed method can be used to estimate flow rates in microvascular networks, based on incomplete boundary data and provides a basis for deducing functional properties of microvessel networks. PMID:22506980
Network Model-Assisted Inference from Respondent-Driven Sampling Data

PubMed Central

Gile, Krista J.; Handcock, Mark S.

2015-01-01

Summary Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population. PMID:26640328
Network Model-Assisted Inference from Respondent-Driven Sampling Data.

PubMed

Gile, Krista J; Handcock, Mark S

2015-06-01

Respondent-Driven Sampling is a widely-used method for sampling hard-to-reach human populations by link-tracing over their social networks. Inference from such data requires specialized techniques because the sampling process is both partially beyond the control of the researcher, and partially implicitly defined. Therefore, it is not generally possible to directly compute the sampling weights for traditional design-based inference, and likelihood inference requires modeling the complex sampling process. As an alternative, we introduce a model-assisted approach, resulting in a design-based estimator leveraging a working network model. We derive a new class of estimators for population means and a corresponding bootstrap standard error estimator. We demonstrate improved performance compared to existing estimators, including adjustment for an initial convenience sample. We also apply the method and an extension to the estimation of HIV prevalence in a high-risk population.
Two new methods to fit models for network meta-analysis with random inconsistency effects.

PubMed

Law, Martin; Jackson, Dan; Turner, Rebecca; Rhodes, Kirsty; Viechtbauer, Wolfgang

2016-07-28

Meta-analysis is a valuable tool for combining evidence from multiple studies. Network meta-analysis is becoming more widely used as a means to compare multiple treatments in the same analysis. However, a network meta-analysis may exhibit inconsistency, whereby the treatment effect estimates do not agree across all trial designs, even after taking between-study heterogeneity into account. We propose two new estimation methods for network meta-analysis models with random inconsistency effects. The model we consider is an extension of the conventional random-effects model for meta-analysis to the network meta-analysis setting and allows for potential inconsistency using random inconsistency effects. Our first new estimation method uses a Bayesian framework with empirically-based prior distributions for both the heterogeneity and the inconsistency variances. We fit the model using importance sampling and thereby avoid some of the difficulties that might be associated with using Markov Chain Monte Carlo (MCMC). However, we confirm the accuracy of our importance sampling method by comparing the results to those obtained using MCMC as the gold standard. The second new estimation method we describe uses a likelihood-based approach, implemented in the metafor package, which can be used to obtain (restricted) maximum-likelihood estimates of the model parameters and profile likelihood confidence intervals of the variance components. We illustrate the application of the methods using two contrasting examples. The first uses all-cause mortality as an outcome, and shows little evidence of between-study heterogeneity or inconsistency. The second uses "ear discharge" as an outcome, and exhibits substantial between-study heterogeneity and inconsistency. Both new estimation methods give results similar to those obtained using MCMC. The extent of heterogeneity and inconsistency should be assessed and reported in any network meta-analysis. Our two new methods can be used to fit models for network meta-analysis with random inconsistency effects. They are easily implemented using the accompanying R code in the Additional file 1. Using these estimation methods, the extent of inconsistency can be assessed and reported.
Imperishable Networks: Complexity Theory and Communication Networking-Bridging the Gap Between Algorithmic Information Theory and Communication Networking

DTIC Science & Technology

2003-04-01

gener- ally considered to be passive data . Instead the genetic material should be capable of being algorith - mic information, that is, program code or...information is estimated to average 1 hour per response, including the time for reviewing instructions, searching existing data sources, gathering and...maintaining the data needed, and completing and reviewing this collection of information. Send comments regarding this burden estimate or any other
[Cost estimation of an epidemiological surveillance network for animal diseases in Central Africa: a case study of the Chad network].

PubMed

Ouagal, M; Berkvens, D; Hendrikx, P; Fecher-Bourgeois, F; Saegerman, C

2012-12-01

In sub-Saharan Africa, most epidemiological surveillance networks for animal diseases were temporarily funded by foreign aid. It should be possible for national public funds to ensure the sustainability of such decision support tools. Taking the epidemiological surveillance network for animal diseases in Chad (REPIMAT) as an example, this study aims to estimate the network's cost by identifying the various costs and expenditures for each level of intervention. The network cost was estimated on the basis of an analysis of the operational organisation of REPIMAT, additional data collected in surveys and interviews with network field workers and a market price listing for Chad. These costs were then compared with those of other epidemiological surveillance networks in West Africa. The study results indicate that REPIMAT costs account for 3% of the State budget allocated to the Ministry of Livestock. In Chad in general, as in other West African countries, fixed costs outweigh variable costs at every level of intervention. The cost of surveillance principally depends on what is needed for surveillance at the local level (monitoring stations) and at the intermediate level (official livestock sectors and regional livestock delegations) and on the cost of the necessary equipment. In African countries, the cost of surveillance per square kilometre depends on livestock density.
Advanced Performance Modeling with Combined Passive and Active Monitoring

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

Dovrolis, Constantine; Sim, Alex

2015-04-15

To improve the efficiency of resource utilization and scheduling of scientific data transfers on high-speed networks, the "Advanced Performance Modeling with combined passive and active monitoring" (APM) project investigates and models a general-purpose, reusable and expandable network performance estimation framework. The predictive estimation model and the framework will be helpful in optimizing the performance and utilization of networks as well as sharing resources with predictable performance for scientific collaborations, especially in data intensive applications. Our prediction model utilizes historical network performance information from various network activity logs as well as live streaming measurements from network peering devices. Historical network performancemore » information is used without putting extra load on the resources by active measurement collection. Performance measurements collected by active probing is used judiciously for improving the accuracy of predictions.« less
Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks

PubMed Central

2017-01-01

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing—with its unique statistical properties—became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca. PMID:28817636
Estimating User Influence in Online Social Networks Subject to Information Overload

NASA Astrophysics Data System (ADS)

Li, Pei; Sun, Yunchuan; Chen, Yingwen; Tian, Zhi

2014-11-01

Online social networks have attracted remarkable attention since they provide various approaches for hundreds of millions of people to stay connected with their friends. Due to the existence of information overload, the research on diffusion dynamics in epidemiology cannot be adopted directly to that in online social networks. In this paper, we consider diffusion dynamics in online social networks subject to information overload, and model the information-processing process of a user by a queue with a batch arrival and a finite buffer. We use the average number of times a message is processed after it is generated by a given user to characterize the user influence, which is then estimated through theoretical analysis for a given network. We validate the accuracy of our estimation by simulations, and apply the results to study the impacts of different factors on the user influence. Among the observations, we find that the impact of network size on the user influence is marginal while the user influence decreases with assortativity due to information overload, which is particularly interesting.
Uncovering robust patterns of microRNA co-expression across cancers using Bayesian Relevance Networks.

PubMed

Ramachandran, Parameswaran; Sánchez-Taltavull, Daniel; Perkins, Theodore J

2017-01-01

Co-expression networks have long been used as a tool for investigating the molecular circuitry governing biological systems. However, most algorithms for constructing co-expression networks were developed in the microarray era, before high-throughput sequencing-with its unique statistical properties-became the norm for expression measurement. Here we develop Bayesian Relevance Networks, an algorithm that uses Bayesian reasoning about expression levels to account for the differing levels of uncertainty in expression measurements between highly- and lowly-expressed entities, and between samples with different sequencing depths. It combines data from groups of samples (e.g., replicates) to estimate group expression levels and confidence ranges. It then computes uncertainty-moderated estimates of cross-group correlations between entities, and uses permutation testing to assess their statistical significance. Using large scale miRNA data from The Cancer Genome Atlas, we show that our Bayesian update of the classical Relevance Networks algorithm provides improved reproducibility in co-expression estimates and lower false discovery rates in the resulting co-expression networks. Software is available at www.perkinslab.ca.
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.
Assessing the carbon benefit of saltmarsh restoration

NASA Astrophysics Data System (ADS)

Taylor, Benjamin; Paterson, David; Hanley, Nicholas

2016-04-01

The quantification of carbon sequestration rates in coastal ecosystems is required to better realise their potential role in climate change mitigation. Through accurate valuation this service can be fully appreciated and perhaps help facilitate efforts to restore vulnerable ecosystems such as saltmarshes. Vegetated coastal ecosystems are suggested to account for approximately 50% of oceanic sedimentary carbon despite their 2% areal extent. Saltmarshes, conservatively estimated to store 430 ± 30 Tg C in surface sediment deposits, have experienced extensive decline in the recent past; through processes such as land use change and coastal squeeze. Saltmarsh habitats offer a range of services that benefit society and the natural world, making their conservation meaningful and beneficial. The associated costs of restoration projects could, in part, be subsidised through payment for ecosystem services, specifically Blue carbon. Additional storage is generated through the (re)vegetation of mudflat areas leading to an altered ecosystem state and function; providing similar benefits to natural saltmarsh areas. The Eden Estuary, Fife, Scotland has been a site of saltmarsh restoration since 2000; providing a temporal and spatial scale to evaluate these additional benefits. The study is being conducted to quantify the carbon benefit of restoration efforts and provide an insight into the evolution of this benefit through sites of different ages. Seasonal sediment deposition and settlement rates are measured across the estuary in: mudflat, young planted saltmarsh, old planted saltmarsh and extant high marsh areas. Carbon values being derived from loss on ignition organic content values. Samples are taken across a tidal cycle on a seasonal basis; providing data on tidal influence, vegetation condition effects and climatic factors on sedimentation and carbon sequestration rates. These data will inform on the annual characteristics of sedimentary processes in the estuary and be used in concert with further data of vertical accretion, vegetation structure and vegetation carbon storage; facilitating the estimation of the total additionality offered by restoration and so its potential value as a subsidy.
Soil nematode assemblages as bioindicators of radiation impact in the Chernobyl Exclusion Zone.

PubMed

Lecomte-Pradines, C; Bonzom, J-M; Della-Vedova, C; Beaugelin-Seiller, K; Villenave, C; Gaschak, S; Coppin, F; Dubourg, N; Maksimenko, A; Adam-Guillermin, C; Garnier-Laplace, J

2014-08-15

In radioecology, the need to understand the long-term ecological effects of radioactive contamination has been emphasised. This requires that the health of field populations is evaluated and linked to an accurate estimate of received radiological dose. The aim of the present study was to assess the effects of current radioactive contamination on nematode assemblages at sites affected by the fallout from the Chernobyl accident. First, we estimated the total dose rates (TDRs) absorbed by nematodes, from measured current soil activity concentrations, Dose Conversion Coefficients (DCCs, calculated using EDEN software) and soil-to-biota concentration ratios (from the ERICA tool database). The impact of current TDRs on nematode assemblages was then evaluated. Nematodes were collected in spring 2011 from 18 forest sites in the Chernobyl Exclusion Zone (CEZ) with external gamma dose rates, measured using radiophotoluminescent dosimeters, varying from 0.2 to 22 μGy h(-1). These values were one order of magnitude below the TDRs. A majority of bacterial-, plant-, and fungal-feeding nematodes and very few of the disturbance sensitive families were identified. No statistically significant association was observed between TDR values and nematode total abundance or the Shannon diversity index (H'). The Nematode Channel Ratio (which defines the relative abundance of bacterial- versus fungal-feeding nematodes) decreased significantly with increasing TDR, suggesting that radioactive contamination may influence nematode assemblages either directly or indirectly by modifying their food resources. A greater Maturity Index (MI), usually characterising better soil quality, was associated with higher pH and TDR values. These results suggest that in the CEZ, nematode assemblages from the forest sites were slightly impacted by chronic exposure at a predicted TDR of 200 μGy h(-1). This may be imputable to a dominant proportion of pollutant resistant nematodes in all sites. This might result from a selection at the expense of sensitive species after the accident. Copyright © 2013 Elsevier B.V. All rights reserved.
Soft sensor based composition estimation and controller design for an ideal reactive distillation column.

PubMed

Vijaya Raghavan, S R; Radhakrishnan, T K; Srinivasan, K

2011-01-01

In this research work, the authors have presented the design and implementation of a recurrent neural network (RNN) based inferential state estimation scheme for an ideal reactive distillation column. Decentralized PI controllers are designed and implemented. The reactive distillation process is controlled by controlling the composition which has been estimated from the available temperature measurements using a type of RNN called Time Delayed Neural Network (TDNN). The performance of the RNN based state estimation scheme under both open loop and closed loop have been compared with a standard Extended Kalman filter (EKF) and a Feed forward Neural Network (FNN). The online training/correction has been done for both RNN and FNN schemes for every ten minutes whenever new un-trained measurements are available from a conventional composition analyzer. The performance of RNN shows better state estimation capability as compared to other state estimation schemes in terms of qualitative and quantitative performance indices. Copyright © 2010 ISA. Published by Elsevier Ltd. All rights reserved.
Bias and precision of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1984

USGS Publications Warehouse

Brooks, M.H.; Schroder, L.J.; Willoughby, T.C.

1987-01-01

The U.S. Geological Survey operated a blind audit sample program during 1974 to test the effects of the sample handling and shipping procedures used by the National Atmospheric Deposition Program and National Trends Network on the quality of wet deposition data produced by the combined networks. Blind audit samples, which were dilutions of standard reference water samples, were submitted by network site operators to the central analytical laboratory disguised as actual wet deposition samples. Results from the analyses of blind audit samples were used to calculate estimates of analyte bias associated with all network wet deposition samples analyzed in 1984 and to estimate analyte precision. Concentration differences between double blind samples that were submitted to the central analytical laboratory and separate analyses of aliquots of those blind audit samples that had not undergone network sample handling and shipping were used to calculate analyte masses that apparently were added to each blind audit sample by routine network handling and shipping procedures. These calculated masses indicated statistically significant biases for magnesium, sodium , potassium, chloride, and sulfate. Median calculated masses were 41.4 micrograms (ug) for calcium, 14.9 ug for magnesium, 23.3 ug for sodium, 0.7 ug for potassium, 16.5 ug for chloride and 55.3 ug for sulfate. Analyte precision was estimated using two different sets of replicate measures performed by the central analytical laboratory. Estimated standard deviations were similar to those previously reported. (Author 's abstract)
Information-geometric measures as robust estimators of connection strengths and external inputs.

PubMed

Tatsuno, Masami; Fellous, Jean-Marc; Amari, Shun-Ichi

2009-08-01

Information geometry has been suggested to provide a powerful tool for analyzing multineuronal spike trains. Among several advantages of this approach, a significant property is the close link between information-geometric measures and neural network architectures. Previous modeling studies established that the first- and second-order information-geometric measures corresponded to the number of external inputs and the connection strengths of the network, respectively. This relationship was, however, limited to a symmetrically connected network, and the number of neurons used in the parameter estimation of the log-linear model needed to be known. Recently, simulation studies of biophysical model neurons have suggested that information geometry can estimate the relative change of connection strengths and external inputs even with asymmetric connections. Inspired by these studies, we analytically investigated the link between the information-geometric measures and the neural network structure with asymmetrically connected networks of N neurons. We focused on the information-geometric measures of orders one and two, which can be derived from the two-neuron log-linear model, because unlike higher-order measures, they can be easily estimated experimentally. Considering the equilibrium state of a network of binary model neurons that obey stochastic dynamics, we analytically showed that the corrected first- and second-order information-geometric measures provided robust and consistent approximation of the external inputs and connection strengths, respectively. These results suggest that information-geometric measures provide useful insights into the neural network architecture and that they will contribute to the study of system-level neuroscience.
Potential impacts of the Alberta fetal alcohol spectrum disorder service networks on secondary disabilities: a cost-benefit analysis.

PubMed

Thanh, Nguyen Xuan; Moffatt, Jessica; Jacobs, Philip; Chuck, Anderson W; Jonsson, Egon

2013-01-01

To estimate the break-even effectiveness of the Alberta Fetal Alcohol Spectrum Disorder (FASD) Service Networks in reducing occurrences of secondary disabilities associated with FASD. The secondary disabilities addressed within this study include crime, homelessness, mental health problems, and school disruption (for children) or unemployment (for adults). We used a cost-benefit analysis approach where benefits of the service networks were the cost difference between the two approaches: having the 12 service networks and having no service network in place, across Alberta. We used a threshold analysis to estimate the break-even effectiveness (i.e. the effectiveness level at which the service networks became cost-saving). If no network was in place throughout the province, the secondary disabilities would cost $22.85 million (including $8.62 million for adults and $14.24 million for children) per year. Given the cost of network was $6.12 million per year, the break-even effectiveness was estimated at 28% (range: 25% to 32%). Although not all benefits associated with the service networks are included, such as the exclusion of the primary benefit to those experiencing FASD, the benefits to FASD caregivers, and the preventative benefits, the economic and social burden associated with secondary disabilities will "pay-off" if the effectiveness of the program in reducing secondary disabilities is 28%.
Advanced Aeroservoelastic Testing and Data Analysis (Les Essais Aeroservoelastiques et l’Analyse des Donnees).

DTIC Science & Technology

1995-11-01

network - based AFS concepts. Neural networks can addition of vanes in each engine exhaust for thrust provide...parameter estimation programs 19-11 8.6 Neural Network Based Methods unknown parameters of the postulated state space model Artificial neural network ...Forward Neural Network the network that the applicability of the recurrent neural and ii) Recurrent Neural Network [117-119]. network to
Hierarchical graphical-based human pose estimation via local multi-resolution convolutional neural network

NASA Astrophysics Data System (ADS)

Zhu, Aichun; Wang, Tian; Snoussi, Hichem

2018-03-01

This paper addresses the problems of the graphical-based human pose estimation in still images, including the diversity of appearances and confounding background clutter. We present a new architecture for estimating human pose using a Convolutional Neural Network (CNN). Firstly, a Relative Mixture Deformable Model (RMDM) is defined by each pair of connected parts to compute the relative spatial information in the graphical model. Secondly, a Local Multi-Resolution Convolutional Neural Network (LMR-CNN) is proposed to train and learn the multi-scale representation of each body parts by combining different levels of part context. Thirdly, a LMR-CNN based hierarchical model is defined to explore the context information of limb parts. Finally, the experimental results demonstrate the effectiveness of the proposed deep learning approach for human pose estimation.
Classification and pose estimation of objects using nonlinear features

NASA Astrophysics Data System (ADS)

Talukder, Ashit; Casasent, David P.

1998-03-01

A new nonlinear feature extraction method called the maximum representation and discrimination feature (MRDF) method is presented for extraction of features from input image data. It implements transformations similar to the Sigma-Pi neural network. However, the weights of the MRDF are obtained in closed form, and offer advantages compared to nonlinear neural network implementations. The features extracted are useful for both object discrimination (classification) and object representation (pose estimation). We show its use in estimating the class and pose of images of real objects and rendered solid CAD models of machine parts from single views using a feature-space trajectory (FST) neural network classifier. We show more accurate classification and pose estimation results than are achieved by standard principal component analysis (PCA) and Fukunaga-Koontz (FK) feature extraction methods.

Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Jet PRopulsion Laboratory (JPL) Deep Space Network (DSN) Data System implementation tasks is described. The resource estimation mdel modifies and combines a number of existing models. The model calibrates the task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit JPL software life-cycle statistics.
Network dynamics of social influence in the wisdom of crowds

PubMed Central

Brackbill, Devon; Centola, Damon

2017-01-01

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton’s discovery of the “wisdom of crowds” [Galton F (1907) Nature 75:450–451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals’ estimates became more similar when subjects observed each other’s beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020–9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error. PMID:28607070
Network dynamics of social influence in the wisdom of crowds.

PubMed

Becker, Joshua; Brackbill, Devon; Centola, Damon

2017-06-27

A longstanding problem in the social, biological, and computational sciences is to determine how groups of distributed individuals can form intelligent collective judgments. Since Galton's discovery of the "wisdom of crowds" [Galton F (1907) Nature 75:450-451], theories of collective intelligence have suggested that the accuracy of group judgments requires individuals to be either independent, with uncorrelated beliefs, or diverse, with negatively correlated beliefs [Page S (2008) The Difference: How the Power of Diversity Creates Better Groups, Firms, Schools, and Societies ]. Previous experimental studies have supported this view by arguing that social influence undermines the wisdom of crowds. These results showed that individuals' estimates became more similar when subjects observed each other's beliefs, thereby reducing diversity without a corresponding increase in group accuracy [Lorenz J, Rauhut H, Schweitzer F, Helbing D (2011) Proc Natl Acad Sci USA 108:9020-9025]. By contrast, we show general network conditions under which social influence improves the accuracy of group estimates, even as individual beliefs become more similar. We present theoretical predictions and experimental results showing that, in decentralized communication networks, group estimates become reliably more accurate as a result of information exchange. We further show that the dynamics of group accuracy change with network structure. In centralized networks, where the influence of central individuals dominates the collective estimation process, group estimates become more likely to increase in error.
Improved rapid magnitude estimation for a community-based, low-cost MEMS accelerometer network

USGS Publications Warehouse

Chung, Angela I.; Cochran, Elizabeth S.; Kaiser, Anna E.; Christensen, Carl M.; Yildirim, Battalgazi; Lawrence, Jesse F.

2015-01-01

Immediately following the Mw 7.2 Darfield, New Zealand, earthquake, over 180 Quake‐Catcher Network (QCN) low‐cost micro‐electro‐mechanical systems accelerometers were deployed in the Canterbury region. Using data recorded by this dense network from 2010 to 2013, we significantly improved the QCN rapid magnitude estimation relationship. The previous scaling relationship (Lawrence et al., 2014) did not accurately estimate the magnitudes of nearby (<35 km) events. The new scaling relationship estimates earthquake magnitudes within 1 magnitude unit of the GNS Science GeoNet earthquake catalog magnitudes for 99% of the events tested, within 0.5 magnitude units for 90% of the events, and within 0.25 magnitude units for 57% of the events. These magnitudes are reliably estimated within 3 s of the initial trigger recorded on at least seven stations. In this report, we present the methods used to calculate a new scaling relationship and demonstrate the accuracy of the revised magnitude estimates using a program that is able to retrospectively estimate event magnitudes using archived data.
Robust neural network with applications to credit portfolio data analysis.

PubMed

Feng, Yijia; Li, Runze; Sudjianto, Agus; Zhang, Yiyun

2010-01-01

In this article, we study nonparametric conditional quantile estimation via neural network structure. We proposed an estimation method that combines quantile regression and neural network (robust neural network, RNN). It provides good smoothing performance in the presence of outliers and can be used to construct prediction bands. A Majorization-Minimization (MM) algorithm was developed for optimization. Monte Carlo simulation study is conducted to assess the performance of RNN. Comparison with other nonparametric regression methods (e.g., local linear regression and regression splines) in real data application demonstrate the advantage of the newly proposed procedure.
Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model

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

Chassin, David P.; Posse, Christian

2005-09-15

The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabási-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using other methods and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.
Evaluating North American Electric Grid Reliability Using the Barabasi-Albert Network Model

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

Chassin, David P.; Posse, Christian

2005-09-15

The reliability of electric transmission systems is examined using a scale-free model of network topology and failure propagation. The topologies of the North American eastern and western electric grids are analyzed to estimate their reliability based on the Barabasi-Albert network model. A commonly used power system reliability index is computed using a simple failure propagation model. The results are compared to the values of power system reliability indices previously obtained using standard power engineering methods, and they suggest that scale-free network models are usable to estimate aggregate electric grid reliability.
An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation

PubMed Central

Wang, Yikai; Kang, Jian; Kemmer, Phebe B.; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package “DensParcorr” can be downloaded from CRAN for implementing the proposed statistical methods. PMID:27242395
An Efficient and Reliable Statistical Method for Estimating Functional Connectivity in Large Scale Brain Networks Using Partial Correlation.

PubMed

Wang, Yikai; Kang, Jian; Kemmer, Phebe B; Guo, Ying

2016-01-01

Currently, network-oriented analysis of fMRI data has become an important tool for understanding brain organization and brain networks. Among the range of network modeling methods, partial correlation has shown great promises in accurately detecting true brain network connections. However, the application of partial correlation in investigating brain connectivity, especially in large-scale brain networks, has been limited so far due to the technical challenges in its estimation. In this paper, we propose an efficient and reliable statistical method for estimating partial correlation in large-scale brain network modeling. Our method derives partial correlation based on the precision matrix estimated via Constrained L1-minimization Approach (CLIME), which is a recently developed statistical method that is more efficient and demonstrates better performance than the existing methods. To help select an appropriate tuning parameter for sparsity control in the network estimation, we propose a new Dens-based selection method that provides a more informative and flexible tool to allow the users to select the tuning parameter based on the desired sparsity level. Another appealing feature of the Dens-based method is that it is much faster than the existing methods, which provides an important advantage in neuroimaging applications. Simulation studies show that the Dens-based method demonstrates comparable or better performance with respect to the existing methods in network estimation. We applied the proposed partial correlation method to investigate resting state functional connectivity using rs-fMRI data from the Philadelphia Neurodevelopmental Cohort (PNC) study. Our results show that partial correlation analysis removed considerable between-module marginal connections identified by full correlation analysis, suggesting these connections were likely caused by global effects or common connection to other nodes. Based on partial correlation, we find that the most significant direct connections are between homologous brain locations in the left and right hemisphere. When comparing partial correlation derived under different sparse tuning parameters, an important finding is that the sparse regularization has more shrinkage effects on negative functional connections than on positive connections, which supports previous findings that many of the negative brain connections are due to non-neurophysiological effects. An R package "DensParcorr" can be downloaded from CRAN for implementing the proposed statistical methods.
Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

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

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less
Nonlinear Dynamic Model-Based Multiobjective Sensor Network Design Algorithm for a Plant with an Estimator-Based Control System

DOE PAGES

Paul, Prokash; Bhattacharyya, Debangsu; Turton, Richard; ...

2017-06-06

Here, a novel sensor network design (SND) algorithm is developed for maximizing process efficiency while minimizing sensor network cost for a nonlinear dynamic process with an estimator-based control system. The multiobjective optimization problem is solved following a lexicographic approach where the process efficiency is maximized first followed by minimization of the sensor network cost. The partial net present value, which combines the capital cost due to the sensor network and the operating cost due to deviation from the optimal efficiency, is proposed as an alternative objective. The unscented Kalman filter is considered as the nonlinear estimator. The large-scale combinatorial optimizationmore » problem is solved using a genetic algorithm. The developed SND algorithm is applied to an acid gas removal (AGR) unit as part of an integrated gasification combined cycle (IGCC) power plant with CO 2 capture. Due to the computational expense, a reduced order nonlinear model of the AGR process is identified and parallel computation is performed during implementation.« less
Expanding Local Cancer Clinical Trial Options: Analysis of the Economic Impact of the Midwest Cancer Alliance in Kansas.

PubMed

Gafford, J Atlee; Gurley-Calvez, Tami; Krebill, Hope; Lai, Sue Min; Christiadi; Doolittle, Gary C

2017-09-01

Patients benefit from receiving cancer treatment closer to home when possible and at high-volume regional centers when specialized care is required. The purpose of this analysis was to estimate the economic impact of retaining more patients in-state for cancer clinical trials and care, which might offset some of the costs of establishing broader cancer trial and treatment networks. Kansas Cancer Registry data were used to estimate the number of patients retained in-state for cancer care following the expansion of local cancer clinical trial options through the Midwest Cancer Alliance based at the University of Kansas Medical Center. The 2014 economic impact of this enhanced local clinical trial network was estimated in four parts: Medical spending was estimated on the basis of National Cancer Institute cost-of-care estimates. Household travel cost savings were estimated as the difference between in-state and out-of-state travel costs. Trial-related grant income was calculated from administrative records. Indirect and induced economic benefits to the state were estimated using an economic impact model. The authors estimated that the enhanced local cancer clinical trial network resulted in approximately $6.9 million in additional economic activity in the state in 2014, or $362,000 per patient retained in-state. This estimate includes $3.6 million in direct spending and $3.3 million in indirect economic activity. The enhanced trial network also resulted in 45 additional jobs. Retaining patients in-state for cancer care and clinical trial participation allows patients to remain closer to home for care and enhances the state economy.
Evaluation and design of a rain gauge network using a statistical optimization method in a severe hydro-geological hazard prone area

NASA Astrophysics Data System (ADS)

Fattoruso, Grazia; Longobardi, Antonia; Pizzuti, Alfredo; Molinara, Mario; Marocco, Claudio; De Vito, Saverio; Tortorella, Francesco; Di Francia, Girolamo

2017-06-01

Rainfall data collection gathered in continuous by a distributed rain gauge network is instrumental to more effective hydro-geological risk forecasting and management services though the input estimated rainfall fields suffer from prediction uncertainty. Optimal rain gauge networks can generate accurate estimated rainfall fields. In this research work, a methodology has been investigated for evaluating an optimal rain gauges network aimed at robust hydrogeological hazard investigations. The rain gauges of the Sarno River basin (Southern Italy) has been evaluated by optimizing a two-objective function that maximizes the estimated accuracy and minimizes the total metering cost through the variance reduction algorithm along with the climatological variogram (time-invariant). This problem has been solved by using an enumerative search algorithm, evaluating the exact Pareto-front by an efficient computational time.
Estimating pole/zero errors in GSN-IRIS/USGS network calibration metadata

USGS Publications Warehouse

Ringler, A.T.; Hutt, C.R.; Aster, R.; Bolton, H.; Gee, L.S.; Storm, T.

2012-01-01

Mapping the digital record of a seismograph into true ground motion requires the correction of the data by some description of the instrument's response. For the Global Seismographic Network (Butler et al., 2004), as well as many other networks, this instrument response is represented as a Laplace domain pole–zero model and published in the Standard for the Exchange of Earthquake Data (SEED) format. This Laplace representation assumes that the seismometer behaves as a linear system, with any abrupt changes described adequately via multiple time-invariant epochs. The SEED format allows for published instrument response errors as well, but these typically have not been estimated or provided to users. We present an iterative three-step method to estimate the instrument response parameters (poles and zeros) and their associated errors using random calibration signals. First, we solve a coarse nonlinear inverse problem using a least-squares grid search to yield a first approximation to the solution. This approach reduces the likelihood of poorly estimated parameters (a local-minimum solution) caused by noise in the calibration records and enhances algorithm convergence. Second, we iteratively solve a nonlinear parameter estimation problem to obtain the least-squares best-fit Laplace pole–zero–gain model. Third, by applying the central limit theorem, we estimate the errors in this pole–zero model by solving the inverse problem at each frequency in a two-thirds octave band centered at each best-fit pole–zero frequency. This procedure yields error estimates of the 99% confidence interval. We demonstrate the method by applying it to a number of recent Incorporated Research Institutions in Seismology/United States Geological Survey (IRIS/USGS) network calibrations (network code IU).
Functional subdivision of group-ICA results of fMRI data collected during cinema viewing.

PubMed

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film ("At land" by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative.
Adaptive enhanced sampling by force-biasing using neural networks

NASA Astrophysics Data System (ADS)

Guo, Ashley Z.; Sevgen, Emre; Sidky, Hythem; Whitmer, Jonathan K.; Hubbell, Jeffrey A.; de Pablo, Juan J.

2018-04-01

A machine learning assisted method is presented for molecular simulation of systems with rugged free energy landscapes. The method is general and can be combined with other advanced sampling techniques. In the particular implementation proposed here, it is illustrated in the context of an adaptive biasing force approach where, rather than relying on discrete force estimates, one can resort to a self-regularizing artificial neural network to generate continuous, estimated generalized forces. By doing so, the proposed approach addresses several shortcomings common to adaptive biasing force and other algorithms. Specifically, the neural network enables (1) smooth estimates of generalized forces in sparsely sampled regions, (2) force estimates in previously unexplored regions, and (3) continuous force estimates with which to bias the simulation, as opposed to biases generated at specific points of a discrete grid. The usefulness of the method is illustrated with three different examples, chosen to highlight the wide range of applicability of the underlying concepts. In all three cases, the new method is found to enhance considerably the underlying traditional adaptive biasing force approach. The method is also found to provide improvements over previous implementations of neural network assisted algorithms.
The Value Estimation of an HFGW Frequency Time Standard for Telecommunications Network Optimization

NASA Astrophysics Data System (ADS)

Harper, Colby; Stephenson, Gary

2007-01-01

The emerging technology of gravitational wave control is used to augment a communication system using a development roadmap suggested in Stephenson (2003) for applications emphasized in Baker (2005). In the present paper consideration is given to the value of a High Frequency Gravitational Wave (HFGW) channel purely as providing a method of frequency and time reference distribution for use within conventional Radio Frequency (RF) telecommunications networks. Specifically, the native value of conventional telecommunications networks may be optimized by using an unperturbed frequency time standard (FTS) to (1) improve terminal navigation and Doppler estimation performance via improved time difference of arrival (TDOA) from a universal time reference, and (2) improve acquisition speed, coding efficiency, and dynamic bandwidth efficiency through the use of a universal frequency reference. A model utilizing a discounted cash flow technique provides an estimation of the additional value using HFGW FTS technology could bring to a mixed technology HFGW/RF network. By applying a simple net present value analysis with supporting reference valuations to such a network, it is demonstrated that an HFGW FTS could create a sizable improvement within an otherwise conventional RF telecommunications network. Our conservative model establishes a low-side value estimate of approximately 50B USD Net Present Value for an HFGW FTS service, with reasonable potential high-side values to significant multiples of this low-side value floor.
Exploring the effects of dimensionality reduction in deep networks for force estimation in robotic-assisted surgery

NASA Astrophysics Data System (ADS)

Aviles, Angelica I.; Alsaleh, Samar; Sobrevilla, Pilar; Casals, Alicia

2016-03-01

Robotic-Assisted Surgery approach overcomes the limitations of the traditional laparoscopic and open surgeries. However, one of its major limitations is the lack of force feedback. Since there is no direct interaction between the surgeon and the tissue, there is no way of knowing how much force the surgeon is applying which can result in irreversible injuries. The use of force sensors is not practical since they impose different constraints. Thus, we make use of a neuro-visual approach to estimate the applied forces, in which the 3D shape recovery together with the geometry of motion are used as input to a deep network based on LSTM-RNN architecture. When deep networks are used in real time, pre-processing of data is a key factor to reduce complexity and improve the network performance. A common pre-processing step is dimensionality reduction which attempts to eliminate redundant and insignificant information by selecting a subset of relevant features to use in model construction. In this work, we show the effects of dimensionality reduction in a real-time application: estimating the applied force in Robotic-Assisted Surgeries. According to the results, we demonstrated positive effects of doing dimensionality reduction on deep networks including: faster training, improved network performance, and overfitting prevention. We also show a significant accuracy improvement, ranging from about 33% to 86%, over existing approaches related to force estimation.
Estimating surface soil moisture from SMAP observations using a neural network technique

USDA-ARS?s Scientific Manuscript database

A Neural Network (NN) algorithm was developed to estimate global surface soil moisture for April 2015 to June 2016 with a 2-3 day repeat frequency using passive microwave observations from the Soil Moisture Active Passive (SMAP) satellite, surface soil temperatures from the NASA Goddard Earth Observ...
Exploration of Heterogeneity in Distributed Research Network Drug Safety Analyses

ERIC Educational Resources Information Center

Hansen, Richard A.; Zeng, Peng; Ryan, Patrick; Gao, Juan; Sonawane, Kalyani; Teeter, Benjamin; Westrich, Kimberly; Dubois, Robert W.

2014-01-01

Distributed data networks representing large diverse populations are an expanding focus of drug safety research. However, interpreting results is difficult when treatment effect estimates vary across datasets (i.e., heterogeneity). In a previous study, risk estimates were generated for selected drugs and potential adverse outcomes. Analyses were…

Cross-section and panel estimates of peer effects in early adolescent cannabis use: With a little help from my 'friends once removed'.

PubMed

Moriarty, John; McVicar, Duncan; Higgins, Kathryn

2016-08-01

Peer effects in adolescent cannabis are difficult to estimate, due in part to the lack of appropriate data on behaviour and social ties. This paper exploits survey data that have many desirable properties and have not previously been used for this purpose. The data set, collected from teenagers in three annual waves from 2002 to 2004 contains longitudinal information about friendship networks within schools (N = 5020). We exploit these data on network structure to estimate peer effects on adolescents from their nominated friends within school using two alternative approaches to identification. First, we present a cross-sectional instrumental variable (IV) estimate of peer effects that exploits network structure at the second degree, i.e. using information on friends of friends who are not themselves ego's friends to instrument for the cannabis use of friends. Second, we present an individual fixed effects estimate of peer effects using the full longitudinal structure of the data. Both innovations allow a greater degree of control for correlated effects than is commonly the case in the substance-use peer effects literature, improving our chances of obtaining estimates of peer effects than can be plausibly interpreted as causal. Both estimates suggest positive peer effects of non-trivial magnitude, although the IV estimate is imprecise. Furthermore, when we specify identical models with behaviour and characteristics of randomly selected school peers in place of friends', we find effectively zero effect from these 'placebo' peers, lending credence to our main estimates. We conclude that cross-sectional data can be used to estimate plausible positive peer effects on cannabis use where network structure information is available and appropriately exploited. Copyright © 2016 Elsevier Ltd. All rights reserved.
Observability and Estimation of Distributed Space Systems via Local Information-Exchange Networks

NASA Technical Reports Server (NTRS)

Fathpour, Nanaz; Hadaegh, Fred Y.; Mesbahi, Mehran; Rahmani, Amirreza

2011-01-01

Spacecraft formation flying involves the coordination of states among multiple spacecraft through relative sensing, inter-spacecraft communication, and control. Most existing formation-flying estimation algorithms can only be supported via highly centralized, all-to-all, static relative sensing. New algorithms are proposed that are scalable, modular, and robust to variations in the topology and link characteristics of the formation exchange network. These distributed algorithms rely on a local information exchange network, relaxing the assumptions on existing algorithms. Distributed space systems rely on a signal transmission network among multiple spacecraft for their operation. Control and coordination among multiple spacecraft in a formation is facilitated via a network of relative sensing and interspacecraft communications. Guidance, navigation, and control rely on the sensing network. This network becomes more complex the more spacecraft are added, or as mission requirements become more complex. The observability of a formation state was observed by a set of local observations from a particular node in the formation. Formation observability can be parameterized in terms of the matrices appearing in the formation dynamics and observation matrices. An agreement protocol was used as a mechanism for observing formation states from local measurements. An agreement protocol is essentially an unforced dynamic system whose trajectory is governed by the interconnection geometry and initial condition of each node, with a goal of reaching a common value of interest. The observability of the interconnected system depends on the geometry of the network, as well as the position of the observer relative to the topology. For the first time, critical GN&C (guidance, navigation, and control estimation) subsystems are synthesized by bringing the contribution of the spacecraft information-exchange network to the forefront of algorithmic analysis and design. The result is a formation estimation algorithm that is modular and robust to variations in the topology and link properties of the underlying formation network.
To trade or not to trade: Link prediction in the virtual water network

NASA Astrophysics Data System (ADS)

Tuninetti, Marta; Tamea, Stefania; Laio, Francesco; Ridolfi, Luca

2017-12-01

In the international trade network, links express the (temporary) presence of a commercial exchange of goods between any two countries. Given the dynamical behaviour of the trade network, where links are created and dismissed every year, predicting the link activation/deactivation is an open research question. Through the international trade network of agricultural goods, water resources are 'virtually' transferred from the country of production to the country of consumption. We propose a novel methodology for link prediction applied to the network of virtual water trade. Starting from the assumption of having links between any two countries, we estimate the associated virtual water flows by means of a gravity-law model using country and link characteristics as drivers. We consider the links with estimated flows higher than 1000 m3/year as active links, while the others as non-active links. Flows traded along estimated active links are then re-estimated using a similar but differently-calibrated gravity-law model. We were able to correctly model 84% of the existing links and 93% of the non-existing links in year 2011. It is worth to note that the predicted active links carry 99% of the global virtual water flow; hence, missed links are mainly those where a minimum volume of virtual water is exchanged. Results indicate that, over the period from 1986 to 2011, population, geographical distances between countries, and agricultural efficiency (through fertilizers use) are the major factors driving the link activation and deactivation. As opposed to other (network-based) models for link prediction, the proposed method is able to reconstruct the network architecture without any prior knowledge of the network topology, using only the nodes and links attributes; it thus represents a general method that can be applied to other networks such as food or value trade networks.
F-MAP: A Bayesian approach to infer the gene regulatory network using external hints

PubMed Central

Shahdoust, Maryam; Mahjub, Hossein; Sadeghi, Mehdi

2017-01-01

The Common topological features of related species gene regulatory networks suggest reconstruction of the network of one species by using the further information from gene expressions profile of related species. We present an algorithm to reconstruct the gene regulatory network named; F-MAP, which applies the knowledge about gene interactions from related species. Our algorithm sets a Bayesian framework to estimate the precision matrix of one species microarray gene expressions dataset to infer the Gaussian Graphical model of the network. The conjugate Wishart prior is used and the information from related species is applied to estimate the hyperparameters of the prior distribution by using the factor analysis. Applying the proposed algorithm on six related species of drosophila shows that the precision of reconstructed networks is improved considerably compared to the precision of networks constructed by other Bayesian approaches. PMID:28938012
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.
BIOREL: the benchmark resource to estimate the relevance of the gene networks.

PubMed

Antonov, Alexey V; Mewes, Hans W

2006-02-06

The progress of high-throughput methodologies in functional genomics has lead to the development of statistical procedures to infer gene networks from various types of high-throughput data. However, due to the lack of common standards, the biological significance of the results of the different studies is hard to compare. To overcome this problem we propose a benchmark procedure and have developed a web resource (BIOREL), which is useful for estimating the biological relevance of any genetic network by integrating different sources of biological information. The associations of each gene from the network are classified as biologically relevant or not. The proportion of genes in the network classified as "relevant" is used as the overall network relevance score. Employing synthetic data we demonstrated that such a score ranks the networks fairly in respect to the relevance level. Using BIOREL as the benchmark resource we compared the quality of experimental and theoretically predicted protein interaction data.
Dynamical estimation of neuron and network properties III: network analysis using neuron spike times.

PubMed

Knowlton, Chris; Meliza, C Daniel; Margoliash, Daniel; Abarbanel, Henry D I

2014-06-01

Estimating the behavior of a network of neurons requires accurate models of the individual neurons along with accurate characterizations of the connections among them. Whereas for a single cell, measurements of the intracellular voltage are technically feasible and sufficient to characterize a useful model of its behavior, making sufficient numbers of simultaneous intracellular measurements to characterize even small networks is infeasible. This paper builds on prior work on single neurons to explore whether knowledge of the time of spiking of neurons in a network, once the nodes (neurons) have been characterized biophysically, can provide enough information to usefully constrain the functional architecture of the network: the existence of synaptic links among neurons and their strength. Using standardized voltage and synaptic gating variable waveforms associated with a spike, we demonstrate that the functional architecture of a small network of model neurons can be established.
An option for measuring maternal mortality in developing countries: a survey using community informants.

PubMed

Qomariyah, Siti Nurul; Braunholtz, David; Achadi, Endang L; Witten, Karen H; Pambudi, Eko Setyo; Anggondowati, Trisari; Latief, Kamaluddin; Graham, Wendy J

2010-11-17

The maternal mortality ratio (MMR) remains high in most developing countries. Local, recent estimates of MMR are needed to motivate policymakers and evaluate interventions. But, estimating MMR, in the absence of vital registration systems, is difficult. This paper describes an efficient approach using village informant networks to capture maternal death cases (Maternal Deaths from Informants/Maternal Death Follow on Review or MADE-IN/MADE-FOR) developed to address this gap, and examines its validity and efficiency. MADE-IN used two village informant networks - heads of neighbourhood units (RTs) and health volunteers (Kaders). Informants were invited to attend separate network meetings - through the village head (for the RT) and through health centre for the kaders. Attached to the letter was a form with written instructions requesting informants list deaths of women of reproductive age (WRA) in the village during the previous two years. At a 'listing meeting' the informants' understanding on the form was checked, informants could correct their forms, and then collectively agreed a consolidated list. MADE-FOR consisted of visits relatives of likely pregnancy related deaths (PRDs) identified from MADE-IN, to confirm the PRD status and gather information about the cause of death. Capture-recapture (CRC) analysis enabled estimation of coverage rates of the two networks, and of total PRDs. The RT network identified a higher proportion of PRDs than the kaders (estimated 0.85 vs. 0.71), but the latter was easier and cheaper to access. Assigned PRD status amongst identified WRA deaths was more accurate for the kader network, and seemingly for more recent deaths, and for deaths from rural areas. Assuming information on live births from an existing source to calculate the MMR, MADE-IN/MADE-FOR cost only $0.1 (US) per women-year risk of exposure, substantially cheaper than alternatives. This study shows that reliable local, recent estimates of MMR can be obtained relatively cheaply using two independent informant networks to identify cases. Neither network captured all PRDs, but capture-recapture analysis allowed self-calibration. However, it requires careful avoidance of false-positives, and matching of cases identified by both networks, which was achieved by the home visit.
Reliability Correction for Functional Connectivity: Theory and Implementation

PubMed Central

Mueller, Sophia; Wang, Danhong; Fox, Michael D.; Pan, Ruiqi; Lu, Jie; Li, Kuncheng; Sun, Wei; Buckner, Randy L.; Liu, Hesheng

2016-01-01

Network properties can be estimated using functional connectivity MRI (fcMRI). However, regional variation of the fMRI signal causes systematic biases in network estimates including correlation attenuation in regions of low measurement reliability. Here we computed the spatial distribution of fcMRI reliability using longitudinal fcMRI datasets and demonstrated how pre-estimated reliability maps can correct for correlation attenuation. As a test case of reliability-based attenuation correction we estimated properties of the default network, where reliability was significantly lower than average in the medial temporal lobe and higher in the posterior medial cortex, heterogeneity that impacts estimation of the network. Accounting for this bias using attenuation correction revealed that the medial temporal lobe’s contribution to the default network is typically underestimated. To render this approach useful to a greater number of datasets, we demonstrate that test-retest reliability maps derived from repeated runs within a single scanning session can be used as a surrogate for multi-session reliability mapping. Using data segments with different scan lengths between 1 and 30 min, we found that test-retest reliability of connectivity estimates increases with scan length while the spatial distribution of reliability is relatively stable even at short scan lengths. Finally, analyses of tertiary data revealed that reliability distribution is influenced by age, neuropsychiatric status and scanner type, suggesting that reliability correction may be especially important when studying between-group differences. Collectively, these results illustrate that reliability-based attenuation correction is an easily implemented strategy that mitigates certain features of fMRI signal nonuniformity. PMID:26493163
Different shades of default mode disturbance in schizophrenia: Subnodal covariance estimation in structure and function.

PubMed

Lefort-Besnard, Jérémy; Bassett, Danielle S; Smallwood, Jonathan; Margulies, Daniel S; Derntl, Birgit; Gruber, Oliver; Aleman, Andre; Jardri, Renaud; Varoquaux, Gaël; Thirion, Bertrand; Eickhoff, Simon B; Bzdok, Danilo

2018-02-01

Schizophrenia is a devastating mental disease with an apparent disruption in the highly associative default mode network (DMN). Interplay between this canonical network and others probably contributes to goal-directed behavior so its disturbance is a candidate neural fingerprint underlying schizophrenia psychopathology. Previous research has reported both hyperconnectivity and hypoconnectivity within the DMN, and both increased and decreased DMN coupling with the multimodal saliency network (SN) and dorsal attention network (DAN). This study systematically revisited network disruption in patients with schizophrenia using data-derived network atlases and multivariate pattern-learning algorithms in a multisite dataset (n = 325). Resting-state fluctuations in unconstrained brain states were used to estimate functional connectivity, and local volume differences between individuals were used to estimate structural co-occurrence within and between the DMN, SN, and DAN. In brain structure and function, sparse inverse covariance estimates of network coupling were used to characterize healthy participants and patients with schizophrenia, and to identify statistically significant group differences. Evidence did not confirm that the backbone of the DMN was the primary driver of brain dysfunction in schizophrenia. Instead, functional and structural aberrations were frequently located outside of the DMN core, such as in the anterior temporoparietal junction and precuneus. Additionally, functional covariation analyses highlighted dysfunctional DMN-DAN coupling, while structural covariation results highlighted aberrant DMN-SN coupling. Our findings reframe the role of the DMN core and its relation to canonical networks in schizophrenia. We thus underline the importance of large-scale neural interactions as effective biomarkers and indicators of how to tailor psychiatric care to single patients. © 2017 Wiley Periodicals, Inc.
A unifying view of synchronization for data assimilation in complex nonlinear networks

NASA Astrophysics Data System (ADS)

Abarbanel, Henry D. I.; Shirman, Sasha; Breen, Daniel; Kadakia, Nirag; Rey, Daniel; Armstrong, Eve; Margoliash, Daniel

2017-12-01

Networks of nonlinear systems contain unknown parameters and dynamical degrees of freedom that may not be observable with existing instruments. From observable state variables, we want to estimate the connectivity of a model of such a network and determine the full state of the model at the termination of a temporal observation window during which measurements transfer information to a model of the network. The model state at the termination of a measurement window acts as an initial condition for predicting the future behavior of the network. This allows the validation (or invalidation) of the model as a representation of the dynamical processes producing the observations. Once the model has been tested against new data, it may be utilized as a predictor of responses to innovative stimuli or forcing. We describe a general framework for the tasks involved in the "inverse" problem of determining properties of a model built to represent measured output from physical, biological, or other processes when the measurements are noisy, the model has errors, and the state of the model is unknown when measurements begin. This framework is called statistical data assimilation and is the best one can do in estimating model properties through the use of the conditional probability distributions of the model state variables, conditioned on observations. There is a very broad arena of applications of the methods described. These include numerical weather prediction, properties of nonlinear electrical circuitry, and determining the biophysical properties of functional networks of neurons. Illustrative examples will be given of (1) estimating the connectivity among neurons with known dynamics in a network of unknown connectivity, and (2) estimating the biophysical properties of individual neurons in vitro taken from a functional network underlying vocalization in songbirds.
Source Localization Using Wireless Sensor Networks

DTIC Science & Technology

2006-06-01

performance of the hybrid SI/ML estimation method. A wireless sensor network is simulated in NS-2 to study the network throughput, delay and jitter...indicate that the wireless sensor network has low delay and can support fast information exchange needed in counter-sniper applications.
Downscaling catchment scale flood risk to contributing sub-catchments to determine the optimum location for flood management.

NASA Astrophysics Data System (ADS)

Pattison, Ian; Lane, Stuart; Hardy, Richard; Reaney, Sim

2010-05-01

The recent increase in flood frequency and magnitude has been hypothesised to have been caused by either climate change or land management. Field scale studies have found that changing land management practices does affect local runoff and streamflow, but upscaling these effects to the catchment scale continues to be problematic, both conceptually and more importantly methodologically. The impact on downstream flood risk is highly dependent upon where the changes are in the catchment, indicating that some areas of the catchment are more important in determining downstream flood risk than others. This is a major flaw in the traditional approach to studying the effect of land use on downstream flood risk: catchment scale hydrological models, which treat every cell in the model equally. We are proposing an alternative ideological approach for doing flood management research, which is underpinned by downscaling the downstream effect (problem i.e. flooding) to the upstream causes (contributing sub-catchments). It is hoped that this approach could have several benefits over the traditional upscaling approach. Firstly, it provides an efficient method to prioritise areas for land use management changes to be implemented to reduce downstream flood risk. Secondly, targets for sub-catchment hydrograph change can be determined which will deliver the required downstream effect. Thirdly, it may be possible to detect the effect of land use changes in upstream areas on downstream flood risk, by weighting the areas of most importance in hydrological models. Two methods for doing this downscaling are proposed; 1) data-based statistical analysis; and 2) hydraulic modelling-based downscaling. These will be outlined using the case study of the River Eden, Cumbria, NW England. The data-based methodology uses the timing and magnitude of floods for each sub-catchment. Principal components analysis (PCA) is used to simplify sub-catchment interactions and optimising stepwise regression is used to predict downstream flood magnitude from the significant principal components. Two particular sub-catchments, the Eamont and the Upper Eden were highlighted as explaining the highest proportion of downstream flood risk, with 21.0% and 19.6% respectively. This approach uses the concept of data mining, whereby commonly available discharge data is used in an innovative way to learn about catchment behaviour. An alternative downscaling approach is hydraulic modelling whereby the input hydrographs from each tributary are changed in turn, both in terms of the magnitudes and the timing of the flows. This basic scenario testing approach can be used to assess the sensitivity of downstream flood risk to upstream contributing tributaries. This approach also highlighted the Upper Eden and Eamont as the most sensitive sub-catchments. A 25% reduction in the flows from these sub-catchments resulted in a 33.1cm and 21.9cm stage reduction downstream respectively, while an 8 hour delay of the peak flow caused a 32.3cm and 27.4cm decrease in downstream stage respectively. This alternative flood management approach is not a replacement to traditional hydrological modelling (upscaling), but a pre-step which allows for more focussed and informed investigation of land management scenarios, in the area where they are most likely to have beneficial impacts on downstream flooding.
Neural networks for tracking of unknown SISO discrete-time nonlinear dynamic systems.

PubMed

Aftab, Muhammad Saleheen; Shafiq, Muhammad

2015-11-01

This article presents a Lyapunov function based neural network tracking (LNT) strategy for single-input, single-output (SISO) discrete-time nonlinear dynamic systems. The proposed LNT architecture is composed of two feedforward neural networks operating as controller and estimator. A Lyapunov function based back propagation learning algorithm is used for online adjustment of the controller and estimator parameters. The controller and estimator error convergence and closed-loop system stability analysis is performed by Lyapunov stability theory. Moreover, two simulation examples and one real-time experiment are investigated as case studies. The achieved results successfully validate the controller performance. Copyright © 2015 ISA. Published by Elsevier Ltd. All rights reserved.
A Method of the UMTS-FDD Network Design Based on Universal Load Characteristics

NASA Astrophysics Data System (ADS)

Gajewski, Slawomir

In the paper an original method of the UMTS radio network design was presented. The method is based on simple way of capacity-coverage trade-off estimation for WCDMA/FDD radio interface. This trade-off is estimated by using universal load characteristics and normalized coverage characteristics. The characteristics are useful for any propagation environment as well as for any service performance requirements. The practical applications of these characteristics on radio network planning and maintenance were described.
Sample EP Flow Analysis of Severely Damaged Networks

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

Werley, Kenneth Alan; McCown, Andrew William

These are slides for a presentation at the working group meeting of the WESC SREMP Software Product Integration Team on sample EP flow analysis of severely damaged networks. The following topics are covered: ERCOT EP Transmission Model; Zoomed in to Houston and Overlaying StreetAtlas; EMPACT Solve/Dispatch/Shedding Options; QACS BaseCase Power Flow Solution; 3 Substation Contingency; Gen. & Load/100 Optimal Dispatch; Dispatch Results; Shed Load for Low V; Network Damage Summary; Estimated Service Areas (Potential); Estimated Outage Areas (potential).
Assessing artificial neural networks and statistical methods for infilling missing soil moisture records

NASA Astrophysics Data System (ADS)

Dumedah, Gift; Walker, Jeffrey P.; Chik, Li

2014-07-01

Soil moisture information is critically important for water management operations including flood forecasting, drought monitoring, and groundwater recharge estimation. While an accurate and continuous record of soil moisture is required for these applications, the available soil moisture data, in practice, is typically fraught with missing values. There are a wide range of methods available to infilling hydrologic variables, but a thorough inter-comparison between statistical methods and artificial neural networks has not been made. This study examines 5 statistical methods including monthly averages, weighted Pearson correlation coefficient, a method based on temporal stability of soil moisture, and a weighted merging of the three methods, together with a method based on the concept of rough sets. Additionally, 9 artificial neural networks are examined, broadly categorized into feedforward, dynamic, and radial basis networks. These 14 infilling methods were used to estimate missing soil moisture records and subsequently validated against known values for 13 soil moisture monitoring stations for three different soil layer depths in the Yanco region in southeast Australia. The evaluation results show that the top three highest performing methods are the nonlinear autoregressive neural network, rough sets method, and monthly replacement. A high estimation accuracy (root mean square error (RMSE) of about 0.03 m/m) was found in the nonlinear autoregressive network, due to its regression based dynamic network which allows feedback connections through discrete-time estimation. An equally high accuracy (0.05 m/m RMSE) in the rough sets procedure illustrates the important role of temporal persistence of soil moisture, with the capability to account for different soil moisture conditions.
Effects of contact network structure on epidemic transmission trees: implications for data required to estimate network structure.

PubMed

Carnegie, Nicole Bohme

2018-01-30

Understanding the dynamics of disease spread is key to developing effective interventions to control or prevent an epidemic. The structure of the network of contacts over which the disease spreads has been shown to have a strong influence on the outcome of the epidemic, but an open question remains as to whether it is possible to estimate contact network features from data collected in an epidemic. The approach taken in this paper is to examine the distributions of epidemic outcomes arising from epidemics on networks with particular structural features to assess whether that structure could be measured from epidemic data and what other constraints might be needed to make the problem identifiable. To this end, we vary the network size, mean degree, and transmissibility of the pathogen, as well as the network feature of interest: clustering, degree assortativity, or attribute-based preferential mixing. We record several standard measures of the size and spread of the epidemic, as well as measures that describe the shape of the transmission tree in order to ascertain whether there are detectable signals in the final data from the outbreak. The results suggest that there is potential to estimate contact network features from transmission trees or pure epidemic data, particularly for diseases with high transmissibility or for which the relevant contact network is of low mean degree. Copyright © 2017 John Wiley & Sons, Ltd. Copyright © 2017 John Wiley & Sons, Ltd.
Connectome sensitivity or specificity: which is more important?

PubMed

Zalesky, Andrew; Fornito, Alex; Cocchi, Luca; Gollo, Leonardo L; van den Heuvel, Martijn P; Breakspear, Michael

2016-11-15

Connectomes with high sensitivity and high specificity are unattainable with current axonal fiber reconstruction methods, particularly at the macro-scale afforded by magnetic resonance imaging. Tensor-guided deterministic tractography yields sparse connectomes that are incomplete and contain false negatives (FNs), whereas probabilistic methods steered by crossing-fiber models yield dense connectomes, often with low specificity due to false positives (FPs). Densely reconstructed probabilistic connectomes are typically thresholded to improve specificity at the cost of a reduction in sensitivity. What is the optimal tradeoff between connectome sensitivity and specificity? We show empirically and theoretically that specificity is paramount. Our evaluations of the impact of FPs and FNs on empirical connectomes indicate that specificity is at least twice as important as sensitivity when estimating key properties of brain networks, including topological measures of network clustering, network efficiency and network modularity. Our asymptotic analysis of small-world networks with idealized modular structure reveals that as the number of nodes grows, specificity becomes exactly twice as important as sensitivity to the estimation of the clustering coefficient. For the estimation of network efficiency, the relative importance of specificity grows linearly with the number of nodes. The greater importance of specificity is due to FPs occurring more prevalently between network modules rather than within them. These spurious inter-modular connections have a dramatic impact on network topology. We argue that efforts to maximize the sensitivity of connectome reconstruction should be realigned with the need to map brain networks with high specificity. Copyright © 2016 Elsevier Inc. All rights reserved.
Discrete-event simulation of a wide-area health care network.

PubMed Central

McDaniel, J G

1995-01-01

OBJECTIVE: Predict the behavior and estimate the telecommunication cost of a wide-area message store-and-forward network for health care providers that uses the telephone system. DESIGN: A tool with which to perform large-scale discrete-event simulations was developed. Network models for star and mesh topologies were constructed to analyze the differences in performances and telecommunication costs. The distribution of nodes in the network models approximates the distribution of physicians, hospitals, medical labs, and insurers in the Province of Saskatchewan, Canada. Modeling parameters were based on measurements taken from a prototype telephone network and a survey conducted at two medical clinics. Simulation studies were conducted for both topologies. RESULTS: For either topology, the telecommunication cost of a network in Saskatchewan is projected to be less than $100 (Canadian) per month per node. The estimated telecommunication cost of the star topology is approximately half that of the mesh. Simulations predict that a mean end-to-end message delivery time of two hours or less is achievable at this cost. A doubling of the data volume results in an increase of less than 50% in the mean end-to-end message transfer time. CONCLUSION: The simulation models provided an estimate of network performance and telecommunication cost in a specific Canadian province. At the expected operating point, network performance appeared to be relatively insensitive to increases in data volume. Similar results might be anticipated in other rural states and provinces in North America where a telephone-based network is desired. PMID:7583646

Pruning Neural Networks with Distribution Estimation Algorithms

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

Cantu-Paz, E

2003-01-15

This paper describes the application of four evolutionary algorithms to the pruning of neural networks used in classification problems. Besides of a simple genetic algorithm (GA), the paper considers three distribution estimation algorithms (DEAs): a compact GA, an extended compact GA, and the Bayesian Optimization Algorithm. The objective is to determine if the DEAs present advantages over the simple GA in terms of accuracy or speed in this problem. The experiments used a feed forward neural network trained with standard back propagation and public-domain and artificial data sets. The pruned networks seemed to have better or equal accuracy than themore » original fully-connected networks. Only in a few cases, pruning resulted in less accurate networks. We found few differences in the accuracy of the networks pruned by the four EAs, but found important differences in the execution time. The results suggest that a simple GA with a small population might be the best algorithm for pruning networks on the data sets we tested.« less
Overarching framework for data-based modelling

NASA Astrophysics Data System (ADS)

Schelter, Björn; Mader, Malenka; Mader, Wolfgang; Sommerlade, Linda; Platt, Bettina; Lai, Ying-Cheng; Grebogi, Celso; Thiel, Marco

2014-02-01

One of the main modelling paradigms for complex physical systems are networks. When estimating the network structure from measured signals, typically several assumptions such as stationarity are made in the estimation process. Violating these assumptions renders standard analysis techniques fruitless. We here propose a framework to estimate the network structure from measurements of arbitrary non-linear, non-stationary, stochastic processes. To this end, we propose a rigorous mathematical theory that underlies this framework. Based on this theory, we present a highly efficient algorithm and the corresponding statistics that are immediately sensibly applicable to measured signals. We demonstrate its performance in a simulation study. In experiments of transitions between vigilance stages in rodents, we infer small network structures with complex, time-dependent interactions; this suggests biomarkers for such transitions, the key to understand and diagnose numerous diseases such as dementia. We argue that the suggested framework combines features that other approaches followed so far lack.
Event-triggered H∞ state estimation for semi-Markov jumping discrete-time neural networks with quantization.

PubMed

Rakkiyappan, R; Maheswari, K; Velmurugan, G; Park, Ju H

2018-05-17

This paper investigates H ∞ state estimation problem for a class of semi-Markovian jumping discrete-time neural networks model with event-triggered scheme and quantization. First, a new event-triggered communication scheme is introduced to determine whether or not the current sampled sensor data should be broad-casted and transmitted to the quantizer, which can save the limited communication resource. Second, a novel communication framework is employed by the logarithmic quantizer that quantifies and reduces the data transmission rate in the network, which apparently improves the communication efficiency of networks. Third, a stabilization criterion is derived based on the sufficient condition which guarantees a prescribed H ∞ performance level in the estimation error system in terms of the linear matrix inequalities. Finally, numerical simulations are given to illustrate the correctness of the proposed scheme. Copyright © 2018 Elsevier Ltd. All rights reserved.
Estimation of Energy Expenditure Using a Patch-Type Sensor Module with an Incremental Radial Basis Function Neural Network

PubMed Central

Li, Meina; Kwak, Keun-Chang; Kim, Youn Tae

2016-01-01

Conventionally, indirect calorimetry has been used to estimate oxygen consumption in an effort to accurately measure human body energy expenditure. However, calorimetry requires the subject to wear a mask that is neither convenient nor comfortable. The purpose of our study is to develop a patch-type sensor module with an embedded incremental radial basis function neural network (RBFNN) for estimating the energy expenditure. The sensor module contains one ECG electrode and a three-axis accelerometer, and can perform real-time heart rate (HR) and movement index (MI) monitoring. The embedded incremental network includes linear regression (LR) and RBFNN based on context-based fuzzy c-means (CFCM) clustering. This incremental network is constructed by building a collection of information granules through CFCM clustering that is guided by the distribution of error of the linear part of the LR model. PMID:27669249
1976 Pennsylvania Public Television Network Audience Research Project: Phase 1. Audience Definition and Estimation.

ERIC Educational Resources Information Center

Lindsay, Carl A.; Southworth, Harrison T.

The first of three phases to provide the Pennsylvania Public Television Network (PPTN) and its seven component stations with information and procedures for defining, estimating and characterizing audiences, as well as developing programs and assessing reactions to them, this study concentrated on establishing audience definitions. The basic…
Sensitivity of Inverse Estimation of 2004 Elemental Carbon Emissions Inventory in the United States to the Choice of Observational Networks

EPA Science Inventory

Choice of observational networks used for inverse re-estimation of elemental (or black) carbon (EC) emissions in the United States impacts results. We convert the Thermal Optical Transmittance (TOT) EC measurements to the Thermal Optical Reflectance (TOR) equivalent to make full...
Performance evaluation of an importance sampling technique in a Jackson network

NASA Astrophysics Data System (ADS)

brahim Mahdipour, E.; Masoud Rahmani, Amir; Setayeshi, Saeed

2014-03-01

Importance sampling is a technique that is commonly used to speed up Monte Carlo simulation of rare events. However, little is known regarding the design of efficient importance sampling algorithms in the context of queueing networks. The standard approach, which simulates the system using an a priori fixed change of measure suggested by large deviation analysis, has been shown to fail in even the simplest network settings. Estimating probabilities associated with rare events has been a topic of great importance in queueing theory, and in applied probability at large. In this article, we analyse the performance of an importance sampling estimator for a rare event probability in a Jackson network. This article carries out strict deadlines to a two-node Jackson network with feedback whose arrival and service rates are modulated by an exogenous finite state Markov process. We have estimated the probability of network blocking for various sets of parameters, and also the probability of missing the deadline of customers for different loads and deadlines. We have finally shown that the probability of total population overflow may be affected by various deadline values, service rates and arrival rates.
Fast Two-Dimensional Bubble Analysis of Biopolymer Filamentous Networks Pore Size from Confocal Microscopy Thin Data Stacks

PubMed Central

Molteni, Matteo; Magatti, Davide; Cardinali, Barbara; Rocco, Mattia; Ferri, Fabio

2013-01-01

The average pore size ξ0 of filamentous networks assembled from biological macromolecules is one of the most important physical parameters affecting their biological functions. Modern optical methods, such as confocal microscopy, can noninvasively image such networks, but extracting a quantitative estimate of ξ0 is a nontrivial task. We present here a fast and simple method based on a two-dimensional bubble approach, which works by analyzing one by one the (thresholded) images of a series of three-dimensional thin data stacks. No skeletonization or reconstruction of the full geometry of the entire network is required. The method was validated by using many isotropic in silico generated networks of different structures, morphologies, and concentrations. For each type of network, the method provides accurate estimates (a few percent) of the average and the standard deviation of the three-dimensional distribution of the pore sizes, defined as the diameters of the largest spheres that can be fit into the pore zones of the entire gel volume. When applied to the analysis of real confocal microscopy images taken on fibrin gels, the method provides an estimate of ξ0 consistent with results from elastic light scattering data. PMID:23473499
Exposure, hazard, and survival analysis of diffusion on social networks.

PubMed

Wu, Jiacheng; Crawford, Forrest W; Kim, David A; Stafford, Derek; Christakis, Nicholas A

2018-04-29

Sociologists, economists, epidemiologists, and others recognize the importance of social networks in the diffusion of ideas and behaviors through human societies. To measure the flow of information on real-world networks, researchers often conduct comprehensive sociometric mapping of social links between individuals and then follow the spread of an "innovation" from reports of adoption or change in behavior over time. The innovation is introduced to a small number of individuals who may also be encouraged to spread it to their network contacts. In conjunction with the known social network, the pattern of adoptions gives researchers insight into the spread of the innovation in the population and factors associated with successful diffusion. Researchers have used widely varying statistical tools to estimate these quantities, and there is disagreement about how to analyze diffusion on fully observed networks. Here, we describe a framework for measuring features of diffusion processes on social networks using the epidemiological concepts of exposure and competing risks. Given a realization of a diffusion process on a fully observed network, we show that classical survival regression models can be adapted to estimate the rate of diffusion, and actor/edge attributes associated with successful transmission or adoption, while accounting for the topology of the social network. We illustrate these tools by applying them to a randomized network intervention trial conducted in Honduras to estimate the rate of adoption of 2 health-related interventions-multivitamins and chlorine bleach for water purification-and determine factors associated with successful social transmission. Copyright © 2018 John Wiley & Sons, Ltd.
Expanding Local Cancer Clinical Trial Options: Analysis of the Economic Impact of the Midwest Cancer Alliance in Kansas

PubMed Central

Gafford, J. Atlee; Krebill, Hope; Lai, Sue Min; Christiadi; Doolittle, Gary C.

2017-01-01

Purpose Patients benefit from receiving cancer treatment closer to home when possible and at high-volume regional centers when specialized care is required. The purpose of this analysis was to estimate the economic impact of retaining more patients in-state for cancer clinical trials and care, which might offset some of the costs of establishing broader cancer trial and treatment networks. Method Kansas Cancer Registry data were used to estimate the number of patients retained in-state for cancer care following the expansion of local cancer clinical trial options through the Midwest Cancer Alliance based at the University of Kansas Medical Center. The 2014 economic impact of this enhanced local clinical trial network was estimated in four parts: Medical spending was estimated on the basis of National Cancer Institute cost-of-care estimates. Household travel cost savings were estimated as the difference between in-state and out-of-state travel costs. Trial-related grant income was calculated from administrative records. Indirect and induced economic benefits to the state were estimated using an economic impact model. Results The authors estimated that the enhanced local cancer clinical trial network resulted in approximately $6.9 million in additional economic activity in the state in 2014, or $362,000 per patient retained in-state. This estimate includes $3.6 million in direct spending and $3.3 million in indirect economic activity. The enhanced trial network also resulted in 45 additional jobs. Conclusions Retaining patients in-state for cancer care and clinical trial participation allows patients to remain closer to home for care and enhances the state economy. PMID:28253204
A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

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

Aziz, H. M. Abdul; Ukkusuri, Satish V.

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less
A novel approach to estimate emissions from large transportation networks: Hierarchical clustering-based link-driving-schedules for EPA-MOVES using dynamic time warping measures

DOE PAGES

Aziz, H. M. Abdul; Ukkusuri, Satish V.

2017-06-29

We present that EPA-MOVES (Motor Vehicle Emission Simulator) is often integrated with traffic simulators to assess emission levels of large-scale urban networks with signalized intersections. High variations in speed profiles exist in the context of congested urban networks with signalized intersections. The traditional average-speed-based emission estimation technique with EPA-MOVES provides faster execution while underestimates the emissions in most cases because of ignoring the speed variation at congested networks with signalized intersections. In contrast, the atomic second-by-second speed profile (i.e., the trajectory of each vehicle)-based technique provides accurate emissions at the cost of excessive computational power and time. We addressed thismore » issue by developing a novel method to determine the link-driving-schedules (LDSs) for the EPA-MOVES tool. Our research developed a hierarchical clustering technique with dynamic time warping similarity measures (HC-DTW) to find the LDS for EPA-MOVES that is capable of producing emission estimates better than the average-speed-based technique with execution time faster than the atomic speed profile approach. We applied the HC-DTW on a sample data from a signalized corridor and found that HC-DTW can significantly reduce computational time without compromising the accuracy. The developed technique in this research can substantially contribute to the EPA-MOVES-based emission estimation process for large-scale urban transportation network by reducing the computational time with reasonably accurate estimates. This method is highly appropriate for transportation networks with higher variation in speed such as signalized intersections. Lastly, experimental results show error difference ranging from 2% to 8% for most pollutants except PM 10.« less
State-space model with deep learning for functional dynamics estimation in resting-state fMRI.

PubMed

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2016-04-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. Copyright © 2016 Elsevier Inc. All rights reserved.
State-space model with deep learning for functional dynamics estimation in resting-state fMRI

PubMed Central

Suk, Heung-Il; Wee, Chong-Yaw; Lee, Seong-Whan; Shen, Dinggang

2017-01-01

Studies on resting-state functional Magnetic Resonance Imaging (rs-fMRI) have shown that different brain regions still actively interact with each other while a subject is at rest, and such functional interaction is not stationary but changes over time. In terms of a large-scale brain network, in this paper, we focus on time-varying patterns of functional networks, i.e., functional dynamics, inherent in rs-fMRI, which is one of the emerging issues along with the network modelling. Specifically, we propose a novel methodological architecture that combines deep learning and state-space modelling, and apply it to rs-fMRI based Mild Cognitive Impairment (MCI) diagnosis. We first devise a Deep Auto-Encoder (DAE) to discover hierarchical non-linear functional relations among regions, by which we transform the regional features into an embedding space, whose bases are complex functional networks. Given the embedded functional features, we then use a Hidden Markov Model (HMM) to estimate dynamic characteristics of functional networks inherent in rs-fMRI via internal states, which are unobservable but can be inferred from observations statistically. By building a generative model with an HMM, we estimate the likelihood of the input features of rs-fMRI as belonging to the corresponding status, i.e., MCI or normal healthy control, based on which we identify the clinical label of a testing subject. In order to validate the effectiveness of the proposed method, we performed experiments on two different datasets and compared with state-of-the-art methods in the literature. We also analyzed the functional networks learned by DAE, estimated the functional connectivities by decoding hidden states in HMM, and investigated the estimated functional connectivities by means of a graph-theoretic approach. PMID:26774612
Nonparametric methods for drought severity estimation at ungauged sites

NASA Astrophysics Data System (ADS)

Sadri, S.; Burn, D. H.

2012-12-01

The objective in frequency analysis is, given extreme events such as drought severity or duration, to estimate the relationship between that event and the associated return periods at a catchment. Neural networks and other artificial intelligence approaches in function estimation and regression analysis are relatively new techniques in engineering, providing an attractive alternative to traditional statistical models. There are, however, few applications of neural networks and support vector machines in the area of severity quantile estimation for drought frequency analysis. In this paper, we compare three methods for this task: multiple linear regression, radial basis function neural networks, and least squares support vector regression (LS-SVR). The area selected for this study includes 32 catchments in the Canadian Prairies. From each catchment drought severities are extracted and fitted to a Pearson type III distribution, which act as observed values. For each method-duration pair, we use a jackknife algorithm to produce estimated values at each site. The results from these three approaches are compared and analyzed, and it is found that LS-SVR provides the best quantile estimates and extrapolating capacity.
Robust state estimation for uncertain fuzzy bidirectional associative memory networks with time-varying delays

NASA Astrophysics Data System (ADS)

Vadivel, P.; Sakthivel, R.; Mathiyalagan, K.; Arunkumar, A.

2013-09-01

This paper addresses the issue of robust state estimation for a class of fuzzy bidirectional associative memory (BAM) neural networks with time-varying delays and parameter uncertainties. By constructing the Lyapunov-Krasovskii functional, which contains the triple-integral term and using the free-weighting matrix technique, a set of sufficient conditions are derived in terms of linear matrix inequalities (LMIs) to estimate the neuron states through available output measurements such that the dynamics of the estimation error system is robustly asymptotically stable. In particular, we consider a generalized activation function in which the traditional assumptions on the boundedness, monotony and differentiability of the activation functions are removed. More precisely, the design of the state estimator for such BAM neural networks can be obtained by solving some LMIs, which are dependent on the size of the time derivative of the time-varying delays. Finally, a numerical example with simulation result is given to illustrate the obtained theoretical results.
LESS: Link Estimation with Sparse Sampling in Intertidal WSNs

PubMed Central

Ji, Xiaoyu; Chen, Yi-chao; Li, Xiaopeng; Xu, Wenyuan

2018-01-01

Deploying wireless sensor networks (WSN) in the intertidal area is an effective approach for environmental monitoring. To sustain reliable data delivery in such a dynamic environment, a link quality estimation mechanism is crucial. However, our observations in two real WSN systems deployed in the intertidal areas reveal that link update in routing protocols often suffers from energy and bandwidth waste due to the frequent link quality measurement and updates. In this paper, we carefully investigate the network dynamics using real-world sensor network data and find it feasible to achieve accurate estimation of link quality using sparse sampling. We design and implement a compressive-sensing-based link quality estimation protocol, LESS, which incorporates both spatial and temporal characteristics of the system to aid the link update in routing protocols. We evaluate LESS in both real WSN systems and a large-scale simulation, and the results show that LESS can reduce energy and bandwidth consumption by up to 50% while still achieving more than 90% link quality estimation accuracy. PMID:29494557
Estimating network parameters from combined dynamics of firing rate and irregularity of single neurons.

PubMed

Hamaguchi, Kosuke; Riehle, Alexa; Brunel, Nicolas

2011-01-01

High firing irregularity is a hallmark of cortical neurons in vivo, and modeling studies suggest a balance of excitation and inhibition is necessary to explain this high irregularity. Such a balance must be generated, at least partly, from local interconnected networks of excitatory and inhibitory neurons, but the details of the local network structure are largely unknown. The dynamics of the neural activity depends on the local network structure; this in turn suggests the possibility of estimating network structure from the dynamics of the firing statistics. Here we report a new method to estimate properties of the local cortical network from the instantaneous firing rate and irregularity (CV(2)) under the assumption that recorded neurons are a part of a randomly connected sparse network. The firing irregularity, measured in monkey motor cortex, exhibits two features; many neurons show relatively stable firing irregularity in time and across different task conditions; the time-averaged CV(2) is widely distributed from quasi-regular to irregular (CV(2) = 0.3-1.0). For each recorded neuron, we estimate the three parameters of a local network [balance of local excitation-inhibition, number of recurrent connections per neuron, and excitatory postsynaptic potential (EPSP) size] that best describe the dynamics of the measured firing rates and irregularities. Our analysis shows that optimal parameter sets form a two-dimensional manifold in the three-dimensional parameter space that is confined for most of the neurons to the inhibition-dominated region. High irregularity neurons tend to be more strongly connected to the local network, either in terms of larger EPSP and inhibitory PSP size or larger number of recurrent connections, compared with the low irregularity neurons, for a given excitatory/inhibitory balance. Incorporating either synaptic short-term depression or conductance-based synapses leads many low CV(2) neurons to move to the excitation-dominated region as well as to an increase of EPSP size.
Estimation of parameters in rational reaction rates of molecular biological systems via weighted least squares

NASA Astrophysics Data System (ADS)

Wu, Fang-Xiang; Mu, Lei; Shi, Zhong-Ke

2010-01-01

The models of gene regulatory networks are often derived from statistical thermodynamics principle or Michaelis-Menten kinetics equation. As a result, the models contain rational reaction rates which are nonlinear in both parameters and states. It is challenging to estimate parameters nonlinear in a model although there have been many traditional nonlinear parameter estimation methods such as Gauss-Newton iteration method and its variants. In this article, we develop a two-step method to estimate the parameters in rational reaction rates of gene regulatory networks via weighted linear least squares. This method takes the special structure of rational reaction rates into consideration. That is, in the rational reaction rates, the numerator and the denominator are linear in parameters. By designing a special weight matrix for the linear least squares, parameters in the numerator and the denominator can be estimated by solving two linear least squares problems. The main advantage of the developed method is that it can produce the analytical solutions to the estimation of parameters in rational reaction rates which originally is nonlinear parameter estimation problem. The developed method is applied to a couple of gene regulatory networks. The simulation results show the superior performance over Gauss-Newton method.
The Ability of Atmospheric Data to Reduce Disagreements in Wetland Methane Flux Estimates over North America

NASA Astrophysics Data System (ADS)

Miller, S. M.; Andrews, A. E.; Benmergui, J. S.; Commane, R.; Dlugokencky, E. J.; Janssens-Maenhout, G.; Melton, J. R.; Michalak, A. M.; Sweeney, C.; Worthy, D. E. J.

2015-12-01

Existing estimates of methane fluxes from wetlands differ in both magnitude and distribution across North America. We discuss seven different bottom-up methane estimates in the context of atmospheric methane data collected across the US and Canada. In the first component of this study, we explore whether the observation network can even detect a methane pattern from wetlands. We find that the observation network can identify a methane pattern from Canadian wetlands but not reliably from US wetlands. Over Canada, the network can even identify spatial patterns at multi-provence scales. Over the US, by contrast, anthropogenic emissions and modeling errors obscure atmospheric patterns from wetland fluxes. In the second component of the study, we then use these observations to reconcile disagreements in the magnitude, seasonal cycle, and spatial distribution of existing estimates. Most existing estimates predict fluxes that are too large with a seasonal cycle that is too narrow. A model known as LPJ-Bern has a spatial distribution most consistent with atmospheric observations. By contrast, a spatially-constant model outperforms the distribution of most existing flux estimates across Canada. The results presented here provide several pathways to reduce disagreements among existing wetland flux estimates across North America.

Visualizing weighted networks: a performance comparison of adjacency matrices versus node-link diagrams

NASA Astrophysics Data System (ADS)

McIntire, John P.; Osesina, O. Isaac; Bartley, Cecilia; Tudoreanu, M. Eduard; Havig, Paul R.; Geiselman, Eric E.

2012-06-01

Ensuring the proper and effective ways to visualize network data is important for many areas of academia, applied sciences, the military, and the public. Fields such as social network analysis, genetics, biochemistry, intelligence, cybersecurity, neural network modeling, transit systems, communications, etc. often deal with large, complex network datasets that can be difficult to interact with, study, and use. There have been surprisingly few human factors performance studies on the relative effectiveness of different graph drawings or network diagram techniques to convey information to a viewer. This is particularly true for weighted networks which include the strength of connections between nodes, not just information about which nodes are linked to other nodes. We describe a human factors study in which participants performed four separate network analysis tasks (finding a direct link between given nodes, finding an interconnected node between given nodes, estimating link strengths, and estimating the most densely interconnected nodes) on two different network visualizations: an adjacency matrix with a heat-map versus a node-link diagram. The results should help shed light on effective methods of visualizing network data for some representative analysis tasks, with the ultimate goal of improving usability and performance for viewers of network data displays.
Functional Subdivision of Group-ICA Results of fMRI Data Collected during Cinema Viewing

PubMed Central

Pamilo, Siina; Malinen, Sanna; Hlushchuk, Yevhen; Seppä, Mika; Tikka, Pia; Hari, Riitta

2012-01-01

Independent component analysis (ICA) can unravel functional brain networks from functional magnetic resonance imaging (fMRI) data. The number of the estimated components affects both the spatial pattern of the identified networks and their time-course estimates. Here group-ICA was applied at four dimensionalities (10, 20, 40, and 58 components) to fMRI data collected from 15 subjects who viewed a 15-min silent film (“At land” by Maya Deren). We focused on the dorsal attention network, the default-mode network, and the sensorimotor network. The lowest dimensionalities demonstrated most prominent activity within the dorsal attention network, combined with the visual areas, and in the default-mode network; the sensorimotor network only appeared with ICA comprising at least 20 components. The results suggest that even very low-dimensional ICA can unravel the most prominent functionally-connected brain networks. However, increasing the number of components gives a more detailed picture and functionally feasible subdivision of the major networks. These results improve our understanding of the hierarchical subdivision of brain networks during viewing of a movie that provides continuous stimulation embedded in an attention-directing narrative. PMID:22860044
Estimating network effect in geocenter motion: Theory

NASA Astrophysics Data System (ADS)

Zannat, Umma Jamila; Tregoning, Paul

2017-10-01

Geophysical models and their interpretations of several processes of interest, such as sea level rise, postseismic relaxation, and glacial isostatic adjustment, are intertwined with the need to realize the International Terrestrial Reference Frame. However, this realization needs to take into account the geocenter motion, that is, the motion of the center of figure of the Earth surface, due to, for example, deformation of the surface by earthquakes or hydrological loading effects. Usually, there is also a discrepancy, known as the network effect, between the theoretically convenient center of figure and the physically accessible center of network frames, because of unavoidable factors such as uneven station distribution, lack of stations in the oceans, disparity in the coverage between the two hemispheres, and the existence of tectonically deforming zones. Here we develop a method to estimate the magnitude of the network effect, that is, the error introduced by the incomplete sampling of the Earth surface, in measuring the geocenter motion, for a network of space geodetic stations of a fixed size N. For this purpose, we use, as our proposed estimate, the standard deviations of the changes in Helmert parameters measured by a random network of the same size N. We show that our estimate scales as 1/√N and give an explicit formula for it in terms of the vector spherical harmonics expansion of the displacement field. In a complementary paper we apply this formalism to coseismic displacements and elastic deformations due to surface water movements.
Canadian Cytogenetic Emergency network (CEN) for biological dosimetry following radiological/nuclear accidents.

PubMed

Miller, Susan M; Ferrarotto, Catherine L; Vlahovich, Slavica; Wilkins, Ruth C; Boreham, Douglas R; Dolling, Jo-Anna

2007-07-01

To test the ability of the cytogenetic emergency network (CEN) of laboratories, currently under development across Canada, to provide rapid biological dosimetry using the dicentric assay for triage assessment, that could be implemented in the event of a large-scale radiation/nuclear emergency. A workshop was held in May 2004 in Toronto, Canada, to introduce the concept of CEN and recruit clinical cytogenetic laboratories at hospitals across the country. Slides were prepared for dicentric assay analysis following in vitro irradiation of blood to a range of gamma-ray doses. A minimum of 50 metaphases per slide were analyzed by 41 people at 22 different laboratories to estimate the exposure level. Dose estimates were calculated based on a dose response curve generated at Health Canada. There were a total of 104 dose estimates and 96 (92.3%) of them fell within the expected range using triage scoring criteria. Half of the laboratories analyzed 50 metaphases in
An investigation of emotion dynamics in major depressive disorder patients and healthy persons using sparse longitudinal networks.

PubMed

de Vos, Stijn; Wardenaar, Klaas J; Bos, Elisabeth H; Wit, Ernst C; Bouwmans, Mara E J; de Jonge, Peter

2017-01-01

Differences in within-person emotion dynamics may be an important source of heterogeneity in depression. To investigate these dynamics, researchers have previously combined multilevel regression analyses with network representations. However, sparse network methods, specifically developed for longitudinal network analyses, have not been applied. Therefore, this study used this approach to investigate population-level and individual-level emotion dynamics in healthy and depressed persons and compared this method with the multilevel approach. Time-series data were collected in pair-matched healthy persons and major depressive disorder (MDD) patients (n = 54). Seven positive affect (PA) and seven negative affect (NA) items were administered electronically at 90 times (30 days; thrice per day). The population-level (healthy vs. MDD) and individual-level time series were analyzed using a sparse longitudinal network model based on vector autoregression. The population-level model was also estimated with a multilevel approach. Effects of different preprocessing steps were evaluated as well. The characteristics of the longitudinal networks were investigated to gain insight into the emotion dynamics. In the population-level networks, longitudinal network connectivity was strongest in the healthy group, with nodes showing more and stronger longitudinal associations with each other. Individually estimated networks varied strongly across individuals. Individual variations in network connectivity were unrelated to baseline characteristics (depression status, neuroticism, severity). A multilevel approach applied to the same data showed higher connectivity in the MDD group, which seemed partly related to the preprocessing approach. The sparse network approach can be useful for the estimation of networks with multiple nodes, where overparameterization is an issue, and for individual-level networks. However, its current inability to model random effects makes it less useful as a population-level approach in case of large heterogeneity. Different preprocessing strategies appeared to strongly influence the results, complicating inferences about network density.
Automated comprehensive Adolescent Idiopathic Scoliosis assessment using MVC-Net.

PubMed

Wu, Hongbo; Bailey, Chris; Rasoulinejad, Parham; Li, Shuo

2018-05-18

Automated quantitative estimation of spinal curvature is an important task for the ongoing evaluation and treatment planning of Adolescent Idiopathic Scoliosis (AIS). It solves the widely accepted disadvantage of manual Cobb angle measurement (time-consuming and unreliable) which is currently the gold standard for AIS assessment. Attempts have been made to improve the reliability of automated Cobb angle estimation. However, it is very challenging to achieve accurate and robust estimation of Cobb angles due to the need for correctly identifying all the required vertebrae in both Anterior-posterior (AP) and Lateral (LAT) view x-rays. The challenge is especially evident in LAT x-ray where occlusion of vertebrae by the ribcage occurs. We therefore propose a novel Multi-View Correlation Network (MVC-Net) architecture that can provide a fully automated end-to-end framework for spinal curvature estimation in multi-view (both AP and LAT) x-rays. The proposed MVC-Net uses our newly designed multi-view convolution layers to incorporate joint features of multi-view x-rays, which allows the network to mitigate the occlusion problem by utilizing the structural dependencies of the two views. The MVC-Net consists of three closely-linked components: (1) a series of X-modules for joint representation of spinal structure (2) a Spinal Landmark Estimator network for robust spinal landmark estimation, and (3) a Cobb Angle Estimator network for accurate Cobb Angles estimation. By utilizing an iterative multi-task training algorithm to train the Spinal Landmark Estimator and Cobb Angle Estimator in tandem, the MVC-Net leverages the multi-task relationship between landmark and angle estimation to reliably detect all the required vertebrae for accurate Cobb angles estimation. Experimental results on 526 x-ray images from 154 patients show an impressive 4.04° Circular Mean Absolute Error (CMAE) in AP Cobb angle and 4.07° CMAE in LAT Cobb angle estimation, which demonstrates the MVC-Net's capability of robust and accurate estimation of Cobb angles in multi-view x-rays. Our method therefore provides clinicians with a framework for efficient, accurate, and reliable estimation of spinal curvature for comprehensive AIS assessment. Copyright © 2018. Published by Elsevier B.V.
Deep space network software cost estimation model

NASA Technical Reports Server (NTRS)

Tausworthe, R. C.

1981-01-01

A parametric software cost estimation model prepared for Deep Space Network (DSN) Data Systems implementation tasks is presented. The resource estimation model incorporates principles and data from a number of existing models. The model calibrates task magnitude and difficulty, development environment, and software technology effects through prompted responses to a set of approximately 50 questions. Parameters in the model are adjusted to fit DSN software life cycle statistics. The estimation model output scales a standard DSN Work Breakdown Structure skeleton, which is then input into a PERT/CPM system, producing a detailed schedule and resource budget for the project being planned.
Cascaded neural networks for sequenced propagation estimation, multiuser detection, and adaptive radio resource control of third-generation wireless networks for multimedia services

NASA Astrophysics Data System (ADS)

Hortos, William S.

1999-03-01

A hybrid neural network approach is presented to estimate radio propagation characteristics and multiuser interference and to evaluate their combined impact on throughput, latency and information loss in third-generation (3G) wireless networks. The latter three performance parameters influence the quality of service (QoS) for multimedia services under consideration for 3G networks. These networks, based on a hierarchical architecture of overlaying macrocells on top of micro- and picocells, are planned to operate in mobile urban and indoor environments with service demands emanating from circuit-switched, packet-switched and satellite-based traffic sources. Candidate radio interfaces for these networks employ a form of wideband CDMA in 5-MHz and wider-bandwidth channels, with possible asynchronous operation of the mobile subscribers. The proposed neural network (NN) architecture allocates network resources to optimize QoS metrics. Parameters of the radio propagation channel are estimated, followed by control of an adaptive antenna array at the base station to minimize interference, and then joint multiuser detection is performed at the base station receiver. These adaptive processing stages are implemented as a sequence of NN techniques that provide their estimates as inputs to a final- stage Kohonen self-organizing feature map (SOFM). The SOFM optimizes the allocation of available network resources to satisfy QoS requirements for variable-rate voice, data and video services. As the first stage of the sequence, a modified feed-forward multilayer perceptron NN is trained on the pilot signals of the mobile subscribers to estimate the parameters of shadowing, multipath fading and delays on the uplinks. A recurrent NN (RNN) forms the second stage to control base stations' adaptive antenna arrays to minimize intra-cell interference. The third stage is based on a Hopfield NN (HNN), modified to detect multiple users on the uplink radio channels to mitigate multiaccess interference, control carrier-sense multiple-access (CSMA) protocols, and refine call handoff procedures. In the final stage, the Kohonen SOFM, operating in a hybrid continuous and discrete space, adaptively allocates the resources of antenna-based cell sectorization, activity monitoring, variable-rate coding, power control, handoff and caller admission to meet user demands for various multimedia services at minimum QoS levels. The performance of the NN cascade is evaluated through simulation of a candidate 3G wireless network using W-CDMA parameters in a small-cell environment. The simulated network consists of a representative number of cells. Mobile users with typical movement patterns are assumed. QoS requirements for different classes of multimedia services are considered. The proposed method is shown to provide relatively low probability of new call blocking and handoff dropping, while maintaining efficient use of the network's radio resources.
EMG-Based Estimation of Limb Movement Using Deep Learning With Recurrent Convolutional Neural Networks.

PubMed

Xia, Peng; Hu, Jie; Peng, Yinghong

2017-10-25

A novel model based on deep learning is proposed to estimate kinematic information for myoelectric control from multi-channel electromyogram (EMG) signals. The neural information of limb movement is embedded in EMG signals that are influenced by all kinds of factors. In order to overcome the negative effects of variability in signals, the proposed model employs the deep architecture combining convolutional neural networks (CNNs) and recurrent neural networks (RNNs). The EMG signals are transformed to time-frequency frames as the input to the model. The limb movement is estimated by the model that is trained with the gradient descent and backpropagation procedure. We tested the model for simultaneous and proportional estimation of limb movement in eight healthy subjects and compared it with support vector regression (SVR) and CNNs on the same data set. The experimental studies show that the proposed model has higher estimation accuracy and better robustness with respect to time. The combination of CNNs and RNNs can improve the model performance compared with using CNNs alone. The model of deep architecture is promising in EMG decoding and optimization of network structures can increase the accuracy and robustness. © 2017 International Center for Artificial Organs and Transplantation and Wiley Periodicals, Inc.
Multiparameter estimation with single photons—linearly-optically generated quantum entanglement beats the shotnoise limit

NASA Astrophysics Data System (ADS)

You, Chenglong; Adhikari, Sushovit; Chi, Yuxi; LaBorde, Margarite L.; Matyas, Corey T.; Zhang, Chenyu; Su, Zuen; Byrnes, Tim; Lu, Chaoyang; Dowling, Jonathan P.; Olson, Jonathan P.

2017-12-01

It was suggested in (Motes et al 2015 Phys. Rev. Lett. 114 170802) that optical networks with relatively inexpensive overheads—single photon Fock states, passive optical elements, and single photon detection—can show significant improvements over classical strategies for single-parameter estimation, when the number of modes in the network is small (n< 7). A similar case was made in (Humphreys et al 2013 Phys. Rev. Lett. 111 070403) for multi-parameter estimation, where measurement is instead made using photon-number resolving detectors. In this paper, we analytically compute the quantum Cramér-Rao bound to show these networks can have a constant-factor quantum advantage in multi-parameter estimation for even large number of modes. Additionally, we provide a simplified measurement scheme using only single-photon (on-off) detectors that is capable of approximately obtaining this sensitivity for a small number of modes.
Environmental assessment: The Eden project

NASA Astrophysics Data System (ADS)

Roza, Christodoulaki

Non domestic buildings account for about one-sixth of the U.K.'s entire C02 emissions and one-third of the building related ones 2 . Their proportion of energy consumption, particularly electricity, has also been growing 2 . New buildings are not necessarily better, with energy use often proving to be much higher than their designers anticipated 2 . Annual C02 emissions of two- and sometimes three- times design expectations are far from unusual, leaving a massive credibility gap 2 . These and other global environmental and human health related concerns have motivated an increasing number of designers, developers and building users to pursue more environmentally sustainable designs and construction strategies 5 . However, these buildings can be difficult to evaluate, since they are large in scale, complex in materials and function and temporally dynamic due to limited service life of building components and changing user requirements 5 . All of these factors make environmental assessment of the buildings challenging. Previous Post Occupancy Review of Buildings and their Engineering (PROBE) building investigations have uncovered serious shortcomings in facilities management, or at least mismatches between a building's management needs and the ability of the occupiers to provide the right level of management 1 . Consequently, large differences between energy performance expectations and outcomes can occur virtually unnoticed, while designers continue to repeat flawed descriptions 2 . This investigation attempts to evaluate the building's operation and to help achieving demonstrable improvements in terms of energy efficiency and occupant satisfaction. The scope of this study is to evaluate the actual environmental performance of a building notable for its advanced design. The Education Resource Centre at the Eden Project was selected to compare design expectations with post occupancy performance. This report contains a small-scale survey of user satisfaction with the chosen building, an analysis of the building's energy use and information about the physical and managerial circumstances operating 24 . The author has attempted to zoom in on specific issues, such as energy performance and lighting consumption. Both successes and failures have been reported, providing owners, designers and end users with valuable, real-world information.
Kinetic fractionation processes recorded in the stalagmites of some limestone caves in Korea

NASA Astrophysics Data System (ADS)

Woo, K. S.; Jo, K.; Edwards, L. R.; Cheng, H.; Wang, Y.; Yoon, H.

2006-12-01

Stable isotope data (oxygen and carbon) of carbonate minerals (mostly calcite, but sometimes aragonite) in stalagmites have been the most commonly and widely used proxies for paleoclimatic research. This is based upon the assumption that carbonate minerals precipitated in isotopic equilibrium with dripping waters from stalactites, thus should reflect paleoclimatic variations. The state of equilibrium, so called "Hendy Test", has been commonly used. Hendy (1971) showed that during kinetic fractionation both oxygen and carbon isotopes behaves in a similar way due to faster degassing rate of cabon dioxide, resulting in the enrichment of both isotopes. The stalagmites from three limestone caves (Gwaneum, Eden and Daeya Caves) in Korea were investigated to understand the effects of kinetic fractionation during their growth. The stalagmites are mostly composed of columnar calcites, but contains the layers of cave coral that is composed of fibrous calcite. The cave coral layers should have grown when the supply rate of dripping water decreased significantly. Stable isotope pattern in three stalagmites do not show the same pattern of disequilibrium process. The cave corals in the Eden stalagmite show the enriched carbon and oxygen isotope values (15 and 5 per mil, respectively) that has the same bimodal pattern as suggested by Hendy (1971). However, the cave corals in the Gwaneum stalagmites show the enriched carbon, but depleted oxygen isotope values (3 and 1 per mil, respectively). Also, the calcite layer precipitated in disequilibrium in the Daeya stalagmite show more enriched carbon isotope values by up to 6 per mil, but show more or less the same oxygen isotopic values, compared to the columnar calcite which was precipitated in equilibrium. Therefore, caution should be made to determine the state of equilibrium precipitation of carbonate minerals in stalagmites. The "Hendy Test" may not be the only solution because other types of speleothems can be formed in stalagmites as the supply rate of dripping water changes. Also, different texture in stalagmites can be used as another criteria to determine the degree of equilibrium.
The Use of Artificial Neural Networks to Estimate Speech Intelligibility from Acoustic Variables: A Preliminary Analysis.

ERIC Educational Resources Information Center

Metz, Dale Evan; And Others

1992-01-01

A preliminary scheme for estimating the speech intelligibility of hearing-impaired speakers from acoustic parameters, using a computerized artificial neural network to process mathematically the acoustic input variables, is outlined. Tests with 60 hearing-impaired speakers found the scheme to be highly accurate in identifying speakers separated by…
Freight Transportation Energy Use : Volume 3. Freight Network and Operations Database.

DOT National Transportation Integrated Search

1979-07-01

The data sources, procedures, and assumptions used to generate the TSC national freight network and operations database are documented. National rail, highway, waterway, and pipeline networks are presented, and estimates of facility capacity, travel ...
PIV-DCNN: cascaded deep convolutional neural networks for particle image velocimetry

NASA Astrophysics Data System (ADS)

Lee, Yong; Yang, Hua; Yin, Zhouping

2017-12-01

Velocity estimation (extracting the displacement vector information) from the particle image pairs is of critical importance for particle image velocimetry. This problem is mostly transformed into finding the sub-pixel peak in a correlation map. To address the original displacement extraction problem, we propose a different evaluation scheme (PIV-DCNN) with four-level regression deep convolutional neural networks. At each level, the networks are trained to predict a vector from two input image patches. The low-level network is skilled at large displacement estimation and the high- level networks are devoted to improving the accuracy. Outlier replacement and symmetric window offset operation glue the well- functioning networks in a cascaded manner. Through comparison with the standard PIV methods (one-pass cross-correlation method, three-pass window deformation), the practicability of the proposed PIV-DCNN is verified by the application to a diversity of synthetic and experimental PIV images.
Optimization of water-level monitoring networks in the eastern Snake River Plain aquifer using a kriging-based genetic algorithm method

USGS Publications Warehouse

Fisher, Jason C.

2013-01-01

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

PubMed Central

Standage, Dominic; You, Hongzhi; Wang, Da-Hui; Dorris, Michael C.

2013-01-01

Our actions take place in space and time, but despite the role of time in decision theory and the growing acknowledgement that the encoding of time is crucial to behaviour, few studies have considered the interactions between neural codes for objects in space and for elapsed time during perceptual decisions. The speed-accuracy trade-off (SAT) provides a window into spatiotemporal interactions. Our hypothesis is that temporal coding determines the rate at which spatial evidence is integrated, controlling the SAT by gain modulation. Here, we propose that local cortical circuits are inherently suited to the relevant spatial and temporal coding. In simulations of an interval estimation task, we use a generic local-circuit model to encode time by ‘climbing’ activity, seen in cortex during tasks with a timing requirement. The model is a network of simulated pyramidal cells and inhibitory interneurons, connected by conductance synapses. A simple learning rule enables the network to quickly produce new interval estimates, which show signature characteristics of estimates by experimental subjects. Analysis of network dynamics formally characterizes this generic, local-circuit timing mechanism. In simulations of a perceptual decision task, we couple two such networks. Network function is determined only by spatial selectivity and NMDA receptor conductance strength; all other parameters are identical. To trade speed and accuracy, the timing network simply learns longer or shorter intervals, driving the rate of downstream decision processing by spatially non-selective input, an established form of gain modulation. Like the timing network's interval estimates, decision times show signature characteristics of those by experimental subjects. Overall, we propose, demonstrate and analyse a generic mechanism for timing, a generic mechanism for modulation of decision processing by temporal codes, and we make predictions for experimental verification. PMID:23592967
The national stream quality accounting network: A flux-basedapproach to monitoring the water quality of large rivers

USGS Publications Warehouse

Hooper, R.P.; Aulenbach, Brent T.; Kelly, V.J.

2001-01-01

Estimating the annual mass flux at a network of fixed stations is one approach to characterizing water quality of large rivers. The interpretive context provided by annual flux includes identifying source and sink areas for constituents and estimating the loadings to receiving waters, such as reservoirs or the ocean. Since 1995, the US Geological Survey's National Stream Quality Accounting Network (NASQAN) has employed this approach at a network of 39 stations in four of the largest river basins of the USA: The Mississippi, the Columbia, the Colorado and the Rio Grande. In this paper, the design of NASQAN is described and its effectiveness at characterizing the water quality of these rivers is evaluated using data from the first 3 years of operation. A broad range of constituents was measured by NASQAN, including trace organic and inorganic chemicals, major ions, sediment and nutrients. Where possible, a regression model relating concentration to discharge and season was used to interpolate between chemical observations for flux estimation. For water-quality network design, the most important finding from NASQAN was the importance of having a specific objective (that is, estimating annual mass flux) and, from that, an explicitly stated data analysis strategy, namely the use of regression models to interpolate between observations. The use of such models aided in the design of sampling strategy and provided a context for data review. The regression models essentially form null hypotheses for concentration variation that can be evaluated by the observed data. The feedback between network operation and data collection established by the hypothesis tests places the water-quality network on a firm scientific footing.
INVESTIGATING DIFFERENCES IN BRAIN FUNCTIONAL NETWORKS USING HIERARCHICAL COVARIATE-ADJUSTED INDEPENDENT COMPONENT ANALYSIS.

PubMed

Shi, Ran; Guo, Ying

2016-12-01

Human brains perform tasks via complex functional networks consisting of separated brain regions. A popular approach to characterize brain functional networks in fMRI studies is independent component analysis (ICA), which is a powerful method to reconstruct latent source signals from their linear mixtures. In many fMRI studies, an important goal is to investigate how brain functional networks change according to specific clinical and demographic variabilities. Existing ICA methods, however, cannot directly incorporate covariate effects in ICA decomposition. Heuristic post-ICA analysis to address this need can be inaccurate and inefficient. In this paper, we propose a hierarchical covariate-adjusted ICA (hc-ICA) model that provides a formal statistical framework for estimating covariate effects and testing differences between brain functional networks. Our method provides a more reliable and powerful statistical tool for evaluating group differences in brain functional networks while appropriately controlling for potential confounding factors. We present an analytically tractable EM algorithm to obtain maximum likelihood estimates of our model. We also develop a subspace-based approximate EM that runs significantly faster while retaining high accuracy. To test the differences in functional networks, we introduce a voxel-wise approximate inference procedure which eliminates the need of computationally expensive covariance matrix estimation and inversion. We demonstrate the advantages of our methods over the existing method via simulation studies. We apply our method to an fMRI study to investigate differences in brain functional networks associated with post-traumatic stress disorder (PTSD).
Complex network analysis of resting-state fMRI of the brain.

PubMed

Anwar, Abdul Rauf; Hashmy, Muhammad Yousaf; Imran, Bilal; Riaz, Muhammad Hussnain; Mehdi, Sabtain Muhammad Muntazir; Muthalib, Makii; Perrey, Stephane; Deuschl, Gunther; Groppa, Sergiu; Muthuraman, Muthuraman

2016-08-01

Due to the fact that the brain activity hardly ever diminishes in healthy individuals, analysis of resting state functionality of the brain seems pertinent. Various resting state networks are active inside the idle brain at any time. Based on various neuro-imaging studies, it is understood that various structurally distant regions of the brain could be functionally connected. Regions of the brain, that are functionally connected, during rest constitutes to the resting state network. In the present study, we employed the complex network measures to estimate the presence of community structures within a network. Such estimate is named as modularity. Instead of using a traditional correlation matrix, we used a coherence matrix taken from the causality measure between different nodes. Our results show that in prolonged resting state the modularity starts to decrease. This decrease was observed in all the resting state networks and on both sides of the brain. Our study highlights the usage of coherence matrix instead of correlation matrix for complex network analysis.

Position estimation of transceivers in communication networks

DOEpatents

Kent, Claudia A [Pleasanton, CA; Dowla, Farid [Castro Valley, CA

2008-06-03

This invention provides a system and method using wireless communication interfaces coupled with statistical processing of time-of-flight data to locate by position estimation unknown wireless receivers. Such an invention can be applied in sensor network applications, such as environmental monitoring of water in the soil or chemicals in the air where the position of the network nodes is deemed critical. Moreover, the present invention can be arranged to operate in areas where a Global Positioning System (GPS) is not available, such as inside buildings, caves, and tunnels.
Volume of Valley Networks on Mars and Its Hydrologic Implications

NASA Astrophysics Data System (ADS)

Luo, W.; Cang, X.; Howard, A. D.; Heo, J.

2015-12-01

Valley networks on Mars are river-like features that offer the best evidence for water activities in its geologic past. Previous studies have extracted valley network lines automatically from digital elevation model (DEM) data and manually from remotely sensed images. The volume of material removed by valley networks is an important parameter that could help us infer the amount of water needed to carve the valleys. A progressive black top hat (PBTH) transformation algorithm has been adapted from image processing to extract valley volume and successfully applied to simulated landform and Ma'adim Valles, Mars. However, the volume of valley network excavation on Mars has not been estimated on a global scale. In this study, the PBTH method was applied to the whole Mars to estimate this important parameter. The process was automated with Python in ArcGIS. Polygons delineating the valley associated depressions were generated by using a multi-flow direction growth method, which started with selected high point seeds on a depth grid (essentially an inverted valley) created by PBTH transformation and grew outward following multi-flow direction on the depth grid. Two published versions of valley network lines were integrated to automatically select depression polygons that represent the valleys. Some crater depressions that are connected with valleys and thus selected in the previous step were removed by using information from a crater database. Because of large distortion associated with global dataset in projected maps, the volume of each cell within a valley was calculated using the depth of the cell multiplied by the spherical area of the cell. The volumes of all the valley cells were then summed to produce the estimate of global valley excavation volume. Our initial result of this estimate was ~2.4×1014 m3. Assuming a sediment density of 2900 kg/m3, a porosity of 0.35, and a sediment load of 1.5 kg/m3, the global volume of water needed to carve the valleys was estimated to be ~7.1×1017 m3. Because of the coarse resolution of MOLA data, this is a conservative lower bound. Comparing with the hypothesized northern ocean volume 2.3×1016 m3 estimated by Carr and Head (2003), our estimate of water volume suggests and confirms an active hydrologic cycle for early Mars. Further hydrologic analysis will improve the estimate accuracy.
Precipitation estimation in mountainous terrain using multivariate geostatistics. Part II: isohyetal maps

USGS Publications Warehouse

Hevesi, Joseph A.; Flint, Alan L.; Istok, Jonathan D.

1992-01-01

Values of average annual precipitation (AAP) may be important for hydrologic characterization of a potential high-level nuclear-waste repository site at Yucca Mountain, Nevada. Reliable measurements of AAP are sparse in the vicinity of Yucca Mountain, and estimates of AAP were needed for an isohyetal mapping over a 2600-square-mile watershed containing Yucca Mountain. Estimates were obtained with a multivariate geostatistical model developed using AAP and elevation data from a network of 42 precipitation stations in southern Nevada and southeastern California. An additional 1531 elevations were obtained to improve estimation accuracy. Isohyets representing estimates obtained using univariate geostatistics (kriging) defined a smooth and continuous surface. Isohyets representing estimates obtained using multivariate geostatistics (cokriging) defined an irregular surface that more accurately represented expected local orographic influences on AAP. Cokriging results included a maximum estimate within the study area of 335 mm at an elevation of 7400 ft, an average estimate of 157 mm for the study area, and an average estimate of 172 mm at eight locations in the vicinity of the potential repository site. Kriging estimates tended to be lower in comparison because the increased AAP expected for remote mountainous topography was not adequately represented by the available sample. Regression results between cokriging estimates and elevation were similar to regression results between measured AAP and elevation. The position of the cokriging 250-mm isohyet relative to the boundaries of pinyon pine and juniper woodlands provided indirect evidence of improved estimation accuracy because the cokriging result agreed well with investigations by others concerning the relationship between elevation, vegetation, and climate in the Great Basin. Calculated estimation variances were also mapped and compared to evaluate improvements in estimation accuracy. Cokriging estimation variances were reduced by an average of 54% relative to kriging variances within the study area. Cokriging reduced estimation variances at the potential repository site by 55% relative to kriging. The usefulness of an existing network of stations for measuring AAP within the study area was evaluated using cokriging variances, and twenty additional stations were located for the purpose of improving the accuracy of future isohyetal mappings. Using the expanded network of stations, the maximum cokriging estimation variance within the study area was reduced by 78% relative to the existing network, and the average estimation variance was reduced by 52%.
Aquatic Nitrate Retention at River Network Scales Across Flow Conditions Determined Using Nested In Situ Sensors

NASA Astrophysics Data System (ADS)

Wollheim, W. M.; Mulukutla, G. K.; Cook, C.; Carey, R. O.

2017-11-01

Nonpoint pollution sources are strongly influenced by hydrology and are therefore sensitive to climate variability. Some pollutants entering aquatic ecosystems, e.g., nitrate, can be mitigated by in-stream processes during transport through river networks. Whole river network nitrate retention is difficult to quantify with observations. High frequency, in situ nitrate sensors, deployed in nested locations within a single watershed, can improve estimates of both nonpoint inputs and aquatic retention at river network scales. We deployed a nested sensor network and associated sampling in the urbanizing Oyster River watershed in coastal New Hampshire, USA, to quantify storm event-scale loading and retention at network scales. An end member analysis used the relative behavior of reactive nitrate and conservative chloride to infer river network fate of nitrate. In the headwater catchments, nitrate and chloride concentrations are both increasingly diluted with increasing storm size. At the mouth of the watershed, chloride is also diluted, but nitrate tended to increase. The end member analysis suggests that this pattern is the result of high retention during small storms (51-78%) that declines to zero during large storms. Although high frequency nitrate sensors did not alter estimates of fluxes over seasonal time periods compared to less frequent grab sampling, they provide the ability to estimate nitrate flux versus storm size at event scales that is critical for such analyses. Nested sensor networks can improve understanding of the controls of both loading and network scale retention, and therefore also improve management of nonpoint source pollution.
Social Network Clustering and the Spread of HIV/AIDS Among Persons Who Inject Drugs in 2 Cities in the Philippines.

PubMed

Verdery, Ashton M; Siripong, Nalyn; Pence, Brian W

2017-09-01

The Philippines has seen rapid increases in HIV prevalence among people who inject drugs. We study 2 neighboring cities where a linked HIV epidemic differed in timing of onset and levels of prevalence. In Cebu, prevalence rose rapidly from below 1% to 54% between 2009 and 2011 and remained high through 2013. In nearby Mandaue, HIV remained below 4% through 2011 then rose rapidly to 38% by 2013. We hypothesize that infection prevalence differences in these cities may owe to aspects of social network structure, specifically levels of network clustering. Building on previous research, we hypothesize that higher levels of network clustering are associated with greater epidemic potential. Data were collected with respondent-driven sampling among men who inject drugs in Cebu and Mandaue in 2013. We first examine sample composition using estimators for population means. We then apply new estimators of network clustering in respondent-driven sampling data to examine associations with HIV prevalence. Samples in both cities were comparable in composition by age, education, and injection locations. Dyadic needle-sharing levels were also similar between the 2 cities, but network clustering in the needle-sharing network differed dramatically. We found higher clustering in Cebu than Mandaue, consistent with expectations that higher clustering is associated with faster epidemic spread. This article is the first to apply estimators of network clustering to empirical respondent-driven samples, and it offers suggestive evidence that researchers should pay greater attention to network structure's role in HIV transmission dynamics.
System Identification for Nonlinear Control Using Neural Networks

NASA Technical Reports Server (NTRS)

Stengel, Robert F.; Linse, Dennis J.

1990-01-01

An approach to incorporating artificial neural networks in nonlinear, adaptive control systems is described. The controller contains three principal elements: a nonlinear inverse dynamic control law whose coefficients depend on a comprehensive model of the plant, a neural network that models system dynamics, and a state estimator whose outputs drive the control law and train the neural network. Attention is focused on the system identification task, which combines an extended Kalman filter with generalized spline function approximation. Continual learning is possible during normal operation, without taking the system off line for specialized training. Nonlinear inverse dynamic control requires smooth derivatives as well as function estimates, imposing stringent goals on the approximating technique.
Estimating network effect in geocenter motion: Applications

NASA Astrophysics Data System (ADS)

Zannat, Umma Jamila; Tregoning, Paul

2017-10-01

The network effect is the error associated with the subsampling of the Earth surface by space geodetic networks. It is an obstacle toward the precise measurement of geocenter motion, that is, the relative motion between the center of mass of the Earth system and the center of figure of the Earth surface. In a complementary paper, we proposed a theoretical approach to estimate the magnitude of this effect from the displacement fields predicted by geophysical models. Here we evaluate the effectiveness of our estimate for two illustrative physical processes: coseismic displacements inducing instantaneous changes in the Helmert parameters and elastic deformation due to surface water movements causing secular drifts in those parameters. For the first, we consider simplified models of the 2004 Sumatra-Andaman and the 2011 Tōhoku-Oki earthquakes, and for the second, we use the observations of the Gravity Recovery and Climate Experiment, complemented by an ocean model. In both case studies, it is found that the magnitude of the network effect, even for a large global network, is often as large as the magnitude of the changes in the Helmert parameters themselves. However, we also show that our proposed modification to the definition of the center of network frame to include weights proportional to the area of the Earth surface that the stations represent can significantly reduce the network effect in most cases.
A novel hybrid approach for estimating total deposition in the United States

NASA Astrophysics Data System (ADS)

Schwede, Donna B.; Lear, Gary G.

2014-08-01

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Network (NTN) to develop values of total deposition of sulfur and nitrogen. Data developed using this method are made available via the CASTNET website.
Top-of-the-atmosphere shortwave flux estimation from satellite observations: an empirical neural network approach applied with data from the A-train constellation

NASA Astrophysics Data System (ADS)

Gupta, Pawan; Joiner, Joanna; Vasilkov, Alexander; Bhartia, Pawan K.

2016-07-01

Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within ±1 % of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.
Top-of-the-Atmosphere Shortwave Flux Estimation from Satellite Observations: An Empirical Neural Network Approach Applied with Data from the A-Train Constellation

NASA Technical Reports Server (NTRS)

Gupta, Pawan; Joiner, Joanna; Vasilkov, Alexander; Bhartia, Pawan K.

2016-01-01

Estimates of top-of-the-atmosphere (TOA) radiative flux are essential for the understanding of Earth's energy budget and climate system. Clouds, aerosols, water vapor, and ozone (O3) are among the most important atmospheric agents impacting the Earth's shortwave (SW) radiation budget. There are several sensors in orbit that provide independent information related to these parameters. Having coincident information from these sensors is important for understanding their potential contributions. The A-train constellation of satellites provides a unique opportunity to analyze data from several of these sensors. In this paper, retrievals of cloud/aerosol parameters and total column ozone (TCO) from the Aura Ozone Monitoring Instrument (OMI) have been collocated with the Aqua Clouds and Earth's Radiant Energy System (CERES) estimates of total reflected TOA outgoing SW flux (SWF). We use these data to develop a variety of neural networks that estimate TOA SWF globally over ocean and land using only OMI data and other ancillary information as inputs and CERES TOA SWF as the output for training purposes. OMI-estimated TOA SWF from the trained neural networks reproduces independent CERES data with high fidelity. The global mean daily TOA SWF calculated from OMI is consistently within 1% of CERES throughout the year 2007. Application of our neural network method to other sensors that provide similar retrieved parameters, both past and future, can produce similar estimates TOA SWF. For example, the well-calibrated Total Ozone Mapping Spectrometer (TOMS) series could provide estimates of TOA SWF dating back to late 1978.
Influence of Choice of Null Network on Small-World Parameters of Structural Correlation Networks

PubMed Central

Hosseini, S. M. Hadi; Kesler, Shelli R.

2013-01-01

In recent years, coordinated variations in brain morphology (e.g., volume, thickness) have been employed as a measure of structural association between brain regions to infer large-scale structural correlation networks. Recent evidence suggests that brain networks constructed in this manner are inherently more clustered than random networks of the same size and degree. Thus, null networks constructed by randomizing topology are not a good choice for benchmarking small-world parameters of these networks. In the present report, we investigated the influence of choice of null networks on small-world parameters of gray matter correlation networks in healthy individuals and survivors of acute lymphoblastic leukemia. Three types of null networks were studied: 1) networks constructed by topology randomization (TOP), 2) networks matched to the distributional properties of the observed covariance matrix (HQS), and 3) networks generated from correlation of randomized input data (COR). The results revealed that the choice of null network not only influences the estimated small-world parameters, it also influences the results of between-group differences in small-world parameters. In addition, at higher network densities, the choice of null network influences the direction of group differences in network measures. Our data suggest that the choice of null network is quite crucial for interpretation of group differences in small-world parameters of structural correlation networks. We argue that none of the available null models is perfect for estimation of small-world parameters for correlation networks and the relative strengths and weaknesses of the selected model should be carefully considered with respect to obtained network measures. PMID:23840672
A Two-Stage Algorithm for Origin-Destination Matrices Estimation Considering Dynamic Dispersion Parameter for Route Choice

PubMed Central

Wang, Yong; Ma, Xiaolei; Liu, Yong; Gong, Ke; Henricakson, Kristian C.; Xu, Maozeng; Wang, Yinhai

2016-01-01

This paper proposes a two-stage algorithm to simultaneously estimate origin-destination (OD) matrix, link choice proportion, and dispersion parameter using partial traffic counts in a congested network. A non-linear optimization model is developed which incorporates a dynamic dispersion parameter, followed by a two-stage algorithm in which Generalized Least Squares (GLS) estimation and a Stochastic User Equilibrium (SUE) assignment model are iteratively applied until the convergence is reached. To evaluate the performance of the algorithm, the proposed approach is implemented in a hypothetical network using input data with high error, and tested under a range of variation coefficients. The root mean squared error (RMSE) of the estimated OD demand and link flows are used to evaluate the model estimation results. The results indicate that the estimated dispersion parameter theta is insensitive to the choice of variation coefficients. The proposed approach is shown to outperform two established OD estimation methods and produce parameter estimates that are close to the ground truth. In addition, the proposed approach is applied to an empirical network in Seattle, WA to validate the robustness and practicality of this methodology. In summary, this study proposes and evaluates an innovative computational approach to accurately estimate OD matrices using link-level traffic flow data, and provides useful insight for optimal parameter selection in modeling travelers’ route choice behavior. PMID:26761209
Estimating wheat and maize daily evapotranspiration using artificial neural network

NASA Astrophysics Data System (ADS)

Abrishami, Nazanin; Sepaskhah, Ali Reza; Shahrokhnia, Mohammad Hossein

2018-02-01

In this research, artificial neural network (ANN) is used for estimating wheat and maize daily standard evapotranspiration. Ten ANN models with different structures were designed for each crop. Daily climatic data [maximum temperature (T max), minimum temperature (T min), average temperature (T ave), maximum relative humidity (RHmax), minimum relative humidity (RHmin), average relative humidity (RHave), wind speed (U 2), sunshine hours (n), net radiation (Rn)], leaf area index (LAI), and plant height (h) were used as inputs. For five structures of ten, the evapotranspiration (ETC) values calculated by ETC = ET0 × K C equation (ET0 from Penman-Monteith equation and K C from FAO-56, ANNC) were used as outputs, and for the other five structures, the ETC values measured by weighing lysimeter (ANNM) were used as outputs. In all structures, a feed forward multiple-layer network with one or two hidden layers and sigmoid transfer function and BR or LM training algorithm was used. Favorite network was selected based on various statistical criteria. The results showed the suitable capability and acceptable accuracy of ANNs, particularly those having two hidden layers in their structure in estimating the daily evapotranspiration. Best model for estimation of maize daily evapotranspiration is «M»ANN1 C (8-4-2-1), with T max, T min, RHmax, RHmin, U 2, n, LAI, and h as input data and LM training rule and its statistical parameters (NRMSE, d, and R2) are 0.178, 0.980, and 0.982, respectively. Best model for estimation of wheat daily evapotranspiration is «W»ANN5 C (5-2-3-1), with T max, T min, Rn, LAI, and h as input data and LM training rule, its statistical parameters (NRMSE, d, and R 2) are 0.108, 0.987, and 0.981 respectively. In addition, if the calculated ETC used as the output of the network for both wheat and maize, higher accurate estimation was obtained. Therefore, ANN is suitable method for estimating evapotranspiration of wheat and maize.
H∞ state estimation for discrete-time memristive recurrent neural networks with stochastic time-delays

NASA Astrophysics Data System (ADS)

Liu, Hongjian; Wang, Zidong; Shen, Bo; Alsaadi, Fuad E.

2016-07-01

This paper deals with the robust H∞ state estimation problem for a class of memristive recurrent neural networks with stochastic time-delays. The stochastic time-delays under consideration are governed by a Bernoulli-distributed stochastic sequence. The purpose of the addressed problem is to design the robust state estimator such that the dynamics of the estimation error is exponentially stable in the mean square, and the prescribed ? performance constraint is met. By utilizing the difference inclusion theory and choosing a proper Lyapunov-Krasovskii functional, the existence condition of the desired estimator is derived. Based on it, the explicit expression of the estimator gain is given in terms of the solution to a linear matrix inequality. Finally, a numerical example is employed to demonstrate the effectiveness and applicability of the proposed estimation approach.
Initial Validation for the Estimation of Resting-State fMRI Effective Connectivity by a Generalization of the Correlation Approach.

PubMed

Xu, Nan; Spreng, R Nathan; Doerschuk, Peter C

2017-01-01

Resting-state functional MRI (rs-fMRI) is widely used to noninvasively study human brain networks. Network functional connectivity is often estimated by calculating the timeseries correlation between blood-oxygen-level dependent (BOLD) signal from different regions of interest (ROIs). However, standard correlation cannot characterize the direction of information flow between regions. In this paper, we introduce and test a new concept, prediction correlation, to estimate effective connectivity in functional brain networks from rs-fMRI. In this approach, the correlation between two BOLD signals is replaced by a correlation between one BOLD signal and a prediction of this signal via a causal system driven by another BOLD signal. Three validations are described: (1) Prediction correlation performed well on simulated data where the ground truth was known, and outperformed four other methods. (2) On simulated data designed to display the "common driver" problem, prediction correlation did not introduce false connections between non-interacting driven ROIs. (3) On experimental data, prediction correlation recovered the previously identified network organization of human brain. Prediction correlation scales well to work with hundreds of ROIs, enabling it to assess whole brain interregional connectivity at the single subject level. These results provide an initial validation that prediction correlation can capture the direction of information flow and estimate the duration of extended temporal delays in information flow between regions of interest ROIs based on BOLD signal. This approach not only maintains the high sensitivity to network connectivity provided by the correlation analysis, but also performs well in the estimation of causal information flow in the brain.
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): A statistically-based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence.

PubMed

Alemohammad, Seyed Hamed; Fang, Bin; Konings, Alexandra G; Aires, Filipe; Green, Julia K; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-01-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed Solar-Induced Fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H and GPP from 2007 to 2015 at 1° × 1° spatial resolution and on monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analysing WECANN retrievals across three extreme drought and heatwave events demonstrates the capability of the retrievals in capturing the extent of these events. Uncertainty estimates of the retrievals are analysed and the inter-annual variability in average global and regional fluxes show the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.
Water, Energy, and Carbon with Artificial Neural Networks (WECANN): a statistically based estimate of global surface turbulent fluxes and gross primary productivity using solar-induced fluorescence

NASA Astrophysics Data System (ADS)

Hamed Alemohammad, Seyed; Fang, Bin; Konings, Alexandra G.; Aires, Filipe; Green, Julia K.; Kolassa, Jana; Miralles, Diego; Prigent, Catherine; Gentine, Pierre

2017-09-01

A new global estimate of surface turbulent fluxes, latent heat flux (LE) and sensible heat flux (H), and gross primary production (GPP) is developed using a machine learning approach informed by novel remotely sensed solar-induced fluorescence (SIF) and other radiative and meteorological variables. This is the first study to jointly retrieve LE, H, and GPP using SIF observations. The approach uses an artificial neural network (ANN) with a target dataset generated from three independent data sources, weighted based on a triple collocation (TC) algorithm. The new retrieval, named Water, Energy, and Carbon with Artificial Neural Networks (WECANN), provides estimates of LE, H, and GPP from 2007 to 2015 at 1° × 1° spatial resolution and at monthly time resolution. The quality of ANN training is assessed using the target data, and the WECANN retrievals are evaluated using eddy covariance tower estimates from the FLUXNET network across various climates and conditions. When compared to eddy covariance estimates, WECANN typically outperforms other products, particularly for sensible and latent heat fluxes. Analyzing WECANN retrievals across three extreme drought and heat wave events demonstrates the capability of the retrievals to capture the extent of these events. Uncertainty estimates of the retrievals are analyzed and the interannual variability in average global and regional fluxes shows the impact of distinct climatic events - such as the 2015 El Niño - on surface turbulent fluxes and GPP.
Toward link predictability of complex networks

PubMed Central

Lü, Linyuan; Pan, Liming; Zhou, Tao; Zhang, Yi-Cheng; Stanley, H. Eugene

2015-01-01

The organization of real networks usually embodies both regularities and irregularities, and, in principle, the former can be modeled. The extent to which the formation of a network can be explained coincides with our ability to predict missing links. To understand network organization, we should be able to estimate link predictability. We assume that the regularity of a network is reflected in the consistency of structural features before and after a random removal of a small set of links. Based on the perturbation of the adjacency matrix, we propose a universal structural consistency index that is free of prior knowledge of network organization. Extensive experiments on disparate real-world networks demonstrate that (i) structural consistency is a good estimation of link predictability and (ii) a derivative algorithm outperforms state-of-the-art link prediction methods in both accuracy and robustness. This analysis has further applications in evaluating link prediction algorithms and monitoring sudden changes in evolving network mechanisms. It will provide unique fundamental insights into the above-mentioned academic research fields, and will foster the development of advanced information filtering technologies of interest to information technology practitioners. PMID:25659742
A Hybrid Approach for Estimating Total Deposition in the ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ
Impact of High Power Interference Sources in Planning and Deployment of Wireless Sensor Networks and Devices in the 2.4 GHz Frequency Band in Heterogeneous Environments

PubMed Central

Iturri, Peio López; Nazábal, Juan Antonio; Azpilicueta, Leire; Rodriguez, Pablo; Beruete, Miguel; Fernández-Valdivielso, Carlos; Falcone, Francisco

2012-01-01

In this work, the impact of radiofrequency radiation leakage from microwave ovens and its effect on 802.15.4 ZigBee-compliant wireless sensor networks operating in the 2.4 GHz Industrial Scientific Medical (ISM) band is analyzed. By means of a novel radioplanning approach, based on electromagnetic field simulation of a microwave oven and determination of equivalent radiation sources applied to an in-house developed 3D ray launching algorithm, estimation of the microwave oven's power leakage is obtained for the complete volume of an indoor scenario. The magnitude and the variable nature of the interference is analyzed and the impact in the radio link quality in operating wireless sensors is estimated and compared with radio channel measurements as well as packet measurements. The measurement results reveal the importance of selecting an adequate 802.15.4 channel, as well as the Wireless Sensor Network deployment strategy within this type of environment, in order to optimize energy consumption and increase the overall network performance. The proposed method enables one to estimate potential interference effects in devices operating within the 2.4 GHz band in the complete scenario, prior to wireless sensor network deployment, which can aid in achieving the most optimal network topology. PMID:23202228

A Novel Hybrid Approach for Estimating Total Deposition in ...

EPA Pesticide Factsheets

Atmospheric deposition of nitrogen and sulfur causes many deleterious effects on ecosystems including acidification and excess eutrophication. Assessments to support development of strategies to mitigate these effects require spatially and temporally continuous values of nitrogen and sulfur deposition. In the U.S., national monitoring networks exist that provide values of wet and dry deposition at discrete locations. While wet deposition can be interpolated between the monitoring locations, dry deposition cannot. Additionally, monitoring networks do not measure the complete suite of chemicals that contribute to total sulfur and nitrogen deposition. Regional air quality models provide spatially continuous values of deposition of monitored species as well as important unmeasured species. However, air quality modeling values are not generally available for an extended continuous time period. Air quality modeling results may also be biased for some chemical species. We developed a novel approach for estimating dry deposition using data from monitoring networks such as the Clean Air Status and Trends Network (CASTNET), the National Atmospheric Deposition Program (NADP) Ammonia Monitoring Network (AMoN), and the Southeastern Aerosol Research and Characterization (SEARCH) network and modeled data from the Community Multiscale Air Quality (CMAQ) model. These dry deposition values estimates are then combined with wet deposition values from the NADP National Trends Networ
Implementation of the ANNs ensembles in macro-BIM cost estimates of buildings' floor structural frames

NASA Astrophysics Data System (ADS)

Juszczyk, Michał

2018-04-01

This paper reports some results of the studies on the use of artificial intelligence tools for the purposes of cost estimation based on building information models. A problem of the cost estimates based on the building information models on a macro level supported by the ensembles of artificial neural networks is concisely discussed. In the course of the research a regression model has been built for the purposes of cost estimation of buildings' floor structural frames, as higher level elements. Building information models are supposed to serve as a repository of data used for the purposes of cost estimation. The core of the model is the ensemble of neural networks. The developed model allows the prediction of cost estimates with satisfactory accuracy.
Non-Data Aided Doppler Shift Estimation for Underwater Acoustic Communication

DTIC Science & Technology

2014-05-01

in underwater acoustic wireless sensor networks . We analyzed the data collected from our experiments using non-data aided (blind) techniques such as...investigated different methods for blind Doppler shift estimation and compensation for a single carrier in underwater acoustic wireless sensor ...distributed underwater sensor networks . Detailed experimental and simulated results based on second order cyclostationary features of the received signals
Estimation of dew point temperature using neuro-fuzzy and neural network techniques

NASA Astrophysics Data System (ADS)

Kisi, Ozgur; Kim, Sungwon; Shiri, Jalal

2013-11-01

This study investigates the ability of two different artificial neural network (ANN) models, generalized regression neural networks model (GRNNM) and Kohonen self-organizing feature maps neural networks model (KSOFM), and two different adaptive neural fuzzy inference system (ANFIS) models, ANFIS model with sub-clustering identification (ANFIS-SC) and ANFIS model with grid partitioning identification (ANFIS-GP), for estimating daily dew point temperature. The climatic data that consisted of 8 years of daily records of air temperature, sunshine hours, wind speed, saturation vapor pressure, relative humidity, and dew point temperature from three weather stations, Daego, Pohang, and Ulsan, in South Korea were used in the study. The estimates of ANN and ANFIS models were compared according to the three different statistics, root mean square errors, mean absolute errors, and determination coefficient. Comparison results revealed that the ANFIS-SC, ANFIS-GP, and GRNNM models showed almost the same accuracy and they performed better than the KSOFM model. Results also indicated that the sunshine hours, wind speed, and saturation vapor pressure have little effect on dew point temperature. It was found that the dew point temperature could be successfully estimated by using T mean and R H variables.
Discrete Indoor Three-Dimensional Localization System Based on Neural Networks Using Visible Light Communication

PubMed Central

Ley-Bosch, Carlos; Quintana-Suárez, Miguel A.

2018-01-01

Indoor localization estimation has become an attractive research topic due to growing interest in location-aware services. Many research works have proposed solving this problem by using wireless communication systems based on radiofrequency. Nevertheless, those approaches usually deliver an accuracy of up to two metres, since they are hindered by multipath propagation. On the other hand, in the last few years, the increasing use of light-emitting diodes in illumination systems has provided the emergence of Visible Light Communication technologies, in which data communication is performed by transmitting through the visible band of the electromagnetic spectrum. This brings a brand new approach to high accuracy indoor positioning because this kind of network is not affected by electromagnetic interferences and the received optical power is more stable than radio signals. Our research focus on to propose a fingerprinting indoor positioning estimation system based on neural networks to predict the device position in a 3D environment. Neural networks are an effective classification and predictive method. The localization system is built using a dataset of received signal strength coming from a grid of different points. From the these values, the position in Cartesian coordinates (x,y,z) is estimated. The use of three neural networks is proposed in this work, where each network is responsible for estimating the position by each axis. Experimental results indicate that the proposed system leads to substantial improvements to accuracy over the widely-used traditional fingerprinting methods, yielding an accuracy above 99% and an average error distance of 0.4 mm. PMID:29601525
Dynamic Data Driven Applications Systems (DDDAS)

DTIC Science & Technology

2013-03-06

INS •  Chip-scale atomic clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser communications •  Frequency-agile RF...atomi clocks •  Ad hoc networks •  Polymorphic networks •  Agile networks •  Laser co munications •  Frequency-agile RF systems...Real-Time Doppler Wind Wind field Sensor observations Energy Estimation Atmospheric Models for On-line Planning Planning and Control
Quantum demultiplexer of quantum parameter-estimation information in quantum networks

NASA Astrophysics Data System (ADS)

Xie, Yanqing; Huang, Yumeng; Wu, Yinzhong; Hao, Xiang

2018-05-01

The quantum demultiplexer is constructed by a series of unitary operators and multipartite entangled states. It is used to realize information broadcasting from an input node to multiple output nodes in quantum networks. The scheme of quantum network communication with respect to phase estimation is put forward through the demultiplexer subjected to amplitude damping noises. The generalized partial measurements can be applied to protect the transferring efficiency from environmental noises in the protocol. It is found out that there are some optimal coherent states which can be prepared to enhance the transmission of phase estimation. The dynamics of state fidelity and quantum Fisher information are investigated to evaluate the feasibility of the network communication. While the state fidelity deteriorates rapidly, the quantum Fisher information can be enhanced to a maximum value and then decreases slowly. The memory effect of the environment induces the oscillations of fidelity and quantum Fisher information. The adjustment of the strength of partial measurements is helpful to increase quantum Fisher information.
A novel application of artificial neural network for wind speed estimation

NASA Astrophysics Data System (ADS)

Fang, Da; Wang, Jianzhou

2017-05-01

Providing accurate multi-steps wind speed estimation models has increasing significance, because of the important technical and economic impacts of wind speed on power grid security and environment benefits. In this study, the combined strategies for wind speed forecasting are proposed based on an intelligent data processing system using artificial neural network (ANN). Generalized regression neural network and Elman neural network are employed to form two hybrid models. The approach employs one of ANN to model the samples achieving data denoising and assimilation and apply the other to predict wind speed using the pre-processed samples. The proposed method is demonstrated in terms of the predicting improvements of the hybrid models compared with single ANN and the typical forecasting method. To give sufficient cases for the study, four observation sites with monthly average wind speed of four given years in Western China were used to test the models. Multiple evaluation methods demonstrated that the proposed method provides a promising alternative technique in monthly average wind speed estimation.
NETWORK ASSISTED ANALYSIS TO REVEAL THE GENETIC BASIS OF AUTISM1

PubMed Central

Liu, Li; Lei, Jing; Roeder, Kathryn

2016-01-01

While studies show that autism is highly heritable, the nature of the genetic basis of this disorder remains illusive. Based on the idea that highly correlated genes are functionally interrelated and more likely to affect risk, we develop a novel statistical tool to find more potentially autism risk genes by combining the genetic association scores with gene co-expression in specific brain regions and periods of development. The gene dependence network is estimated using a novel partial neighborhood selection (PNS) algorithm, where node specific properties are incorporated into network estimation for improved statistical and computational efficiency. Then we adopt a hidden Markov random field (HMRF) model to combine the estimated network and the genetic association scores in a systematic manner. The proposed modeling framework can be naturally extended to incorporate additional structural information concerning the dependence between genes. Using currently available genetic association data from whole exome sequencing studies and brain gene expression levels, the proposed algorithm successfully identified 333 genes that plausibly affect autism risk. PMID:27134692
Simulations in site error estimation for direction finders

NASA Astrophysics Data System (ADS)

López, Raúl E.; Passi, Ranjit M.

1991-08-01

The performance of an algorithm for the recovery of site-specific errors of direction finder (DF) networks is tested under controlled simulated conditions. The simulations show that the algorithm has some inherent shortcomings for the recovery of site errors from the measured azimuth data. These limitations are fundamental to the problem of site error estimation using azimuth information. Several ways for resolving or ameliorating these basic complications are tested by means of simulations. From these it appears that for the effective implementation of the site error determination algorithm, one should design the networks with at least four DFs, improve the alignment of the antennas, and increase the gain of the DFs as much as it is compatible with other operational requirements. The use of a nonzero initial estimate of the site errors when working with data from networks of four or more DFs also improves the accuracy of the site error recovery. Even for networks of three DFs, reasonable site error corrections could be obtained if the antennas could be well aligned.
Mixed H2/H∞-Based Fusion Estimation for Energy-Limited Multi-Sensors in Wearable Body Networks

PubMed Central

Li, Chao; Zhang, Zhenjiang; Chao, Han-Chieh

2017-01-01

In wireless sensor networks, sensor nodes collect plenty of data for each time period. If all of data are transmitted to a Fusion Center (FC), the power of sensor node would run out rapidly. On the other hand, the data also needs a filter to remove the noise. Therefore, an efficient fusion estimation model, which can save the energy of the sensor nodes while maintaining higher accuracy, is needed. This paper proposes a novel mixed H2/H∞-based energy-efficient fusion estimation model (MHEEFE) for energy-limited Wearable Body Networks. In the proposed model, the communication cost is firstly reduced efficiently while keeping the estimation accuracy. Then, the parameters in quantization method are discussed, and we confirm them by an optimization method with some prior knowledge. Besides, some calculation methods of important parameters are researched which make the final estimates more stable. Finally, an iteration-based weight calculation algorithm is presented, which can improve the fault tolerance of the final estimate. In the simulation, the impacts of some pivotal parameters are discussed. Meanwhile, compared with the other related models, the MHEEFE shows a better performance in accuracy, energy-efficiency and fault tolerance. PMID:29280950
An Optimization-Based State Estimatioin Framework for Large-Scale Natural Gas Networks

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

Jalving, Jordan; Zavala, Victor M.

We propose an optimization-based state estimation framework to track internal spacetime flow and pressure profiles of natural gas networks during dynamic transients. We find that the estimation problem is ill-posed (because of the infinite-dimensional nature of the states) and that this leads to instability of the estimator when short estimation horizons are used. To circumvent this issue, we propose moving horizon strategies that incorporate prior information. In particular, we propose a strategy that initializes the prior using steady-state information and compare its performance against a strategy that does not initialize the prior. We find that both strategies are capable ofmore » tracking the state profiles but we also find that superior performance is obtained with steady-state prior initialization. We also find that, under the proposed framework, pressure sensor information at junctions is sufficient to track the state profiles. We also derive approximate transport models and show that some of these can be used to achieve significant computational speed-ups without sacrificing estimation performance. We show that the estimator can be easily implemented in the graph-based modeling framework Plasmo.jl and use a multipipeline network study to demonstrate the developments.« less
Large-cell Monte Carlo renormalization of irreversible growth processes

NASA Technical Reports Server (NTRS)

Nakanishi, H.; Family, F.

1985-01-01

Monte Carlo sampling is applied to a recently formulated direct-cell renormalization method for irreversible, disorderly growth processes. Large-cell Monte Carlo renormalization is carried out for various nonequilibrium problems based on the formulation dealing with relative probabilities. Specifically, the method is demonstrated by application to the 'true' self-avoiding walk and the Eden model of growing animals for d = 2, 3, and 4 and to the invasion percolation problem for d = 2 and 3. The results are asymptotically in agreement with expectations; however, unexpected complications arise, suggesting the possibility of crossovers, and in any case, demonstrating the danger of using small cells alone, because of the very slow convergence as the cell size b is extrapolated to infinity. The difficulty of applying the present method to the diffusion-limited-aggregation model, is commented on.
Enhanced Radio Frequency (RF) Collection With Distributed Wireless Sensor Networks

DTIC Science & Technology

2007-06-01

48 4. Controlling the Size of the Beamwidth ............................................50 C. SPECTRAL ESTIMATION...55 Figure 35. Spectral Estimation results 157 MHz. .............................................................58 Figure 36. Spectral ...Estimation results 800 MHz. .............................................................59 Figure 37. Spectral Estimation results 2.4 GHz
Temperature and relative humidity estimation and prediction in the tobacco drying process using Artificial Neural Networks.

PubMed

Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M; Aguiar, Javier M; Carro, Belén

2012-10-17

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed.
Temperature and Relative Humidity Estimation and Prediction in the Tobacco Drying Process Using Artificial Neural Networks

PubMed Central

Martínez-Martínez, Víctor; Baladrón, Carlos; Gomez-Gil, Jaime; Ruiz-Ruiz, Gonzalo; Navas-Gracia, Luis M.; Aguiar, Javier M.; Carro, Belén

2012-01-01

This paper presents a system based on an Artificial Neural Network (ANN) for estimating and predicting environmental variables related to tobacco drying processes. This system has been validated with temperature and relative humidity data obtained from a real tobacco dryer with a Wireless Sensor Network (WSN). A fitting ANN was used to estimate temperature and relative humidity in different locations inside the tobacco dryer and to predict them with different time horizons. An error under 2% can be achieved when estimating temperature as a function of temperature and relative humidity in other locations. Moreover, an error around 1.5 times lower than that obtained with an interpolation method can be achieved when predicting the temperature inside the tobacco mass as a function of its present and past values with time horizons over 150 minutes. These results show that the tobacco drying process can be improved taking into account the predicted future value of the monitored variables and the estimated actual value of other variables using a fitting ANN as proposed. PMID:23202032
Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks

PubMed Central

Lam, William H. K.; Li, Qingquan

2017-01-01

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks. PMID:29210978
Heterogeneous Data Fusion Method to Estimate Travel Time Distributions in Congested Road Networks.

PubMed

Shi, Chaoyang; Chen, Bi Yu; Lam, William H K; Li, Qingquan

2017-12-06

Travel times in congested urban road networks are highly stochastic. Provision of travel time distribution information, including both mean and variance, can be very useful for travelers to make reliable path choice decisions to ensure higher probability of on-time arrival. To this end, a heterogeneous data fusion method is proposed to estimate travel time distributions by fusing heterogeneous data from point and interval detectors. In the proposed method, link travel time distributions are first estimated from point detector observations. The travel time distributions of links without point detectors are imputed based on their spatial correlations with links that have point detectors. The estimated link travel time distributions are then fused with path travel time distributions obtained from the interval detectors using Dempster-Shafer evidence theory. Based on fused path travel time distribution, an optimization technique is further introduced to update link travel time distributions and their spatial correlations. A case study was performed using real-world data from Hong Kong and showed that the proposed method obtained accurate and robust estimations of link and path travel time distributions in congested road networks.
The Derivation of Fault Volumetric Properties from 3D Trace Maps Using Outcrop Constrained Discrete Fracture Network Models

NASA Astrophysics Data System (ADS)

Hodgetts, David; Seers, Thomas

2015-04-01

Fault systems are important structural elements within many petroleum reservoirs, acting as potential conduits, baffles or barriers to hydrocarbon migration. Large, seismic-scale faults often serve as reservoir bounding seals, forming structural traps which have proved to be prolific plays in many petroleum provinces. Though inconspicuous within most seismic datasets, smaller subsidiary faults, commonly within the damage zones of parent structures, may also play an important role. These smaller faults typically form narrow, tabular low permeability zones which serve to compartmentalize the reservoir, negatively impacting upon hydrocarbon recovery. Though considerable improvements have been made in the visualization field to reservoir-scale fault systems with the advent of 3D seismic surveys, the occlusion of smaller scale faults in such datasets is a source of significant uncertainty during prospect evaluation. The limited capacity of conventional subsurface datasets to probe the spatial distribution of these smaller scale faults has given rise to a large number of outcrop based studies, allowing their intensity, connectivity and size distributions to be explored in detail. Whilst these studies have yielded an improved theoretical understanding of the style and distribution of sub-seismic scale faults, the ability to transform observations from outcrop to quantities that are relatable to reservoir volumes remains elusive. These issues arise from the fact that outcrops essentially offer a pseudo-3D window into the rock volume, making the extrapolation of surficial fault properties such as areal density (fracture length per unit area: P21), to equivalent volumetric measures (i.e. fracture area per unit volume: P32) applicable to fracture modelling extremely challenging. Here, we demonstrate an approach which harnesses advances in the extraction of 3D trace maps from surface reconstructions using calibrated image sequences, in combination with a novel semi-deterministic, outcrop constrained discrete fracture network modeling code to derive volumetric fault intensity measures (fault area per unit volume / fault volume per unit volume). Producing per-vertex measures of volumetric intensity; our method captures the spatial variability in 3D fault density across a surveyed outcrop, enabling first order controls to be probed. We demonstrate our approach on pervasively faulted exposures of a Permian aged reservoir analogue from the Vale of Eden Basin, UK.
Unraveling spurious properties of interaction networks with tailored random networks.

PubMed

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures--known for their complex spatial and temporal dynamics--we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis.

Unraveling Spurious Properties of Interaction Networks with Tailored Random Networks

PubMed Central

Bialonski, Stephan; Wendler, Martin; Lehnertz, Klaus

2011-01-01

We investigate interaction networks that we derive from multivariate time series with methods frequently employed in diverse scientific fields such as biology, quantitative finance, physics, earth and climate sciences, and the neurosciences. Mimicking experimental situations, we generate time series with finite length and varying frequency content but from independent stochastic processes. Using the correlation coefficient and the maximum cross-correlation, we estimate interdependencies between these time series. With clustering coefficient and average shortest path length, we observe unweighted interaction networks, derived via thresholding the values of interdependence, to possess non-trivial topologies as compared to Erdös-Rényi networks, which would indicate small-world characteristics. These topologies reflect the mostly unavoidable finiteness of the data, which limits the reliability of typically used estimators of signal interdependence. We propose random networks that are tailored to the way interaction networks are derived from empirical data. Through an exemplary investigation of multichannel electroencephalographic recordings of epileptic seizures – known for their complex spatial and temporal dynamics – we show that such random networks help to distinguish network properties of interdependence structures related to seizure dynamics from those spuriously induced by the applied methods of analysis. PMID:21850239
Short-term estimation of GNSS TEC using a neural network model in Brazil

NASA Astrophysics Data System (ADS)

Ferreira, Arthur Amaral; Borges, Renato Alves; Paparini, Claudia; Ciraolo, Luigi; Radicella, Sandro M.

2017-10-01

This work presents a novel Neural Network (NN) model to estimate Total Electron Content (TEC) from Global Navigation Satellite Systems (GNSS) measurements in three distinct sectors in Brazil. The purpose of this work is to start the investigations on the development of a regional model that can be used to determine the vertical TEC over Brazil, aiming future applications on a near real-time frame estimations and short-term forecasting. The NN is used to estimate the GNSS TEC values at void locations, where no dual-frequency GNSS receiver that may be used as a source of data to GNSS TEC estimation is available. This approach is particularly useful for GNSS single-frequency users that rely on corrections of ionospheric range errors by TEC models. GNSS data from the first GLONASS network for research and development (GLONASS R&D network) installed in Latin America, and from the Brazilian Network for Continuous Monitoring of the GNSS (RMBC) were used on TEC calibration. The input parameters of the NN model are based on features known to influence TEC values, such as geographic location of the GNSS receiver, magnetic activity, seasonal and diurnal variations, and solar activity. Data from two ten-days periods (from DoY 154 to 163 and from 282 to 291) are used to train the network. Three distinct analyses have been carried out in order to assess time-varying and spatial performance of the model. At the spatial performance analysis, for each region, a set of stations is chosen to provide training data to the NN, and after the training procedure, the NN is used to estimate vTEC behavior for the test station which data were not presented to the NN in training process. An analysis is done by comparing, for each testing station, the estimated NN vTEC delivered by the NN and reference calibrated vTEC. Also, as a second analysis, the network ability to forecast one day after the time interval (DoY 292) based on information of the second period of investigation is also assessed in order to verify the feasibility on using low amount of data for short-term forecasting. In a third analysis, the spatial performance of the NN model is assessed and compared against CODE Global Ionospheric Maps during the geomagnetic storm registered on 13th and 14th October 2016. The results obtained from the three described analyses indicate that even using a ten-days period of data to train the network, the proposed NN model provides good spatial performance and presents to be a promising tool for short-term forecasting. The results obtained in the analysis presented a root mean squared error less than 7.9 TECU in all scenarios under investigation.
Spread of Epidemic on Complex Networks Under Voluntary Vaccination Mechanism

NASA Astrophysics Data System (ADS)

Xue, Shengjun; Ruan, Feng; Yin, Chuanyang; Zhang, Haifeng; Wang, Binghong

Under the assumption that the decision of vaccination is a voluntary behavior, in this paper, we use two forms of risk functions to characterize how susceptible individuals estimate the perceived risk of infection. One is uniform case, where each susceptible individual estimates the perceived risk of infection only based on the density of infection at each time step, so the risk function is only a function of the density of infection; another is preferential case, where each susceptible individual estimates the perceived risk of infection not only based on the density of infection but only related to its own activities/immediate neighbors (in network terminology, the activity or the number of immediate neighbors is the degree of node), so the risk function is a function of the density of infection and the degree of individuals. By investigating two different ways of estimating the risk of infection for susceptible individuals on complex network, we find that, for the preferential case, the spread of epidemic can be effectively controlled; yet, for the uniform case, voluntary vaccination mechanism is almost invalid in controlling the spread of epidemic on networks. Furthermore, given the temporality of some vaccines, the waves of epidemic for two cases are also different. Therefore, our work insight that the way of estimating the perceived risk of infection determines the decision on vaccination options, and then determines the success or failure of control strategy.
Application of AFINCH as a tool for evaluating the effects of streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the southeast Lake Michigan hydrologic subregion

USGS Publications Warehouse

Koltun, G.F.; Holtschlag, David J.

2010-01-01

Bootstrapping techniques employing random subsampling were used with the AFINCH (Analysis of Flows In Networks of CHannels) model to gain insights into the effects of variation in streamflow-gaging-network size and composition on the accuracy and precision of streamflow estimates at ungaged locations in the 0405 (Southeast Lake Michigan) hydrologic subregion. AFINCH uses stepwise-regression techniques to estimate monthly water yields from catchments based on geospatial-climate and land-cover data in combination with available streamflow and water-use data. Calculations are performed on a hydrologic-subregion scale for each catchment and stream reach contained in a National Hydrography Dataset Plus (NHDPlus) subregion. Water yields from contributing catchments are multiplied by catchment areas and resulting flow values are accumulated to compute streamflows in stream reaches which are referred to as flow lines. AFINCH imposes constraints on water yields to ensure that observed streamflows are conserved at gaged locations. Data from the 0405 hydrologic subregion (referred to as Southeast Lake Michigan) were used for the analyses. Daily streamflow data were measured in the subregion for 1 or more years at a total of 75 streamflow-gaging stations during the analysis period which spanned water years 1971–2003. The number of streamflow gages in operation each year during the analysis period ranged from 42 to 56 and averaged 47. Six sets (one set for each censoring level), each composed of 30 random subsets of the 75 streamflow gages, were created by censoring (removing) approximately 10, 20, 30, 40, 50, and 75 percent of the streamflow gages (the actual percentage of operating streamflow gages censored for each set varied from year to year, and within the year from subset to subset, but averaged approximately the indicated percentages).Streamflow estimates for six flow lines each were aggregated by censoring level, and results were analyzed to assess (a) how the size and composition of the streamflow-gaging network affected the average apparent errors and variability of the estimated flows and (b) whether results for certain months were more variable than for others. The six flow lines were categorized into one of three types depending upon their network topology and position relative to operating streamflow-gaging stations. Statistical analysis of the model results indicates that (1) less precise (that is, more variable) estimates resulted from smaller streamflow-gaging networks as compared to larger streamflow-gaging networks, (2) precision of AFINCH flow estimates at an ungaged flow line is improved by operation of one or more streamflow gages upstream and (or) downstream in the enclosing basin, (3) no consistent seasonal trend in estimate variability was evident, and (4) flow lines from ungaged basins appeared to exhibit the smallest absolute apparent percent errors (APEs) and smallest changes in average APE as a function of increasing censoring level. The counterintuitive results described in item (4) above likely reflect both the nature of the base-streamflow estimate from which the errors were computed and insensitivity in the average model-derived estimates to changes in the streamflow-gaging-network size and composition. Another analysis demonstrated that errors for flow lines in ungaged basins have the potential to be much larger than indicated by their APEs if measured relative to their true (but unknown) flows. “Missing gage” analyses, based on examination of censoring subset results where the streamflow gage of interest was omitted from the calibration data set, were done to better understand the true error characteristics for ungaged flow lines as a function of network size. Results examined for 2 water years indicated that the probability of computing a monthly streamflow estimate within 10 percent of the true value with AFINCH decreased from greater than 0.9 at about a 10-percent network-censoring level to less than 0.6 as the censoring level approached 75 percent. In addition, estimates for typically dry months tended to be characterized by larger percent errors than typically wetter months.
The Game of Contacts: Estimating the Social Visibility of Groups.

PubMed

Salganik, Matthew J; Mello, Maeve B; Abdo, Alexandre H; Bertoni, Neilane; Fazito, Dimitri; Bastos, Francisco I

2011-01-01

Estimating the sizes of hard-to-count populations is a challenging and important problem that occurs frequently in social science, public health, and public policy. This problem is particularly pressing in HIV/AIDS research because estimates of the sizes of the most at-risk populations-illicit drug users, men who have sex with men, and sex workers-are needed for designing, evaluating, and funding programs to curb the spread of the disease. A promising new approach in this area is the network scale-up method, which uses information about the personal networks of respondents to make population size estimates. However, if the target population has low social visibility, as is likely to be the case in HIV/AIDS research, scale-up estimates will be too low. In this paper we develop a game-like activity that we call the game of contacts in order to estimate the social visibility of groups, and report results from a study of heavy drug users in Curitiba, Brazil (n = 294). The game produced estimates of social visibility that were consistent with qualitative expectations but of surprising magnitude. Further, a number of checks suggest that the data are high-quality. While motivated by the specific problem of population size estimation, our method could be used by researchers more broadly and adds to long-standing efforts to combine the richness of social network analysis with the power and scale of sample surveys.
Extension algorithm for generic low-voltage networks

NASA Astrophysics Data System (ADS)

Marwitz, S.; Olk, C.

2018-02-01

Distributed energy resources (DERs) are increasingly penetrating the energy system which is driven by climate and sustainability goals. These technologies are mostly connected to low- voltage electrical networks and change the demand and supply situation in these networks. This can cause critical network states. Network topologies vary significantly and depend on several conditions including geography, historical development, network design or number of network connections. In the past, only some of these aspects were taken into account when estimating the network investment needs for Germany on the low-voltage level. Typically, fixed network topologies are examined or a Monte Carlo approach is used to quantify the investment needs at this voltage level. Recent research has revealed that DERs differ substantially between rural, suburban and urban regions. The low-voltage network topologies have different design concepts in these regions, so that different network topologies have to be considered when assessing the need for network extensions and investments due to DERs. An extension algorithm is needed to calculate network extensions and investment needs for the different typologies of generic low-voltage networks. We therefore present a new algorithm, which is capable of calculating the extension for generic low-voltage networks of any given topology based on voltage range deviations and thermal overloads. The algorithm requires information about line and cable lengths, their topology and the network state only. We test the algorithm on a radial, a loop, and a heavily meshed network. Here we show that the algorithm functions for electrical networks with these topologies. We found that the algorithm is able to extend different networks efficiently by placing cables between network nodes. The main value of the algorithm is that it does not require any information about routes for additional cables or positions for additional substations when it comes to estimating network extension needs.
RENEB intercomparisons applying the conventional Dicentric Chromosome Assay (DCA).

PubMed

Oestreicher, Ursula; Samaga, Daniel; Ainsbury, Elizabeth; Antunes, Ana Catarina; Baeyens, Ans; Barrios, Leonardo; Beinke, Christina; Beukes, Philip; Blakely, William F; Cucu, Alexandra; De Amicis, Andrea; Depuydt, Julie; De Sanctis, Stefania; Di Giorgio, Marina; Dobos, Katalin; Dominguez, Inmaculada; Duy, Pham Ngoc; Espinoza, Marco E; Flegal, Farrah N; Figel, Markus; Garcia, Omar; Monteiro Gil, Octávia; Gregoire, Eric; Guerrero-Carbajal, C; Güçlü, İnci; Hadjidekova, Valeria; Hande, Prakash; Kulka, Ulrike; Lemon, Jennifer; Lindholm, Carita; Lista, Florigio; Lumniczky, Katalin; Martinez-Lopez, Wilner; Maznyk, Nataliya; Meschini, Roberta; M'kacher, Radia; Montoro, Alegria; Moquet, Jayne; Moreno, Mercedes; Noditi, Mihaela; Pajic, Jelena; Radl, Analía; Ricoul, Michelle; Romm, Horst; Roy, Laurence; Sabatier, Laure; Sebastià, Natividad; Slabbert, Jacobus; Sommer, Sylwester; Stuck Oliveira, Monica; Subramanian, Uma; Suto, Yumiko; Que, Tran; Testa, Antonella; Terzoudi, Georgia; Vral, Anne; Wilkins, Ruth; Yanti, LusiYanti; Zafiropoulos, Demetre; Wojcik, Andrzej

2017-01-01

Two quality controlled inter-laboratory exercises were organized within the EU project 'Realizing the European Network of Biodosimetry (RENEB)' to further optimize the dicentric chromosome assay (DCA) and to identify needs for training and harmonization activities within the RENEB network. The general study design included blood shipment, sample processing, analysis of chromosome aberrations and radiation dose assessment. After manual scoring of dicentric chromosomes in different cell numbers dose estimations and corresponding 95% confidence intervals were submitted by the participants. The shipment of blood samples to the partners in the European Community (EU) were performed successfully. Outside the EU unacceptable delays occurred. The results of the dose estimation demonstrate a very successful classification of the blood samples in medically relevant groups. In comparison to the 1st exercise the 2nd intercomparison showed an improvement in the accuracy of dose estimations especially for the high dose point. In case of a large-scale radiological incident, the pooling of ressources by networks can enhance the rapid classification of individuals in medically relevant treatment groups based on the DCA. The performance of the RENEB network as a whole has clearly benefited from harmonization processes and specific training activities for the network partners.
Inclusion of Topological Measurements into Analytic Estimates of Effective Permeability in Fractured Media

NASA Astrophysics Data System (ADS)

Sævik, P. N.; Nixon, C. W.

2017-11-01

We demonstrate how topology-based measures of connectivity can be used to improve analytical estimates of effective permeability in 2-D fracture networks, which is one of the key parameters necessary for fluid flow simulations at the reservoir scale. Existing methods in this field usually compute fracture connectivity using the average fracture length. This approach is valid for ideally shaped, randomly distributed fractures, but is not immediately applicable to natural fracture networks. In particular, natural networks tend to be more connected than randomly positioned fractures of comparable lengths, since natural fractures often terminate in each other. The proposed topological connectivity measure is based on the number of intersections and fracture terminations per sampling area, which for statistically stationary networks can be obtained directly from limited outcrop exposures. To evaluate the method, numerical permeability upscaling was performed on a large number of synthetic and natural fracture networks, with varying topology and geometry. The proposed method was seen to provide much more reliable permeability estimates than the length-based approach, across a wide range of fracture patterns. We summarize our results in a single, explicit formula for the effective permeability.
Estimation of the Botanical Composition of Clover-Grass Leys from RGB Images Using Data Simulation and Fully Convolutional Neural Networks

PubMed Central

Steen, Kim Arild; Green, Ole; Karstoft, Henrik

2017-01-01

Optimal fertilization of clover-grass fields relies on knowledge of the clover and grass fractions. This study shows how knowledge can be obtained by analyzing images collected in fields automatically. A fully convolutional neural network was trained to create a pixel-wise classification of clover, grass, and weeds in red, green, and blue (RGB) images of clover-grass mixtures. The estimated clover fractions of the dry matter from the images were found to be highly correlated with the real clover fractions of the dry matter, making this a cheap and non-destructive way of monitoring clover-grass fields. The network was trained solely on simulated top-down images of clover-grass fields. This enables the network to distinguish clover, grass, and weed pixels in real images. The use of simulated images for training reduces the manual labor to a few hours, as compared to more than 3000 h when all the real images are annotated for training. The network was tested on images with varied clover/grass ratios and achieved an overall pixel classification accuracy of 83.4%, while estimating the dry matter clover fraction with a standard deviation of 7.8%. PMID:29258215
Spatial Representativeness Error in the Ground-Level Observation Networks for Black Carbon Radiation Absorption

NASA Astrophysics Data System (ADS)

Wang, Rong; Andrews, Elisabeth; Balkanski, Yves; Boucher, Olivier; Myhre, Gunnar; Samset, Bjørn Hallvard; Schulz, Michael; Schuster, Gregory L.; Valari, Myrto; Tao, Shu

2018-02-01

There is high uncertainty in the direct radiative forcing of black carbon (BC), an aerosol that strongly absorbs solar radiation. The observation-constrained estimate, which is several times larger than the bottom-up estimate, is influenced by the spatial representativeness error due to the mesoscale inhomogeneity of the aerosol fields and the relatively low resolution of global chemistry-transport models. Here we evaluated the spatial representativeness error for two widely used observational networks (AErosol RObotic NETwork and Global Atmosphere Watch) by downscaling the geospatial grid in a global model of BC aerosol absorption optical depth to 0.1° × 0.1°. Comparing the models at a spatial resolution of 2° × 2° with BC aerosol absorption at AErosol RObotic NETwork sites (which are commonly located near emission hot spots) tends to cause a global spatial representativeness error of 30%, as a positive bias for the current top-down estimate of global BC direct radiative forcing. By contrast, the global spatial representativeness error will be 7% for the Global Atmosphere Watch network, because the sites are located in such a way that there are almost an equal number of sites with positive or negative representativeness error.
Health impact assessment of cycling network expansions in European cities.

PubMed

Mueller, Natalie; Rojas-Rueda, David; Salmon, Maëlle; Martinez, David; Ambros, Albert; Brand, Christian; de Nazelle, Audrey; Dons, Evi; Gaupp-Berghausen, Mailin; Gerike, Regine; Götschi, Thomas; Iacorossi, Francesco; Int Panis, Luc; Kahlmeier, Sonja; Raser, Elisabeth; Nieuwenhuijsen, Mark

2018-04-01

We conducted a health impact assessment (HIA) of cycling network expansions in seven European cities. We modeled the association between cycling network length and cycling mode share and estimated health impacts of the expansion of cycling networks. First, we performed a non-linear least square regression to assess the relationship between cycling network length and cycling mode share for 167 European cities. Second, we conducted a quantitative HIA for the seven cities of different scenarios (S) assessing how an expansion of the cycling network [i.e. 10% (S1); 50% (S2); 100% (S3), and all-streets (S4)] would lead to an increase in cycling mode share and estimated mortality impacts thereof. We quantified mortality impacts for changes in physical activity, air pollution and traffic incidents. Third, we conducted a cost-benefit analysis. The cycling network length was associated with a cycling mode share of up to 24.7% in European cities. The all-streets scenario (S4) produced greatest benefits through increases in cycling for London with 1,210 premature deaths (95% CI: 447-1,972) avoidable annually, followed by Rome (433; 95% CI: 170-695), Barcelona (248; 95% CI: 86-410), Vienna (146; 95% CI: 40-252), Zurich (58; 95% CI: 16-100) and Antwerp (7; 95% CI: 3-11). The largest cost-benefit ratios were found for the 10% increase in cycling networks (S1). If all 167 European cities achieved a cycling mode share of 24.7% over 10,000 premature deaths could be avoided annually. In European cities, expansions of cycling networks were associated with increases in cycling and estimated to provide health and economic benefits. Copyright © 2018 Elsevier Inc. All rights reserved.
Validation of a smartphone app to map social networks of proximity

PubMed Central

Larsen, Mark E.; Townsend, Samuel; Christensen, Helen

2017-01-01

Social network analysis is a prominent approach to investigate interpersonal relationships. Most studies use self-report data to quantify the connections between participants and construct social networks. In recent years smartphones have been used as an alternative to map networks by assessing the proximity between participants based on Bluetooth and GPS data. While most studies have handed out specially programmed smartphones to study participants, we developed an application for iOS and Android to collect Bluetooth data from participants’ own smartphones. In this study, we compared the networks estimated with the smartphone app to those obtained from sociometric badges and self-report data. Participants (n = 21) installed the app on their phone and wore a sociometric badge during office hours. Proximity data was collected for 4 weeks. A contingency table revealed a significant association between proximity data (ϕ = 0.17, p<0.0001), but the marginal odds were higher for the app (8.6%) than for the badges (1.3%), indicating that dyads were more often detected by the app. We then compared the networks that were estimated using the proximity and self-report data. All three networks were significantly correlated, although the correlation with self-reported data was lower for the app (ρ = 0.25) than for badges (ρ = 0.67). The scanning rates of the app varied considerably between devices and was lower on iOS than on Android. The association between the app and the badges increased when the network was estimated between participants whose app recorded more regularly. These findings suggest that the accuracy of proximity networks can be further improved by reducing missing data and restricting the interpersonal distance at which interactions are detected. PMID:29261782
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.
Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

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

Kubic, William Louis; Jenkins, Rhodri W.; Moore, Cameron M.

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.
Artificial Neural Network Based Group Contribution Method for Estimating Cetane and Octane Numbers of Hydrocarbons and Oxygenated Organic Compounds

DOE PAGES

Kubic, William Louis; Jenkins, Rhodri W.; Moore, Cameron M.; ...

2017-09-28

Chemical pathways for converting biomass into fuels produce compounds for which key physical and chemical property data are unavailable. We developed an artificial neural network based group contribution method for estimating cetane and octane numbers that captures the complex dependence of fuel properties of pure compounds on chemical structure and is statistically superior to current methods.
On the estimability of parameters in undifferenced, uncombined GNSS network and PPP-RTK user models by means of $mathcal {S}$ S -system theory

NASA Astrophysics Data System (ADS)

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

2016-01-01

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

PubMed

Sauser Zachrison, Kori; Iwashyna, Theodore J; Gebremariam, Achamyeleh; Hutchins, Meghan; Lee, Joyce M

2016-12-28

Connected individuals (or nodes) in a network are more likely to be similar than two randomly selected nodes due to homophily and/or network influence. Distinguishing between these two influences is an important goal in network analysis, and generalized estimating equation (GEE) analyses of longitudinal dyadic network data are an attractive approach. It is not known to what extent such regressions can accurately extract underlying data generating processes. Therefore our primary objective is to determine to what extent, and under what conditions, does the GEE-approach recreate the actual dynamics in an agent-based model. We generated simulated cohorts with pre-specified network characteristics and attachments in both static and dynamic networks, and we varied the presence of homophily and network influence. We then used statistical regression and examined the GEE model performance in each cohort to determine whether the model was able to detect the presence of homophily and network influence. In cohorts with both static and dynamic networks, we find that the GEE models have excellent sensitivity and reasonable specificity for determining the presence or absence of network influence, but little ability to distinguish whether or not homophily is present. The GEE models are a valuable tool to examine for the presence of network influence in longitudinal data, but are quite limited with respect to homophily.
By land, sea and air (and space): Verifying UK methane emissions at a range of scales by integrating multiple measurement platforms

NASA Astrophysics Data System (ADS)

Rigby, M. L.; Lunt, M. F.; Ganesan, A.

2015-12-01

The Greenhouse gAs Uk and Global Emissions (GAUGE) programme and Department of Energy and Climate Change (DECC) network aim to quantify the magnitude and uncertainty of UK greenhouse gas (GHG) emissions at a resolution and accuracy higher than has previously been possible. The on going DECC tall tower network consists of three sites, and an eastern background site in Ireland. The GAUGE project includes instruments at two additional tall tower sites, a high-density measurement network over agricultural land in eastern England, a ferry that performs near-daily transects along the east coast of the UK, and a research aircraft that has been deployed on a campaign basis. Together with data collected by the GOSAT satellite, these data represent the GAUGE/DECC GHG measurement network that is being used to quantify UK GHG fluxes. As part of the wider GAUGE modelling efforts, we have derived methane flux estimates for the UK and northwest Europe using the UK Met Office NAME atmospheric transport model and a novel hierarchical Bayesian "trans-dimensional" inversion framework. We will show that our estimated fluxes for the UK as a whole are largely consistent between individual measurement platforms, albeit with very different uncertainties. Our novel inversion approach uses the data to objectively determine the extent to which we can further refine our national estimates to the level of large urban areas, major hotspots or larger sub-national regions. In this talk, we will outline some initial findings of the GAUGE project, tackling questions such as: At what spatial scale can we effectively derive greenhouse gas fluxes with a dense, multi-platform national network? Can we resolve individual metropolitan areas or major hotspots? What is relative impact of individual stations, platforms and network configurations on flux estimates for a country of the size of the UK? How can we effectively use multi-platform observations to cross-validate flux estimates and determine likely errors in model transport?
Cloud-based shaft torque estimation for electric vehicle equipped with integrated motor-transmission system

NASA Astrophysics Data System (ADS)

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

2018-01-01

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

NASA Astrophysics Data System (ADS)

Singal, J.; Shmakova, M.; Gerke, B.; Griffith, R. L.; Lotz, J.

2011-05-01

We present a determination of the effects of including galaxy morphological parameters in photometric redshift estimation with an artificial neural network method. Neural networks, which recognize patterns in the information content of data in an unbiased way, can be a useful estimator of the additional information contained in extra parameters, such as those describing morphology, if the input data are treated on an equal footing. We use imaging and five band photometric magnitudes from the All-wavelength Extended Groth Strip International Survey (AEGIS). It is shown that certain principal components of the morphology information are correlated with galaxy type. However, we find that for the data used the inclusion of morphological information does not have a statistically significant benefit for photometric redshift estimation with the techniques employed here. The inclusion of these parameters may result in a tradeoff between extra information and additional noise, with the additional noise becoming more dominant as more parameters are added.

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