Dissociable changes in functional network topology underlie early category learning and development of automaticity

PubMed Central

Soto, Fabian A.; Bassett, Danielle S.; Ashby, F. Gregory

2016-01-01

Recent work has shown that multimodal association areas–including frontal, temporal and parietal cortex–are focal points of functional network reconfiguration during human learning and performance of cognitive tasks. On the other hand, neurocomputational theories of category learning suggest that the basal ganglia and related subcortical structures are focal points of functional network reconfiguration during early learning of some categorization tasks, but become less so with the development of automatic categorization performance. Using a combination of network science and multilevel regression, we explore how changes in the connectivity of small brain regions can predict behavioral changes during training in a visual categorization task. We find that initial category learning, as indexed by changes in accuracy, is predicted by increasingly efficient integrative processing in subcortical areas, with higher functional specialization, more efficient integration across modules, but a lower cost in terms of redundancy of information processing. The development of automaticity, as indexed by changes in the speed of correct responses, was predicted by lower clustering (particularly in subcortical areas), higher strength (highest in cortical areas) and higher betweenness centrality. By combining neurocomputational theories and network scientific methods, these results synthesize the dissociative roles of multimodal association areas and subcortical structures in the development of automaticity during category learning. PMID:27453156

A Computational Solution to Automatically Map Metabolite Libraries in the Context of Genome Scale Metabolic Networks.

PubMed

Merlet, Benjamin; Paulhe, Nils; Vinson, Florence; Frainay, Clément; Chazalviel, Maxime; Poupin, Nathalie; Gloaguen, Yoann; Giacomoni, Franck; Jourdan, Fabien

2016-01-01

This article describes a generic programmatic method for mapping chemical compound libraries on organism-specific metabolic networks from various databases (KEGG, BioCyc) and flat file formats (SBML and Matlab files). We show how this pipeline was successfully applied to decipher the coverage of chemical libraries set up by two metabolomics facilities MetaboHub (French National infrastructure for metabolomics and fluxomics) and Glasgow Polyomics (GP) on the metabolic networks available in the MetExplore web server. The present generic protocol is designed to formalize and reduce the volume of information transfer between the library and the network database. Matching of metabolites between libraries and metabolic networks is based on InChIs or InChIKeys and therefore requires that these identifiers are specified in both libraries and networks. In addition to providing covering statistics, this pipeline also allows the visualization of mapping results in the context of metabolic networks. In order to achieve this goal, we tackled issues on programmatic interaction between two servers, improvement of metabolite annotation in metabolic networks and automatic loading of a mapping in genome scale metabolic network analysis tool MetExplore. It is important to note that this mapping can also be performed on a single or a selection of organisms of interest and is thus not limited to large facilities.

Toward automatic time-series forecasting using neural networks.

PubMed

Yan, Weizhong

2012-07-01

Over the past few decades, application of artificial neural networks (ANN) to time-series forecasting (TSF) has been growing rapidly due to several unique features of ANN models. However, to date, a consistent ANN performance over different studies has not been achieved. Many factors contribute to the inconsistency in the performance of neural network models. One such factor is that ANN modeling involves determining a large number of design parameters, and the current design practice is essentially heuristic and ad hoc, this does not exploit the full potential of neural networks. Systematic ANN modeling processes and strategies for TSF are, therefore, greatly needed. Motivated by this need, this paper attempts to develop an automatic ANN modeling scheme. It is based on the generalized regression neural network (GRNN), a special type of neural network. By taking advantage of several GRNN properties (i.e., a single design parameter and fast learning) and by incorporating several design strategies (e.g., fusing multiple GRNNs), we have been able to make the proposed modeling scheme to be effective for modeling large-scale business time series. The initial model was entered into the NN3 time-series competition. It was awarded the best prediction on the reduced dataset among approximately 60 different models submitted by scholars worldwide.

Automatic analysis of attack data from distributed honeypot network

NASA Astrophysics Data System (ADS)

Safarik, Jakub; Voznak, MIroslav; Rezac, Filip; Partila, Pavol; Tomala, Karel

2013-05-01

There are many ways of getting real data about malicious activity in a network. One of them relies on masquerading monitoring servers as a production one. These servers are called honeypots and data about attacks on them brings us valuable information about actual attacks and techniques used by hackers. The article describes distributed topology of honeypots, which was developed with a strong orientation on monitoring of IP telephony traffic. IP telephony servers can be easily exposed to various types of attacks, and without protection, this situation can lead to loss of money and other unpleasant consequences. Using a distributed topology with honeypots placed in different geological locations and networks provides more valuable and independent results. With automatic system of gathering information from all honeypots, it is possible to work with all information on one centralized point. Communication between honeypots and centralized data store use secure SSH tunnels and server communicates only with authorized honeypots. The centralized server also automatically analyses data from each honeypot. Results of this analysis and also other statistical data about malicious activity are simply accessible through a built-in web server. All statistical and analysis reports serve as information basis for an algorithm which classifies different types of used VoIP attacks. The web interface then brings a tool for quick comparison and evaluation of actual attacks in all monitored networks. The article describes both, the honeypots nodes in distributed architecture, which monitor suspicious activity, and also methods and algorithms used on the server side for analysis of gathered data.

Automatic recognition of holistic functional brain networks using iteratively optimized convolutional neural networks (IO-CNN) with weak label initialization.

PubMed

Zhao, Yu; Ge, Fangfei; Liu, Tianming

2018-07-01

fMRI data decomposition techniques have advanced significantly from shallow models such as Independent Component Analysis (ICA) and Sparse Coding and Dictionary Learning (SCDL) to deep learning models such Deep Belief Networks (DBN) and Convolutional Autoencoder (DCAE). However, interpretations of those decomposed networks are still open questions due to the lack of functional brain atlases, no correspondence across decomposed or reconstructed networks across different subjects, and significant individual variabilities. Recent studies showed that deep learning, especially deep convolutional neural networks (CNN), has extraordinary ability of accommodating spatial object patterns, e.g., our recent works using 3D CNN for fMRI-derived network classifications achieved high accuracy with a remarkable tolerance for mistakenly labelled training brain networks. However, the training data preparation is one of the biggest obstacles in these supervised deep learning models for functional brain network map recognitions, since manual labelling requires tedious and time-consuming labours which will sometimes even introduce label mistakes. Especially for mapping functional networks in large scale datasets such as hundreds of thousands of brain networks used in this paper, the manual labelling method will become almost infeasible. In response, in this work, we tackled both the network recognition and training data labelling tasks by proposing a new iteratively optimized deep learning CNN (IO-CNN) framework with an automatic weak label initialization, which enables the functional brain networks recognition task to a fully automatic large-scale classification procedure. Our extensive experiments based on ABIDE-II 1099 brains' fMRI data showed the great promise of our IO-CNN framework. Copyright © 2018 Elsevier B.V. All rights reserved.

Study on application of adaptive fuzzy control and neural network in the automatic leveling system

NASA Astrophysics Data System (ADS)

Xu, Xiping; Zhao, Zizhao; Lan, Weiyong; Sha, Lei; Qian, Cheng

2015-04-01

This paper discusses the adaptive fuzzy control and neural network BP algorithm in large flat automatic leveling control system application. The purpose is to develop a measurement system with a flat quick leveling, Make the installation on the leveling system of measurement with tablet, to be able to achieve a level in precision measurement work quickly, improve the efficiency of the precision measurement. This paper focuses on the automatic leveling system analysis based on fuzzy controller, Use of the method of combining fuzzy controller and BP neural network, using BP algorithm improve the experience rules .Construct an adaptive fuzzy control system. Meanwhile the learning rate of the BP algorithm has also been run-rate adjusted to accelerate convergence. The simulation results show that the proposed control method can effectively improve the leveling precision of automatic leveling system and shorten the time of leveling.

Changes in default mode network as automaticity develops in a categorization task.

PubMed

Shamloo, Farzin; Helie, Sebastien

2016-10-15

The default mode network (DMN) is a set of brain regions in which blood oxygen level dependent signal is suppressed during attentional focus on the external environment. Because automatic task processing requires less attention, development of automaticity in a rule-based categorization task may result in less deactivation and altered functional connectivity of the DMN when compared to the initial learning stage. We tested this hypothesis by re-analyzing functional magnetic resonance imaging data of participants trained in rule-based categorization for over 10,000 trials (Helie et al., 2010) [12,13]. The results show that some DMN regions are deactivated in initial training but not after automaticity has developed. There is also a significant decrease in DMN deactivation after extensive practice. Seed-based functional connectivity analyses with the precuneus, medial prefrontal cortex (two important DMN regions) and Brodmann area 6 (an important region in automatic categorization) were also performed. The results show increased functional connectivity with both DMN and non-DMN regions after the development of automaticity, and a decrease in functional connectivity between the medial prefrontal cortex and ventromedial orbitofrontal cortex. Together, these results further support the hypothesis of a strategy shift in automatic categorization and bridge the cognitive and neuroscientific conceptions of automaticity in showing that the reduced need for cognitive resources in automatic processing is accompanied by a disinhibition of the DMN and stronger functional connectivity between DMN and task-related brain regions. Copyright © 2016 Elsevier B.V. All rights reserved.

Neural Network Classification of Receiver Functions as a Step Towards Automatic Crustal Parameter Determination

NASA Astrophysics Data System (ADS)

Jemberie, A.; Dugda, M. T.; Reusch, D.; Nyblade, A.

2006-12-01

Neural networks are decision making mathematical/engineering tools, which if trained properly, can do jobs automatically (and objectively) that normally require particular expertise and/or tedious repetition. Here we explore two techniques from the field of artificial neural networks (ANNs) that seek to reduce the time requirements and increase the objectivity of quality control (QC) and Event Identification (EI) on seismic datasets. We explore to apply the multiplayer Feed Forward (FF) Artificial Neural Networks (ANN) and Self- Organizing Maps (SOM) in combination with Hk stacking of receiver functions in an attempt to test the extent of the usefulness of automatic classification of receiver functions for crustal parameter determination. Feed- forward ANNs (FFNNs) are a supervised classification tool while self-organizing maps (SOMs) are able to provide unsupervised classification of large, complex geophysical data sets into a fixed number of distinct generalized patterns or modes. Hk stacking is a methodology that is used to stack receiver functions based on the relative arrival times of P-to-S converted phase and next two reverberations to determine crustal thickness H and Vp-to-Vs ratio (k). We use receiver functions from teleseismic events recorded by the 2000- 2002 Ethiopia Broadband Seismic Experiment. Preliminary results of applying FFNN neural network and Hk stacking of receiver functions for automatic receiver functions classification as a step towards an effort of automatic crustal parameter determination look encouraging. After training a FFNN neural network, the network could classify the best receiver functions from bad ones with a success rate of about 75 to 95%. Applying H? stacking on the receiver functions classified by this FFNN as the best receiver functions, we could obtain crustal thickness and Vp/Vs ratio of 31±4 km and 1.75±0.05, respectively, for the crust beneath station ARBA in the Main Ethiopian Rift. To make comparison, we applied Hk

Automatic Clustering of Rolling Element Bearings Defects with Artificial Neural Network

NASA Astrophysics Data System (ADS)

Antonini, M.; Faglia, R.; Pedersoli, M.; Tiboni, M.

2006-06-01

The paper presents the optimization of a methodology for automatic clustering based on Artificial Neural Networks to detect the presence of defects in rolling bearings. The research activity was developed in co-operation with an Italian company which is expert in the production of water pumps for automotive use (Industrie Saleri Italo). The final goal of the work is to develop a system for the automatic control of the pumps, at the end of the production line. In this viewpoint, we are gradually considering the main elements of the water pump, which can cause malfunctioning. The first elements we have considered are the rolling bearing, a very critic component for the system. The experimental activity is based on the vibration measuring of rolling bearings opportunely damaged; vibration signals are in the second phase elaborated; the third and last phase is an automatic clustering. Different signal elaboration techniques are compared to optimize the methodology.

Automatic reconstruction of a bacterial regulatory network using Natural Language Processing

PubMed Central

Rodríguez-Penagos, Carlos; Salgado, Heladia; Martínez-Flores, Irma; Collado-Vides, Julio

2007-01-01

Background Manual curation of biological databases, an expensive and labor-intensive process, is essential for high quality integrated data. In this paper we report the implementation of a state-of-the-art Natural Language Processing system that creates computer-readable networks of regulatory interactions directly from different collections of abstracts and full-text papers. Our major aim is to understand how automatic annotation using Text-Mining techniques can complement manual curation of biological databases. We implemented a rule-based system to generate networks from different sets of documents dealing with regulation in Escherichia coli K-12. Results Performance evaluation is based on the most comprehensive transcriptional regulation database for any organism, the manually-curated RegulonDB, 45% of which we were able to recreate automatically. From our automated analysis we were also able to find some new interactions from papers not already curated, or that were missed in the manual filtering and review of the literature. We also put forward a novel Regulatory Interaction Markup Language better suited than SBML for simultaneously representing data of interest for biologists and text miners. Conclusion Manual curation of the output of automatic processing of text is a good way to complement a more detailed review of the literature, either for validating the results of what has been already annotated, or for discovering facts and information that might have been overlooked at the triage or curation stages. PMID:17683642

Integrating the automatic and the controlled: Strategies in Semantic Priming in an Attractor Network with Latching Dynamics

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2014-01-01

Semantic priming has long been recognized to reflect, along with automatic semantic mechanisms, the contribution of controlled strategies. However, previous theories of controlled priming were mostly qualitative, lacking common grounds with modern mathematical models of automatic priming based on neural networks. Recently, we have introduced a novel attractor network model of automatic semantic priming with latching dynamics. Here, we extend this work to show how the same model can also account for important findings regarding controlled processes. Assuming the rate of semantic transitions in the network can be adapted using simple reinforcement learning, we show how basic findings attributed to controlled processes in priming can be achieved, including their dependency on stimulus onset asynchrony and relatedness proportion and their unique effect on associative, category-exemplar, mediated and backward prime-target relations. We discuss how our mechanism relates to the classic expectancy theory and how it can be further extended in future developments of the model. PMID:24890261

National networks of Healthy Cities in Europe.

PubMed

Janss Lafond, Leah; Heritage, Zoë

2009-11-01

National networks of Healthy Cities emerged in the late 1980s as a spontaneous reaction to a great demand by cities to participate in the Healthy Cities movement. Today, they engage at least 1300 cities in the European region and form the backbone of the Healthy Cities movement. This article provides an analysis of the results of the regular surveys of national networks that have been carried out principally since 1997. The main functions and achievements of national networks are presented alongside some of their most pressing challenges. Although networks have differing priorities and organizational characteristics, they do share common goals and strategic directions based on the Healthy Cities model (see other articles in this special edition of HPI). Therefore, it has been possible to identify a set of organizational and strategic factors that contribute to the success of networks. These factors form the basis of a set of accreditation criteria for national networks and provide guidance for the establishment of new national networks. Although national networks have made substantial achievements, they continue to face a number of dilemmas that are discussed in the article. Problems a national network must deal with include how to obtain sustainable funding, how to raise the standard of work in cities without creating exclusive participation criteria and how to balance the need to provide direct support to cities with its role as a national player. These dilemmas are similar to other public sector networks. During the last 15 years, the pooling of practical expertise in urban health has made Healthy Cities networks an important resource for national as well as local governments. Not only do they provide valuable support to their members but they often advise ministries and other national institutions on effective models to promote sustainable urban health development.

Looking for underlying features in automatic and reviewed seismic bulletins through a neural network

NASA Astrophysics Data System (ADS)

Carluccio, R.; Console, R.; Chiappini, M.; Chiappini, S.

2009-12-01

SEL1 bulletins are, among all IDC products, a fundamental tool for NDCs in their task of national assessment of compliance with the CTBT. This is because SEL1s are expected to be disseminated within 2 hours from the occurrence of any detected waveform event, and the National Authorities are supposed to take a political decision in nearly real time, especially in the case when the event could triggers the request for an on site inspection. In this context not only the rapidity, but also the reliability of the SEL1 is a fundamental requirement. Our last years experience gained in the comparison between SEL1 and Italian Seismic Bulletin events has shown that SEL1s usually contain a big fraction of bogus events (sometimes close to 50%). This is due to many factors, all related to the availability of processing data and to the fast automatic algorithms involved. On the other hand, REBs are much more reliable as proved by our experience. Therefore, in spite of their relevant time delay by which they are distributed, which prevents their real-time use, REBs can be still useful in a retrospective way as reference information for comparison with SEL1s. This study tries to set up a sort of logical filter on the SEL1s that, while maintaining the rapidity requirements, improves their reliability. Our idea is based on the assumption that the SEL1s are produced by systematic algorithm of phase association and therefore some patterns among the input and output data could exist and be recognized. Our approach was initially based on a set of rules suggested by human experts on their personal experience, and its application on large datasets on a global scale. Other approaches not involving human interaction (data mining techniques) do exist. This study refers specifically to a semi-automatic approach: fitting of multi-parametric relationships hidden in the data set, through the application of neural networks by an algorithm of supervised learning. Full SEL1 and REB bulletins from

Morphological self-organizing feature map neural network with applications to automatic target recognition

NASA Astrophysics Data System (ADS)

Zhang, Shijun; Jing, Zhongliang; Li, Jianxun

2005-01-01

The rotation invariant feature of the target is obtained using the multi-direction feature extraction property of the steerable filter. Combining the morphological operation top-hat transform with the self-organizing feature map neural network, the adaptive topological region is selected. Using the erosion operation, the topological region shrinkage is achieved. The steerable filter based morphological self-organizing feature map neural network is applied to automatic target recognition of binary standard patterns and real-world infrared sequence images. Compared with Hamming network and morphological shared-weight networks respectively, the higher recognition correct rate, robust adaptability, quick training, and better generalization of the proposed method are achieved.

Automatic construction of a recurrent neural network based classifier for vehicle passage detection

NASA Astrophysics Data System (ADS)

Burnaev, Evgeny; Koptelov, Ivan; Novikov, German; Khanipov, Timur

2017-03-01

Recurrent Neural Networks (RNNs) are extensively used for time-series modeling and prediction. We propose an approach for automatic construction of a binary classifier based on Long Short-Term Memory RNNs (LSTM-RNNs) for detection of a vehicle passage through a checkpoint. As an input to the classifier we use multidimensional signals of various sensors that are installed on the checkpoint. Obtained results demonstrate that the previous approach to handcrafting a classifier, consisting of a set of deterministic rules, can be successfully replaced by an automatic RNN training on an appropriately labelled data.

Modernization of the Slovenian National Seismic Network

NASA Astrophysics Data System (ADS)

Vidrih, R.; Godec, M.; Gosar, A.; Sincic, P.; Tasic, I.; Zivcic, M.

2003-04-01

The Environmental Agency of the Republic of Slovenia, the Seismology Office is responsible for the fast and reliable information about earthquakes, originating in the area of Slovenia and nearby. In the year 2000 the project Modernization of the Slovenian National Seismic Network started. The purpose of a modernized seismic network is to enable fast and accurate automatic location of earthquakes, to determine earthquake parameters and to collect data of local, regional and global earthquakes. The modernized network will be finished in the year 2004 and will consist of 25 Q730 remote broadband data loggers based seismic station subsystems transmitting in real-time data to the Data Center in Ljubljana, where the Seismology Office is located. The remote broadband station subsystems include 16 surface broadband seismometers CMG-40T, 5 broadband seismometers CMG-40T with strong motion accelerographs EpiSensor, 4 borehole broadband seismometers CMG-40T, all with accurate timing provided by GPS receivers. The seismic network will cover the entire Slovenian territory, involving an area of 20,256 km2. The network is planned in this way; more seismic stations will be around bigger urban centres and in regions with greater vulnerability (NW Slovenia, Krsko Brezice region). By the end of the year 2002, three old seismic stations were modernized and ten new seismic stations were built. All seismic stations transmit data to UNIX-based computers running Antelope system software. The data is transmitted in real time using TCP/IP protocols over the Goverment Wide Area Network . Real-time data is also exchanged with seismic networks in the neighbouring countries, where the data are collected from the seismic stations, close to the Slovenian border. A typical seismic station consists of the seismic shaft with the sensor and the data acquisition system and, the service shaft with communication equipment (modem, router) and power supply with a battery box. which provides energy in case

23 CFR 658.21 - Identification of National Network.

Code of Federal Regulations, 2011 CFR

2011-04-01

... 23 Highways 1 2011-04-01 2011-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

23 CFR 658.21 - Identification of National Network.

Code of Federal Regulations, 2010 CFR

2010-04-01

... 23 Highways 1 2010-04-01 2010-04-01 false Identification of National Network. 658.21 Section 658... Identification of National Network. (a) To identify the National Network, a State may sign the routes or provide maps of lists of highways describing the National Network. (b) Exceptional local conditions on the...

Recent advances in automatic alignment system for the National Ignition Facility

NASA Astrophysics Data System (ADS)

Wilhelmsen, Karl; Awwal, Abdul A. S.; Kalantar, Dan; Leach, Richard; Lowe-Webb, Roger; McGuigan, David; Miller Kamm, Vicki

2011-03-01

The automatic alignment system for the National Ignition Facility (NIF) is a large-scale parallel system that directs all 192 laser beams along the 300-m optical path to a 50-micron focus at target chamber in less than 50 minutes. The system automatically commands 9,000 stepping motors to adjust mirrors and other optics based upon images acquired from high-resolution digital cameras viewing beams at various locations. Forty-five control loops per beamline request image processing services running on a LINUX cluster to analyze these images of the beams and references, and automatically steer the beams toward the target. This paper discusses the upgrades to the NIF automatic alignment system to handle new alignment needs and evolving requirements as related to various types of experiments performed. As NIF becomes a continuously-operated system and more experiments are performed, performance monitoring is increasingly important for maintenance and commissioning work. Data, collected during operations, is analyzed for tuning of the laser and targeting maintenance work. Handling evolving alignment and maintenance needs is expected for the planned 30-year operational life of NIF.

Fully automatic oil spill detection from COSMO-SkyMed imagery using a neural network approach

NASA Astrophysics Data System (ADS)

Avezzano, Ruggero G.; Del Frate, Fabio; Latini, Daniele

2012-09-01

The increased amount of available Synthetic Aperture Radar (SAR) images acquired over the ocean represents an extraordinary potential for improving oil spill detection activities. On the other side this involves a growing workload on the operators at analysis centers. In addition, even if the operators go through extensive training to learn manual oil spill detection, they can provide different and subjective responses. Hence, the upgrade and improvements of algorithms for automatic detection that can help in screening the images and prioritizing the alarms are of great benefit. In the framework of an ASI Announcement of Opportunity for the exploitation of COSMO-SkyMed data, a research activity (ASI contract L/020/09/0) aiming at studying the possibility to use neural networks architectures to set up fully automatic processing chains using COSMO-SkyMed imagery has been carried out and results are presented in this paper. The automatic identification of an oil spill is seen as a three step process based on segmentation, feature extraction and classification. We observed that a PCNN (Pulse Coupled Neural Network) was capable of providing a satisfactory performance in the different dark spots extraction, close to what it would be produced by manual editing. For the classification task a Multi-Layer Perceptron (MLP) Neural Network was employed.

A grid layout algorithm for automatic drawing of biochemical networks.

PubMed

Li, Weijiang; Kurata, Hiroyuki

2005-05-01

Visualization is indispensable in the research of complex biochemical networks. Available graph layout algorithms are not adequate for satisfactorily drawing such networks. New methods are required to visualize automatically the topological architectures and facilitate the understanding of the functions of the networks. We propose a novel layout algorithm to draw complex biochemical networks. A network is modeled as a system of interacting nodes on squared grids. A discrete cost function between each node pair is designed based on the topological relation and the geometric positions of the two nodes. The layouts are produced by minimizing the total cost. We design a fast algorithm to minimize the discrete cost function, by which candidate layouts can be produced efficiently. A simulated annealing procedure is used to choose better candidates. Our algorithm demonstrates its ability to exhibit cluster structures clearly in relatively compact layout areas without any prior knowledge. We developed Windows software to implement the algorithm for CADLIVE. All materials can be freely downloaded from http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/ http://kurata21.bio.kyutech.ac.jp/grid/grid_layout.htm; http://www.cadlive.jp/

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

PubMed

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

2018-05-01

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

National Highway Safety Administration. Automatic collision notice field test summary.

PubMed

2001-10-01

From 1995 to 2000, the National Highway Traffic Safety Administration (NHTSA) sponsored an initiative to create and operate an Automatic Collision Notification (ACN) system on a demonstration basis in a rural area to provide faster and smarter emergency medical responses and in an attempt to save lives and reduce disabilities from injuries. This article is a brief summary of that demonstration.

Automatic data processing and analysis system for monitoring region around a planned nuclear power plant

NASA Astrophysics Data System (ADS)

Kortström, Jari; Tiira, Timo; Kaisko, Outi

2016-03-01

The Institute of Seismology of University of Helsinki is building a new local seismic network, called OBF network, around planned nuclear power plant in Northern Ostrobothnia, Finland. The network will consist of nine new stations and one existing station. The network should be dense enough to provide azimuthal coverage better than 180° and automatic detection capability down to ML -0.1 within a radius of 25 km from the site.The network construction work began in 2012 and the first four stations started operation at the end of May 2013. We applied an automatic seismic signal detection and event location system to a network of 13 stations consisting of the four new stations and the nearest stations of Finnish and Swedish national seismic networks. Between the end of May and December 2013 the network detected 214 events inside the predefined area of 50 km radius surrounding the planned nuclear power plant site. Of those detections, 120 were identified as spurious events. A total of 74 events were associated with known quarries and mining areas. The average location error, calculated as a difference between the announced location from environment authorities and companies and the automatic location, was 2.9 km. During the same time period eight earthquakes between magnitude range 0.1-1.0 occurred within the area. Of these seven could be automatically detected. The results from the phase 1 stations of the OBF network indicates that the planned network can achieve its goals.

Integration of wireless sensor networks into automatic irrigation scheduling of a center pivot

USDA-ARS?s Scientific Manuscript database

A six-span center pivot system was used as a platform for testing two wireless sensor networks (WSN) of infrared thermometers. The cropped field was a semi-circle, divided into six pie shaped sections of which three were irrigated manually and three were irrigated automatically based on the time tem...

DiffNet: automatic differential functional summarization of dE-MAP networks.

PubMed

Seah, Boon-Siew; Bhowmick, Sourav S; Dewey, C Forbes

2014-10-01

The study of genetic interaction networks that respond to changing conditions is an emerging research problem. Recently, Bandyopadhyay et al. (2010) proposed a technique to construct a differential network (dE-MAPnetwork) from two static gene interaction networks in order to map the interaction differences between them under environment or condition change (e.g., DNA-damaging agent). This differential network is then manually analyzed to conclude that DNA repair is differentially effected by the condition change. Unfortunately, manual construction of differential functional summary from a dE-MAP network that summarizes all pertinent functional responses is time-consuming, laborious and error-prone, impeding large-scale analysis on it. To this end, we propose DiffNet, a novel data-driven algorithm that leverages Gene Ontology (go) annotations to automatically summarize a dE-MAP network to obtain a high-level map of functional responses due to condition change. We tested DiffNet on the dynamic interaction networks following MMS treatment and demonstrated the superiority of our approach in generating differential functional summaries compared to state-of-the-art graph clustering methods. We studied the effects of parameters in DiffNet in controlling the quality of the summary. We also performed a case study that illustrates its utility. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic speech recognition using a predictive echo state network classifier.

PubMed

Skowronski, Mark D; Harris, John G

2007-04-01

We have combined an echo state network (ESN) with a competitive state machine framework to create a classification engine called the predictive ESN classifier. We derive the expressions for training the predictive ESN classifier and show that the model was significantly more noise robust compared to a hidden Markov model in noisy speech classification experiments by 8+/-1 dB signal-to-noise ratio. The simple training algorithm and noise robustness of the predictive ESN classifier make it an attractive classification engine for automatic speech recognition.

Automatic network coupling analysis for dynamical systems based on detailed kinetic models.

PubMed

Lebiedz, Dirk; Kammerer, Julia; Brandt-Pollmann, Ulrich

2005-10-01

We introduce a numerical complexity reduction method for the automatic identification and analysis of dynamic network decompositions in (bio)chemical kinetics based on error-controlled computation of a minimal model dimension represented by the number of (locally) active dynamical modes. Our algorithm exploits a generalized sensitivity analysis along state trajectories and subsequent singular value decomposition of sensitivity matrices for the identification of these dominant dynamical modes. It allows for a dynamic coupling analysis of (bio)chemical species in kinetic models that can be exploited for the piecewise computation of a minimal model on small time intervals and offers valuable functional insight into highly nonlinear reaction mechanisms and network dynamics. We present results for the identification of network decompositions in a simple oscillatory chemical reaction, time scale separation based model reduction in a Michaelis-Menten enzyme system and network decomposition of a detailed model for the oscillatory peroxidase-oxidase enzyme system.

Automatic, time-interval traffic counts for recreation area management planning

Treesearch

D. L. Erickson; C. J. Liu; H. K. Cordell

1980-01-01

Automatic, time-interval recorders were used to count directional vehicular traffic on a multiple entry/exit road network in the Red River Gorge Geological Area, Daniel Boone National Forest. Hourly counts of entering and exiting traffic differed according to recorder location, but an aggregated distribution showed a delayed peak in exiting traffic thought to be...

The Italian National Seismic Network

NASA Astrophysics Data System (ADS)

Michelini, Alberto

2016-04-01

The Italian National Seismic Network is composed by about 400 stations, mainly broadband, installed in the Country and in the surrounding regions. About 110 stations feature also collocated strong motion instruments. The Centro Nazionale Terremoti, (National Earthquake Center), CNT, has installed and operates most of these stations, although a considerable number of stations contributing to the INGV surveillance has been installed and is maintained by other INGV sections (Napoli, Catania, Bologna, Milano) or even other Italian or European Institutions. The important technological upgrades carried out in the last years has allowed for significant improvements of the seismic monitoring of Italy and of the Euro-Mediterranean Countries. The adopted data transmission systems include satellite, wireless connections and wired lines. The Seedlink protocol has been adopted for data transmission. INGV is a primary node of EIDA (European Integrated Data Archive) for archiving and distributing, continuous, quality checked data. The data acquisition system was designed to accomplish, in near-real-time, automatic earthquake detection and hypocenter and magnitude determination (moment tensors, shake maps, etc.). Database archiving of all parametric results are closely linked to the existing procedures of the INGV seismic monitoring environment. Overall, the Italian earthquake surveillance service provides, in quasi real-time, hypocenter parameters which are then revised routinely by the analysts of the Bollettino Sismico Nazionale. The results are published on the web page http://cnt.rm.ingv.it/ and are publicly available to both the scientific community and the the general public. This presentation will describe the various activities and resulting products of the Centro Nazionale Terremoti. spanning from data acquisition to archiving, distribution and specialised products.

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network.

PubMed

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-07-08

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods.

Multi-National Banknote Classification Based on Visible-light Line Sensor and Convolutional Neural Network

PubMed Central

Pham, Tuyen Danh; Lee, Dong Eun; Park, Kang Ryoung

2017-01-01

Automatic recognition of banknotes is applied in payment facilities, such as automated teller machines (ATMs) and banknote counters. Besides the popular approaches that focus on studying the methods applied to various individual types of currencies, there have been studies conducted on simultaneous classification of banknotes from multiple countries. However, their methods were conducted with limited numbers of banknote images, national currencies, and denominations. To address this issue, we propose a multi-national banknote classification method based on visible-light banknote images captured by a one-dimensional line sensor and classified by a convolutional neural network (CNN) considering the size information of each denomination. Experiments conducted on the combined banknote image database of six countries with 62 denominations gave a classification accuracy of 100%, and results show that our proposed algorithm outperforms previous methods. PMID:28698466

QUALITY ASSURANCE PROGRAM FOR WET DEPOSITION SAMPLING AND CHEMICAL ANALYSES FOR THE NATIONAL TRENDS NETWORK.

USGS Publications Warehouse

Schroder, LeRoy J.; Malo, Bernard A.; ,

1985-01-01

The purpose of the National Trends Network is to delineate the major inorganic constituents in the wet deposition in the United States. The approach chosen to monitor the Nation's wet deposition is to install approximately 150 automatic sampling devices with at least one collector in each state. Samples are collected at one week intervals, removed from collectors, and transported to an analytical laboratory for chemical analysis. The quality assurance program has divided wet deposition monitoring into 5 parts: (1) Sampling site selection, (2) sampling device, (3) sample container, (4) sample handling, and (5) laboratory analysis. Each of these five components is being examined using existing designs or new designs. Each existing or proposed sampling site is visited and a criteria audit is performed.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

Short-Term Load Forecasting-Based Automatic Distribution Network Reconfiguration

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In a traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of the load forecasting technique can provide an accurate prediction of the load power that will happen in a future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during a longer time period instead of using a snapshot of the load at the time when the reconfiguration happens; thus, the distribution system operatormore » can use this information to better operate the system reconfiguration and achieve optimal solutions. This paper proposes a short-term load forecasting approach to automatically reconfigure distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with a forecaster based on support vector regression and parallel parameters optimization. The network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum amount of loss at the future time. The simulation results validate and evaluate the proposed approach.« less

A PC-based computer package for automatic detection and location of earthquakes: Application to a seismic network in eastern sicity (Italy)

NASA Astrophysics Data System (ADS)

Patanè, Domenico; Ferrari, Ferruccio; Giampiccolo, Elisabetta; Gresta, Stefano

Few automated data acquisition and processing systems operate on mainframes, some run on UNIX-based workstations and others on personal computers, equipped with either DOS/WINDOWS or UNIX-derived operating systems. Several large and complex software packages for automatic and interactive analysis of seismic data have been developed in recent years (mainly for UNIX-based systems). Some of these programs use a variety of artificial intelligence techniques. The first operational version of a new software package, named PC-Seism, for analyzing seismic data from a local network is presented in Patanè et al. (1999). This package, composed of three separate modules, provides an example of a new generation of visual object-oriented programs for interactive and automatic seismic data-processing running on a personal computer. In this work, we mainly discuss the automatic procedures implemented in the ASDP (Automatic Seismic Data-Processing) module and real time application to data acquired by a seismic network running in eastern Sicily. This software uses a multi-algorithm approach and a new procedure MSA (multi-station-analysis) for signal detection, phase grouping and event identification and location. It is designed for an efficient and accurate processing of local earthquake records provided by single-site and array stations. Results from ASDP processing of two different data sets recorded at Mt. Etna volcano by a regional network are analyzed to evaluate its performance. By comparing the ASDP pickings with those revised manually, the detection and subsequently the location capabilities of this software are assessed. The first data set is composed of 330 local earthquakes recorded in the Mt. Etna erea during 1997 by the telemetry analog seismic network. The second data set comprises about 970 automatic locations of more than 2600 local events recorded at Mt. Etna during the last eruption (July 2001) at the present network. For the former data set, a comparison of the

National law enforcement telecommunications network

NASA Technical Reports Server (NTRS)

Reilly, N. B.; Garrison, G. W.; Sohn, R. L.; Gallop, D. L.; Goldstein, B. L.

1975-01-01

Alternative approaches are analyzed to a National Law Enforcement Telecommunications Network (NALECOM) designed to service all state-to-state and state-to-national criminal justice communications traffic needs in the United States. Network topology options were analyzed, and equipment and personnel requirements for each option were defined in accordance with NALECOM functional specifications and design guidelines. Evaluation criteria were developed and applied to each of the options leading to specific conclusions. Detailed treatments of methods for determining traffic requirements, communication line costs, switcher configurations and costs, microwave costs, satellite system configurations and costs, facilities, operations and engineering costs, network delay analysis and network availability analysis are presented. It is concluded that a single regional switcher configuration is the optimum choice based on cost and technical factors. A two-region configuration is competitive. Multiple-region configurations are less competitive due to increasing costs without attending benefits.

Automatic Detection of Welding Defects using Deep Neural Network

NASA Astrophysics Data System (ADS)

Hou, Wenhui; Wei, Ye; Guo, Jie; Jin, Yi; Zhu, Chang'an

2018-01-01

In this paper, we propose an automatic detection schema including three stages for weld defects in x-ray images. Firstly, the preprocessing procedure for the image is implemented to locate the weld region; Then a classification model which is trained and tested by the patches cropped from x-ray images is constructed based on deep neural network. And this model can learn the intrinsic feature of images without extra calculation; Finally, the sliding-window approach is utilized to detect the whole images based on the trained model. In order to evaluate the performance of the model, we carry out several experiments. The results demonstrate that the classification model we proposed is effective in the detection of welded joints quality.

Automatic quality assessment of apical four-chamber echocardiograms using deep convolutional neural networks

NASA Astrophysics Data System (ADS)

Abdi, Amir H.; Luong, Christina; Tsang, Teresa; Allan, Gregory; Nouranian, Saman; Jue, John; Hawley, Dale; Fleming, Sarah; Gin, Ken; Swift, Jody; Rohling, Robert; Abolmaesumi, Purang

2017-02-01

Echocardiography (echo) is the most common test for diagnosis and management of patients with cardiac condi- tions. While most medical imaging modalities benefit from a relatively automated procedure, this is not the case for echo and the quality of the final echo view depends on the competency and experience of the sonographer. It is not uncommon that the sonographer does not have adequate experience to adjust the transducer and acquire a high quality echo, which may further affect the clinical diagnosis. In this work, we aim to aid the operator during image acquisition by automatically assessing the quality of the echo and generating the Automatic Echo Score (AES). This quality assessment method is based on a deep convolutional neural network, trained in an end-to-end fashion on a large dataset of apical four-chamber (A4C) echo images. For this project, an expert car- diologist went through 2,904 A4C images obtained from independent studies and assessed their condition based on a 6-scale grading system. The scores assigned by the expert ranged from 0 to 5. The distribution of scores among the 6 levels were almost uniform. The network was then trained on 80% of the data (2,345 samples). The average absolute error of the trained model in calculating the AES was 0.8 +/- 0:72. The computation time of the GPU implementation of the neural network was estimated at 5 ms per frame, which is sufficient for real-time deployment.

OpenSim: A Flexible Distributed Neural Network Simulator with Automatic Interactive Graphics.

PubMed

Jarosch, Andreas; Leber, Jean Francois

1997-06-01

An object-oriented simulator called OpenSim is presented that achieves a high degree of flexibility by relying on a small set of building blocks. The state variables and algorithms put in this framework can easily be accessed through a command shell. This allows one to distribute a large-scale simulation over several workstations and to generate the interactive graphics automatically. OpenSim opens new possibilities for cooperation among Neural Network researchers. Copyright 1997 Elsevier Science Ltd.

A guidebook for using automatic passenger counter data for National Transit Database (NTD) reporting

DOT National Transportation Integrated Search

2010-12-01

This document provides guidance for transit agencies to use data from their automatic passenger counters (APCs) for reporting to the National Transit Database (NTD). It first reviews both the traditional data requirements on the data items to be repo...

Automatic sleep stage classification of single-channel EEG by using complex-valued convolutional neural network.

PubMed

Zhang, Junming; Wu, Yan

2018-03-28

Many systems are developed for automatic sleep stage classification. However, nearly all models are based on handcrafted features. Because of the large feature space, there are so many features that feature selection should be used. Meanwhile, designing handcrafted features is a difficult and time-consuming task because the feature designing needs domain knowledge of experienced experts. Results vary when different sets of features are chosen to identify sleep stages. Additionally, many features that we may be unaware of exist. However, these features may be important for sleep stage classification. Therefore, a new sleep stage classification system, which is based on the complex-valued convolutional neural network (CCNN), is proposed in this study. Unlike the existing sleep stage methods, our method can automatically extract features from raw electroencephalography data and then classify sleep stage based on the learned features. Additionally, we also prove that the decision boundaries for the real and imaginary parts of a complex-valued convolutional neuron intersect orthogonally. The classification performances of handcrafted features are compared with those of learned features via CCNN. Experimental results show that the proposed method is comparable to the existing methods. CCNN obtains a better classification performance and considerably faster convergence speed than convolutional neural network. Experimental results also show that the proposed method is a useful decision-support tool for automatic sleep stage classification.

Short-Term Load Forecasting Based Automatic Distribution Network Reconfiguration: Preprint

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

Jiang, Huaiguang; Ding, Fei; Zhang, Yingchen

In the traditional dynamic network reconfiguration study, the optimal topology is determined at every scheduled time point by using the real load data measured at that time. The development of load forecasting technique can provide accurate prediction of load power that will happen in future time and provide more information about load changes. With the inclusion of load forecasting, the optimal topology can be determined based on the predicted load conditions during the longer time period instead of using the snapshot of load at the time when the reconfiguration happens, and thus it can provide information to the distribution systemmore » operator (DSO) to better operate the system reconfiguration to achieve optimal solutions. Thus, this paper proposes a short-term load forecasting based approach for automatically reconfiguring distribution systems in a dynamic and pre-event manner. Specifically, a short-term and high-resolution distribution system load forecasting approach is proposed with support vector regression (SVR) based forecaster and parallel parameters optimization. And the network reconfiguration problem is solved by using the forecasted load continuously to determine the optimal network topology with the minimum loss at the future time. The simulation results validate and evaluate the proposed approach.« less

High Efficiency Automatic-Power-Controlled and Gain-Clamped EDFA for Broadband Passive Optical Networking Systems

NASA Astrophysics Data System (ADS)

Shen, Jyi-Lai; Wei, Shui-Ken; Lin, Chin-Yuan; Iong Li, Ssu; Huang, Chih-Chuan

2010-04-01

The configuration of a simple improved high efficiency automatic-power-controlled and gain-clamped EDFA (APC-GC-EDFA) for broadband passive optical networking systems (BPON) is presented here. In order to compensate the phase and amplitude variation due to the different distance between the optical line terminal (OLT) and optical network units (ONU), the APC-GC-EDFA need to be employed. A single 980 nm laser module is employed as the primary pump. To extend the bandwidth, all C-band ASE is recycled as the secondary pump to enhance the gain efficiency. An electrical feedback circuit is used as a multi-wavelength channel transmitter monitor for the automatic power control to improve the gain-flattened flatness for stable amplification. The experimental results prove that the EDFA system can provide flatter clamped gain in both C-band and L-band configurations. The gain flatness wavelength ranging from 1530 to 1610 nm is within 32.83 ± 0.64 dB, i.e. below 1.95 %. The gains are clamped at 33.85 ± 0.65 dB for the input signal power of -40 dBm to -10 dBm. The range of noise figure is between 6.37 and 6.56, which is slightly lower compared to that of unclamped amplifiers. This will be very useful for measuring the gain flatness of APC-GC-EDFA. Finally, we have also demonstrated the records of the overall simultaneous dynamics measurements for the new system stabilization. The carrier to noise ratio (CNR) is 49.5 to 50.8 dBc which is above the National Television System Committee (NTSC) standard of 43 dBc, and both composite second order (CSO) 69.2 to 71.5 dBc and composite triple beat (CTB) of 69.8 to 72.2 dBc are above 53 dBc. The recorded corresponding rise-time of 1.087 ms indicates that the system does not exhibit any overshoot of gain or ASE variation due to the signal at the beginning of the pulse.

A semi-automatic method for extracting thin line structures in images as rooted tree network

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

Brazzini, Jacopo; Dillard, Scott; Soille, Pierre

2010-01-01

This paper addresses the problem of semi-automatic extraction of line networks in digital images - e.g., road or hydrographic networks in satellite images, blood vessels in medical images, robust. For that purpose, we improve a generic method derived from morphological and hydrological concepts and consisting in minimum cost path estimation and flow simulation. While this approach fully exploits the local contrast and shape of the network, as well as its arborescent nature, we further incorporate local directional information about the structures in the image. Namely, an appropriate anisotropic metric is designed by using both the characteristic features of the targetmore » network and the eigen-decomposition of the gradient structure tensor of the image. Following, the geodesic propagation from a given seed with this metric is combined with hydrological operators for overland flow simulation to extract the line network. The algorithm is demonstrated for the extraction of blood vessels in a retina image and of a river network in a satellite image.« less

NCI National Clinical Trials Network Structure

Cancer.gov

Learn about how the National Clinical Trials Network (NCTN) is structured. The NCTN is a program of the National Cancer Institute that gives funds and other support to cancer research organizations to conduct cancer clinical trials.

National research and education network

NASA Technical Reports Server (NTRS)

Villasenor, Tony

1991-01-01

Some goals of this network are as follows: Extend U.S. technological leadership in high performance computing and computer communications; Provide wide dissemination and application of the technologies both to the speed and the pace of innovation and to serve the national economy, national security, education, and the global environment; and Spur gains in the U.S. productivity and industrial competitiveness by making high performance computing and networking technologies an integral part of the design and production process. Strategies for achieving these goals are as follows: Support solutions to important scientific and technical challenges through a vigorous R and D effort; Reduce the uncertainties to industry for R and D and use of this technology through increased cooperation between government, industry, and universities and by the continued use of government and government funded facilities as a prototype user for early commercial HPCC products; and Support underlying research, network, and computational infrastructures on which U.S. high performance computing technology is based.

Empirical study on neural network based predictive techniques for automatic number plate recognition

NASA Astrophysics Data System (ADS)

Shashidhara, M. S.; Indrakumar, S. S.

2011-10-01

The objective of this study is to provide an easy, accurate and effective technology for the Bangalore city traffic control. This is based on the techniques of image processing and laser beam technology. The core concept chosen here is an image processing technology by the method of automatic number plate recognition system. First number plate is recognized if any vehicle breaks the traffic rules in the signals. The number is fetched from the database of the RTO office by the process of automatic database fetching. Next this sends the notice and penalty related information to the vehicle owner email-id and an SMS sent to vehicle owner. In this paper, we use of cameras with zooming options & laser beams to get accurate pictures further applied image processing techniques such as Edge detection to understand the vehicle, Identifying the location of the number plate, Identifying the number plate for further use, Plain plate number, Number plate with additional information, Number plates in the different fonts. Accessing the database of the vehicle registration office to identify the name and address and other information of the vehicle number. The updates to be made to the database for the recording of the violation and penalty issues. A feed forward artificial neural network is used for OCR. This procedure is particularly important for glyphs that are visually similar such as '8' and '9' and results in training sets of between 25,000 and 40,000 training samples. Over training of the neural network is prevented by Bayesian regularization. The neural network output value is set to 0.05 when the input is not desired glyph, and 0.95 for correct input.

The National Biomedical Communications Network as a Developing Structure *

PubMed Central

Davis, Ruth M.

1971-01-01

The National Biomedical Communications Network has evolved both from a set of conceptual recommendations over the last twelve years and an accumulation of needs manifesting themselves in the requests of members of the medical community. With a short history of three years this network and its developing structure have exhibited most of the stresses of technology interfacing with customer groups, and of a structure attempting to build itself upon many existing fragmentary unconnected segments of a potentially viable resourcesharing capability. In addition to addressing these topics, the paper treats a design appropriate to any network devoted to information transfer in a special interest user community. It discusses fundamentals of network design, highlighting that network structure most appropriate to a national information network. Examples are given of cost analyses of information services and certain conjectures are offered concerning the roles of national networks. PMID:5542912

United States National seismograph network

USGS Publications Warehouse

Masse, R.P.; Filson, J.R.; Murphy, A.

1989-01-01

The USGS National Earthquake Information Center (NEIC) has planned and is developing a broadband digital seismograph network for the United States. The network will consist of approximately 150 seismograph stations distributed across the contiguous 48 states and across Alaska, Hawaii, Puerto Rico and the Virgin Islands. Data transmission will be via two-way satellite telemetry from the network sites to a central recording facility at the NEIC in Golden, Colorado. The design goal for the network is the on-scale recording by at least five well-distributed stations of any seismic event of magnitude 2.5 or greater in all areas of the United States except possibly part of Alaska. All event data from the network will be distributed to the scientific community on compact disc with read-only memory (CD-ROM). ?? 1989.

Designs on a National Research Network.

ERIC Educational Resources Information Center

Walsh, John

1988-01-01

Discusses the addition of the National Aeronautics and Space Administration database to the National Science Foundation's NSFnet data communication network. Outlines the history of databases in the United States and enumerates proposed upgrades from a new Office of Science and Technology policy report. (TW)

CADLIVE toolbox for MATLAB: automatic dynamic modeling of biochemical networks with comprehensive system analysis.

PubMed

Inoue, Kentaro; Maeda, Kazuhiro; Miyabe, Takaaki; Matsuoka, Yu; Kurata, Hiroyuki

2014-09-01

Mathematical modeling has become a standard technique to understand the dynamics of complex biochemical systems. To promote the modeling, we had developed the CADLIVE dynamic simulator that automatically converted a biochemical map into its associated mathematical model, simulated its dynamic behaviors and analyzed its robustness. To enhance the feasibility by CADLIVE and extend its functions, we propose the CADLIVE toolbox available for MATLAB, which implements not only the existing functions of the CADLIVE dynamic simulator, but also the latest tools including global parameter search methods with robustness analysis. The seamless, bottom-up processes consisting of biochemical network construction, automatic construction of its dynamic model, simulation, optimization, and S-system analysis greatly facilitate dynamic modeling, contributing to the research of systems biology and synthetic biology. This application can be freely downloaded from http://www.cadlive.jp/CADLIVE_MATLAB/ together with an instruction.

Cellular neural network-based hybrid approach toward automatic image registration

NASA Astrophysics Data System (ADS)

Arun, Pattathal VijayaKumar; Katiyar, Sunil Kumar

2013-01-01

Image registration is a key component of various image processing operations that involve the analysis of different image data sets. Automatic image registration domains have witnessed the application of many intelligent methodologies over the past decade; however, inability to properly model object shape as well as contextual information has limited the attainable accuracy. A framework for accurate feature shape modeling and adaptive resampling using advanced techniques such as vector machines, cellular neural network (CNN), scale invariant feature transform (SIFT), coreset, and cellular automata is proposed. CNN has been found to be effective in improving feature matching as well as resampling stages of registration and complexity of the approach has been considerably reduced using coreset optimization. The salient features of this work are cellular neural network approach-based SIFT feature point optimization, adaptive resampling, and intelligent object modelling. Developed methodology has been compared with contemporary methods using different statistical measures. Investigations over various satellite images revealed that considerable success was achieved with the approach. This system has dynamically used spectral and spatial information for representing contextual knowledge using CNN-prolog approach. This methodology is also illustrated to be effective in providing intelligent interpretation and adaptive resampling.

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery

PubMed Central

Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B.; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases. PMID:28985229

A deep convolutional neural network-based automatic delineation strategy for multiple brain metastases stereotactic radiosurgery.

PubMed

Liu, Yan; Stojadinovic, Strahinja; Hrycushko, Brian; Wardak, Zabi; Lau, Steven; Lu, Weiguo; Yan, Yulong; Jiang, Steve B; Zhen, Xin; Timmerman, Robert; Nedzi, Lucien; Gu, Xuejun

2017-01-01

Accurate and automatic brain metastases target delineation is a key step for efficient and effective stereotactic radiosurgery (SRS) treatment planning. In this work, we developed a deep learning convolutional neural network (CNN) algorithm for segmenting brain metastases on contrast-enhanced T1-weighted magnetic resonance imaging (MRI) datasets. We integrated the CNN-based algorithm into an automatic brain metastases segmentation workflow and validated on both Multimodal Brain Tumor Image Segmentation challenge (BRATS) data and clinical patients' data. Validation on BRATS data yielded average DICE coefficients (DCs) of 0.75±0.07 in the tumor core and 0.81±0.04 in the enhancing tumor, which outperformed most techniques in the 2015 BRATS challenge. Segmentation results of patient cases showed an average of DCs 0.67±0.03 and achieved an area under the receiver operating characteristic curve of 0.98±0.01. The developed automatic segmentation strategy surpasses current benchmark levels and offers a promising tool for SRS treatment planning for multiple brain metastases.

Automatic comparison of striation marks and automatic classification of shoe prints

NASA Astrophysics Data System (ADS)

Geradts, Zeno J.; Keijzer, Jan; Keereweer, Isaac

1995-09-01

A database for toolmarks (named TRAX) and a database for footwear outsole designs (named REBEZO) have been developed on a PC. The databases are filled with video-images and administrative data about the toolmarks and the footwear designs. An algorithm for the automatic comparison of the digitized striation patterns has been developed for TRAX. The algorithm appears to work well for deep and complete striation marks and will be implemented in TRAX. For REBEZO some efforts have been made to the automatic classification of outsole patterns. The algorithm first segments the shoeprofile. Fourier-features are selected for the separate elements and are classified with a neural network. In future developments information on invariant moments of the shape and rotation angle will be included in the neural network.

HNET - A National Computerized Health Network

PubMed Central

Casey, Mark; Hamilton, Richard

1988-01-01

The HNET system demonstrated conceptually and technically a national text (and limited bit mapped graphics) computer network for use between innovative members of the health care industry. The HNET configuration of a leased high speed national packet switching network connecting any number of mainframe, mini, and micro computers was unique in it's relatively low capital costs and freedom from obsolescence. With multiple simultaneous conferences, databases, bulletin boards, calendars, and advanced electronic mail and surveys, it is marketable to innovative hospitals, clinics, physicians, health care associations and societies, nurses, multisite research projects libraries, etc.. Electronic publishing and education capabilities along with integrated voice and video transmission are identified as future enhancements.

The National Network of Libraries of Medicine

MedlinePlus

... New England Region: University of Massachusetts Bringing the World of Medical Information to Your Neighborhood By Angela ... D., Head, NN/LM National Network Office The world's largest medical library is the National Library of ...

Ultrasound image-based thyroid nodule automatic segmentation using convolutional neural networks.

PubMed

Ma, Jinlian; Wu, Fa; Jiang, Tian'an; Zhao, Qiyu; Kong, Dexing

2017-11-01

Delineation of thyroid nodule boundaries from ultrasound images plays an important role in calculation of clinical indices and diagnosis of thyroid diseases. However, it is challenging for accurate and automatic segmentation of thyroid nodules because of their heterogeneous appearance and components similar to the background. In this study, we employ a deep convolutional neural network (CNN) to automatically segment thyroid nodules from ultrasound images. Our CNN-based method formulates a thyroid nodule segmentation problem as a patch classification task, where the relationship among patches is ignored. Specifically, the CNN used image patches from images of normal thyroids and thyroid nodules as inputs and then generated the segmentation probability maps as outputs. A multi-view strategy is used to improve the performance of the CNN-based model. Additionally, we compared the performance of our approach with that of the commonly used segmentation methods on the same dataset. The experimental results suggest that our proposed method outperforms prior methods on thyroid nodule segmentation. Moreover, the results show that the CNN-based model is able to delineate multiple nodules in thyroid ultrasound images accurately and effectively. In detail, our CNN-based model can achieve an average of the overlap metric, dice ratio, true positive rate, false positive rate, and modified Hausdorff distance as [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text], [Formula: see text] on overall folds, respectively. Our proposed method is fully automatic without any user interaction. Quantitative results also indicate that our method is so efficient and accurate that it can be good enough to replace the time-consuming and tedious manual segmentation approach, demonstrating the potential clinical applications.

ESTABLISHING A NATIONAL ENVIRONMENTAL PUBLIC HEALTH TRACKING NETWORK

EPA Science Inventory

This paper describes the CDC's efforts to develop a National Environmental Public Health Tracking Network Tracking Network) with particular focus on air related issues and collaboration with EPA. A Tracking Network is needed in the United States to improve the health of communit...

The UNESCO Global Network of National Geoparks

NASA Astrophysics Data System (ADS)

Mc Keever1, P.; Zouros, N.; Patzak, M.; Missotten, R.

2009-12-01

The UNESCO Global Network of National Geoparks was founded in 2004, following the model successfully established by the European Geoparks Network in 2000. It now comprises 63 members in 19 nations across the world. A Global Geopark is an area with geological heritage of international value but where that heritage is being used for the sustainable economic benefit if the local inhabitants, primarily through education and tourism. Supported by IUGS and IUCN, the aim of the Global Geoparks Network is to facilitate exchange and sharing between members to assist in the protection and conservation of the geological heritage of our planet but to do so in way where local communities can take ownership of these special places and where they can get some sustainable economic benefit from them. While allowing for the sustainable economic development of geoparks, the network explicitly forbids the destruction or sale of the geological value of a geopark. This paper outlines the ethos of the Global Geoparks Network and describes the typical activities of geoparks and how the network functions. Using two examples it also illustrates how members of the Global Geoparks Network provide good examples as tools not only for holistic nature conservation but also for economic development.

The "Golden Projects": China's National Networking Initiative.

ERIC Educational Resources Information Center

Lovelock, Peter; Clark, Theodore C.; Petrazzini, Ben A.

1996-01-01

For China, information technology and communications networks are a new solution to an old problem, reconstituting hierarchical state power. This article examines China's National Networking Initiative, "Golden Projects," within the context of economic and political reform to demonstrate an alternative to traditional economic based…

A National Perspective on Women Owning Woodlands (WOW) Networks

ERIC Educational Resources Information Center

Huff, Emily S.

2017-01-01

This article provides a national overview of women owning woodlands (WOW) networks and the barriers and successes they encounter. Qualitative interview data with key network leaders were used for increasing understanding of how these networks operate. Network leaders were all connected professionally, and all successful WOW networks involved…

Variable Discretisation for Anomaly Detection using Bayesian Networks

DTIC Science & Technology

2017-01-01

UNCLASSIFIED DST- Group –TR–3328 1 Introduction Bayesian network implementations usually require each variable to take on a finite number of mutually...UNCLASSIFIED Variable Discretisation for Anomaly Detection using Bayesian Networks Jonathan Legg National Security and ISR Division Defence Science...and Technology Group DST- Group –TR–3328 ABSTRACT Anomaly detection is the process by which low probability events are automatically found against a

Automatic breast density classification using a convolutional neural network architecture search procedure

NASA Astrophysics Data System (ADS)

Fonseca, Pablo; Mendoza, Julio; Wainer, Jacques; Ferrer, Jose; Pinto, Joseph; Guerrero, Jorge; Castaneda, Benjamin

2015-03-01

Breast parenchymal density is considered a strong indicator of breast cancer risk and therefore useful for preventive tasks. Measurement of breast density is often qualitative and requires the subjective judgment of radiologists. Here we explore an automatic breast composition classification workflow based on convolutional neural networks for feature extraction in combination with a support vector machines classifier. This is compared to the assessments of seven experienced radiologists. The experiments yielded an average kappa value of 0.58 when using the mode of the radiologists' classifications as ground truth. Individual radiologist performance against this ground truth yielded kappa values between 0.56 and 0.79.

Automatic QRS complex detection using two-level convolutional neural network.

PubMed

Xiang, Yande; Lin, Zhitao; Meng, Jianyi

2018-01-29

The QRS complex is the most noticeable feature in the electrocardiogram (ECG) signal, therefore, its detection is critical for ECG signal analysis. The existing detection methods largely depend on hand-crafted manual features and parameters, which may introduce significant computational complexity, especially in the transform domains. In addition, fixed features and parameters are not suitable for detecting various kinds of QRS complexes under different circumstances. In this study, based on 1-D convolutional neural network (CNN), an accurate method for QRS complex detection is proposed. The CNN consists of object-level and part-level CNNs for extracting different grained ECG morphological features automatically. All the extracted morphological features are used by multi-layer perceptron (MLP) for QRS complex detection. Additionally, a simple ECG signal preprocessing technique which only contains difference operation in temporal domain is adopted. Based on the MIT-BIH arrhythmia (MIT-BIH-AR) database, the proposed detection method achieves overall sensitivity Sen = 99.77%, positive predictivity rate PPR = 99.91%, and detection error rate DER = 0.32%. In addition, the performance variation is performed according to different signal-to-noise ratio (SNR) values. An automatic QRS detection method using two-level 1-D CNN and simple signal preprocessing technique is proposed for QRS complex detection. Compared with the state-of-the-art QRS complex detection approaches, experimental results show that the proposed method acquires comparable accuracy.

An Automatic Diagnosis Method of Facial Acne Vulgaris Based on Convolutional Neural Network.

PubMed

Shen, Xiaolei; Zhang, Jiachi; Yan, Chenjun; Zhou, Hong

2018-04-11

In this paper, we present a new automatic diagnosis method for facial acne vulgaris which is based on convolutional neural networks (CNNs). To overcome the shortcomings of previous methods which were the inability to classify enough types of acne vulgaris. The core of our method is to extract features of images based on CNNs and achieve classification by classifier. A binary-classifier of skin-and-non-skin is used to detect skin area and a seven-classifier is used to achieve the classification task of facial acne vulgaris and healthy skin. In the experiments, we compare the effectiveness of our CNN and the VGG16 neural network which is pre-trained on the ImageNet data set. We use a ROC curve to evaluate the performance of binary-classifier and use a normalized confusion matrix to evaluate the performance of seven-classifier. The results of our experiments show that the pre-trained VGG16 neural network is effective in extracting features from facial acne vulgaris images. And the features are very useful for the follow-up classifiers. Finally, we try applying the classifiers both based on the pre-trained VGG16 neural network to assist doctors in facial acne vulgaris diagnosis.

Program Spotlight: National Outreach Network's Community Health Educators

Cancer.gov

National Outreach Network of Community Health Educators located at Community Network Program Centers, Partnerships to Advance Cancer Health Equity, and NCI-designated cancer centers help patients and their families receive survivorship support.

National Seismic Network of Georgia

NASA Astrophysics Data System (ADS)

Tumanova, N.; Kakhoberashvili, S.; Omarashvili, V.; Tserodze, M.; Akubardia, D.

2016-12-01

Georgia, as a part of the Southern Caucasus, is tectonically active and structurally complex region. It is one of the most active segments of the Alpine-Himalayan collision belt. The deformation and the associated seismicity are due to the continent-continent collision between the Arabian and Eurasian plates. Seismic Monitoring of country and the quality of seismic data is the major tool for the rapid response policy, population safety, basic scientific research and in the end for the sustainable development of the country. National Seismic Network of Georgia has been developing since the end of 19th century. Digital era of the network started from 2003. Recently continuous data streams from 25 stations acquired and analyzed in the real time. Data is combined to calculate rapid location and magnitude for the earthquake. Information for the bigger events (Ml>=3.5) is simultaneously transferred to the website of the monitoring center and to the related governmental agencies. To improve rapid earthquake location and magnitude estimation the seismic network was enhanced by installing additional 7 new stations. Each new station is equipped with coupled Broadband and Strong Motion seismometers and permanent GPS system as well. To select the sites for the 7 new base stations, we used standard network optimization techniques. To choose the optimal sites for new stations we've taken into account geometry of the existed seismic network, topographic conditions of the site. For each site we studied local geology (Vs30 was mandatory for each site), local noise level and seismic vault construction parameters. Due to the country elevation, stations were installed in the high mountains, no accessible in winter due to the heavy snow conditions. To secure online data transmission we used satellite data transmission as well as cell data network coverage from the different local companies. As a result we've already have the improved earthquake location and event magnitudes. We

National Stream Quality Accounting Network and National Monitoring Network Basin Boundary Geospatial Dataset, 2008–13

USGS Publications Warehouse

Baker, Nancy T.

2011-01-01

This report and the accompanying geospatial data were created to assist in analysis and interpretation of water-quality data provided by the U.S. Geological Survey's National Stream Quality Accounting Network (NASQAN) and by the U.S. Coastal Waters and Tributaries National Monitoring Network (NMN), which is a cooperative monitoring program of Federal, regional, and State agencies. The report describes the methods used to develop the geospatial data, which was primarily derived from the National Watershed Boundary Dataset. The geospatial data contains polygon shapefiles of basin boundaries for 33 NASQAN and 5 NMN streamflow and water-quality monitoring stations. In addition, 30 polygon shapefiles of the closed and noncontributing basins contained within the NASQAN or NMN boundaries are included. Also included is a point shapefile of the NASQAN and NMN monitoring stations and associated basin and station attributes. Geospatial data for basin delineations, associated closed and noncontributing basins, and monitoring station locations are available at http://water.usgs.gov/GIS/metadata/usgswrd/XML/ds641_nasqan_wbd12.xml.

34 CFR 412.4 - What is the National Network of Directors Council?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 3 2010-07-01 2010-07-01 false What is the National Network of Directors Council? 412...) OFFICE OF VOCATIONAL AND ADULT EDUCATION, DEPARTMENT OF EDUCATION NATIONAL NETWORK FOR CURRICULUM COORDINATION IN VOCATIONAL AND TECHNICAL EDUCATION General § 412.4 What is the National Network of Directors...

34 CFR 412.4 - What is the National Network of Directors Council?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 34 Education 3 2011-07-01 2011-07-01 false What is the National Network of Directors Council? 412...) OFFICE OF VOCATIONAL AND ADULT EDUCATION, DEPARTMENT OF EDUCATION NATIONAL NETWORK FOR CURRICULUM COORDINATION IN VOCATIONAL AND TECHNICAL EDUCATION General § 412.4 What is the National Network of Directors...

Convolutional neural networks for an automatic classification of prostate tissue slides with high-grade Gleason score

NASA Astrophysics Data System (ADS)

Jiménez del Toro, Oscar; Atzori, Manfredo; Otálora, Sebastian; Andersson, Mats; Eurén, Kristian; Hedlund, Martin; Rönnquist, Peter; Müller, Henning

2017-03-01

The Gleason grading system was developed for assessing prostate histopathology slides. It is correlated to the outcome and incidence of relapse in prostate cancer. Although this grading is part of a standard protocol performed by pathologists, visual inspection of whole slide images (WSIs) has an inherent subjectivity when evaluated by different pathologists. Computer aided pathology has been proposed to generate an objective and reproducible assessment that can help pathologists in their evaluation of new tissue samples. Deep convolutional neural networks are a promising approach for the automatic classification of histopathology images and can hierarchically learn subtle visual features from the data. However, a large number of manual annotations from pathologists are commonly required to obtain sufficient statistical generalization when training new models that can evaluate the daily generated large amounts of pathology data. A fully automatic approach that detects prostatectomy WSIs with high-grade Gleason score is proposed. We evaluate the performance of various deep learning architectures training them with patches extracted from automatically generated regions-of-interest rather than from manually segmented ones. Relevant parameters for training the deep learning model such as size and number of patches as well as the inclusion or not of data augmentation are compared between the tested deep learning architectures. 235 prostate tissue WSIs with their pathology report from the publicly available TCGA data set were used. An accuracy of 78% was obtained in a balanced set of 46 unseen test images with different Gleason grades in a 2-class decision: high vs. low Gleason grade. Grades 7-8, which represent the boundary decision of the proposed task, were particularly well classified. The method is scalable to larger data sets with straightforward re-training of the model to include data from multiple sources, scanners and acquisition techniques. Automatically

Automatic delineation and 3D visualization of the human ventricular system using probabilistic neural networks

NASA Astrophysics Data System (ADS)

Hatfield, Fraser N.; Dehmeshki, Jamshid

1998-09-01

Neurosurgery is an extremely specialized area of medical practice, requiring many years of training. It has been suggested that virtual reality models of the complex structures within the brain may aid in the training of neurosurgeons as well as playing an important role in the preparation for surgery. This paper focuses on the application of a probabilistic neural network to the automatic segmentation of the ventricles from magnetic resonance images of the brain, and their three dimensional visualization.

Artificial Epigenetic Networks: Automatic Decomposition of Dynamical Control Tasks Using Topological Self-Modification.

PubMed

Turner, Alexander P; Caves, Leo S D; Stepney, Susan; Tyrrell, Andy M; Lones, Michael A

2017-01-01

This paper describes the artificial epigenetic network, a recurrent connectionist architecture that is able to dynamically modify its topology in order to automatically decompose and solve dynamical problems. The approach is motivated by the behavior of gene regulatory networks, particularly the epigenetic process of chromatin remodeling that leads to topological change and which underlies the differentiation of cells within complex biological organisms. We expected this approach to be useful in situations where there is a need to switch between different dynamical behaviors, and do so in a sensitive and robust manner in the absence of a priori information about problem structure. This hypothesis was tested using a series of dynamical control tasks, each requiring solutions that could express different dynamical behaviors at different stages within the task. In each case, the addition of topological self-modification was shown to improve the performance and robustness of controllers. We believe this is due to the ability of topological changes to stabilize attractors, promoting stability within a dynamical regime while allowing rapid switching between different regimes. Post hoc analysis of the controllers also demonstrated how the partitioning of the networks could provide new insights into problem structure.

Spreading Activation in an Attractor Network with Latching Dynamics: Automatic Semantic Priming Revisited

PubMed Central

Lerner, Itamar; Bentin, Shlomo; Shriki, Oren

2012-01-01

Localist models of spreading activation (SA) and models assuming distributed-representations offer very different takes on semantic priming, a widely investigated paradigm in word recognition and semantic memory research. In the present study we implemented SA in an attractor neural network model with distributed representations and created a unified framework for the two approaches. Our models assumes a synaptic depression mechanism leading to autonomous transitions between encoded memory patterns (latching dynamics), which account for the major characteristics of automatic semantic priming in humans. Using computer simulations we demonstrated how findings that challenged attractor-based networks in the past, such as mediated and asymmetric priming, are a natural consequence of our present model’s dynamics. Puzzling results regarding backward priming were also given a straightforward explanation. In addition, the current model addresses some of the differences between semantic and associative relatedness and explains how these differences interact with stimulus onset asynchrony in priming experiments. PMID:23094718

Prevalence and test characteristics of national health safety network ventilator-associated events.

PubMed

Lilly, Craig M; Landry, Karen E; Sood, Rahul N; Dunnington, Cheryl H; Ellison, Richard T; Bagley, Peter H; Baker, Stephen P; Cody, Shawn; Irwin, Richard S

2014-09-01

The primary aim of the study was to measure the test characteristics of the National Health Safety Network ventilator-associated event/ventilator-associated condition constructs for detecting ventilator-associated pneumonia. Its secondary aims were to report the clinical features of patients with National Health Safety Network ventilator-associated event/ventilator-associated condition, measure costs of surveillance, and its susceptibility to manipulation. Prospective cohort study. Two inpatient campuses of an academic medical center. Eight thousand four hundred eight mechanically ventilated adults discharged from an ICU. None. The National Health Safety Network ventilator-associated event/ventilator-associated condition constructs detected less than a third of ventilator-associated pneumonia cases with a sensitivity of 0.325 and a positive predictive value of 0.07. Most National Health Safety Network ventilator-associated event/ventilator-associated condition cases (93%) did not have ventilator-associated pneumonia or other hospital-acquired complications; 71% met the definition for acute respiratory distress syndrome. Similarly, most patients with National Health Safety Network probable ventilator-associated pneumonia did not have ventilator-associated pneumonia because radiographic criteria were not met. National Health Safety Network ventilator-associated event/ventilator-associated condition rates were reduced 93% by an unsophisticated manipulation of ventilator management protocols. The National Health Safety Network ventilator-associated event/ventilator-associated condition constructs failed to detect many patients who had ventilator-associated pneumonia, detected many cases that did not have a hospital complication, and were susceptible to manipulation. National Health Safety Network ventilator-associated event/ventilator-associated condition surveillance did not perform as well as ventilator-associated pneumonia surveillance and had several undesirable

Attention to Automatic Movements in Parkinson's Disease: Modified Automatic Mode in the Striatum

PubMed Central

Wu, Tao; Liu, Jun; Zhang, Hejia; Hallett, Mark; Zheng, Zheng; Chan, Piu

2015-01-01

We investigated neural correlates when attending to a movement that could be made automatically in healthy subjects and Parkinson's disease (PD) patients. Subjects practiced a visuomotor association task until they could perform it automatically, and then directed their attention back to the automated task. Functional MRI was obtained during the early-learning, automatic stage, and when re-attending. In controls, attention to automatic movement induced more activation in the dorsolateral prefrontal cortex (DLPFC), anterior cingulate cortex, and rostral supplementary motor area. The motor cortex received more influence from the cortical motor association regions. In contrast, the pattern of the activity and connectivity of the striatum remained at the level of the automatic stage. In PD patients, attention enhanced activity in the DLPFC, premotor cortex, and cerebellum, but the connectivity from the putamen to the motor cortex decreased. Our findings demonstrate that, in controls, when a movement achieves the automatic stage, attention can influence the attentional networks and cortical motor association areas, but has no apparent effect on the striatum. In PD patients, attention induces a shift from the automatic mode back to the controlled pattern within the striatum. The shifting between controlled and automatic behaviors relies in part on striatal function. PMID:24925772

A National Strategy for Civic Networking: A Vision of Change.

ERIC Educational Resources Information Center

Civille, Richard

1993-01-01

Presents a vision and a national strategy for civic networking based on the development of the National Information Infrastructure. Topics addressed include a public interest communications policy; benefits of civic networking, including improving services and reducing government costs, reducing poverty and health care costs, and improving…

77 FR 33229 - Notice of Proposed Information Collection: Comment Request; National Resource Network

Federal Register 2010, 2011, 2012, 2013, 2014

2012-06-05

... Information Collection: Comment Request; National Resource Network AGENCY: Office of the Assistant Secretary... information: Title of Proposal: National Resource Network. OMB Control Number, if applicable: None... and reporting information related to the proposed National Resource Network. The U.S. Department of...

78 FR 8686 - Establishment of the National Freight Network

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-06

... Network AGENCY: Federal Highway Administration (FHWA), DOT. ACTION: Notice. SUMMARY: This notice defines the planned process for the designation of the national freight network as required by Section 1115 of... the initial designation of the primary freight network, the designation of additional miles critical...

Privacy Issues of a National Research and Education Network.

ERIC Educational Resources Information Center

Katz, James E.; Graveman, Richard F.

1991-01-01

Discussion of the right to privacy of communications focuses on privacy expectations within a National Research and Education Network (NREN). Highlights include privacy needs in scientific and education communications; academic and research networks; network security and privacy concerns; protection strategies; and consequences of privacy…

76 FR 38124 - Applications for New Awards; Americans With Disabilities Act (ADA) National Network Regional...

Federal Register 2010, 2011, 2012, 2013, 2014

2011-06-29

...) National Network Regional Centers and ADA National Network Collaborative Research Projects AGENCY: Office... National Network Regional Centers (formerly the Disability Business Technical Assistance Centers (DBTACs), and ADA National Network Collaborative Research Projects. Notice inviting applications for new awards...

"Getting Practical" and the National Network of Science Learning Centres

ERIC Educational Resources Information Center

Chapman, Georgina; Langley, Mark; Skilling, Gus; Walker, John

2011-01-01

The national network of Science Learning Centres is a co-ordinating partner in the Getting Practical--Improving Practical Work in Science programme. The principle of training provision for the "Getting Practical" programme is a cascade model. Regional trainers employed by the national network of Science Learning Centres trained the cohort of local…

Particle swarm optimization-based automatic parameter selection for deep neural networks and its applications in large-scale and high-dimensional data

PubMed Central

2017-01-01

In this paper, we propose a new automatic hyperparameter selection approach for determining the optimal network configuration (network structure and hyperparameters) for deep neural networks using particle swarm optimization (PSO) in combination with a steepest gradient descent algorithm. In the proposed approach, network configurations were coded as a set of real-number m-dimensional vectors as the individuals of the PSO algorithm in the search procedure. During the search procedure, the PSO algorithm is employed to search for optimal network configurations via the particles moving in a finite search space, and the steepest gradient descent algorithm is used to train the DNN classifier with a few training epochs (to find a local optimal solution) during the population evaluation of PSO. After the optimization scheme, the steepest gradient descent algorithm is performed with more epochs and the final solutions (pbest and gbest) of the PSO algorithm to train a final ensemble model and individual DNN classifiers, respectively. The local search ability of the steepest gradient descent algorithm and the global search capabilities of the PSO algorithm are exploited to determine an optimal solution that is close to the global optimum. We constructed several experiments on hand-written characters and biological activity prediction datasets to show that the DNN classifiers trained by the network configurations expressed by the final solutions of the PSO algorithm, employed to construct an ensemble model and individual classifier, outperform the random approach in terms of the generalization performance. Therefore, the proposed approach can be regarded an alternative tool for automatic network structure and parameter selection for deep neural networks. PMID:29236718

European national healthy city networks: the impact of an elite epistemic community.

PubMed

Heritage, Zoë; Green, Geoff

2013-10-01

National healthy cities networks (NNs) were created 20 years ago to support the development of healthy cities within the WHO Europe Region. Using the concept of epistemic communities, the evolution and impact of NNs is considered, as is their future development. Healthy cities national networks are providing information, training and support to member cities. In many cases, they are also involved in supporting national public health policy development and disseminating out healthy city principles to other local authorities. National networks are a fragile but an extremely valuable resource for sharing public health knowledge.

An automatic method to generate domain-specific investigator networks using PubMed abstracts.

PubMed

Yu, Wei; Yesupriya, Ajay; Wulf, Anja; Qu, Junfeng; Gwinn, Marta; Khoury, Muin J

2007-06-20

Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70-90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. We successfully created a web-based prototype capable of creating domain

An automatic method to generate domain-specific investigator networks using PubMed abstracts

PubMed Central

Yu, Wei; Yesupriya, Ajay; Wulf, Anja; Qu, Junfeng; Gwinn, Marta; Khoury, Muin J

2007-01-01

Background Collaboration among investigators has become critical to scientific research. This includes ad hoc collaboration established through personal contacts as well as formal consortia established by funding agencies. Continued growth in online resources for scientific research and communication has promoted the development of highly networked research communities. Extending these networks globally requires identifying additional investigators in a given domain, profiling their research interests, and collecting current contact information. We present a novel strategy for building investigator networks dynamically and producing detailed investigator profiles using data available in PubMed abstracts. Results We developed a novel strategy to obtain detailed investigator information by automatically parsing the affiliation string in PubMed records. We illustrated the results by using a published literature database in human genome epidemiology (HuGE Pub Lit) as a test case. Our parsing strategy extracted country information from 92.1% of the affiliation strings in a random sample of PubMed records and in 97.0% of HuGE records, with accuracies of 94.0% and 91.0%, respectively. Institution information was parsed from 91.3% of the general PubMed records (accuracy 86.8%) and from 94.2% of HuGE PubMed records (accuracy 87.0). We demonstrated the application of our approach to dynamic creation of investigator networks by creating a prototype information system containing a large database of PubMed abstracts relevant to human genome epidemiology (HuGE Pub Lit), indexed using PubMed medical subject headings converted to Unified Medical Language System concepts. Our method was able to identify 70–90% of the investigators/collaborators in three different human genetics fields; it also successfully identified 9 of 10 genetics investigators within the PREBIC network, an existing preterm birth research network. Conclusion We successfully created a web-based prototype

Computer systems for automatic earthquake detection

USGS Publications Warehouse

Stewart, S.W.

1974-01-01

U.S Geological Survey seismologists in Menlo park, California, are utilizing the speed, reliability, and efficiency of minicomputers to monitor seismograph stations and to automatically detect earthquakes. An earthquake detection computer system, believed to be the only one of its kind in operation, automatically reports about 90 percent of all local earthquakes recorded by a network of over 100 central California seismograph stations. The system also monitors the stations for signs of malfunction or abnormal operation. Before the automatic system was put in operation, all of the earthquakes recorded had to be detected by manually searching the records, a time-consuming process. With the automatic detection system, the stations are efficiently monitored continuously. 

Automatic Skin Lesion Segmentation Using Deep Fully Convolutional Networks With Jaccard Distance.

PubMed

Yuan, Yading; Chao, Ming; Lo, Yeh-Chi

2017-09-01

Automatic skin lesion segmentation in dermoscopic images is a challenging task due to the low contrast between lesion and the surrounding skin, the irregular and fuzzy lesion borders, the existence of various artifacts, and various imaging acquisition conditions. In this paper, we present a fully automatic method for skin lesion segmentation by leveraging 19-layer deep convolutional neural networks that is trained end-to-end and does not rely on prior knowledge of the data. We propose a set of strategies to ensure effective and efficient learning with limited training data. Furthermore, we design a novel loss function based on Jaccard distance to eliminate the need of sample re-weighting, a typical procedure when using cross entropy as the loss function for image segmentation due to the strong imbalance between the number of foreground and background pixels. We evaluated the effectiveness, efficiency, as well as the generalization capability of the proposed framework on two publicly available databases. One is from ISBI 2016 skin lesion analysis towards melanoma detection challenge, and the other is the PH2 database. Experimental results showed that the proposed method outperformed other state-of-the-art algorithms on these two databases. Our method is general enough and only needs minimum pre- and post-processing, which allows its adoption in a variety of medical image segmentation tasks.

Cord blood banking in France: reorganising the national network.

PubMed

Katz, Gregory; Mills, Antonia

2010-06-01

Paradoxically, France is one of the leading exporters of cord blood units worldwide, but ranks only 17th in terms of cord blood units per inhabitant, and imports 64% of cord blood grafts to meet national transplantation demands. With three operational banks in 2008, the French allogeneic cord blood network is now entering an important phase of development with the creation of seven new banks collecting from local clusters of maternities. Although the French network of public banks is demonstrating a strong commitment to reorganise and scale up its activities, the revision of France's bioethics law in 2010 has sparked a debate concerning the legalisation of commercial autologous banking. The paper discusses key elements for a comprehensive national plan that would strengthen the allogeneic banking network through which France could meet its national medical needs and guarantee equal access to healthcare. Copyright 2010. Published by Elsevier Ltd.

Automatic temporal segment detection via bilateral long short-term memory recurrent neural networks

NASA Astrophysics Data System (ADS)

Sun, Bo; Cao, Siming; He, Jun; Yu, Lejun; Li, Liandong

2017-03-01

Constrained by the physiology, the temporal factors associated with human behavior, irrespective of facial movement or body gesture, are described by four phases: neutral, onset, apex, and offset. Although they may benefit related recognition tasks, it is not easy to accurately detect such temporal segments. An automatic temporal segment detection framework using bilateral long short-term memory recurrent neural networks (BLSTM-RNN) to learn high-level temporal-spatial features, which synthesizes the local and global temporal-spatial information more efficiently, is presented. The framework is evaluated in detail over the face and body database (FABO). The comparison shows that the proposed framework outperforms state-of-the-art methods for solving the problem of temporal segment detection.

The impact of capacity growth in national telecommunications networks.

PubMed

Lord, Andrew; Soppera, Andrea; Jacquet, Arnaud

2016-03-06

This paper discusses both UK-based and global Internet data bandwidth growth, beginning with historical data for the BT network. We examine the time variations in consumer behaviour and how this is statistically aggregated into larger traffic loads on national core fibre communications networks. The random nature of consumer Internet behaviour, where very few consumers require maximum bandwidth simultaneously, provides the opportunity for a significant statistical gain. The paper looks at predictions for how this growth might continue over the next 10-20 years, giving estimates for the amount of bandwidth that networks should support in the future. The paper then explains how national networks are designed to accommodate these traffic levels, and the various network roles, including access, metro and core, are described. The physical layer network is put into the context of how the packet and service layers are designed and the applications and location of content are also included in an overall network overview. The specific role of content servers in alleviating core network traffic loads is highlighted. The status of the relevant transmission technologies in the access, metro and core is given, showing that these technologies, with adequate research, should be sufficient to provide bandwidth for consumers in the next 10-20 years. © 2016 The Author(s).

National information network and database system of hazardous waste management in China

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

Ma Hongchang

1996-12-31

Industries in China generate large volumes of hazardous waste, which makes it essential for the nation to pay more attention to hazardous waste management. National laws and regulations, waste surveys, and manifest tracking and permission systems have been initiated. Some centralized hazardous waste disposal facilities are under construction. China`s National Environmental Protection Agency (NEPA) has also obtained valuable information on hazardous waste management from developed countries. To effectively share this information with local environmental protection bureaus, NEPA developed a national information network and database system for hazardous waste management. This information network will have such functions as information collection, inquiry,more » and connection. The long-term objective is to establish and develop a national and local hazardous waste management information network. This network will significantly help decision makers and researchers because it will be easy to obtain information (e.g., experiences of developed countries in hazardous waste management) to enhance hazardous waste management in China. The information network consists of five parts: technology consulting, import-export management, regulation inquiry, waste survey, and literature inquiry.« less

Overview of the new National Near-Road Air Quality Monitoring Network

EPA Science Inventory

In 2010, EPA promulgated new National Ambient Air Quality Standards (NAAQS) for nitrogen dioxide (NO2). As part of this new NAAQS, EPA required the establishment of a national near-road air quality monitoring network. This network will consist of one NO2 near-road monitoring st...

"It Takes a Network": Building National Capacity for Climate Change Interpretation

NASA Astrophysics Data System (ADS)

Spitzer, W.

2014-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. More than 1,500 informal science venues (science centers, museums, aquariums, zoos, nature centers, national parks) are visited annually by 61% of the U.S. population. These visitors expect reliable information about environmental issues and solutions. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. Beyond providing in-depth training, we have found that our "alumni network" is assuming an increasingly important role in achieving our goals: 1. Ongoing learning - Training must be ongoing given continuous advances in climate and social science research. 2. Implementation support - Social support is critical as interpreters move from learning to practice, given complex and potentially contentious subject matter. 3. Leadership development - We rely on a national cadre of interpretive leaders to conduct workshops, facilitate study circle trainings, and support alumni. 4. Coalition building - A peer network helps to build and maintain connections with colleagues, and supports further dissemination through the informal science community. We are experimenting with a variety of online and face to face strategies to support the growing alumni network. Our goals are to achieve a systemic national

Implementation of the NCI’s National Clinical Trials Network

Cancer.gov

NCI is launching a new clinical trials research network intended to improve treatment for the more than 1.6 million Americans diagnosed with cancer each year. The new system, NCI’s National Clinical Trials Network (NCTN), will facilitate the rapid initia

Automatic crown cover mapping to improve forest inventory

Treesearch

Claude Vidal; Jean-Guy Boureau; Nicolas Robert; Nicolas Py; Josiane Zerubia; Xavier Descombes; Guillaume Perrin

2009-01-01

To automatically analyze near infrared aerial photographs, the French National Institute for Research in Computer Science and Control developed together with the French National Forest Inventory (NFI) a method for automatic crown cover mapping. This method uses a Reverse Jump Monte Carlo Markov Chain algorithm to locate the crowns and describe those using ellipses or...

Automatic 3D liver location and segmentation via convolutional neural network and graph cut.

PubMed

Lu, Fang; Wu, Fa; Hu, Peijun; Peng, Zhiyi; Kong, Dexing

2017-02-01

Segmentation of the liver from abdominal computed tomography (CT) images is an essential step in some computer-assisted clinical interventions, such as surgery planning for living donor liver transplant, radiotherapy and volume measurement. In this work, we develop a deep learning algorithm with graph cut refinement to automatically segment the liver in CT scans. The proposed method consists of two main steps: (i) simultaneously liver detection and probabilistic segmentation using 3D convolutional neural network; (ii) accuracy refinement of the initial segmentation with graph cut and the previously learned probability map. The proposed approach was validated on forty CT volumes taken from two public databases MICCAI-Sliver07 and 3Dircadb1. For the MICCAI-Sliver07 test dataset, the calculated mean ratios of volumetric overlap error (VOE), relative volume difference (RVD), average symmetric surface distance (ASD), root-mean-square symmetric surface distance (RMSD) and maximum symmetric surface distance (MSD) are 5.9, 2.7 %, 0.91, 1.88 and 18.94 mm, respectively. For the 3Dircadb1 dataset, the calculated mean ratios of VOE, RVD, ASD, RMSD and MSD are 9.36, 0.97 %, 1.89, 4.15 and 33.14 mm, respectively. The proposed method is fully automatic without any user interaction. Quantitative results reveal that the proposed approach is efficient and accurate for hepatic volume estimation in a clinical setup. The high correlation between the automatic and manual references shows that the proposed method can be good enough to replace the time-consuming and nonreproducible manual segmentation method.

NASDA knowledge-based network planning system

NASA Technical Reports Server (NTRS)

Yamaya, K.; Fujiwara, M.; Kosugi, S.; Yambe, M.; Ohmori, M.

1993-01-01

One of the SODS (space operation and data system) sub-systems, NP (network planning) was the first expert system used by NASDA (national space development agency of Japan) for tracking and control of satellite. The major responsibilities of the NP system are: first, the allocation of network and satellite control resources and, second, the generation of the network operation plan data (NOP) used in automated control of the stations and control center facilities. Up to now, the first task of network resource scheduling was done by network operators. NP system automatically generates schedules using its knowledge base, which contains information on satellite orbits, station availability, which computer is dedicated to which satellite, and how many stations must be available for a particular satellite pass or a certain time period. The NP system is introduced.

USA National Phenology Network observational data documentation

USGS Publications Warehouse

Rosemartin, Alyssa H.; Denny, Ellen G.; Gerst, Katharine L.; Marsh, R. Lee; Posthumus, Erin E.; Crimmins, Theresa M.; Weltzin, Jake F.

2018-04-25

The goals of the USA National Phenology Network (USA-NPN, www.usanpn.org) are to advance science, inform decisions, and communicate and connect with the public regarding phenology and species’ responses to environmental variation and climate change. The USA-NPN seeks to advance the science of phenology and facilitate ecosystem stewardship by providing phenological information freely and openly. To accomplish these goals, the USA-NPN National Coordinating Office (NCO) delivers observational data on plant and animal phenology in several formats, including minimally processed status and intensity datasets and derived phenometrics for individual plants, sites, and regions. This document describes the suite of observational data products delivered by the USA National Phenology Network, covering the period 2009–present for the United States and accessible via the Phenology Observation Portal (http://dx.doi.org/10.5066/F78S4N1V) and via an Application Programming Interface. The data described here have been used in diverse research and management applications, including over 30 publications in fields such as remote sensing, plant evolution, and resource management.

Bibliographic Services for a National Network.

ERIC Educational Resources Information Center

Avram, Henriette D.; Pulsifer, Josephine S.

The thesis of this paper is that efficient functioning of a network is dependent upon the organization of bibliographic services so that the basic record for each bibliographic item is created once. This record must be minimally capable of serving the needs of libraries, information centers, abstracting and indexing services, and national and…

Spatial spreading of infectious disease via local and national mobility networks in South Korea

NASA Astrophysics Data System (ADS)

Kwon, Okyu; Son, Woo-Sik

2017-12-01

We study the spread of infectious disease based on local- and national-scale mobility networks. We construct a local mobility network using data on urban bus services to estimate local-scale movement of people. We also construct a national mobility network from orientation-destination data of vehicular traffic between highway tollgates to evaluate national-scale movement of people. A metapopulation model is used to simulate the spread of epidemics. Thus, the number of infected people is simulated using a susceptible-infectious-recovered (SIR) model within the administrative division, and inter-division spread of infected people is determined through local and national mobility networks. In this paper, we consider two scenarios for epidemic spread. In the first, the infectious disease only spreads through local-scale movement of people, that is, the local mobility network. In the second, it spreads via both local and national mobility networks. For the former, the simulation results show infected people sequentially spread to neighboring divisions. Yet for the latter, we observe a faster spreading pattern to distant divisions. Thus, we confirm the national mobility network enhances synchronization among the incidence profiles of all administrative divisions.

USA National Phenology Network gridded products documentation

USGS Publications Warehouse

Crimmins, Theresa M.; Marsh, R. Lee; Switzer, Jeff R.; Crimmins, Michael A.; Gerst, Katharine L.; Rosemartin, Alyssa H.; Weltzin, Jake F.

2017-02-23

The goals of the USA National Phenology Network (USA-NPN, www.usanpn.org) are to advance science, inform decisions, and communicate and connect with the public regarding phenology and species’ responses to environmental variation and climate change. The USA-NPN seeks to facilitate informed ecosystem stewardship and management by providing phenological information freely and openly. One way the USA-NPN is endeavoring to accomplish these goals is by providing data and data products in a wide range of formats, including gridded real-time, short-term forecasted, and historical maps of phenological events, patterns and trends. This document describes the suite of gridded phenologically relevant data products produced and provided by the USA National Phenology Network, which can be accessed at www.usanpn.org/data/phenology_maps and also through web services at geoserver.usanpn.org/geoserver/wms?request=GetCapabilities.

78 FR 24154 - Notice of Availability of a National Animal Health Laboratory Network Reorganization Concept Paper

Federal Register 2010, 2011, 2012, 2013, 2014

2013-04-24

...] Notice of Availability of a National Animal Health Laboratory Network Reorganization Concept Paper AGENCY... Network (NAHLN) for public review and comment. The NAHLN is a nationally coordinated network and... Coordinator, National Animal Health Laboratory Network, Veterinary Services, APHIS, 2140 Centre Avenue...

Automatic programming of simulation models

NASA Technical Reports Server (NTRS)

Schroer, Bernard J.; Tseng, Fan T.; Zhang, Shou X.; Dwan, Wen S.

1990-01-01

The concepts of software engineering were used to improve the simulation modeling environment. Emphasis was placed on the application of an element of rapid prototyping, or automatic programming, to assist the modeler define the problem specification. Then, once the problem specification has been defined, an automatic code generator is used to write the simulation code. The following two domains were selected for evaluating the concepts of software engineering for discrete event simulation: manufacturing domain and a spacecraft countdown network sequence. The specific tasks were to: (1) define the software requirements for a graphical user interface to the Automatic Manufacturing Programming System (AMPS) system; (2) develop a graphical user interface for AMPS; and (3) compare the AMPS graphical interface with the AMPS interactive user interface.

Gap-free segmentation of vascular networks with automatic image processing pipeline.

PubMed

Hsu, Chih-Yang; Ghaffari, Mahsa; Alaraj, Ali; Flannery, Michael; Zhou, Xiaohong Joe; Linninger, Andreas

2017-03-01

Current image processing techniques capture large vessels reliably but often fail to preserve connectivity in bifurcations and small vessels. Imaging artifacts and noise can create gaps and discontinuity of intensity that hinders segmentation of vascular trees. However, topological analysis of vascular trees require proper connectivity without gaps, loops or dangling segments. Proper tree connectivity is also important for high quality rendering of surface meshes for scientific visualization or 3D printing. We present a fully automated vessel enhancement pipeline with automated parameter settings for vessel enhancement of tree-like structures from customary imaging sources, including 3D rotational angiography, magnetic resonance angiography, magnetic resonance venography, and computed tomography angiography. The output of the filter pipeline is a vessel-enhanced image which is ideal for generating anatomical consistent network representations of the cerebral angioarchitecture for further topological or statistical analysis. The filter pipeline combined with computational modeling can potentially improve computer-aided diagnosis of cerebrovascular diseases by delivering biometrics and anatomy of the vasculature. It may serve as the first step in fully automatic epidemiological analysis of large clinical datasets. The automatic analysis would enable rigorous statistical comparison of biometrics in subject-specific vascular trees. The robust and accurate image segmentation using a validated filter pipeline would also eliminate operator dependency that has been observed in manual segmentation. Moreover, manual segmentation is time prohibitive given that vascular trees have more than thousands of segments and bifurcations so that interactive segmentation consumes excessive human resources. Subject-specific trees are a first step toward patient-specific hemodynamic simulations for assessing treatment outcomes. Copyright © 2017 Elsevier Ltd. All rights reserved.

Estimating National-scale Emissions using Dense Monitoring Networks

NASA Astrophysics Data System (ADS)

Ganesan, A.; Manning, A.; Grant, A.; Young, D.; Oram, D.; Sturges, W. T.; Moncrieff, J. B.; O'Doherty, S.

2014-12-01

The UK's DECC (Deriving Emissions linked to Climate Change) network consists of four greenhouse gas measurement stations that are situated to constrain emissions from the UK and Northwest Europe. These four stations are located in Mace Head (West Coast of Ireland), and on telecommunication towers at Ridge Hill (Western England), Tacolneston (Eastern England) and Angus (Eastern Scotland). With the exception of Angus, which currently only measures carbon dioxide (CO2) and methane (CH4), the remaining sites are additionally equipped to monitor nitrous oxide (N2O). We present an analysis of the network's CH4 and N2O observations from 2011-2013 and compare derived top-down regional emissions with bottom-up inventories, including a recently produced high-resolution inventory (UK National Atmospheric Emissions Inventory). As countries are moving toward national-level emissions estimation, we also address some of the considerations that need to be made when designing these national networks. One of the novel aspects of this work is that we use a hierarchical Bayesian inversion framework. This methodology, which has newly been applied to greenhouse gas emissions estimation, is designed to estimate temporally and spatially varying model-measurement uncertainties and correlation scales, in addition to fluxes. Through this analysis, we demonstrate the importance of characterizing these covariance parameters in order to properly use data from high-density monitoring networks. This UK case study highlights the ways in which this new inverse framework can be used to address some of the limitations of traditional Bayesian inverse methods.

Research on Application of Automatic Weather Station Based on Internet of Things

NASA Astrophysics Data System (ADS)

Jianyun, Chen; Yunfan, Sun; Chunyan, Lin

2017-12-01

In this paper, the Internet of Things is briefly introduced, and then its application in the weather station is studied. A method of data acquisition and transmission based on NB-iot communication mode is proposed, Introduction of Internet of things technology, Sensor digital and independent power supply as the technical basis, In the construction of Automatic To realize the intelligent interconnection of the automatic weather station, and then to form an automatic weather station based on the Internet of things. A network structure of automatic weather station based on Internet of things technology is constructed to realize the independent operation of intelligent sensors and wireless data transmission. Research on networking data collection and dissemination of meteorological data, through the data platform for data analysis, the preliminary work of meteorological information publishing standards, networking of meteorological information receiving terminal provides the data interface, to the wisdom of the city, the wisdom of the purpose of the meteorological service.

Automatic anatomy recognition using neural network learning of object relationships via virtual landmarks

NASA Astrophysics Data System (ADS)

Yan, Fengxia; Udupa, Jayaram K.; Tong, Yubing; Xu, Guoping; Odhner, Dewey; Torigian, Drew A.

2018-03-01

The recently developed body-wide Automatic Anatomy Recognition (AAR) methodology depends on fuzzy modeling of individual objects, hierarchically arranging objects, constructing an anatomy ensemble of these models, and a dichotomous object recognition-delineation process. The parent-to-offspring spatial relationship in the object hierarchy is crucial in the AAR method. We have found this relationship to be quite complex, and as such any improvement in capturing this relationship information in the anatomy model will improve the process of recognition itself. Currently, the method encodes this relationship based on the layout of the geometric centers of the objects. Motivated by the concept of virtual landmarks (VLs), this paper presents a new one-shot AAR recognition method that utilizes the VLs to learn object relationships by training a neural network to predict the pose and the VLs of an offspring object given the VLs of the parent object in the hierarchy. We set up two neural networks for each parent-offspring object pair in a body region, one for predicting the VLs and another for predicting the pose parameters. The VL-based learning/prediction method is evaluated on two object hierarchies involving 14 objects. We utilize 54 computed tomography (CT) image data sets of head and neck cancer patients and the associated object contours drawn by dosimetrists for routine radiation therapy treatment planning. The VL neural network method is found to yield more accurate object localization than the currently used simple AAR method.

Federated queries of clinical data repositories: Scaling to a national network.

PubMed

Weber, Griffin M

2015-06-01

Federated networks of clinical research data repositories are rapidly growing in size from a handful of sites to true national networks with more than 100 hospitals. This study creates a conceptual framework for predicting how various properties of these systems will scale as they continue to expand. Starting with actual data from Harvard's four-site Shared Health Research Information Network (SHRINE), the framework is used to imagine a future 4000 site network, representing the majority of hospitals in the United States. From this it becomes clear that several common assumptions of small networks fail to scale to a national level, such as all sites being online at all times or containing data from the same date range. On the other hand, a large network enables researchers to select subsets of sites that are most appropriate for particular research questions. Developers of federated clinical data networks should be aware of how the properties of these networks change at different scales and design their software accordingly. Copyright © 2015 Elsevier Inc. All rights reserved.

A Handbook for Automatic Data Processing Equipment Acquisition.

DTIC Science & Technology

1981-12-01

Navy ADPE Procurement Policies (Automatic Data Processing Equipment (ADPE) procurement by federal agencies is governed by an interlocking network of...ADPE) procurement by federal agencies is governed by an interlocking network of policies and directives issued by federal agencies, the Department...SECNAVINST) and local procedures governing the acquisition of ADPE. Obtaining and understanding this interlocking network of policies is often difficult

Automatic detection and segmentation of brain metastases on multimodal MR images with a deep convolutional neural network.

PubMed

Charron, Odelin; Lallement, Alex; Jarnet, Delphine; Noblet, Vincent; Clavier, Jean-Baptiste; Meyer, Philippe

2018-04-01

Stereotactic treatments are today the reference techniques for the irradiation of brain metastases in radiotherapy. The dose per fraction is very high, and delivered in small volumes (diameter <1 cm). As part of these treatments, effective detection and precise segmentation of lesions are imperative. Many methods based on deep-learning approaches have been developed for the automatic segmentation of gliomas, but very little for that of brain metastases. We adapted an existing 3D convolutional neural network (DeepMedic) to detect and segment brain metastases on MRI. At first, we sought to adapt the network parameters to brain metastases. We then explored the single or combined use of different MRI modalities, by evaluating network performance in terms of detection and segmentation. We also studied the interest of increasing the database with virtual patients or of using an additional database in which the active parts of the metastases are separated from the necrotic parts. Our results indicated that a deep network approach is promising for the detection and the segmentation of brain metastases on multimodal MRI. Copyright © 2018 Elsevier Ltd. All rights reserved.

Fully automatic acute ischemic lesion segmentation in DWI using convolutional neural networks.

PubMed

Chen, Liang; Bentley, Paul; Rueckert, Daniel

2017-01-01

Stroke is an acute cerebral vascular disease, which is likely to cause long-term disabilities and death. Acute ischemic lesions occur in most stroke patients. These lesions are treatable under accurate diagnosis and treatments. Although diffusion-weighted MR imaging (DWI) is sensitive to these lesions, localizing and quantifying them manually is costly and challenging for clinicians. In this paper, we propose a novel framework to automatically segment stroke lesions in DWI. Our framework consists of two convolutional neural networks (CNNs): one is an ensemble of two DeconvNets (Noh et al., 2015), which is the EDD Net; the second CNN is the multi-scale convolutional label evaluation net (MUSCLE Net), which aims to evaluate the lesions detected by the EDD Net in order to remove potential false positives. To the best of our knowledge, it is the first attempt to solve this problem and using both CNNs achieves very good results. Furthermore, we study the network architectures and key configurations in detail to ensure the best performance. It is validated on a large dataset comprising clinical acquired DW images from 741 subjects. A mean accuracy of Dice coefficient obtained is 0.67 in total. The mean Dice scores based on subjects with only small and large lesions are 0.61 and 0.83, respectively. The lesion detection rate achieved is 0.94.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels

NASA Astrophysics Data System (ADS)

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness.

Building A National Network for Ocean and Climate Change Interpretation (Invited)

NASA Astrophysics Data System (ADS)

Spitzer, W.; Anderson, J.

2013-12-01

In the US, more than 1,500 informal science venues (science centers, museums, aquariums, zoos, nature centers, national parks) are visited annually by 61% of the population. Research shows that these visitors are receptive to learning about climate change, and expect these institutions to provide reliable information about environmental issues and solutions. Given that we spend less than 5% of our lifetime in a classroom, informal science venues play a critical role in shaping public understanding. Since 2007, the New England Aquarium (NEAq) has led a national effort to increase the capacity of informal science education institutions (ISEIs) to effectively communicate about the impacts of climate change on the oceans. NEAq is now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI's design is based on best practices in informal science learning, cognitive/social psychology, community and network building: Interpreters as Communication Strategists - Interpreters can serve not merely as educators disseminating information, but can also be leaders in influencing public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. Communities of Practice - Learning is a social activity that is created through engagement in a supportive community context. Social support is particularly important in addressing a complex, contentious and distressing subject. Diffusion of Innovation - Peer networks are of primary importance in spreading innovations. Leaders serve as 'early adopters' and influence others to achieve a critical mass of implementation. Over the next five years, NNOCCI will achieve a

Network structure exploration in networks with node attributes

NASA Astrophysics Data System (ADS)

Chen, Yi; Wang, Xiaolong; Bu, Junzhao; Tang, Buzhou; Xiang, Xin

2016-05-01

Complex networks provide a powerful way to represent complex systems and have been widely studied during the past several years. One of the most important tasks of network analysis is to detect structures (also called structural regularities) embedded in networks by determining group number and group partition. Most of network structure exploration models only consider network links. However, in real world networks, nodes may have attributes that are useful for network structure exploration. In this paper, we propose a novel Bayesian nonparametric (BNP) model to explore structural regularities in networks with node attributes, called Bayesian nonparametric attribute (BNPA) model. This model does not only take full advantage of both links between nodes and node attributes for group partition via shared hidden variables, but also determine group number automatically via the Bayesian nonparametric theory. Experiments conducted on a number of real and synthetic networks show that our BNPA model is able to automatically explore structural regularities in networks with node attributes and is competitive with other state-of-the-art models.

The Global Special Operations Forces Network from a Partner-Nation Perspective

DTIC Science & Technology

2014-12-01

in networks vs . management of Networks. ................................80  Figure 17.  A national SOF network with SOCOM as the manager of networks...context and are asked in the natural course of things; there is no predetermination of question topics or wording. 10 descriptive section is the...struggles and challenges that occur naturally over time. As depicted in Figure 2, the network will constantly have to examine how it is evolving and, if

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era.

PubMed

Best, Michele; Sakande, Jean

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state's public health system and is critical to the development of a robust national laboratory response network to meet global health security threats.

Practical recommendations for strengthening national and regional laboratory networks in Africa in the Global Health Security era

PubMed Central

2016-01-01

The role of national health laboratories in support of public health response has expanded beyond laboratory testing to include a number of other core functions such as emergency response, training and outreach, communications, laboratory-based surveillance and data management. These functions can only be accomplished by an efficient and resilient national laboratory network that includes public health, reference, clinical and other laboratories. It is a primary responsibility of the national health laboratory in the Ministry of Health to develop and maintain the national laboratory network in the country. In this article, we present practical recommendations based on 17 years of network development experience for the development of effective national laboratory networks. These recommendations and examples of current laboratory networks, are provided to facilitate laboratory network development in other states. The development of resilient, integrated laboratory networks will enhance each state’s public health system and is critical to the development of a robust national laboratory response network to meet global health security threats. PMID:28879137

The ADVANCE network: accelerating data value across a national community health center network

PubMed Central

DeVoe, Jennifer E; Gold, Rachel; Cottrell, Erika; Bauer, Vance; Brickman, Andrew; Puro, Jon; Nelson, Christine; Mayer, Kenneth H; Sears, Abigail; Burdick, Tim; Merrell, Jonathan; Matthews, Paul; Fields, Scott

2014-01-01

The ADVANCE (Accelerating Data Value Across a National Community Health Center Network) clinical data research network (CDRN) is led by the OCHIN Community Health Information Network in partnership with Health Choice Network and Fenway Health. The ADVANCE CDRN will ‘horizontally’ integrate outpatient electronic health record data for over one million federally qualified health center patients, and ‘vertically’ integrate hospital, health plan, and community data for these patients, often under-represented in research studies. Patient investigators, community investigators, and academic investigators with diverse expertise will work together to meet project goals related to data integration, patient engagement and recruitment, and the development of streamlined regulatory policies. By enhancing the data and research infrastructure of participating organizations, the ADVANCE CDRN will serve as a ‘community laboratory’ for including disadvantaged and vulnerable patients in patient-centered outcomes research that is aligned with the priorities of patients, clinics, and communities in our network. PMID:24821740

3D convolutional neural network for automatic detection of lung nodules in chest CT

NASA Astrophysics Data System (ADS)

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-03-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

3D Convolutional Neural Network for Automatic Detection of Lung Nodules in Chest CT.

PubMed

Hamidian, Sardar; Sahiner, Berkman; Petrick, Nicholas; Pezeshk, Aria

2017-01-01

Deep convolutional neural networks (CNNs) form the backbone of many state-of-the-art computer vision systems for classification and segmentation of 2D images. The same principles and architectures can be extended to three dimensions to obtain 3D CNNs that are suitable for volumetric data such as CT scans. In this work, we train a 3D CNN for automatic detection of pulmonary nodules in chest CT images using volumes of interest extracted from the LIDC dataset. We then convert the 3D CNN which has a fixed field of view to a 3D fully convolutional network (FCN) which can generate the score map for the entire volume efficiently in a single pass. Compared to the sliding window approach for applying a CNN across the entire input volume, the FCN leads to a nearly 800-fold speed-up, and thereby fast generation of output scores for a single case. This screening FCN is used to generate difficult negative examples that are used to train a new discriminant CNN. The overall system consists of the screening FCN for fast generation of candidate regions of interest, followed by the discrimination CNN.

Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment.

PubMed

Wang, Jianing; Niu, Xintao; Zheng, Lingjiao; Zheng, Chuantao; Wang, Yiding

2016-11-18

In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO₂) sensor based on non-dispersive infrared (NDIR) with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI), to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO₂ control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO₂ concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID) algorithm realized on a LabVIEW platform, the CO₂ concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of <±35 ppm in an acre of greenhouse.

Automatic classification of retinal three-dimensional optical coherence tomography images using principal component analysis network with composite kernels.

PubMed

Fang, Leyuan; Wang, Chong; Li, Shutao; Yan, Jun; Chen, Xiangdong; Rabbani, Hossein

2017-11-01

We present an automatic method, termed as the principal component analysis network with composite kernel (PCANet-CK), for the classification of three-dimensional (3-D) retinal optical coherence tomography (OCT) images. Specifically, the proposed PCANet-CK method first utilizes the PCANet to automatically learn features from each B-scan of the 3-D retinal OCT images. Then, multiple kernels are separately applied to a set of very important features of the B-scans and these kernels are fused together, which can jointly exploit the correlations among features of the 3-D OCT images. Finally, the fused (composite) kernel is incorporated into an extreme learning machine for the OCT image classification. We tested our proposed algorithm on two real 3-D spectral domain OCT (SD-OCT) datasets (of normal subjects and subjects with the macular edema and age-related macular degeneration), which demonstrated its effectiveness. (2017) COPYRIGHT Society of Photo-Optical Instrumentation Engineers (SPIE).

Evolving plans for the USA National Phenology Network

USGS Publications Warehouse

Betancourt, Julio L.; Schwartz, Mark D.; Breshears, David D.; Brewer, Carol A.; Frazer, Gary; Gross, John E.; Mazer, Susan J.; Reed, Bradley C.; Wilson, Bruce E.

2007-01-01

Phenology is the study of periodic plant and animal life cycle events, how these are influenced by seasonal and interannual variations in climate, and how they modulate the abundance, diversity, and interactions of organisms. The USA National Phenology Network (USA-NPN) is currently being organized to engage federal agencies, environmental networks and field stations, educational institutions, and citizen scientists. The first USA-NPN planning workshop was held August 2005, in Tucson, Ariz. (Betancourt et al. [2005]; http://www.uwm.edu/Dept/Geography/npn/; by 1 June 2007, also see http://www.usanpn.org). With sponsorship from the U.S. National Science Foundation, the U.S. Geological Survey (USGS), the U.S. Fish and Wildlife Service, and NASA, the second USA-NPN planning workshop was held at the University of Wisconsin-Milwaukee on 10–12 October 2006 to (1) develop lists of target species and observation protocols; (2) identify existing networks that could comprise the backbone of nationwide observations by 2008; (3) develop opportunities for education, citizen science, and outreach beginning in spring 2007; (4) design strategies for implementing the remote sensing component of USA-NPN; and (5) draft a data management and cyberinfrastructure plan.

The USA National Phenology Network: A national science and monitoring program for understanding climate change

NASA Astrophysics Data System (ADS)

Weltzin, J.

2009-04-01

Patterns of phenology for plants and animals control ecosystem processes, determine land surface properties, control biosphere-atmosphere interactions, and affect food production, health, conservation, and recreation. Although phenological data and models have applications related to scientific research, education and outreach, agriculture, tourism and recreation, human health, and natural resource conservation and management, until recently there was no coordinated effort to understand phenology at the national scale in the United States. The USA National Phenology Network (USA-NPN; www.usanpn.org), established in 2007, is an emerging and exciting partnership between federal agencies, the academic community, and the general public to establish a national science and monitoring initiative focused on phenology. The first year of operation of USA-NPN produced many new phenology products and venues for phenology research and citizen involvement. Products include a new web-site (www.usanpn.org) that went live in June 2008; the web-site includes a tool for on-line data entry, and serves as a clearinghouse for products and information to facilitate research and communication related to phenology. The new core Plant Phenology Program includes profiles for 200 vetted local, regional, and national plant species with descriptions and (BBCH-consistent) monitoring protocols, as well as templates for addition of new species. A partnership program describes how other monitoring networks can engage with USA-NPN to collect, manage or disseminate phenological information for science, health, education, management or predictive service applications. Project BudBurst, a USA-NPN field campaign for citizen scientists, went live in February 2008, and now includes over 3000 registered observers monitoring 4000 plants across the nation. For 2009 and beyond, we will initiate a new Wildlife Phenology Program, create an on-line clearing-house for phenology education and outreach, strengthen

Data from selected U.S. Geological Survey National Stream Water-Quality Networks (WQN)

USGS Publications Warehouse

Alexander, Richard B.; Slack, J.R.; Ludtke, A.S.; Fitzgerald, K.K.; Schertz, T.L.; Briel, L.I.; Buttleman, K.P.

1996-01-01

This CD-ROM set contains data from two USGS national stream water-quality networks, the Hydrologic Benchmark Network (HBN) and the National Stream Quality Accounting Network (NASQAN), operated during the past 30 years. These networks were established to provide national and regional descriptions of stream water-quality conditions and trends, based on uniform monitoring of selected watersheds throughout the United States, and to improve our understanding of the effects of the natural environment and human activities on water quality. The HBN, consisting of 63 relatively small, minimally disturbed watersheds, provides data for investigating naturally induced changes in streamflow and water quality and the effects of airborne substances on water quality. NASQAN, consisting of 618 larger, more culturally influenced watersheds, provides information for tracking water-quality conditions in major U.S. rivers and streams.

Automatic analysis and classification of surface electromyography.

PubMed

Abou-Chadi, F E; Nashar, A; Saad, M

2001-01-01

In this paper, parametric modeling of surface electromyography (EMG) algorithms that facilitates automatic SEMG feature extraction and artificial neural networks (ANN) are combined for providing an integrated system for the automatic analysis and diagnosis of myopathic disorders. Three paradigms of ANN were investigated: the multilayer backpropagation algorithm, the self-organizing feature map algorithm and a probabilistic neural network model. The performance of the three classifiers was compared with that of the old Fisher linear discriminant (FLD) classifiers. The results have shown that the three ANN models give higher performance. The percentage of correct classification reaches 90%. Poorer diagnostic performance was obtained from the FLD classifier. The system presented here indicates that surface EMG, when properly processed, can be used to provide the physician with a diagnostic assist device.

Automatic sleep stage classification based on EEG signals by using neural networks and wavelet packet coefficients.

PubMed

Ebrahimi, Farideh; Mikaeili, Mohammad; Estrada, Edson; Nazeran, Homer

2008-01-01

Currently in the world there is an alarming number of people who suffer from sleep disorders. A number of biomedical signals, such as EEG, EMG, ECG and EOG are used in sleep labs among others for diagnosis and treatment of sleep related disorders. The usual method for sleep stage classification is visual inspection by a sleep specialist. This is a very time consuming and laborious exercise. Automatic sleep stage classification can facilitate this process. The definition of sleep stages and the sleep literature show that EEG signals are similar in Stage 1 of non-rapid eye movement (NREM) sleep and rapid eye movement (REM) sleep. Therefore, in this work an attempt was made to classify four sleep stages consisting of Awake, Stage 1 + REM, Stage 2 and Slow Wave Stage based on the EEG signal alone. Wavelet packet coefficients and artificial neural networks were deployed for this purpose. Seven all night recordings from Physionet database were used in the study. The results demonstrated that these four sleep stages could be automatically discriminated from each other with a specificity of 94.4 +/- 4.5%, a of sensitivity 84.2+3.9% and an accuracy of 93.0 +/- 4.0%.

Open College Networks and National Vocational Qualifications. A Development Paper.

ERIC Educational Resources Information Center

National Council for Vocational Qualifications, London (England).

Both the National Council for Vocational Qualifications (NCVQ) and Open College Networks or Federations (OCNs) have the objective of creating nationally coherent frameworks of qualification and training in Britain. However, they are very different organizations and have distinct, though potentially complementary, roles. Issues where the two…

Building National Capacity for Climate Change Interpretation: The Role of Leaders, Partnerships, and Networks

NASA Astrophysics Data System (ADS)

Spitzer, W.

2015-12-01

Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. NNOCCI enables teams of informal science interpreters across the country to serve as "communication strategists" - beyond merely conveying information they can influence public perceptions, given their high level of commitment, knowledge, public trust, social networks, and visitor contact. We provide in-depth training as well as an alumni network for ongoing learning, implementation support, leadership development, and coalition building. Our goals are to achieve a systemic national impact, embed our work within multiple ongoing regional and national climate change education networks, and leave an enduring legacy. Our project represents a cross-disciplinary partnership among climate scientists, social and cognitive scientists, and informal education practitioners. We have built a growing national network of more than 250 alumni, including approximately 15-20 peer leaders who co-lead both in-depth training programs and introductory workshops. We have found that this alumni network has been assuming increasing importance in providing for ongoing learning, support for implementation, leadership development, and coalition building. As we look toward the future, we are exploring potential partnerships with other existing networks, both to sustain our impact and to expand our reach. This presentation will address what we have learned in terms of network impacts, best practices, factors for success, and future directions.

Documentary of MFENET, a national computer network

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

Shuttleworth, B.O.

1977-06-01

The national Magnetic Fusion Energy Computer Network (MFENET) is a newly operational star network of geographically separated heterogeneous hosts and a communications subnetwork of PDP-11 processors. Host processors interfaced to the subnetwork currently include a CDC 7600 at the Central Computer Center (CCC) and several DECsystem-10's at User Service Centers (USC's). The network was funded by a U.S. government agency (ERDA) to provide in an economical manner the needed computational resources to magnetic confinement fusion researchers. Phase I operation of MFENET distributed the processing power of the CDC 7600 among the USC's through the provision of file transport between anymore » two hosts and remote job entry to the 7600. Extending the capabilities of Phase I, MFENET Phase II provided interactive terminal access to the CDC 7600 from the USC's. A file management system is maintained at the CCC for all network users. The history and development of MFENET are discussed, with emphasis on the protocols used to link the host computers and the USC software. Comparisons are made of MFENET versus ARPANET (Advanced Research Projects Agency Computer Network) and DECNET (Digital Distributed Network Architecture). DECNET and MFENET host-to host, host-to-CCP, and link protocols are discussed in detail. The USC--CCP interface is described briefly. 43 figures, 2 tables.« less

External quality-assurance project report for the National Atmospheric Deposition Program/National Trends Network and Mercury Deposition Network, 2009-2010

USGS Publications Warehouse

Wetherbee, Gregory A.; Martin, RoseAnn; Rhodes, Mark F.; Chesney, Tanya A.

2014-01-01

The U.S. Geological Survey operated six distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program/National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2009–2010. The field-audit program assessed the effects of onsite exposure, sample handling, and shipping on the chemistry of NTN samples; a system-blank program assessed the same effects for MDN. Two interlaboratory-comparison programs assessed the bias and variability of the chemical analysis data from the Central Analytical Laboratory (CAL) and Mercury (Hg) Analytical Laboratory (HAL). The blind-audit program was also implemented for the MDN to evaluate analytical bias in total Hg concentration data produced by the HAL. The co-located-sampler program was used to identify and quantify potential shifts in NADP data resulting from replacement of original network instrumentation with new electronic recording rain gages (E-gages) and precipitation collectors that use optical sensors. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends of chemical constituents in wet deposition across the United States. Results also suggest that retrofit of the NADP networks with the new precipitation collectors could cause –8 to +14 percent shifts in NADP annual precipitation-weighted mean concentrations and total deposition values for ammonium, nitrate, sulfate, and hydrogen ion, and larger shifts (+13 to +74 percent) for calcium, magnesium, sodium, potassium, and chloride. The prototype N-CON Systems bucket collector is more efficient in the catch of precipitation in winter than Aerochem Metrics Model 301 collector, especially for light snowfall.

pSCANNER: patient-centered Scalable National Network for Effectiveness Research

PubMed Central

Ohno-Machado, Lucila; Agha, Zia; Bell, Douglas S; Dahm, Lisa; Day, Michele E; Doctor, Jason N; Gabriel, Davera; Kahlon, Maninder K; Kim, Katherine K; Hogarth, Michael; Matheny, Michael E; Meeker, Daniella; Nebeker, Jonathan R

2014-01-01

This article describes the patient-centered Scalable National Network for Effectiveness Research (pSCANNER), which is part of the recently formed PCORnet, a national network composed of learning healthcare systems and patient-powered research networks funded by the Patient Centered Outcomes Research Institute (PCORI). It is designed to be a stakeholder-governed federated network that uses a distributed architecture to integrate data from three existing networks covering over 21 million patients in all 50 states: (1) VA Informatics and Computing Infrastructure (VINCI), with data from Veteran Health Administration's 151 inpatient and 909 ambulatory care and community-based outpatient clinics; (2) the University of California Research exchange (UC-ReX) network, with data from UC Davis, Irvine, Los Angeles, San Francisco, and San Diego; and (3) SCANNER, a consortium of UCSD, Tennessee VA, and three federally qualified health systems in the Los Angeles area supplemented with claims and health information exchange data, led by the University of Southern California. Initial use cases will focus on three conditions: (1) congestive heart failure; (2) Kawasaki disease; (3) obesity. Stakeholders, such as patients, clinicians, and health service researchers, will be engaged to prioritize research questions to be answered through the network. We will use a privacy-preserving distributed computation model with synchronous and asynchronous modes. The distributed system will be based on a common data model that allows the construction and evaluation of distributed multivariate models for a variety of statistical analyses. PMID:24780722

Automatic Phase Picker for Local and Teleseismic Events Using Wavelet Transform and Simulated Annealing

NASA Astrophysics Data System (ADS)

Gaillot, P.; Bardaine, T.; Lyon-Caen, H.

2004-12-01

Since recent years, various automatic phase pickers based on the wavelet transform have been developed. The main motivation for using wavelet transform is that they are excellent at finding the characteristics of transient signals, they have good time resolution at all periods, and they are easy to program for fast execution. Thus, the time-scale properties and flexibility of the wavelets allow detection of P and S phases in a broad frequency range making their utilization possible in various context. However, the direct application of an automatic picking program in a different context/network than the one for which it has been initially developed is quickly tedious. In fact, independently of the strategy involved in automatic picking algorithms (window average, autoregressive, beamforming, optimization filtering, neuronal network), all developed algorithms use different parameters that depend on the objective of the seismological study, the region and the seismological network. Classically, these parameters are manually defined by trial-error or calibrated learning stage. In order to facilitate this laborious process, we have developed an automated method that provide optimal parameters for the picking programs. The set of parameters can be explored using simulated annealing which is a generic name for a family of optimization algorithms based on the principle of stochastic relaxation. The optimization process amounts to systematically modifying an initial realization so as to decrease the value of the objective function, getting the realization acceptably close to the target statistics. Different formulations of the optimization problem (objective function) are discussed using (1) world seismicity data recorded by the French national seismic monitoring network (ReNass), (2) regional seismicity data recorded in the framework of the Corinth Rift Laboratory (CRL) experiment, (3) induced seismicity data from the gas field of Lacq (Western Pyrenees), and (4) micro

Automatic classification of transiently evoked otoacoustic emissions using an artificial neural network.

PubMed

Buller, G; Lutman, M E

1998-08-01

The increasing use of transiently evoked otoacoustic emissions (TEOAE) in large neonatal hearing screening programmes makes a standardized method of response classification desirable. Until now methods have been either subjective or based on arbitrary response characteristics. This study takes an expert system approach to standardize the subjective judgements of an experienced scorer. The method that is developed comprises three stages. First, it transforms TEOAEs from waveforms in the time domain into a simplified parameter set. Second, the parameter set is classified by an artificial neural network that has been taught on a large database TEOAE waveforms and corresponding expert scores. Third, additional fuzzy logic rules automatically detect probable artefacts in the waveforms and synchronized spontaneous emission components. In this way, the knowledge of the experienced scorer is encapsulated in the expert system software and thereafter can be accessed by non-experts. Teaching and evaluation of the neural network was based on TEOAEs from a database totalling 2190 neonatal hearing screening tests. The database was divided into learning and test groups with 820 and 1370 waveforms respectively. From each recorded waveform a set of 12 parameters was calculated, representing signal static and dynamic properties. The artifical network was taught with parameter sets of only the learning groups. Reproduction of the human scorer classification by the neural net in the learning group showed a sensitivity for detecting screen fails of 99.3% (299 from 301 failed results on subjective scoring) and a specificity for detecting screen passes of 81.1% (421 of 519 pass results). To quantify the post hoc performance of the net (generalization), the test group was then presented to the network input. Sensitivity was 99.4% (474 from 477) and specificity was 87.3% (780 from 893). To check the efficiency of the classification method, a second learning group was selected out of the

A Network of Automatic Control Web-Based Laboratories

ERIC Educational Resources Information Center

Vargas, Hector; Sanchez Moreno, J.; Jara, Carlos A.; Candelas, F. A.; Torres, Fernando; Dormido, Sebastian

2011-01-01

This article presents an innovative project in the context of remote experimentation applied to control engineering education. Specifically, the authors describe their experience regarding the analysis, design, development, and exploitation of web-based technologies within the scope of automatic control. This work is part of an inter-university…

34 CFR 412.1 - What is the National Network for Curriculum Coordination in Vocational and Technical Education?

Code of Federal Regulations, 2011 CFR

2011-07-01

... 34 Education 3 2011-07-01 2011-07-01 false What is the National Network for Curriculum... EDUCATION NATIONAL NETWORK FOR CURRICULUM COORDINATION IN VOCATIONAL AND TECHNICAL EDUCATION General § 412.1 What is the National Network for Curriculum Coordination in Vocational and Technical Education? The...

34 CFR 412.1 - What is the National Network for Curriculum Coordination in Vocational and Technical Education?

Code of Federal Regulations, 2010 CFR

2010-07-01

... 34 Education 3 2010-07-01 2010-07-01 false What is the National Network for Curriculum... EDUCATION NATIONAL NETWORK FOR CURRICULUM COORDINATION IN VOCATIONAL AND TECHNICAL EDUCATION General § 412.1 What is the National Network for Curriculum Coordination in Vocational and Technical Education? The...

Automatic abdominal multi-organ segmentation using deep convolutional neural network and time-implicit level sets.

PubMed

Hu, Peijun; Wu, Fa; Peng, Jialin; Bao, Yuanyuan; Chen, Feng; Kong, Dexing

2017-03-01

Multi-organ segmentation from CT images is an essential step for computer-aided diagnosis and surgery planning. However, manual delineation of the organs by radiologists is tedious, time-consuming and poorly reproducible. Therefore, we propose a fully automatic method for the segmentation of multiple organs from three-dimensional abdominal CT images. The proposed method employs deep fully convolutional neural networks (CNNs) for organ detection and segmentation, which is further refined by a time-implicit multi-phase evolution method. Firstly, a 3D CNN is trained to automatically localize and delineate the organs of interest with a probability prediction map. The learned probability map provides both subject-specific spatial priors and initialization for subsequent fine segmentation. Then, for the refinement of the multi-organ segmentation, image intensity models, probability priors as well as a disjoint region constraint are incorporated into an unified energy functional. Finally, a novel time-implicit multi-phase level-set algorithm is utilized to efficiently optimize the proposed energy functional model. Our method has been evaluated on 140 abdominal CT scans for the segmentation of four organs (liver, spleen and both kidneys). With respect to the ground truth, average Dice overlap ratios for the liver, spleen and both kidneys are 96.0, 94.2 and 95.4%, respectively, and average symmetric surface distance is less than 1.3 mm for all the segmented organs. The computation time for a CT volume is 125 s in average. The achieved accuracy compares well to state-of-the-art methods with much higher efficiency. A fully automatic method for multi-organ segmentation from abdominal CT images was developed and evaluated. The results demonstrated its potential in clinical usage with high effectiveness, robustness and efficiency.

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network.

PubMed

Cui, Shaoguo; Mao, Lei; Jiang, Jingfeng; Liu, Chang; Xiong, Shuyu

2018-01-01

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice.

Automatic Semantic Segmentation of Brain Gliomas from MRI Images Using a Deep Cascaded Neural Network

PubMed Central

Mao, Lei; Liu, Chang; Xiong, Shuyu

2018-01-01

Brain tumors can appear anywhere in the brain and have vastly different sizes and morphology. Additionally, these tumors are often diffused and poorly contrasted. Consequently, the segmentation of brain tumor and intratumor subregions using magnetic resonance imaging (MRI) data with minimal human interventions remains a challenging task. In this paper, we present a novel fully automatic segmentation method from MRI data containing in vivo brain gliomas. This approach can not only localize the entire tumor region but can also accurately segment the intratumor structure. The proposed work was based on a cascaded deep learning convolutional neural network consisting of two subnetworks: (1) a tumor localization network (TLN) and (2) an intratumor classification network (ITCN). The TLN, a fully convolutional network (FCN) in conjunction with the transfer learning technology, was used to first process MRI data. The goal of the first subnetwork was to define the tumor region from an MRI slice. Then, the ITCN was used to label the defined tumor region into multiple subregions. Particularly, ITCN exploited a convolutional neural network (CNN) with deeper architecture and smaller kernel. The proposed approach was validated on multimodal brain tumor segmentation (BRATS 2015) datasets, which contain 220 high-grade glioma (HGG) and 54 low-grade glioma (LGG) cases. Dice similarity coefficient (DSC), positive predictive value (PPV), and sensitivity were used as evaluation metrics. Our experimental results indicated that our method could obtain the promising segmentation results and had a faster segmentation speed. More specifically, the proposed method obtained comparable and overall better DSC values (0.89, 0.77, and 0.80) on the combined (HGG + LGG) testing set, as compared to other methods reported in the literature. Additionally, the proposed approach was able to complete a segmentation task at a rate of 1.54 seconds per slice. PMID:29755716

36 CFR 1260.56 - What are NARA considerations when implementing automatic declassification?

Code of Federal Regulations, 2014 CFR

2014-07-01

... 36 Parks, Forests, and Public Property 3 2014-07-01 2014-07-01 false What are NARA considerations when implementing automatic declassification? 1260.56 Section 1260.56 Parks, Forests, and Public Property NATIONAL ARCHIVES AND RECORDS ADMINISTRATION DECLASSIFICATION DECLASSIFICATION OF NATIONAL SECURITY INFORMATION Automatic Declassification §...

23 CFR 658.21 - Identification of National Network.

Code of Federal Regulations, 2012 CFR

2012-04-01

... the efficiency of the total traffic flow, such as time of day prohibitions, or lane use controls. (2....21 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION ENGINEERING AND TRAFFIC... National Network shall be signed. All signs shall conform to the Manual on Uniform Traffic Control Devices...

23 CFR 658.21 - Identification of National Network.

Code of Federal Regulations, 2013 CFR

2013-04-01

... the efficiency of the total traffic flow, such as time of day prohibitions, or lane use controls. (2....21 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION ENGINEERING AND TRAFFIC... National Network shall be signed. All signs shall conform to the Manual on Uniform Traffic Control Devices...

23 CFR 658.21 - Identification of National Network.

Code of Federal Regulations, 2014 CFR

2014-04-01

... the efficiency of the total traffic flow, such as time of day prohibitions, or lane use controls. (2....21 Highways FEDERAL HIGHWAY ADMINISTRATION, DEPARTMENT OF TRANSPORTATION ENGINEERING AND TRAFFIC... National Network shall be signed. All signs shall conform to the Manual on Uniform Traffic Control Devices...

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms.

PubMed

Weber, Griffin M; Barnett, William; Conlon, Mike; Eichmann, David; Kibbe, Warren; Falk-Krzesinski, Holly; Halaas, Michael; Johnson, Layne; Meeks, Eric; Mitchell, Donald; Schleyer, Titus; Stallings, Sarah; Warden, Michael; Kahlon, Maninder

2011-12-01

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users.

Direct2Experts: a pilot national network to demonstrate interoperability among research-networking platforms

PubMed Central

Barnett, William; Conlon, Mike; Eichmann, David; Kibbe, Warren; Falk-Krzesinski, Holly; Halaas, Michael; Johnson, Layne; Meeks, Eric; Mitchell, Donald; Schleyer, Titus; Stallings, Sarah; Warden, Michael; Kahlon, Maninder

2011-01-01

Research-networking tools use data-mining and social networking to enable expertise discovery, matchmaking and collaboration, which are important facets of team science and translational research. Several commercial and academic platforms have been built, and many institutions have deployed these products to help their investigators find local collaborators. Recent studies, though, have shown the growing importance of multiuniversity teams in science. Unfortunately, the lack of a standard data-exchange model and resistance of universities to share information about their faculty have presented barriers to forming an institutionally supported national network. This case report describes an initiative, which, in only 6 months, achieved interoperability among seven major research-networking products at 28 universities by taking an approach that focused on addressing institutional concerns and encouraging their participation. With this necessary groundwork in place, the second phase of this effort can begin, which will expand the network's functionality and focus on the end users. PMID:22037890

Automatic diagnosis of imbalanced ophthalmic images using a cost-sensitive deep convolutional neural network.

PubMed

Jiang, Jiewei; Liu, Xiyang; Zhang, Kai; Long, Erping; Wang, Liming; Li, Wangting; Liu, Lin; Wang, Shuai; Zhu, Mingmin; Cui, Jiangtao; Liu, Zhenzhen; Lin, Zhuoling; Li, Xiaoyan; Chen, Jingjing; Cao, Qianzhong; Li, Jing; Wu, Xiaohang; Wang, Dongni; Wang, Jinghui; Lin, Haotian

2017-11-21

Ocular images play an essential role in ophthalmological diagnoses. Having an imbalanced dataset is an inevitable issue in automated ocular diseases diagnosis; the scarcity of positive samples always tends to result in the misdiagnosis of severe patients during the classification task. Exploring an effective computer-aided diagnostic method to deal with imbalanced ophthalmological dataset is crucial. In this paper, we develop an effective cost-sensitive deep residual convolutional neural network (CS-ResCNN) classifier to diagnose ophthalmic diseases using retro-illumination images. First, the regions of interest (crystalline lens) are automatically identified via twice-applied Canny detection and Hough transformation. Then, the localized zones are fed into the CS-ResCNN to extract high-level features for subsequent use in automatic diagnosis. Second, the impacts of cost factors on the CS-ResCNN are further analyzed using a grid-search procedure to verify that our proposed system is robust and efficient. Qualitative analyses and quantitative experimental results demonstrate that our proposed method outperforms other conventional approaches and offers exceptional mean accuracy (92.24%), specificity (93.19%), sensitivity (89.66%) and AUC (97.11%) results. Moreover, the sensitivity of the CS-ResCNN is enhanced by over 13.6% compared to the native CNN method. Our study provides a practical strategy for addressing imbalanced ophthalmological datasets and has the potential to be applied to other medical images. The developed and deployed CS-ResCNN could serve as computer-aided diagnosis software for ophthalmologists in clinical application.

Automatic detection of kidney in 3D pediatric ultrasound images using deep neural networks

NASA Astrophysics Data System (ADS)

Tabrizi, Pooneh R.; Mansoor, Awais; Biggs, Elijah; Jago, James; Linguraru, Marius George

2018-02-01

Ultrasound (US) imaging is the routine and safe diagnostic modality for detecting pediatric urology problems, such as hydronephrosis in the kidney. Hydronephrosis is the swelling of one or both kidneys because of the build-up of urine. Early detection of hydronephrosis can lead to a substantial improvement in kidney health outcomes. Generally, US imaging is a challenging modality for the evaluation of pediatric kidneys with different shape, size, and texture characteristics. The aim of this study is to present an automatic detection method to help kidney analysis in pediatric 3DUS images. The method localizes the kidney based on its minimum volume oriented bounding box) using deep neural networks. Separate deep neural networks are trained to estimate the kidney position, orientation, and scale, making the method computationally efficient by avoiding full parameter training. The performance of the method was evaluated using a dataset of 45 kidneys (18 normal and 27 diseased kidneys diagnosed with hydronephrosis) through the leave-one-out cross validation method. Quantitative results show the proposed detection method could extract the kidney position, orientation, and scale ratio with root mean square values of 1.3 +/- 0.9 mm, 6.34 +/- 4.32 degrees, and 1.73 +/- 0.04, respectively. This method could be helpful in automating kidney segmentation for routine clinical evaluation.

Support vector machine for automatic pain recognition

NASA Astrophysics Data System (ADS)

Monwar, Md Maruf; Rezaei, Siamak

2009-02-01

Facial expressions are a key index of emotion and the interpretation of such expressions of emotion is critical to everyday social functioning. In this paper, we present an efficient video analysis technique for recognition of a specific expression, pain, from human faces. We employ an automatic face detector which detects face from the stored video frame using skin color modeling technique. For pain recognition, location and shape features of the detected faces are computed. These features are then used as inputs to a support vector machine (SVM) for classification. We compare the results with neural network based and eigenimage based automatic pain recognition systems. The experiment results indicate that using support vector machine as classifier can certainly improve the performance of automatic pain recognition system.

SpecialNet. A National Computer-Based Communications Network.

ERIC Educational Resources Information Center

Morin, Alfred J.

1986-01-01

"SpecialNet," a computer-based communications network for educators at all administrative levels, has been established and is managed by National Systems Management, Inc. Users can send and receive electronic mail, share information on electronic bulletin boards, participate in electronic conferences, and send reports and other documents to each…

12 CFR 7.1018 - Automatic payment plan account.

Code of Federal Regulations, 2011 CFR

2011-01-01

... 12 Banks and Banking 1 2011-01-01 2011-01-01 false Automatic payment plan account. 7.1018 Section 7.1018 Banks and Banking COMPTROLLER OF THE CURRENCY, DEPARTMENT OF THE TREASURY BANK ACTIVITIES AND OPERATIONS Bank Powers § 7.1018 Automatic payment plan account. A national bank may, for the benefit and...

Wireless Mid-Infrared Spectroscopy Sensor Network for Automatic Carbon Dioxide Fertilization in a Greenhouse Environment

PubMed Central

Wang, Jianing; Niu, Xintao; Zheng, Lingjiao; Zheng, Chuantao; Wang, Yiding

2016-01-01

In this paper, a wireless mid-infrared spectroscopy sensor network was designed and implemented for carbon dioxide fertilization in a greenhouse environment. A mid-infrared carbon dioxide (CO2) sensor based on non-dispersive infrared (NDIR) with the functionalities of wireless communication and anti-condensation prevention was realized as the sensor node. Smart transmission power regulation was applied in the wireless sensor network, according to the Received Signal Strength Indication (RSSI), to realize high communication stability and low-power consumption deployment. Besides real-time monitoring, this system also provides a CO2 control facility for manual and automatic control through a LabVIEW platform. According to simulations and field tests, the implemented sensor node has a satisfying anti-condensation ability and reliable measurement performance on CO2 concentrations ranging from 30 ppm to 5000 ppm. As an application, based on the Fuzzy proportional, integral, and derivative (PID) algorithm realized on a LabVIEW platform, the CO2 concentration was regulated to some desired concentrations, such as 800 ppm and 1200 ppm, in 30 min with a controlled fluctuation of <±35 ppm in an acre of greenhouse. PMID:27869725

Automatic Car Identification - an Evaluation

DOT National Transportation Integrated Search

1972-03-01

In response to a Federal Railroad Administration request, the Transportation Systems Center evaluated the Automatic Car Identification System (ACI) used on the nation's railroads. The ACI scanner was found to be adequate for reliable data output whil...

Fully automatic cervical vertebrae segmentation framework for X-ray images.

PubMed

Al Arif, S M Masudur Rahman; Knapp, Karen; Slabaugh, Greg

2018-04-01

The cervical spine is a highly flexible anatomy and therefore vulnerable to injuries. Unfortunately, a large number of injuries in lateral cervical X-ray images remain undiagnosed due to human errors. Computer-aided injury detection has the potential to reduce the risk of misdiagnosis. Towards building an automatic injury detection system, in this paper, we propose a deep learning-based fully automatic framework for segmentation of cervical vertebrae in X-ray images. The framework first localizes the spinal region in the image using a deep fully convolutional neural network. Then vertebra centers are localized using a novel deep probabilistic spatial regression network. Finally, a novel shape-aware deep segmentation network is used to segment the vertebrae in the image. The framework can take an X-ray image and produce a vertebrae segmentation result without any manual intervention. Each block of the fully automatic framework has been trained on a set of 124 X-ray images and tested on another 172 images, all collected from real-life hospital emergency rooms. A Dice similarity coefficient of 0.84 and a shape error of 1.69 mm have been achieved. Copyright © 2018 Elsevier B.V. All rights reserved.

Deep residual networks for automatic segmentation of laparoscopic videos of the liver

NASA Astrophysics Data System (ADS)

Gibson, Eli; Robu, Maria R.; Thompson, Stephen; Edwards, P. Eddie; Schneider, Crispin; Gurusamy, Kurinchi; Davidson, Brian; Hawkes, David J.; Barratt, Dean C.; Clarkson, Matthew J.

2017-03-01

Motivation: For primary and metastatic liver cancer patients undergoing liver resection, a laparoscopic approach can reduce recovery times and morbidity while offering equivalent curative results; however, only about 10% of tumours reside in anatomical locations that are currently accessible for laparoscopic resection. Augmenting laparoscopic video with registered vascular anatomical models from pre-procedure imaging could support using laparoscopy in a wider population. Segmentation of liver tissue on laparoscopic video supports the robust registration of anatomical liver models by filtering out false anatomical correspondences between pre-procedure and intra-procedure images. In this paper, we present a convolutional neural network (CNN) approach to liver segmentation in laparoscopic liver procedure videos. Method: We defined a CNN architecture comprising fully-convolutional deep residual networks with multi-resolution loss functions. The CNN was trained in a leave-one-patient-out cross-validation on 2050 video frames from 6 liver resections and 7 laparoscopic staging procedures, and evaluated using the Dice score. Results: The CNN yielded segmentations with Dice scores >=0.95 for the majority of images; however, the inter-patient variability in median Dice score was substantial. Four failure modes were identified from low scoring segmentations: minimal visible liver tissue, inter-patient variability in liver appearance, automatic exposure correction, and pathological liver tissue that mimics non-liver tissue appearance. Conclusion: CNNs offer a feasible approach for accurately segmenting liver from other anatomy on laparoscopic video, but additional data or computational advances are necessary to address challenges due to the high inter-patient variability in liver appearance.

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network

PubMed Central

Dietz, Hans Peter; D’hooge, Jan; Barratt, Dean; Deprest, Jan

2018-01-01

Abstract. Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled by three operators, is used for training and evaluation in a leave-one-patient-out cross validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams’ index of 1.03), and outperforming a U-Net architecture without the need for batch normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach. PMID:29340289

Automatic segmentation method of pelvic floor levator hiatus in ultrasound using a self-normalizing neural network.

PubMed

Bonmati, Ester; Hu, Yipeng; Sindhwani, Nikhil; Dietz, Hans Peter; D'hooge, Jan; Barratt, Dean; Deprest, Jan; Vercauteren, Tom

2018-04-01

Segmentation of the levator hiatus in ultrasound allows the extraction of biometrics, which are of importance for pelvic floor disorder assessment. We present a fully automatic method using a convolutional neural network (CNN) to outline the levator hiatus in a two-dimensional image extracted from a three-dimensional ultrasound volume. In particular, our method uses a recently developed scaled exponential linear unit (SELU) as a nonlinear self-normalizing activation function, which for the first time has been applied in medical imaging with CNN. SELU has important advantages such as being parameter-free and mini-batch independent, which may help to overcome memory constraints during training. A dataset with 91 images from 35 patients during Valsalva, contraction, and rest, all labeled by three operators, is used for training and evaluation in a leave-one-patient-out cross validation. Results show a median Dice similarity coefficient of 0.90 with an interquartile range of 0.08, with equivalent performance to the three operators (with a Williams' index of 1.03), and outperforming a U-Net architecture without the need for batch normalization. We conclude that the proposed fully automatic method achieved equivalent accuracy in segmenting the pelvic floor levator hiatus compared to a previous semiautomatic approach.

Mexican national pyronometer network calibration

NASA Astrophysics Data System (ADS)

VAldes, M.; Villarreal, L.; Estevez, H.; Riveros, D.

2013-12-01

In order to take advantage of the solar radiation as an alternate energy source it is necessary to evaluate the spatial and temporal availability. The Mexican National Meterological Service (SMN) has a network with 136 meteorological stations, each coupled with a pyronometer for measuring the global solar radiation. Some of these stations had not been calibrated in several years. The Mexican Department of Energy (SENER) in order to count on a reliable evaluation of the solar resource funded this project to calibrate the SMN pyrometer network and validate the data. The calibration of the 136 pyronometers by the intercomparison method recommended by the World Meterological Organization (WMO) requires lengthy observations and specific environmental conditions such as clear skies and a stable atmosphere, circumstances that determine the site and season of the calibration. The Solar Radiation Section of the Instituto de Geofísica of the Universidad Nacional Autónoma de México is a Regional Center of the WMO and is certified to carry out the calibration procedures and emit certificates. We are responsible for the recalibration of the pyronometer network of the SMN. A continuous emission solar simulator with exposed areas with 30cm diameters was acquired to reduce the calibration time and not depend on atmospheric conditions. We present the results of the calibration of 10 thermopile pyronometers and one photovoltaic cell by the intercomparison method with more than 10000 observations each and those obtained with the solar simulator.

Preliminary Design Study for a National Digital Seismograph Network

USGS Publications Warehouse

Peterson, Jon; Hutt, Charles R.

1981-01-01

Introduction Recently, the National Research Council published a report by the Panel on National, Regional, and Local Seismograph Networks of the Committee on Seismology in which the principal recommendation was for the establishment of a national digital seismograph network (NDSN). The Panel Report (Bolt, 1980) addresses both the need and the scientific requirements for the new national network. The purpose of this study has been to translate the scientific requirements into an instrumentation concept for the NSDS. There are literally hundreds, perhaps thousands, of seismographs in operation within the United States. Each serves an important purpose, but most have limited objectives in time, in region, or in the types of data that are being recorded. The concept of a national network, funded and operated by the Federal Government, is based on broader objectives that include continuity of time, uniform coverage, standardization of data format and instruments, and widespread use of the data for a variety of research purposes. A national digital seismograph network will be an important data resource for many years to come; hence, its design is likely to be of interest to most seismologists. Seismologists have traditionally been involved in the development and field operation of seismic systems and thus have been familiar with both the potential value and the limitations of the data. However, in recent years of increasing technological sophistication, the development of data sstems has fallen more to system engineers, and this trend is likely to continue. One danger in this is that the engineers may misinterpret scientific objectives or subordinate them to purely technological considerations. Another risk is that the data users may misuse or misinterpret the data because they are not aware of the limitations of the data system. Perhaps the most important purpose of a design study such as this is to stimulate a dialogue between system engineers and potential data users

A national neurological excellence centers network.

PubMed

Pazzi, S; Cristiani, P; Cavallini, A

1998-02-01

The most relevant problems related to the management of neurological disorders are (i) the frequent hospitalization in nonspecialist departments, with the need for neurological consultation, and (ii) the frequent requests of GPs for highly specialized investigations that are very expensive and of little value in arriving at a correct diagnosis. In 1996, the Consorzio di Bioingegneria e Informatica Medica in Italy realized the CISNet project (in collaboration with the Consorzio Istituti Scientifici Neuroscienze e Tecnologie Biomediche and funded by the Centro Studi of the National Public Health Council) for the implementation of a national neurological excellence centers network (CISNet). In the CISNet project, neurologists will be able to give on-line interactive consultation and off-line consulting services identifying correct diagnostic/therapeutic procedures, evaluating the need for both examination in specialist centers and admission to specialized centers, and identifying the most appropriate ones.

A statistical summary of data from the U.S. Geological Survey's national water quality networks

USGS Publications Warehouse

Smith, R.A.; Alexander, R.B.

1983-01-01

The U.S. Geological Survey Operates two nationwide networks to monitor water quality, the National Hydrologic Bench-Mark Network and the National Stream Quality Accounting Network (NASQAN). The Bench-Mark network is composed of 51 stations in small drainage basins which are as close as possible to their natural state, with no human influence and little likelihood of future development. Stations in the NASQAN program are located to monitor flow from accounting units (subregional drainage basins) which collectively encompass the entire land surface of the nation. Data collected at both networks include streamflow, concentrations of major inorganic constituents, nutrients, and trace metals. The goals of the two water quality sampling programs include the determination of mean constituent concentrations and transport rates as well as the analysis of long-term trends in those variables. This report presents a station-by-station statistical summary of data from the two networks for the period 1974 through 1981. (Author 's abstract)

Developing A National Groundwater-Monitoring Network In Korea

NASA Astrophysics Data System (ADS)

Kim, N. J.; Cho, M. J.; Woo, N. C.

1995-04-01

Since the 1960's, the groundwater resources of Korea have been developed without a proper regulatory system for monitoring and preservation, resulting in significant source depletion, land subsidence, water contamination, and sea-water intrusion. With the activation of the "Groundwater Law" in June 1994, the government initiated a project to develop a groundwater-monitoring network to describe general groundwater quality, to define its long-term changes, and to identify major factors affecting changes in groundwater quality and yield. In selecting monitoring locations nationwide, criteria considered are 1) spatial distribution, 2) aquifer characteristics of hydrogeologic units, 3) local groundwater flow regime, 4) linkage with surface hydrology observations, 5) site accessibility, and 6) financial situations. A total of 310 sites in 78 small hydrologic basins were selected to compose the monitoring network. Installation of monitoring wells is scheduled to start in 1995 for 15 sites; the remainder are scheduled to be completed by 2001. At each site, a nest of monitoring wells was designed; shallow and deep groundwater will be monitored for water temperature, pH, EC, DO and TDS every month. Water-level fluctuations will also be measured by automatic recorders equipped with pressure transducers. As a next step, the government plans to develop a groundwater-database management system, which could be linked with surface hydrologic data.

A novel network module for medical devices.

PubMed

Chen, Ping-Yu

2008-01-01

In order to allow medical devices to upload the vital signs to a server on a network without manually configuring for end-users, a new network module is proposed. The proposed network module, called Medical Hub (MH), functions as a bridge to fetch the data from all connecting medical devices, and then upload these data to the server. When powering on, the MH can immediately establish network configuration automatically. Network Address Translation (NAT) traversal is also supported by the MH with the UPnP Internet Gateway Device (IGD) methodology. Besides the network configuration, other configuration in the MH is automatically established by using the remote management protocol TR-069. On the other hand, a mechanism for updating software automatically according to the variant connected medical device is proposed. With this mechanism, newcome medical devices can be detected and supported by the MH without manual operation.

Renewal of K-NET (National Strong-motion Observation Network of Japan)

NASA Astrophysics Data System (ADS)

Kunugi, T.; Fujiwara, H.; Aoi, S.; Adachi, S.

2004-12-01

The National Research Institute for Earth Science and Disaster Prevention (NIED) operates K-NET (Kyoshin Network), the national strong-motion observation network, which evenly covers the whole of Japan at intervals of 25 km on average. K-NET was constructed after the Hyogoken-Nambu (Kobe) earthquake in January 1995, and began operation in June 1996. Thus, eight years have passed since K-NET started, and large amounts of strong-motion records have been obtained. As technology has progressed and new technologies have become available, NIED has developed a new K-NET with improved functionality. New seismographs have been installed at 443 observatories mainly in southwestern Japan where there is a risk of strong-motion due to the Nankai and Tonankai earthquakes. The new system went into operation in June 2004, although seismographs have still to be replaced in other areas. The new seismograph (K-NET02) consists of a sensor module, a measurement module and a communication module. A UPS, a GPS antenna and a dial-up router are also installed together with a K-NET02. A triaxial accelerometer, FBA-ES-DECK (Kinemetrics Inc.) is built into the sensor module. The measurement module functions as a conventional strong-motion seismograph for high-precision observation. The communication module can perform sophisticated processes, such as calculation of the Japan Meteorological Agency (JMA) seismic intensity, continuous recording of data and near real-time data transmission. It connects to the Data Management Center (DMC) using an ISDN line. In case of a power failure, the measurement module can control the power supply to the router and the communication module to conserve battery power. One of the main features of K-NET02 is a function for processing JMA seismic intensity. K-NET02 functions as a proper seismic intensity meter that complies with the official requirements of JMA, although the old strong-motion seismograph (K-NET95) does not calculate seismic intensity. Another

Earthquake Monitoring: SeisComp3 at the Swiss National Seismic Network

NASA Astrophysics Data System (ADS)

Clinton, J. F.; Diehl, T.; Cauzzi, C.; Kaestli, P.

2011-12-01

The Swiss Seismological Service (SED) has an ongoing responsibility to improve the seismicity monitoring capability for Switzerland. This is a crucial issue for a country with low background seismicity but where a large M6+ earthquake is expected in the next decades. With over 30 stations with spacing of ~25km, the SED operates one of the densest broadband networks in the world, which is complimented by ~ 50 realtime strong motion stations. The strong motion network is expected to grow with an additional ~80 stations over the next few years. Furthermore, the backbone of the network is complemented by broadband data from surrounding countries and temporary sub-networks for local monitoring of microseismicity (e.g. at geothermal sites). The variety of seismic monitoring responsibilities as well as the anticipated densifications of our network demands highly flexible processing software. We are transitioning all software to the SeisComP3 (SC3) framework. SC3 is a fully featured automated real-time earthquake monitoring software developed by GeoForschungZentrum Potsdam in collaboration with commercial partner, gempa GmbH. It is in its core open source, and becoming a community standard software for earthquake detection and waveform processing for regional and global networks across the globe. SC3 was originally developed for regional and global rapid monitoring of potentially tsunamagenic earthquakes. In order to fulfill the requirements of a local network recording moderate seismicity, SED has tuned configurations and added several modules. In this contribution, we present our SC3 implementation strategy, focusing on the detection and identification of seismicity on different scales. We operate several parallel processing "pipelines" to detect and locate local, regional and global seismicity. Additional pipelines with lower detection thresholds can be defined to monitor seismicity within dense subnets of the network. To be consistent with existing processing

Primary Strategy Learning Networks: A Local Study of a National Initiative

ERIC Educational Resources Information Center

Moore, Tessa A.; Rutherford, Desmond

2012-01-01

The use of networks as a means of communicating knowledge and ideas and in promoting innovation among schools has emerged globally over the past decade. Currently, inter-school collaboration is not only at the fore nationally in England, but also has become integral to the school improvement agenda. However, networking theory is a disparate field…

NATIONAL CROP LOSS ASSESSMENT NETWORK (NCLAN) 1982 ANNUAL REPORT

EPA Science Inventory

The National Crop Loss Assessment Network (NCLAN) is a group of organizations cooperating in research to assess the short- and long-term economic impact of air pollution on crop production. The primary objectives are (1) to define relationships between yield of major agricultural...

Low-altitude photographic transects of the Arctic network of national park units and Selawik National Wildlife Refuge, Alaska, July 2013

Treesearch

Bruce G. Marcot; M. Torre Jorgenson; Anthony R. DeGange

2014-01-01

During July 16–18, 2013, low-level photography flights were conducted (with a Cessna 185 with floats and a Cessna 206 with tundra tires) over the five administrative units of the National Park Service Arctic Network (Bering Land Bridge National Preserve, Cape Krusenstern National Monument, Gates of the Arctic National Park and Preserve, Kobuk Valley National Park, and...

Prototyping sensor network system for automatic vital signs collection. Evaluation of a location based automated assignment of measured vital signs to patients.

PubMed

Kuroda, T; Noma, H; Naito, C; Tada, M; Yamanaka, H; Takemura, T; Nin, K; Yoshihara, H

2013-01-01

Development of a clinical sensor network system that automatically collects vital sign and its supplemental data, and evaluation the effect of automatic vital sensor value assignment to patients based on locations of sensors. The sensor network estimates the data-source, a target patient, from the position of a vital sign sensor obtained from a newly developed proximity sensing system. The proximity sensing system estimates the positions of the devices using a Bluetooth inquiry process. Using Bluetooth access points and the positioning system newly developed in this project, the sensor network collects vital sign and its 4W (who, where, what, and when) supplemental data from any Bluetooth ready vital sign sensors such as Continua-ready devices. The prototype was evaluated in a pseudo clinical setting at Kyoto University Hospital using a cyclic paired comparison and statistical analysis. The result of the cyclic paired analysis shows the subjects evaluated the proposed system is more effective and safer than POCS as well as paper-based operation. It halves the times for vital signs input and eliminates input errors. On the other hand, the prototype failed in its position estimation for 12.6% of all attempts, and the nurses overlooked half of the errors. A detailed investigation clears that an advanced interface to show the system's "confidence", i.e. the probability of estimation error, must be effective to reduce the oversights. This paper proposed a clinical sensor network system that relieves nurses from vital signs input tasks. The result clearly shows that the proposed system increases the efficiency and safety of the nursing process both subjectively and objectively. It is a step toward new generation of point of nursing care systems where sensors take over the tasks of data input from the nurses.

HOLA: Human-like Orthogonal Network Layout.

PubMed

Kieffer, Steve; Dwyer, Tim; Marriott, Kim; Wybrow, Michael

2016-01-01

Over the last 50 years a wide variety of automatic network layout algorithms have been developed. Some are fast heuristic techniques suitable for networks with hundreds of thousands of nodes while others are multi-stage frameworks for higher-quality layout of smaller networks. However, despite decades of research currently no algorithm produces layout of comparable quality to that of a human. We give a new "human-centred" methodology for automatic network layout algorithm design that is intended to overcome this deficiency. User studies are first used to identify the aesthetic criteria algorithms should encode, then an algorithm is developed that is informed by these criteria and finally, a follow-up study evaluates the algorithm output. We have used this new methodology to develop an automatic orthogonal network layout method, HOLA, that achieves measurably better (by user study) layout than the best available orthogonal layout algorithm and which produces layouts of comparable quality to those produced by hand.

An automatic microseismic or acoustic emission arrival identification scheme with deep recurrent neural networks

NASA Astrophysics Data System (ADS)

Zheng, Jing; Lu, Jiren; Peng, Suping; Jiang, Tianqi

2018-02-01

The conventional arrival pick-up algorithms cannot avoid the manual modification of the parameters for the simultaneous identification of multiple events under different signal-to-noise ratios (SNRs). Therefore, in order to automatically obtain the arrivals of multiple events with high precision under different SNRs, in this study an algorithm was proposed which had the ability to pick up the arrival of microseismic or acoustic emission events based on deep recurrent neural networks. The arrival identification was performed using two important steps, which included a training phase and a testing phase. The training process was mathematically modelled by deep recurrent neural networks using Long Short-Term Memory architecture. During the testing phase, the learned weights were utilized to identify the arrivals through the microseismic/acoustic emission data sets. The data sets were obtained by rock physics experiments of the acoustic emission. In order to obtain the data sets under different SNRs, this study added random noise to the raw experiments' data sets. The results showed that the outcome of the proposed method was able to attain an above 80 per cent hit-rate at SNR 0 dB, and an approximately 70 per cent hit-rate at SNR -5 dB, with an absolute error in 10 sampling points. These results indicated that the proposed method had high selection precision and robustness.

Landbird Monitoring Protocol for National Parks in the North Coast and Cascades Network

USGS Publications Warehouse

Siegel, Rodney B.; Wilkerson, Robert L.; Jenkins, Kurt J.; Kuntz, Robert C.; Boetsch, John R.; Schaberl, James P.; Happe, Patricia J.

2007-01-01

This protocol narrative outlines the rationale, sampling design and methods for monitoring landbirds in the North Coast and Cascades Network (NCCN) during the breeding season. The NCCN, one of 32 networks of parks in the National Park System, comprises seven national park units in the Pacific Northwest, including three large, mountainous, natural area parks (Mount Rainier [MORA] and Olympic [OLYM] National Parks, North Cascades National Park Service Complex [NOCA]), and four small historic cultural parks (Ebey's Landing National Historical Reserve [EBLA], Lewis and Clark National Historical Park [LEWI], Fort Vancouver National Historical Park [FOVA], and San Juan Island National Historical Park [SAJH]). The protocol reflects decisions made by the NCCN avian monitoring group, which includes NPS representatives from each of the large parks in the Network as well as personnel from the U.S. Geological Survey Forest and Rangeland Ecosystem Science Center (USGS-FRESC) Olympic Field Station, and The Institute for Bird Populations, at meetings held between 2000 (Siegel and Kuntz, 2000) and 2005. The protocol narrative describes the monitoring program in relatively broad terms, and its structure and content adhere to the outline and recommendations developed by Oakley and others (2003) and adopted by NPS. Finer details of the methodology are addressed in a set of standard operating procedures (SOPs) that accompany the protocol narrative. We also provide appendixes containing additional supporting materials that do not clearly belong in either the protocol narrative or the standard operating procedures.

Cart'Eaux: an automatic mapping procedure for wastewater networks using machine learning and data mining

NASA Astrophysics Data System (ADS)

Bailly, J. S.; Delenne, C.; Chahinian, N.; Bringay, S.; Commandré, B.; Chaumont, M.; Derras, M.; Deruelle, L.; Roche, M.; Rodriguez, F.; Subsol, G.; Teisseire, M.

2017-12-01

In France, local government institutions must establish a detailed description of wastewater networks. The information should be available, but it remains fragmented (different formats held by different stakeholders) and incomplete. In the "Cart'Eaux" project, a multidisciplinary team, including an industrial partner, develops a global methodology using Machine Learning and Data Mining approaches applied to various types of large data to recover information in the aim of mapping urban sewage systems for hydraulic modelling. Deep-learning is first applied using a Convolution Neural Network to localize manhole covers on 5 cm resolution aerial RGB images. The detected manhole covers are then automatically connected using a tree-shaped graph constrained by industry rules. Based on a Delaunay triangulation, connections are chosen to minimize a cost function depending on pipe length, slope and possible intersection with roads or buildings. A stochastic version of this algorithm is currently being developed to account for positional uncertainty and detection errors, and generate sets of probable networks. As more information is required for hydraulic modeling (slopes, diameters, materials, etc.), text data mining is used to extract network characteristics from data posted on the Web or available through governmental or specific databases. Using an appropriate list of keywords, the web is scoured for documents which are saved in text format. The thematic entities are identified and linked to the surrounding spatial and temporal entities. The methodology is developed and tested on two towns in southern France. The primary results are encouraging: 54% of manhole covers are detected with few false detections, enabling the reconstruction of probable networks. The data mining results are still being investigated. It is clear at this stage that getting numerical values on specific pipes will be challenging. Thus, when no information is found, decision rules will be used to

Automatic learning rate adjustment for self-supervising autonomous robot control

NASA Technical Reports Server (NTRS)

Arras, Michael K.; Protzel, Peter W.; Palumbo, Daniel L.

1992-01-01

Described is an application in which an Artificial Neural Network (ANN) controls the positioning of a robot arm with five degrees of freedom by using visual feedback provided by two cameras. This application and the specific ANN model, local liner maps, are based on the work of Ritter, Martinetz, and Schulten. We extended their approach by generating a filtered, average positioning error from the continuous camera feedback and by coupling the learning rate to this error. When the network learns to position the arm, the positioning error decreases and so does the learning rate until the system stabilizes at a minimum error and learning rate. This abolishes the need for a predetermined cooling schedule. The automatic cooling procedure results in a closed loop control with no distinction between a learning phase and a production phase. If the positioning error suddenly starts to increase due to an internal failure such as a broken joint, or an environmental change such as a camera moving, the learning rate increases accordingly. Thus, learning is automatically activated and the network adapts to the new condition after which the error decreases again and learning is 'shut off'. The automatic cooling is therefore a prerequisite for the autonomy and the fault tolerance of the system.

Automatically Scoring Short Essays for Content. CRESST Report 836

ERIC Educational Resources Information Center

Kerr, Deirdre; Mousavi, Hamid; Iseli, Markus R.

2013-01-01

The Common Core assessments emphasize short essay constructed response items over multiple choice items because they are more precise measures of understanding. However, such items are too costly and time consuming to be used in national assessments unless a way is found to score them automatically. Current automatic essay scoring techniques are…

Wireless sensor network-based greenhouse environment monitoring and automatic control system for dew condensation prevention.

PubMed

Park, Dae-Heon; Park, Jang-Woo

2011-01-01

Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop's surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control.

78 FR 10249 - Establishment of the National Freight Network

Federal Register 2010, 2011, 2012, 2013, 2014

2013-02-13

... DEPARTMENT OF TRANSPORTATION Federal Highway Administration Establishment of the National Freight Network Correction In notice document 2013-02580 appearing on pages 8686-8689, in the issue of Wednesday, February 6, 2013, make the following correction: In the Table appearing on page 8687, in the third column...

Toward implementation of a national ground water monitoring network

USGS Publications Warehouse

Schreiber, Robert P.; Cunningham, William L.; Copeland, Rick; Frederick, Kevin D.

2008-01-01

The Federal Advisory Committee on Water Information's (ACWI) Subcommittee on Ground Water (SOGW) has been working steadily to develop and encourage implementation of a nationwide, long-term ground-water quantity and quality monitoring framework. Significant progress includes the planned submission this fall of a draft framework document to the full committee. The document will include recommendations for implementation of the network and continued acknowledgment at the federal and state level of ACWI's potential role in national monitoring toward an improved assessment of the nation's water reserves. The SOGW mission includes addressing several issues regarding network design, as well as developing plans for concept testing, evaluation of costs and benefits, and encouraging the movement from pilot-test results to full-scale implementation within a reasonable time period. With the recent attention to water resource sustainability driven by severe droughts, concerns over global warming effects, and persistent water supply problems, the SOGW mission is now even more critical.

Building Capacity: The National Network for Ocean and Climate Change Interpretation

NASA Astrophysics Data System (ADS)

Spitzer, W.

2014-12-01

In the US, more than 1,500 informal science venues (science centers, museums, aquariums, zoos, nature centers, national parks) are visited annually by 61% of the population. Research shows that these visitors are receptive to learning about climate change, and expect these institutions to provide reliable information about environmental issues and solutions. These informal science venues play a critical role in shaping public understanding. Since 2007, the New England Aquarium has led a national effort to increase the capacity of informal science venues to effectively communicate about climate change. We are now leading the NSF-funded National Network for Ocean and Climate Change Interpretation (NNOCCI), partnering with the Association of Zoos and Aquariums, FrameWorks Institute, Woods Hole Oceanographic Institution, Monterey Bay Aquarium, and National Aquarium, with evaluation conducted by the New Knowledge Organization, Pennsylvania State University, and Ohio State University. After two years of project implementation, key findings include: 1. Importance of adaptive management - We continue to make ongoing changes in training format, content, and roles of facilitators and participants. 2. Impacts on interpreters - We have multiple lines of evidence for changes in knowledge, skills, attitudes, and behaviors. 3. Social radiation - Trained interpreters have a significant influence on their friends, family and colleagues. 4. Visitor impacts - "Exposure to "strategically framed" interpretation does change visitors' perceptions about climate change. 5. Community of practice - We are seeing evidence of growing participation, leadership, and sustainability. 6. Diffusion of innovation - Peer networks are facilitating dissemination throughout the informal science education community. Over the next five years, NNOCCI will achieve a systemic national impact across the ISE community, embed its work within multiple ongoing regional and national climate change education

Using stochastic activity networks to study the energy feasibility of automatic weather stations

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

Cassano, Luca; Cesarini, Daniel; Avvenuti, Marco

Automatic Weather Stations (AWSs) are systems equipped with a number of environmental sensors and communication interfaces used to monitor harsh environments, such as glaciers and deserts. Designing such systems is challenging, since designers have to maximize the amount of sampled and transmitted data while considering the energy needs of the system that, in most cases, is powered by rechargeable batteries and exploits energy harvesting, e.g., solar cells and wind turbines. To support designers of AWSs in the definition of the software tasks and of the hardware configuration of the AWS we designed and implemented an energy-aware simulator of such systems.more » The simulator relies on the Stochastic Activity Networks (SANs) formalism and has been developed using the Möbius tool. In this paper we first show how we used the SAN formalism to model the various components of an AWS, we then report results from an experiment carried out to validate the simulator against a real-world AWS and we finally show some examples of usage of the proposed simulator.« less

Automatic picker of P & S first arrivals and robust event locator

NASA Astrophysics Data System (ADS)

Pinsky, V.; Polozov, A.; Hofstetter, A.

2003-12-01

of the bell-shaped characteristic functions, used to emphasize true pickings and eliminate outliers. The final solution is a grid point that provides maximum to the target function. The procedure was applied to a list of ML > 4 earthquakes recorded by the Israel Seismic Network (ISN) in the 1999-2002 time period. Most of them are badly constrained relative the network. However, the results of location with average normalized error relative bulletin solutions e=dr/R of 5% were obtained, in each of the distance ranges. The first version of the procedure was incorporated in the national Early Warning System in 2001. Recently, we started to send automatic Early Warn ing reports, to the EMSC Real Time Bulletin. Initially reported some teleseismic location discrepancies have been eliminated by introduction of station corrections.

External quality assurance project report for the National Atmospheric Deposition Program’s National Trends Network and Mercury Deposition Network, 2015–16

USGS Publications Warehouse

Wetherbee, Gregory A.; Martin, RoseAnn

2018-06-29

The U.S. Geological Survey Precipitation Chemistry Quality Assurance project operated five distinct programs to provide external quality assurance monitoring for the National Atmospheric Deposition Program’s (NADP) National Trends Network and Mercury Deposition Network during 2015–16. The National Trends Network programs include (1) a field audit program to evaluate sample contamination and stability, (2) an interlaboratory comparison program to evaluate analytical laboratory performance, and (3) a colocated sampler program to evaluate bias and variability attributed to automated precipitation samplers. The Mercury Deposition Network programs include the (4) system blank program and (5) an interlaboratory comparison program. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends for chemical constituents in wet deposition.The field audit program results indicate increased sample contamination for calcium, magnesium, and potassium relative to 2010 levels, and slight fluctuation in sodium contamination. Nitrate contamination levels dropped slightly during 2014–16, and chloride contamination leveled off between 2007 and 2016. Sulfate contamination is similar to the 2000 level. Hydrogen ion contamination has steadily decreased since 2012. Losses of ammonium and nitrate resulting from potential sample instability were negligible.The NADP Central Analytical Laboratory produced interlaboratory comparison results with low bias and variability compared to other domestic and international laboratories that support atmospheric deposition monitoring. Significant absolute bias above the magnitudes of the detection limits was observed for nitrate and sulfate concentrations, but no analyte determinations exceeded the detection limits for blanks.Colocated sampler program results from dissimilar colocated collectors indicate that the retrofit of the National Trends Network with N-CON Systems Company

[Construction of automatic elucidation platform for mechanism of traditional Chinese medicine].

PubMed

Zhang, Bai-xia; Luo, Si-jun; Yan, Jing; Gu, Hao; Luo, Ji; Zhang, Yan-ling; Tao, Ou; Wang, Yun

2015-10-01

Aim at the two problems in the field of traditional Chinese medicine (TCM) mechanism elucidation, one is the lack of detailed biological processes information, next is the low efficient in constructing network models, we constructed an auxiliary elucidation system for the TCM mechanism and realize the automatic establishment of biological network model. This study used the Entity Grammar Systems (EGS) as the theoretical framework, integrated the data of formulae, herbs, chemical components, targets of component, biological reactions, signaling pathways and disease related proteins, established the formal models, wrote the reasoning engine, constructed the auxiliary elucidation system for the TCM mechanism elucidation. The platform provides an automatic modeling method for biological network model of TCM mechanism. It would be benefit to perform the in-depth research on TCM theory of natures and combination and provides the scientific references for R&D of TCM.

Database Software Selection for the Egyptian National STI Network.

ERIC Educational Resources Information Center

Slamecka, Vladimir

The evaluation and selection of information/data management system software for the Egyptian National Scientific and Technical (STI) Network are described. An overview of the state-of-the-art of database technology elaborates on the differences between information retrieval and database management systems (DBMS). The desirable characteristics of…

Establishing the ACORN National Practitioner Database: Strategies to Recruit Practitioners to a National Practice-Based Research Network.

PubMed

Adams, Jon; Steel, Amie; Moore, Craig; Amorin-Woods, Lyndon; Sibbritt, David

2016-10-01

The purpose of this paper is to report on the recruitment and promotion strategies employed by the Australian Chiropractic Research Network (ACORN) project aimed at helping recruit a substantial national sample of participants and to describe the features of our practice-based research network (PBRN) design that may provide key insights to others looking to establish a similar network or draw on the ACORN project to conduct sub-studies. The ACORN project followed a multifaceted recruitment and promotion strategy drawing on distinct branding, a practitioner-focused promotion campaign, and a strategically designed questionnaire and distribution/recruitment approach to attract sufficient participation from the ranks of registered chiropractors across Australia. From the 4684 chiropractors registered at the time of recruitment, the project achieved a database response rate of 36% (n = 1680), resulting in a large, nationally representative sample across age, gender, and location. This sample constitutes the largest proportional coverage of participants from any voluntary national PBRN across any single health care profession. It does appear that a number of key promotional and recruitment features of the ACORN project may have helped establish the high response rate for the PBRN, which constitutes an important sustainable resource for future national and international efforts to grow the chiropractic evidence base and research capacity. Further rigorous enquiry is needed to help evaluate the direct contribution of specific promotional and recruitment strategies in attaining high response rates from practitioner populations who may be invited to participate in future PBRNs. Copyright © 2016. Published by Elsevier Inc.

Neural network model for automatic traffic incident detection : executive summary.

DOT National Transportation Integrated Search

2001-04-01

Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelli...

Project: Toward a National Educational Testing Network. Final Report.

ERIC Educational Resources Information Center

Bock, Darrell R.

Three fiscal year 1987 deliverables due for the "Toward a National Educational Testing Network: Feasibility Study of Duplex Design" are presented. The study is concerned with implementation of statewide and interstate testing of student attainment. The report includes: (1) a duplex design (DD) review paper discussing the means by which…

Effective Instruction. National Dropout Prevention Center/Network Newsletter. Volume 21, Number 2

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2009-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Strategies for Success (Charles W. Hatch); (2) 2009 NDPN Crystal Star Winners; (3) Strategies for More Effective Instruction (Micki Gibson); (4) Some Thoughts on Teaching…

Service-Learning. National Dropout Prevention Center/Network Newsletter. Volume 22, Number 4

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2011-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Dropouts and Democracy (Robert Shumer); (2) 2011 NDPN Crystal Star Winners; (3) Service-Learning as Dropout Intervention and More (Michael VanKeulen); and (4) Teacher…

Implementation and integration of regional health care data networks in the Hellenic National Health Service.

PubMed

Lampsas, Petros; Vidalis, Ioannis; Papanikolaou, Christos; Vagelatos, Aristides

2002-12-01

Modern health care is provided with close cooperation among many different institutions and professionals, using their specialized expertise in a common effort to deliver best-quality and, at the same time, cost-effective services. Within this context of the growing need for information exchange, the demand for realization of data networks interconnecting various health care institutions at a regional level, as well as a national level, has become a practical necessity. To present the technical solution that is under consideration for implementing and interconnecting regional health care data networks in the Hellenic National Health System. The most critical requirements for deploying such a regional health care data network were identified as: fast implementation, security, quality of service, availability, performance, and technical support. The solution proposed is the use of proper virtual private network technologies for implementing functionally-interconnected regional health care data networks. The regional health care data network is considered to be a critical infrastructure for further development and penetration of information and communication technologies in the Hellenic National Health System. Therefore, a technical approach was planned, in order to have a fast cost-effective implementation, conforming to certain specifications.

Implementation and Integration of Regional Health Care Data Networks in the Hellenic National Health Service

PubMed Central

Vidalis, Ioannis; Papanikolaou, Christos; Vagelatos, Aristides

2002-01-01

Background Modern health care is provided with close cooperation among many different institutions and professionals, using their specialized expertise in a common effort to deliver best-quality and, at the same time, cost-effective services. Within this context of the growing need for information exchange, the demand for realization of data networks interconnecting various health care institutions at a regional level, as well as a national level, has become a practical necessity. Objectives To present the technical solution that is under consideration for implementing and interconnecting regional health care data networks in the Hellenic National Health System. Methods The most critical requirements for deploying such a regional health care data network were identified as: fast implementation, security, quality of service, availability, performance, and technical support. Results The solution proposed is the use of proper virtual private network technologies for implementing functionally-interconnected regional health care data networks. Conclusions The regional health care data network is considered to be a critical infrastructure for further development and penetration of information and communication technologies in the Hellenic National Health System. Therefore, a technical approach was planned, in order to have a fast cost-effective implementation, conforming to certain specifications. PMID:12554551

Automatic identification of resting state networks: an extended version of multiple template-matching

NASA Astrophysics Data System (ADS)

Guaje, Javier; Molina, Juan; Rudas, Jorge; Demertzi, Athena; Heine, Lizette; Tshibanda, Luaba; Soddu, Andrea; Laureys, Steven; Gómez, Francisco

2015-12-01

Functional magnetic resonance imaging in resting state (fMRI-RS) constitutes an informative protocol to investigate several pathological and pharmacological conditions. A common approach to study this data source is through the analysis of changes in the so called resting state networks (RSNs). These networks correspond to well-defined functional entities that have been associated to different low and high brain order functions. RSNs may be characterized by using Independent Component Analysis (ICA). ICA provides a decomposition of the fMRI-RS signal into sources of brain activity, but it lacks of information about the nature of the signal, i.e., if the source is artifactual or not. Recently, a multiple template-matching (MTM) approach was proposed to automatically recognize RSNs in a set of Independent Components (ICs). This method provides valuable information to assess subjects at individual level. Nevertheless, it lacks of a mechanism to quantify how much certainty there is about the existence/absence of each network. This information may be important for the assessment of patients with severely damaged brains, in which RSNs may be greatly affected as a result of the pathological condition. In this work we propose a set of changes to the original MTM that improves the RSNs recognition task and also extends the functionality of the method. The key points of this improvement is a standardization strategy and a modification of method's constraints that adds flexibility to the approach. Additionally, we also introduce an analysis to the trustworthiness measurement of each RSN obtained by using template-matching approach. This analysis consists of a thresholding strategy applied over the computed Goodness-of-Fit (GOF) between the set of templates and the ICs. The proposed method was validated on 2 two independent studies (Baltimore, 23 healthy subjects and Liege, 27 healthy subjects) with different configurations of MTM. Results suggest that the method will provide

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)

Cross-over between discrete and continuous protein structure space: insights into automatic classification and networks of protein structures.

PubMed

Pascual-García, Alberto; Abia, David; Ortiz, Angel R; Bastolla, Ugo

2009-03-01

Structural classifications of proteins assume the existence of the fold, which is an intrinsic equivalence class of protein domains. Here, we test in which conditions such an equivalence class is compatible with objective similarity measures. We base our analysis on the transitive property of the equivalence relationship, requiring that similarity of A with B and B with C implies that A and C are also similar. Divergent gene evolution leads us to expect that the transitive property should approximately hold. However, if protein domains are a combination of recurrent short polypeptide fragments, as proposed by several authors, then similarity of partial fragments may violate the transitive property, favouring the continuous view of the protein structure space. We propose a measure to quantify the violations of the transitive property when a clustering algorithm joins elements into clusters, and we find out that such violations present a well defined and detectable cross-over point, from an approximately transitive regime at high structure similarity to a regime with large transitivity violations and large differences in length at low similarity. We argue that protein structure space is discrete and hierarchic classification is justified up to this cross-over point, whereas at lower similarities the structure space is continuous and it should be represented as a network. We have tested the qualitative behaviour of this measure, varying all the choices involved in the automatic classification procedure, i.e., domain decomposition, alignment algorithm, similarity score, and clustering algorithm, and we have found out that this behaviour is quite robust. The final classification depends on the chosen algorithms. We used the values of the clustering coefficient and the transitivity violations to select the optimal choices among those that we tested. Interestingly, this criterion also favours the agreement between automatic and expert classifications. As a domain set, we

Assessment of the National Park network of mainland Spain by the Insecurity Index of vertebrate species.

PubMed

Estrada, Alba; Real, Raimundo

2018-01-01

The evaluation of protected area networks on their capacity to preserve species distributions is a key topic in conservation biology. There are different types of protected areas, with National Parks those with highest level of protection. National Parks can be declared attending to many ecological features that include the presence of certain animal species. Here, we selected 37 vertebrate species that were highlighted as having relevant natural value for at least one of the 10 National Parks of mainland Spain. We modelled species distributions with the favourability function, and applied the Insecurity Index to detect the degree of protection of favourable areas for each species. Two metrics of Insecurity Index were defined for each species: the Insecurity Index in each of the cells, and the Overall Insecurity Index of a species. The former allows the identification of insecure areas for each species that can be used to establish spatial conservation priorities. The latter gives a value of Insecurity for each species, which we used to calculate the Representativeness of favourable areas for the species in the network. As expected, due to the limited extension of the National Park network, all species have high values of Insecurity; i.e., just a narrow proportion of their favourable areas are covered by a National Park. However, the majority of species favourable areas are well represented in the network, i.e., the percentage of favourable areas covered by the National Park network is higher than the percentage of mainland Spain covered by the network (result also supported by a randomization approach). Even if a reserve network only covers a low percentage of a country, the Overall Insecurity Index allows an objective assessment of its capacity to represent species. Beyond the results presented here, the Insecurity Index has the potential to be extrapolated to other areas and to cover a wide range of species.

Linking Geophysical Networks to International Economic Development Through Integration of Global and National Monitoring

NASA Astrophysics Data System (ADS)

Lerner-Lam, A.

2007-05-01

Outside of the research community and mission agencies, global geophysical monitoring rarely receives sustained attention except in the aftermath of a humanitarian disaster. The recovery and rebuilding period focuses attention and resources for a short time on regional needs for geophysical observation, often at the national or sub-national level. This can result in the rapid deployment of national monitoring networks, but may overlook the longer-term benefits of integration with global networks. Even in the case of multinational disasters, such as the Indian Ocean tsunami, it has proved difficult to promote the integration of national solutions with global monitoring, research and operations infrastructure. More importantly, continuing operations at the national or sub-national scale are difficult to sustain once the resources associated with recovery and rebuilding are depleted. Except for some notable examples, the vast infrastructure associated with global geophysical monitoring is not utilized constructively to promote the integration of national networks with international efforts. This represents a missed opportunity not only for monitoring, but for developing the international research and educational collaborations necessary for technological transfer and capacity building. The recent confluence of highly visible disasters, global multi-hazard risk assessments, evaluations of the relationships between natural disasters and socio-economic development, and shifts in development agency policies, provides an opportunity to link global geophysical monitoring initiatives to central issues in international development. Natural hazard risk reduction has not been the first priority of international development agendas for understandable, mainly humanitarian reasons. However, it is now recognized that the so-called risk premium associated with making development projects more risk conscious or risk resilient is relatively small relative to potential losses. Thus

Wireless Sensor Network-Based Greenhouse Environment Monitoring and Automatic Control System for Dew Condensation Prevention

PubMed Central

Park, Dae-Heon; Park, Jang-Woo

2011-01-01

Dew condensation on the leaf surface of greenhouse crops can promote diseases caused by fungus and bacteria, affecting the growth of the crops. In this paper, we present a WSN (Wireless Sensor Network)-based automatic monitoring system to prevent dew condensation in a greenhouse environment. The system is composed of sensor nodes for collecting data, base nodes for processing collected data, relay nodes for driving devices for adjusting the environment inside greenhouse and an environment server for data storage and processing. Using the Barenbrug formula for calculating the dew point on the leaves, this system is realized to prevent dew condensation phenomena on the crop’s surface acting as an important element for prevention of diseases infections. We also constructed a physical model resembling the typical greenhouse in order to verify the performance of our system with regard to dew condensation control. PMID:22163813

77 FR 20010 - Notice of Public Workshop: “Designing for Impact: Workshop on Building the National Network for...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-04-03

...: ``Designing for Impact: Workshop on Building the National Network for Manufacturing Innovation'' AGENCY...), housed at the National Institute of Standards and Technology (NIST), announces the first of a series of public workshops entitled ``Designing for Impact: Workshop on Building the National Network for...

PACS quality control and automatic problem notifier

NASA Astrophysics Data System (ADS)

Honeyman-Buck, Janice C.; Jones, Douglas; Frost, Meryll M.; Staab, Edward V.

1997-05-01

One side effect of installing a clinical PACS Is that users become dependent upon the technology and in some cases it can be very difficult to revert back to a film based system if components fail. The nature of system failures range from slow deterioration of function as seen in the loss of monitor luminance through sudden catastrophic loss of the entire PACS networks. This paper describes the quality control procedures in place at the University of Florida and the automatic notification system that alerts PACS personnel when a failure has happened or is anticipated. The goal is to recover from a failure with a minimum of downtime and no data loss. Routine quality control is practiced on all aspects of PACS, from acquisition, through network routing, through display, and including archiving. Whenever possible, the system components perform self and between platform checks for active processes, file system status, errors in log files, and system uptime. When an error is detected or a exception occurs, an automatic page is sent to a pager with a diagnostic code. Documentation on each code, trouble shooting procedures, and repairs are kept on an intranet server accessible only to people involved in maintaining the PACS. In addition to the automatic paging system for error conditions, acquisition is assured by an automatic fax report sent on a daily basis to all technologists acquiring PACS images to be used as a cross check that all studies are archived prior to being removed from the acquisition systems. Daily quality control is preformed to assure that studies can be moved from each acquisition and contrast adjustment. The results of selected quality control reports will be presented. The intranet documentation server will be described with the automatic pager system. Monitor quality control reports will be described and the cost of quality control will be quantified. As PACS is accepted as a clinical tool, the same standards of quality control must be established

U.S. EPA's National Dioxin Air Monitoring Network: Analytical Issues

EPA Science Inventory

The U.S. EPA has established a National Dioxin Air Monitoring Network (NDAMN) to determine the temporal and geographical variability of atmospheric chlorinated dibenzo-p-dioxins (CDDs), furans (CDFs), and coplanar polychlorinated biphenyls (PCBs) at rural and non-impacted locatio...

Project UNIFY. National Dropout Prevention Center/Network Newsletter. Volume 22, Number 1

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2011-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Special Olympics Project UNIFY (Andrea Cahn); (2) The Impact of Project UNIFY; (3) Project UNIFY Brings Youth Together to Learn and Graduate (William H. Hughes); (4)…

"Repeating Events" as Estimator of Location Precision: The China National Seismograph Network

NASA Astrophysics Data System (ADS)

Jiang, Changsheng; Wu, Zhongliang; Li, Yutong; Ma, Tengfei

2014-03-01

"Repeating earthquakes" identified by waveform cross-correlation, with inter-event separation of no more than 1 km, can be used for assessment of location precision. Assuming that the network-measured apparent inter-epicenter distance X of the "repeating doublets" indicates the location precision, we estimated the regionalized location quality of the China National Seismograph Network by comparing the "repeating events" in and around China by S chaff and R ichards (Science 303: 1176-1178, 2004; J Geophys Res 116: B03309, 2011) and the monthly catalogue of the China Earthquake Networks Center. The comparison shows that the average X value of the China National Seismograph Network is approximately 10 km. The mis-location is larger for the Tibetan Plateau, west and north of Xinjiang, and east of Inner Mongolia, as indicated by larger X values. Mis-location is correlated with the completeness magnitude of the earthquake catalogue. Using the data from the Beijing Capital Circle Region, the dependence of the mis-location on the distribution of seismic stations can be further confirmed.

The Masked Semantic Priming Effect Is Task Dependent: Reconsidering the Automatic Spreading Activation Process

ERIC Educational Resources Information Center

de Wit, Bianca; Kinoshita, Sachiko

2015-01-01

Semantic priming effects are popularly explained in terms of an automatic spreading activation process, according to which the activation of a node in a semantic network spreads automatically to interconnected nodes, preactivating a semantically related word. It is expected from this account that semantic priming effects should be routinely…

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network

NASA Astrophysics Data System (ADS)

Yakubu, A.; Oluremi, O. I. A.; Ekpo, E. I.

2018-03-01

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R 2 = 0.961, adjusted R 2 = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R 2 = 0.966; adjusted R 2 = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

Family Engagement. National Dropout Prevention Center/Network Newsletter. Volume 20, Number 2

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2008-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Family/School Relationships: Relationships That Matter; (2) Program Profile; (3) Engaging Families in the Pathway to College: Lessons From Schools That Are Beating the Odds (Anne T.…

Automatic generation of investigator bibliographies for institutional research networking systems.

PubMed

Johnson, Stephen B; Bales, Michael E; Dine, Daniel; Bakken, Suzanne; Albert, Paul J; Weng, Chunhua

2014-10-01

Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss' kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators. Copyright © 2014 Elsevier Inc. All rights reserved.

Automatic generation of investigator bibliographies for institutional research networking systems

PubMed Central

Johnson, Stephen B.; Bales, Michael E.; Dine, Daniel; Bakken, Suzanne; Albert, Paul J.; Weng, Chunhua

2014-01-01

Objective Publications are a key data source for investigator profiles and research networking systems. We developed ReCiter, an algorithm that automatically extracts bibliographies from PubMed using institutional information about the target investigators. Methods ReCiter executes a broad query against PubMed, groups the results into clusters that appear to constitute distinct author identities and selects the cluster that best matches the target investigator. Using information about investigators from one of our institutions, we compared ReCiter results to queries based on author name and institution and to citations extracted manually from the Scopus database. Five judges created a gold standard using citations of a random sample of 200 investigators. Results About half of the 10,471 potential investigators had no matching citations in PubMed, and about 45% had fewer than 70 citations. Interrater agreement (Fleiss’ kappa) for the gold standard was 0.81. Scopus achieved the best recall (sensitivity) of 0.81, while name-based queries had 0.78 and ReCiter had 0.69. ReCiter attained the best precision (positive predictive value) of 0.93 while Scopus had 0.85 and name-based queries had 0.31. Discussion ReCiter accesses the most current citation data, uses limited computational resources and minimizes manual entry by investigators. Generation of bibliographies using named-based queries will not yield high accuracy. Proprietary databases can perform well but requite manual effort. Automated generation with higher recall is possible but requires additional knowledge about investigators. PMID:24694772

ADVANCED SURVEILLANCE OF ENVIROMENTAL RADIATION IN AUTOMATIC NETWORKS.

PubMed

Benito, G; Sáez, J C; Blázquez, J B; Quiñones, J

2018-06-01

The objective of this study is the verification of the operation of a radiation monitoring network conformed by several sensors. The malfunction of a surveillance network has security and economic consequences, which derive from its maintenance and could be avoided with an early detection. The proposed method is based on a kind of multivariate distance, and the verification for the methodology has been tested at CIEMAT's local radiological early warning network.

The Continuing Growth of Global Cooperation Networks in Research: A Conundrum for National Governments

PubMed Central

Wagner, Caroline S.; Park, Han Woo; Leydesdorff, Loet

2015-01-01

Global collaboration continues to grow as a share of all scientific cooperation, measured as coauthorships of peer-reviewed, published papers. The percent of all scientific papers that are internationally coauthored has more than doubled in 20 years, and they account for all the growth in output among the scientifically advanced countries. Emerging countries, particularly China, have increased their participation in global science, in part by doubling their spending on R&D; they are increasingly likely to appear as partners on internationally coauthored scientific papers. Given the growth of connections at the international level, it is helpful to examine the phenomenon as a communications network and to consider the network as a new organization on the world stage that adds to and complements national systems. When examined as interconnections across the globe over two decades, a global network has grown denser but not more clustered, meaning there are many more connections but they are not grouping into exclusive ‘cliques’. This suggests that power relationships are not reproducing those of the political system. The network has features an open system, attracting productive scientists to participate in international projects. National governments could gain efficiencies and influence by developing policies and strategies designed to maximize network benefits—a model different from those designed for national systems. PMID:26196296

National network television news coverage of contraception - a content analysis.

PubMed

Patton, Elizabeth W; Moniz, Michelle H; Hughes, Lauren S; Buis, Lorraine; Howell, Joel

2017-01-01

The objective was to describe and analyze national network television news framing of contraception, recognizing that onscreen news can influence the public's knowledge and beliefs. We used the Vanderbilt Television News Archives and LexisNexis Database to obtain video and print transcripts of all relevant national network television news segments covering contraception from January 2010 to June 2014. We conducted a content analysis of 116 TV news segments covering contraception during the rollout of the Affordable Care Act. Segments were quantitatively coded for contraceptive methods covered, story sources used, and inclusion of medical and nonmedical content (intercoder reliability using Krippendorf's alpha ranged 0.6-1 for coded categories). Most (55%) news stories focused on contraception in general rather than specific methods. The most effective contraceptive methods were rarely discussed (implant, 1%; intrauterine device, 4%). The most frequently used sources were political figures (40%), advocates (25%), the general public (25%) and Catholic Church leaders (16%); medical professionals (11%) and health researchers (4%) appeared in a minority of stories. A minority of stories (31%) featured medical content. National network news coverage of contraception frequently focuses on contraception in political and social terms and uses nonmedical figures such as politicians and church leaders as sources. This focus deemphasizes the public health aspect of contraception, leading medical professionals and health content to be rarely featured. Media coverage of contraception may influence patients' views about contraception. Understanding the content, sources and medical accuracy of current media portrayals of contraception may enable health care professionals to dispel popular misperceptions. Published by Elsevier Inc.

Summer Learning. National Dropout Prevention Center/Network Newsletter. Volume 21, Number 3

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2010-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) A New Vision of Summer Learning (Brenda McLaughlin); (2) Using Summers More Strategically to Bridge the 8th-9th Grade Transition (Brenda McLaughlin and Hillary Hardt); (3)…

GPs’ use of defibrillators and the national radio network in emergency primary healthcare in Norway

PubMed Central

Zakariassen, Erik; Hunskaar, Steinar

2008-01-01

Objective To study the geographic size of out-of-hours districts, the availability of defibrillators and use of the national radio network in Norway. Design Survey. Setting The emergency primary healthcare system in Norway. Subjects A total of 282 host municipalities responsible for 260 out-of-hours districts. Main outcome measures Size of out-of-hours districts, use of national radio network and access to a defibrillator in emergency situations. Results The out-of-hours districts have a wide range of areas, which gives a large variation in driving time for doctors on call. The median longest transport time for doctors in Norway is 45 minutes. In 46% of out-of-hours districts doctors bring their own defibrillator on emergency callouts. Doctors always use the national radio network in 52% of out-of-hours districts. Use of the radio network and access to a defibrillator are significantly greater in out-of-hours districts with a host municipality of fewer then 5000 inhabitants compared with host municipalities of more than 20 000 inhabitants. Conclusion In half of out-of-hours districts doctors on call always use the national radio network. Doctors in out-of-hours districts with a host municipality of fewer than 5000 inhabitants are in a better state of readiness to attend an emergency, compared with doctors working in larger host municipalities. PMID:18570012

Evidence for a Role of a Cortico-Subcortical Network for Automatic and Unconscious Motor Inhibition of Manual Responses

PubMed Central

D’Ostilio, Kevin; Collette, Fabienne; Phillips, Christophe; Garraux, Gaëtan

2012-01-01

It is now clear that non-consciously perceived stimuli can bias our decisions. Although previous researches highlighted the importance of automatic and unconscious processes involved in voluntary action, the neural correlates of such processes remain unclear. Basal ganglia dysfunctions have long been associated with impairment in automatic motor control. In addition, a key role of the medial frontal cortex has been suggested by administrating a subliminal masked prime task to a patient with a small lesion restricted to the supplementary motor area (SMA). In this task, invisible masked arrows stimuli were followed by visible arrow targets for a left or right hand response at different interstimuli intervals (ISI), producing a traditional facilitation effect for compatible trials at short ISI and a reversal inhibitory effect at longer ISI. Here, by using fast event-related fMRI and a weighted parametric analysis, we showed BOLD related activity changes in a cortico-subcortical network, especially in the SMA and the striatum, directly linked to the individual behavioral pattern. This new imaging result corroborates previous works on subliminal priming using lesional approaches. This finding implies that one of the roles of these regions was to suppress a partially activated movement below the threshold of awareness. PMID:23110158

Evidence for a role of a cortico-subcortical network for automatic and unconscious motor inhibition of manual responses.

PubMed

D'Ostilio, Kevin; Collette, Fabienne; Phillips, Christophe; Garraux, Gaëtan

2012-01-01

It is now clear that non-consciously perceived stimuli can bias our decisions. Although previous researches highlighted the importance of automatic and unconscious processes involved in voluntary action, the neural correlates of such processes remain unclear. Basal ganglia dysfunctions have long been associated with impairment in automatic motor control. In addition, a key role of the medial frontal cortex has been suggested by administrating a subliminal masked prime task to a patient with a small lesion restricted to the supplementary motor area (SMA). In this task, invisible masked arrows stimuli were followed by visible arrow targets for a left or right hand response at different interstimuli intervals (ISI), producing a traditional facilitation effect for compatible trials at short ISI and a reversal inhibitory effect at longer ISI. Here, by using fast event-related fMRI and a weighted parametric analysis, we showed BOLD related activity changes in a cortico-subcortical network, especially in the SMA and the striatum, directly linked to the individual behavioral pattern. This new imaging result corroborates previous works on subliminal priming using lesional approaches. This finding implies that one of the roles of these regions was to suppress a partially activated movement below the threshold of awareness.

Deep neural networks for automatic detection of osteoporotic vertebral fractures on CT scans.

PubMed

Tomita, Naofumi; Cheung, Yvonne Y; Hassanpour, Saeed

2018-07-01

Osteoporotic vertebral fractures (OVFs) are prevalent in older adults and are associated with substantial personal suffering and socio-economic burden. Early diagnosis and treatment of OVFs are critical to prevent further fractures and morbidity. However, OVFs are often under-diagnosed and under-reported in computed tomography (CT) exams as they can be asymptomatic at an early stage. In this paper, we present and evaluate an automatic system that can detect incidental OVFs in chest, abdomen, and pelvis CT examinations at the level of practicing radiologists. Our OVF detection system leverages a deep convolutional neural network (CNN) to extract radiological features from each slice in a CT scan. These extracted features are processed through a feature aggregation module to make the final diagnosis for the full CT scan. In this work, we explored different methods for this feature aggregation, including the use of a long short-term memory (LSTM) network. We trained and evaluated our system on 1432 CT scans, comprised of 10,546 two-dimensional (2D) images in sagittal view. Our system achieved an accuracy of 89.2% and an F1 score of 90.8% based on our evaluation on a held-out test set of 129 CT scans, which were established as reference standards through standard semiquantitative and quantitative methods. The results of our system matched the performance of practicing radiologists on this test set in real-world clinical circumstances. We expect the proposed system will assist and improve OVF diagnosis in clinical settings by pre-screening routine CT examinations and flagging suspicious cases prior to review by radiologists. Copyright © 2018 Elsevier Ltd. All rights reserved.

Automatic emotional expression analysis from eye area

NASA Astrophysics Data System (ADS)

Akkoç, Betül; Arslan, Ahmet

2015-02-01

Eyes play an important role in expressing emotions in nonverbal communication. In the present study, emotional expression classification was performed based on the features that were automatically extracted from the eye area. Fırst, the face area and the eye area were automatically extracted from the captured image. Afterwards, the parameters to be used for the analysis through discrete wavelet transformation were obtained from the eye area. Using these parameters, emotional expression analysis was performed through artificial intelligence techniques. As the result of the experimental studies, 6 universal emotions consisting of expressions of happiness, sadness, surprise, disgust, anger and fear were classified at a success rate of 84% using artificial neural networks.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing.

PubMed

Hristova, Desislava; Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals.

The International Postal Network and Other Global Flows as Proxies for National Wellbeing

PubMed Central

Rutherford, Alex; Anson, Jose; Luengo-Oroz, Miguel; Mascolo, Cecilia

2016-01-01

The digital exhaust left by flows of physical and digital commodities provides a rich measure of the nature, strength and significance of relationships between countries in the global network. With this work, we examine how these traces and the network structure can reveal the socioeconomic profile of different countries. We take into account multiple international networks of physical and digital flows, including the previously unexplored international postal network. By measuring the position of each country in the Trade, Postal, Migration, International Flights, IP and Digital Communications networks, we are able to build proxies for a number of crucial socioeconomic indicators such as GDP per capita and the Human Development Index ranking along with twelve other indicators used as benchmarks of national well-being by the United Nations and other international organisations. In this context, we have also proposed and evaluated a global connectivity degree measure applying multiplex theory across the six networks that accounts for the strength of relationships between countries. We conclude by showing how countries with shared community membership over multiple networks have similar socioeconomic profiles. Combining multiple flow data sources can help understand the forces which drive economic activity on a global level. Such an ability to infer proxy indicators in a context of incomplete information is extremely timely in light of recent discussions on measurement of indicators relevant to the Sustainable Development Goals. PMID:27248142

Structure-based manual screening and automatic networking for systematically exploring sansanmycin analogues using high performance liquid chromatography tandem mass spectroscopy.

PubMed

Jiang, Zhi-Bo; Ren, Wei-Cong; Shi, Yuan-Yuan; Li, Xing-Xing; Lei, Xuan; Fan, Jia-Hui; Zhang, Cong; Gu, Ren-Jie; Wang, Li-Fei; Xie, Yun-Ying; Hong, Bin

2018-05-18

Sansanmycins (SS), one of several known uridyl peptide antibiotics (UPAs) possessing a unique chemical scaffold, showed a good inhibitory effect on the highly refractory pathogens Pseudomonas aeruginosa and Mycobacterium tuberculosis, especially on the multi-drug resistant M. tuberculosis. This study employed high performance liquid chromatography-mass spectrometry detector (HPLC-MSD) ion trap and LTQ orbitrap tandem mass spectrometry (MS/MS) to explore sansanmycin analogues manually and automatically by re-analysis of the Streptomyces sp. SS fermentation broth. The structure-based manual screening method, based on analysis of the fragmentation pathway of known UPAs and on comparisons of the MS/MS spectra with that of sansanmycin A (SS-A), resulted in identifying twenty sansanmycin analogues, including twelve new structures (1-12). Furthermore, to deeply explore sansanmycin analogues, we utilized a GNPS based molecular networking workflow to re-analyze the HPLC-MS/MS data automatically. As a result, eight more new sansanmycins (13-20) were discovered. Compound 1 was discovered to lose two amino acids of residue 1 (AA 1 ) and (2S, 3S)-N 3 -methyl-2,3-diamino butyric acid (DABA) from the N-terminus, and compounds 6, 11 and 12 were found to contain a 2',3'-dehydrated 4',5'-enamine-3'-deoxyuridyl moiety, which have not been reported before. Interestingly, three trace components with novel 5,6-dihydro-5'-aminouridyl group (16-18) were detected for the first time in the sansanmycin-producing strain. Their structures were primarily determined by detail analysis of the data from MS/MS. Compounds 8 and 10 were further confirmed by nuclear magnetic resonance (NMR) data, which proved the efficiency and accuracy of the method of HPLC-MS/MS for exploration of novel UPAs. Comparing to manual screening, the networking method can provide systematic visualization results. Manual screening and networking method may complement with each other to facilitate the mining of novel UPAs

Target recognition based on convolutional neural network

NASA Astrophysics Data System (ADS)

Wang, Liqiang; Wang, Xin; Xi, Fubiao; Dong, Jian

2017-11-01

One of the important part of object target recognition is the feature extraction, which can be classified into feature extraction and automatic feature extraction. The traditional neural network is one of the automatic feature extraction methods, while it causes high possibility of over-fitting due to the global connection. The deep learning algorithm used in this paper is a hierarchical automatic feature extraction method, trained with the layer-by-layer convolutional neural network (CNN), which can extract the features from lower layers to higher layers. The features are more discriminative and it is beneficial to the object target recognition.

Wireless sensor network effectively controls center pivot irrigation of sorghum

USDA-ARS?s Scientific Manuscript database

Robust automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automat...

Automatic classification of ovarian cancer types from cytological images using deep convolutional neural networks.

PubMed

Wu, Miao; Yan, Chuanbo; Liu, Huiqiang; Liu, Qian

2018-06-29

Ovarian cancer is one of the most common gynecologic malignancies. Accurate classification of ovarian cancer types (serous carcinoma, mucous carcinoma, endometrioid carcinoma, transparent cell carcinoma) is an essential part in the different diagnosis. Computer-aided diagnosis (CADx) can provide useful advice for pathologists to determine the diagnosis correctly. In our study, we employed a Deep Convolutional Neural Networks (DCNN) based on AlexNet to automatically classify the different types of ovarian cancers from cytological images. The DCNN consists of five convolutional layers, three max pooling layers, and two full reconnect layers. Then we trained the model by two group input data separately, one was original image data and the other one was augmented image data including image enhancement and image rotation. The testing results are obtained by the method of 10-fold cross-validation, showing that the accuracy of classification models has been improved from 72.76 to 78.20% by using augmented images as training data. The developed scheme was useful for classifying ovarian cancers from cytological images. © 2018 The Author(s).

An historical overview of the National Network of Libraries of Medicine, 1985-2015.

PubMed

Speaker, Susan L

2018-04-01

The National Network of Libraries of Medicine (NNLM), established as the Regional Medical Library Program in 1965, has a rich and remarkable history. The network's first twenty years were documented in a detailed 1987 history by Alison Bunting, AHIP, FMLA. This article traces the major trends in the network's development since then: reconceiving the Regional Medical Library staff as a "field force" for developing, marketing, and distributing a growing number of National Library of Medicine (NLM) products and services; subsequent expansion of outreach to health professionals who are unaffiliated with academic medical centers, particularly those in public health; the advent of the Internet during the 1990s, which brought the migration of NLM and NNLM resources and services to the World Wide Web, and a mandate to encourage and facilitate Internet connectivity in the network; and the further expansion of the NLM and NNLM mission to include providing consumer health resources to satisfy growing public demand. The concluding section discusses the many challenges that NNLM staff faced as they transformed the network from a system that served mainly academic medical researchers to a larger, denser organization that offers health information resources to everyone.

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

PubMed

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

2017-12-01

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

Update on Plans to Establish a National Phenology Network in the U.S.A.

NASA Astrophysics Data System (ADS)

Betancourt, J.; Schwartz, M.; Breshears, D.; Cayan, D.; Dettinger, M.; Inouye, D.; Post, E.; Reed, B.; Gray, S.

2005-12-01

The passing of the seasons is the most pervasive source of climatic and biological variability on Earth, yet phenological monitoring has been spotty worldwide. Formal phenological networks were recently established in Europe and Canada, and we are now following their lead in organizing a National Phenology Network (NPN) for the U.S.A. With support from federal agencies (NSF, USGS, NPS, USDA-FS, EPA, NOAA, NASA), on Aug. 22-26 we organized a workshop in Tucson, Arizona to begin planning a national-scale, multi-tiered phenological network. A prototype for a web-based NPN and preliminary workshop results are available at http://www.npn.uwm.edu. The main goals of NPN will be to: (1) facilitate thorough understanding of phenological phenomena, including causes and effects; (2) provide ground truthing to make the most of heavy public investment in remote sensing data; (3) allow detection and prediction of environmental change for a wide of variety of applications; (4) harness the power of mass participation and engage tens of thousands of "citizen scientists" in meeting national needs in Education, Health, Commerce, Natural Resources and Agriculture; (5) develop a model system for substantive collaboration across different levels of government, academia and the private sector. Just as the national networks of weather stations and stream gauges are critical for providing weather, climate and water-related information, NPN will help safeguard and procure goods and services that ecosystems provide. We expect that NPN will consist of a four-tiered, expandable structure: 1) a backbone network linked to existing weather stations, run by recruited public observers; 2) A smaller, second tier of intensive observations, run by scientists at established research sites; 3) a much larger network of observations made by citizen scientists; and 4) remote sensing observations that can be validated with surface observations, thereby providing wall-to-wall coverage for the U.S.A. Key to

Predicting heat stress index in Sasso hens using automatic linear modeling and artificial neural network.

PubMed

Yakubu, A; Oluremi, O I A; Ekpo, E I

2018-03-17

There is an increasing use of robust analytical algorithms in the prediction of heat stress. The present investigation therefore, was carried out to forecast heat stress index (HSI) in Sasso laying hens. One hundred and sixty seven records on the thermo-physiological parameters of the birds were utilized. They were reared on deep litter and battery cage systems. Data were collected when the birds were 42- and 52-week of age. The independent variables fitted were housing system, age of birds, rectal temperature (RT), pulse rate (PR), and respiratory rate (RR). The response variable was HSI. Data were analyzed using automatic linear modeling (ALM) and artificial neural network (ANN) procedures. The ALM model building method involved Forward Stepwise using the F Statistic criterion. As regards ANN, multilayer perceptron (MLP) with back-propagation network was used. The ANN network was trained with 90% of the data set while 10% were dedicated to testing for model validation. RR and PR were the two parameters of utmost importance in the prediction of HSI. However, the fractional importance of RR was higher than that of PR in both ALM (0.947 versus 0.053) and ANN (0.677 versus 0.274) models. The two models also predicted HSI effectively with high degree of accuracy [r = 0.980, R 2  = 0.961, adjusted R 2  = 0.961, and RMSE = 0.05168 (ALM); r = 0.983, R 2  = 0.966; adjusted R 2  = 0.966, and RMSE = 0.04806 (ANN)]. The present information may be exploited in the development of a heat stress chart based largely on RR. This may aid detection of thermal discomfort in a poultry house under tropical and subtropical conditions.

Site characterization of the national seismic network of Italy

NASA Astrophysics Data System (ADS)

Bordoni, Paola; Pacor, Francesca; Cultrera, Giovanna; Casale, Paolo; Cara, Fabrizio; Di Giulio, Giuseppe; Famiani, Daniela; Ladina, Chiara; PIschiutta, Marta; Quintiliani, Matteo

2017-04-01

The national seismic network of Italy (Rete Sismica Nazionale, RSN) run by Istituto Nazionale di Geofisica e Vulcanologia (INGV) consists of more than 400 seismic stations connected in real time to the institute data center in order to locate earthquakes for civil defense purposes. A critical issue in the performance of a network is the characterization of site condition at the recording stations. Recently INGV has started addressing this subject through the revision of all available geological and geophysical data, the acquisition of new information by means of ad-hoc field measurements and the analysis of seismic waveforms. The main effort is towards building a database, integrated with the other INGV infrastructures, designed to archive homogeneous parameters through the seismic network useful for a complete site characterization, including housing, geological, seismological and geotechnical features as well as the site class according to the European and Italian building codes. Here we present the ongoing INGV activities.

Anticipated Ethics and Regulatory Challenges in PCORnet: The National Patient-Centered Clinical Research Network.

PubMed

Ali, Joseph; Califf, Robert; Sugarman, Jeremy

2016-01-01

PCORnet, the National Patient-Centered Clinical Research Network, seeks to establish a robust national health data network for patient-centered comparative effectiveness research. This article reports the results of a PCORnet survey designed to identify the ethics and regulatory challenges anticipated in network implementation. A 12-item online survey was developed by leadership of the PCORnet Ethics and Regulatory Task Force; responses were collected from the 29 PCORnet networks. The most pressing ethics issues identified related to informed consent, patient engagement, privacy and confidentiality, and data sharing. High priority regulatory issues included IRB coordination, privacy and confidentiality, informed consent, and data sharing. Over 150 IRBs and five different approaches to managing multisite IRB review were identified within PCORnet. Further empirical and scholarly work, as well as practical and policy guidance, is essential if important initiatives that rely on comparative effectiveness research are to move forward.

U.S. Geological Survey external quality-assurance project report for the National Atmospheric Deposition Program / National Trends Network and Mercury Deposition Network, 2011-2012

USGS Publications Warehouse

Wetherbee, Gregory A.; Martin, RoseAnn

2014-01-01

The U.S. Geological Survey operated six distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program (NADP) / National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2011–2012. The field-audit program assessed the effects of onsite exposure, sample handling, and shipping on the chemistry of NTN samples; a system-blank program assessed the same effects for MDN. Two interlaboratory-comparison programs assessed the bias and variability of the chemical analysis data from the Central Analytical Laboratory and Mercury Analytical Laboratory (HAL). A blind-audit program was implemented for the MDN during 2011 to evaluate analytical bias in HAL total mercury concentration data. The co-located–sampler program was used to identify and quantify potential shifts in NADP data resulting from the replacement of original network instrumentation with new electronic recording rain gages and precipitation collectors that use optical precipitation sensors. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends of chemical constituents in wet deposition across the United States. Co-located rain gage results indicate -3.7 to +6.5 percent bias in NADP precipitation-depth measurements. Co-located collector results suggest that the retrofit of the NADP networks with the new precipitation collectors could cause +10 to +36 percent shifts in NADP annual deposition values for ammonium, nitrate, and sulfate; -7.5 to +41 percent shifts for hydrogen-ion deposition; and larger shifts (-51 to +52 percent) for calcium, magnesium, sodium, potassium, and chloride. The prototype N-CON Systems bucket collector typically catches more precipitation than the NADP-approved Aerochem Metrics Model 301 collector.

Planning and Establishment of a National Teledocumentation Network--Guidelines Based on the Spanish Experience.

ERIC Educational Resources Information Center

Mahon, F. V., Ed.

Finding that the promotion of a national information industry can best be pursued through the planning and establishment of a national teledocumentation network, this study (based on the experiences of Spain) offers a model that may be of interest to UNESCO (United Nations Educational, Scientific and Cultural Organization) member states wishing to…

The National Network forTechnology Entrepreneurship and Commercialization (N2TEC): Bringing New Technologies to Market

NASA Astrophysics Data System (ADS)

Allen, Kathleen

2003-03-01

N2TEC, the National Network for Technology Entrepreneurship and Commercialization, is a National Science Foundation "Partnerships for Innovation" initiative designed to raise the level of innovation and technology commercialization in colleges, universities, and communities across the nation. N2TEC is creating a network of people and institutions, and a set of technology tools that will facilitate the pooling of resources and knowledge and enable faculty and students to share those resources and collaborate without regard to geographic boundaries. N2TEC will become the backbone by which educational institutions across the nation can move their technologies into new venture startups. The ultimate goal is to create new wealth and strengthen local, regional and national economies.

Celebrating 25 Years. National Dropout Prevention Center/Network Newsletter. Volume 22, Number 3

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2011-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Leading the Way in Dropout Prevention; (2) The 15 Effective Strategies in Action; (3) Technology Changes 1986-2011 (Marty Duckenfield); (4) 25 Years of Research and Support…

78 FR 68030 - Draft Guidance on Intellectual Property Rights for the National Network for Manufacturing...

Federal Register 2010, 2011, 2012, 2013, 2014

2013-11-13

... Additive Manufacturing showed great promise for the defense, energy, space and commercial sectors of the Nation. In August, 2012, the selection of the National Additive Manufacturing Innovation Institute (NAMII...-01] Draft Guidance on Intellectual Property Rights for the National Network for Manufacturing...

Automatic Railway Traffic Object Detection System Using Feature Fusion Refine Neural Network under Shunting Mode.

PubMed

Ye, Tao; Wang, Baocheng; Song, Ping; Li, Juan

2018-06-12

Many accidents happen under shunting mode when the speed of a train is below 45 km/h. In this mode, train attendants observe the railway condition ahead using the traditional manual method and tell the observation results to the driver in order to avoid danger. To address this problem, an automatic object detection system based on convolutional neural network (CNN) is proposed to detect objects ahead in shunting mode, which is called Feature Fusion Refine neural network (FR-Net). It consists of three connected modules, i.e., the depthwise-pointwise convolution, the coarse detection module, and the object detection module. Depth-wise-pointwise convolutions are used to improve the detection in real time. The coarse detection module coarsely refine the locations and sizes of prior anchors to provide better initialization for the subsequent module and also reduces search space for the classification, whereas the object detection module aims to regress accurate object locations and predict the class labels for the prior anchors. The experimental results on the railway traffic dataset show that FR-Net achieves 0.8953 mAP with 72.3 FPS performance on a machine with a GeForce GTX1080Ti with the input size of 320 × 320 pixels. The results imply that FR-Net takes a good tradeoff both on effectiveness and real time performance. The proposed method can meet the needs of practical application in shunting mode.

Automatic Implementation of Ttethernet-Based Time-Triggered Avionics Applications

NASA Astrophysics Data System (ADS)

Gorcitz, Raul Adrian; Carle, Thomas; Lesens, David; Monchaux, David; Potop-Butucaruy, Dumitru; Sorel, Yves

2015-09-01

The design of safety-critical embedded systems such as those used in avionics still involves largely manual phases. But in avionics the definition of standard interfaces embodied in standards such as ARINC 653 or TTEthernet should allow the definition of fully automatic code generation flows that reduce the costs while improving the quality of the generated code, much like compilers have done when replacing manual assembly coding. In this paper, we briefly present such a fully automatic implementation tool, called Lopht, for ARINC653-based time-triggered systems, and then explain how it is currently extended to include support for TTEthernet networks.

Strategic factors in the development of the National Technology Transfer Network

NASA Technical Reports Server (NTRS)

Root, Jonathan F.; Stone, Barbara A.

1993-01-01

Broad consensus among industry and government leaders has developed over the last decade on the importance of applying the U.S. leadership in research and development (R&D) to strengthen competitiveness in the global marketplace, and thus enhance national prosperity. This consensus has emerged against the backdrop of increasing economic competition, and the dramatic reduction of military threats to national security with the end of the Cold War. This paper reviews the key factors and considerations that shaped - and continue to influence - the development of the Regional Technoloty Transfer Centers (RTTC) and the National Technology Transfer Center (NTTC). Also, the future role of the national network in support of emerging technology policy initiatives will be explored.

Advancing environmental health surveillance in the US through a national human biomonitoring network.

PubMed

Latshaw, Megan Weil; Degeberg, Ruhiyyih; Patel, Surili Sutaria; Rhodes, Blaine; King, Ewa; Chaudhuri, Sanwat; Nassif, Julianne

2017-03-01

The United States lacks a comprehensive, nationally-coordinated, state-based environmental health surveillance system. This lack of infrastructure leads to: • varying levels of understanding of chemical exposures at the state & local levels • often inefficient public health responses to chemical exposure emergencies (such as those that occurred in the Flint drinking water crisis, the Gold King mine spill, the Elk river spill and the Gulf Coast oil spill) • reduced ability to measure the impact of public health interventions or environmental policies • less efficient use of resources for cleaning up environmental contamination Establishing the National Biomonitoring Network serves as a step toward building a national, state-based environmental health surveillance system. The Network builds upon CDC investments in emergency preparedness and environmental public health tracking, which have created advanced chemical analysis and information sharing capabilities in the state public health systems. The short-term goal of the network is to harmonize approaches to human biomonitoring in the US, thus increasing the comparability of human biomonitoring data across states and communities. The long-term goal is to compile baseline data on exposures at the state level, similar to data found in CDC's National Report on Human Exposure to Environmental Chemicals. Barriers to success for this network include: available resources, effective risk communication strategies, data comparability & sharing, and political will. Anticipated benefits include high quality data on which to base public health and environmental decisions, data with which to assess the success of public health interventions, improved risk assessments for chemicals, and new ways to prioritize environmental health research. Copyright © 2016 Elsevier GmbH. All rights reserved.

The USA National Phenology Network's Model for Collaborative Data Generation and Dissemination

NASA Astrophysics Data System (ADS)

Rosemartin, A.; Lincicome, A.; Denny, E. G.; Marsh, L.; Wilson, B. E.

2010-12-01

The USA National Phenology Network (USA-NPN) serves science and society by promoting a broad understanding of plant and animal phenology and the relationships among phenological patterns and all aspects of environmental change. The Network was founded as an NSF-funded Research Coordination Network, for the purpose of fostering collaboration among scientists, policy-makers and the general public to address the challenges posed by global change and its impact on ecosystems and human health. With this mission in mind, the USA-NPN has developed an Information Management System (IMS) to facilitate collaboration and participatory data collection and digitization. The IMS includes components for data storage, such as the National Phenology Database, as well as a Drupal website for information-sharing and data visualization, and a Java application for collection of contemporary observational data. The National Phenology Database is designed to efficiently accommodate large quantities of phenology data and to be flexible to the changing needs of the network. The database allows for the collection, storage and output of phenology data from multiple sources (e.g., partner organizations, researchers and citizen observers), as well as integration with legacy data sets. Participants in the network can submit records (as Drupal content types) for publications, legacy data sets and phenology-related festivals. The USA-NPN’s contemporary phenology data collection effort, Nature’s Notebook also draws on the contributions of participants. Citizen scientists around the country submit data through this Java application (paired with the Drupal site through a shared login) on the life cycle stages of plants and animals in their yards and parks. The North American Bird Phenology Program, now a part of the USA-NPN, also relies on web-based crowdsourcing. Participants in this program are transcribing 6 million scanned paper cards that were collected by observers across the United States

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

EPA Science Inventory

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

Effects of equipment performance on data quality from the National Atmospheric Deposition Program/National Trends Network and the Mercury Deposition Network

USGS Publications Warehouse

Wetherbee, Gregory A.; Rhodes, Mark F.

2013-01-01

The U.S. Geological Survey Branch of Quality Systems operates the Precipitation Chemistry Quality Assurance project (PCQA) to provide independent, external quality-assurance for the National Atmospheric Deposition Program (NADP). NADP is composed of five monitoring networks that measure the chemical composition of precipitation and ambient air. PCQA and the NADP Program Office completed five short-term studies to investigate the effects of equipment performance with respect to the National Trends Network (NTN) and Mercury Deposition Network (MDN) data quality: sample evaporation from NTN collectors; sample volume and mercury loss from MDN collectors; mercury adsorption to MDN collector glassware, grid-type precipitation sensors for precipitation collectors, and the effects of an NTN collector wind shield on sample catch efficiency. Sample-volume evaporation from an NTN Aerochem Metrics (ACM) collector ranged between 1.1–33 percent with a median of 4.7 percent. The results suggest that weekly NTN sample evaporation is small relative to sample volume. MDN sample evaporation occurs predominantly in western and southern regions of the United States (U.S.) and more frequently with modified ACM collectors than with N-CON Systems Inc. collectors due to differences in airflow through the collectors. Variations in mercury concentrations, measured to be as high as 47.5 percent per week with a median of 5 percent, are associated with MDN sample-volume loss. Small amounts of mercury are also lost from MDN samples by adsorption to collector glassware irrespective of collector type. MDN 11-grid sensors were found to open collectors sooner, keep them open longer, and cause fewer lid cycles than NTN 7-grid sensors. Wind shielding an NTN ACM collector resulted in collection of larger quantities of precipitation while also preserving sample integrity.

The National Research and Education Network (NREN): Promise of New Information Environments. ERIC Digest.

ERIC Educational Resources Information Center

Bishop, Ann P.

This digest describes proposed legislation for the implementation of the National Research and Education Network (NREN). Issues and implications for teachers, students, researchers, and librarians are suggested and the emergence of the electronic network as a general communication and research tool is described. Developments in electronic…

Albemarle Sound demonstration study of the national monitoring network for US coastal waters and their tributaries

Treesearch

Michelle Moorman; Sharon Fitzgerald; Keith Loftin; Elizabeth Fensin

2016-01-01

The U.S. Geological Survey’s (USGS) is implementing a demonstration project in the Albemarle Sound for the National Monitoring Network for U.S. coastal waters and their tributaries. The goal of the National Monitoring Network is to provide information about the health of our oceans and coastal ecosystems and inland influences on coastal waters for improved resource...

National Health Care Network for children with oral clefts: organization, functioning, and preliminary outcomes.

PubMed

Cassinelli, Agustina; Pauselli, Nadia; Piola, Agustina; Martinelli, Claudia; Alves de Azeved, José L; Bidondo, María P; Groisman, Boris; Barbero, Pablo; Liascovich, Rosa; Sala, Ana

2018-02-01

Oral clefts are major congenital anomalies that may affect the lip and/or palate, and that may also involve the nose and nostrils. In Argentina, their prevalence is approximately 15 per 10 000 births. In 2015, the Ministry of Health of Argentina created a national health care network for children with oral clefts in Argentina through the joint work with the National Registry of Congenital Anomalies (Red Nacional de Anomalías Congénitas, RENAC) (coordinating center for the national network) and the SUMAR Program. The objective of this study was to describe the health care network and its preliminary outcomes. A total of 61 centers that provided a comprehensive treatment for oral clefts or in collaboration with other centers were identified and accredited. Maternity centers were connected with treating centers grouped in health care network nodes. In the period between March 2015 and February 2016, 550 newborn infants who were exclusively covered by the public health care system were identified. Among these, 18% had a cleft lip; 62%, cleft lip and palate; and 20%, cleft palate only; 75% were isolated cases and 25%, in association with other congenital anomalies. Approximately 70% of children were assessed by a certified treating institution and are receiving treatment. The network seeks to improve data systematization, include the largest number of centers possible, strengthen interdisciplinary team work, and promote high-quality standards for treatments. Sociedad Argentina de Pediatría

Global Observation Information Networking: Using the Distributed Image Spreadsheet (DISS)

NASA Technical Reports Server (NTRS)

Hasler, Fritz

1999-01-01

The DISS and many other tools will be used to present visualizations which span the period from the original Suomi/Hasler animations of the first ATS-1 GEO weather satellite images in 1966 ....... to the latest 1999 NASA Earth Science Vision for the next 25 years. Hot off the SGI Onyx Graphics-Supercomputers are NASA's visualizations of Hurricanes Mitch, Georges, Fran and Linda. These storms have been recently featured on the covers of National Geographic, Time, Newsweek and Popular Science and used repeatedly this season on National and International network TV. Results will be presented from a new paper on automatic wind measurements in Hurricane Luis from 1-min GOES images that appeared in the November BAMS.

Automatic detection of voice impairments by means of short-term cepstral parameters and neural network based detectors.

PubMed

Godino-Llorente, J I; Gómez-Vilda, P

2004-02-01

It is well known that vocal and voice diseases do not necessarily cause perceptible changes in the acoustic voice signal. Acoustic analysis is a useful tool to diagnose voice diseases being a complementary technique to other methods based on direct observation of the vocal folds by laryngoscopy. Through the present paper two neural-network based classification approaches applied to the automatic detection of voice disorders will be studied. Structures studied are multilayer perceptron and learning vector quantization fed using short-term vectors calculated accordingly to the well-known Mel Frequency Coefficient cepstral parameterization. The paper shows that these architectures allow the detection of voice disorders--including glottic cancer--under highly reliable conditions. Within this context, the Learning Vector quantization methodology demonstrated to be more reliable than the multilayer perceptron architecture yielding 96% frame accuracy under similar working conditions.

Georgia's Surface-Water Resources and Streamflow Monitoring Network, 2006

USGS Publications Warehouse

Nobles, Patricia L.; ,

2006-01-01

The U.S. Geological Survey (USGS) network of 223 real-time monitoring stations, the 'Georgia HydroWatch,' provides real-time water-stage data, with streamflow computed at 198 locations, and rainfall recorded at 187 stations. These sites continuously record data on 15-minute intervals and transmit the data via satellite to be incorporated into the USGS National Water Information System database. These data are automatically posted to the USGS Web site for public dissemination (http://waterdata.usgs.gov/ga/nwis/nwis). The real-time capability of this network provides information to help emergency-management officials protect human life and property during floods, and mitigate the effects of prolonged drought. The map at right shows the USGS streamflow monitoring network for Georgia and major watersheds. Streamflow is monitored at 198 sites statewide, more than 80 percent of which include precipitation gages. Various Federal, State, and local agencies fund these streamflow monitoring stations.

Automatic Seismic-Event Classification with Convolutional Neural Networks.

NASA Astrophysics Data System (ADS)

Bueno Rodriguez, A.; Titos Luzón, M.; Garcia Martinez, L.; Benitez, C.; Ibáñez, J. M.

2017-12-01

Active volcanoes exhibit a wide range of seismic signals, providing vast amounts of unlabelled volcano-seismic data that can be analyzed through the lens of artificial intelligence. However, obtaining high-quality labelled data is time-consuming and expensive. Deep neural networks can process data in their raw form, compute high-level features and provide a better representation of the input data distribution. These systems can be deployed to classify seismic data at scale, enhance current early-warning systems and build extensive seismic catalogs. In this research, we aim to classify spectrograms from seven different seismic events registered at "Volcán de Fuego" (Colima, Mexico), during four eruptive periods. Our approach is based on convolutional neural networks (CNNs), a sub-type of deep neural networks that can exploit grid structure from the data. Volcano-seismic signals can be mapped into a grid-like structure using the spectrogram: a representation of the temporal evolution in terms of time and frequency. Spectrograms were computed from the data using Hamming windows with 4 seconds length, 2.5 seconds overlapping and 128 points FFT resolution. Results are compared to deep neural networks, random forest and SVMs. Experiments show that CNNs can exploit temporal and frequency information, attaining a classification accuracy of 93%, similar to deep networks 91% but outperforming SVM and random forest. These results empirically show that CNNs are powerful models to classify a wide range of volcano-seismic signals, and achieve good generalization. Furthermore, volcano-seismic spectrograms contains useful discriminative information for the CNN, as higher layers of the network combine high-level features computed for each frequency band, helping to detect simultaneous events in time. Being at the intersection of deep learning and geophysics, this research enables future studies of how CNNs can be used in volcano monitoring to accurately determine the detection and

The USA National Phenology Network; taking the pulse of our planet

USGS Publications Warehouse

Weltzin, Jake F.

2011-01-01

People have tracked phenology for centuries and for the most practical reasons: it helped them know when to hunt and fish, when to plant and harvest crops, and when to navigate waterways. Now phenology is being used as a tool to assess climate change and its effects on both natural and modified ecosystems. How is the timing of events in plant and animal life cycles, like flowering or migration, responding to climate change? And how are those responses, in turn, affecting people and ecosystems? The USA National Phenology Network (the Network) is working to answer these questions for science and society by promoting a broad understanding of plant and animal phenology and their relationship to environmental change. The Network is a consortium of organizations and individuals that collect, share, and use phenology data, models, and related information to enable scientists, resource managers, and the public to adapt in response to changing climates and environments. In addition, the Network encourages people of all ages and backgrounds to observe and record phenology as a way to discover and explore the nature and pace of our dynamic world. The National Coordinating Office (NCO) of the Network is a resource center that facilitates and encourages widespread collection, integration, and sharing of phenology data and related information (for example, meteorological and hydrological data). The NCO develops and promotes standardized methods for field data collection and maintains several online user interfaces for data upload and download, as well as data exploration, visualization, and analysis. The NCO also facilitates basic and applied research related to phenology, the development of decision-support tools for resource managers and planners, and the design of educational and outreach materials

77 FR 50469 - Notice of Public Workshop: “Designing for Impact III: Workshop on Building the National Network...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-08-21

... series of public workshops entitled ``Designing for Impact: Workshop on Building the National Network for...-president-manufacturing-and-economy . The Designing for Impact workshop series is organized by the federal...: ``Designing for Impact III: Workshop on Building the National Network for Manufacturing Innovation'' AGENCY...

Hybrid Network Architectures for the Next Generation NAS

NASA Technical Reports Server (NTRS)

Madubata, Christian

2003-01-01

To meet the needs of the 21st Century NAS, an integrated, network-centric infrastructure is essential that is characterized by secure, high bandwidth, digital communication systems that support precision navigation capable of reducing position errors for all aircraft to within a few meters. This system will also require precision surveillance systems capable of accurately locating all aircraft, and automatically detecting any deviations from an approved path within seconds and be able to deliver high resolution weather forecasts - critical to create 4- dimensional (space and time) profiles for up to 6 hours for all atmospheric conditions affecting aviation, including wake vortices. The 21st Century NAS will be characterized by highly accurate digital data bases depicting terrain, obstacle, and airport information no matter what visibility conditions exist. This research task will be to perform a high-level requirements analysis of the applications, information and services required by the next generation National Airspace System. The investigation and analysis is expected to lead to the development and design of several national network-centric communications architectures that would be capable of supporting the Next Generation NAS.

Building Capacity for a Long-Term, in-Situ, National-Scale Phenology Monitoring Network: Successes, Challenges and Lessons Learned

NASA Astrophysics Data System (ADS)

Weltzin, J. F.; Browning, D. M.

2014-12-01

The USA National Phenology Network (USA-NPN; www.usanpn.org) is a national-scale science and monitoring initiative focused on phenology - the study of seasonal life-cycle events such as leafing, flowering, reproduction, and migration - as a tool to understand the response of biodiversity to environmental variation and change. USA-NPN provides a hierarchical, national monitoring framework that enables other organizations to leverage the capacity of the Network for their own applications - minimizing investment and duplication of effort - while promoting interoperability. Network participants can leverage: (1) Standardized monitoring protocols that have been broadly vetted, tested and published; (2) A centralized National Phenology Database (NPDb) for maintaining, archiving and replicating data, with standard metadata, terms-of-use, web-services, and documentation of QA/QC, plus tools for discovery, visualization and download of raw data and derived data products; and/or (3) A national in-situ, multi-taxa phenological monitoring system, Nature's Notebook, which enables participants to observe and record phenology of plants and animals - based on the protocols and information management system (IMS) described above - via either web or mobile applications. The protocols, NPDb and IMS, and Nature's Notebook represent a hierarchy of opportunities for involvement by a broad range of interested stakeholders, from individuals to agencies. For example, some organizations have adopted (e.g., the National Ecological Observatory Network or NEON) -- or are considering adopting (e.g., the Long-Term Agroecosystems Network or LTAR) -- the USA-NPN standardized protocols, but will develop their own database and IMS with web services to promote sharing of data with the NPDb. Other organizations (e.g., the Inventory and Monitoring Programs of the National Wildlife Refuge System and the National Park Service) have elected to use Nature's Notebook to support their phenological monitoring

U.S. Geological Survey external quality-assurance project report to the National Atmospheric Deposition Program / National Trends Network and Mercury Deposition Network, 2007-08

USGS Publications Warehouse

Wetherbee, Gregory A.; Latysh, Natalie E.; Chesney, Tanya A.

2010-01-01

The U.S. Geological Survey (USGS) used six distinct programs to provide external quality-assurance monitoring for the National Atmospheric Deposition Program / National Trends Network (NTN) and Mercury Deposition Network (MDN) during 2007-08. The field-audit program assessed the effects of onsite exposure, sample handling, and shipping on the chemistry of NTN samples, and a system-blank program assessed the same effects for MDN. Two interlaboratory-comparison programs assessed the bias and variability of the chemical analysis data from the Central Analytical Laboratory (CAL), Mercury (Hg) Analytical Laboratory (HAL), and 12 other participating laboratories. A blind-audit program was also implemented for the MDN to evaluate analytical bias in HAL total Hg concentration data. A co-located-sampler program was used to identify and quantify potential shifts in NADP data resulting from replacement of original network instrumentation with new electronic recording rain gages (E-gages) and prototype precipitation collectors. The results indicate that NADP data continue to be of sufficient quality for the analysis of spatial distributions and time trends of chemical constituents in wet deposition across the U.S. NADP data-quality objectives continued to be achieved during 2007-08. Results also indicate that retrofit of the NADP networks with the new E-gages is not likely to create step-function type shifts in NADP precipitation-depth records, except for sites where annual precipitation depth is dominated by snow because the E-gages tend to catch more snow than the original NADP rain gages. Evaluation of prototype precipitation collectors revealed no difference in sample volumes and analyte concentrations between the original NADP collectors and modified, deep-bucket collectors, but the Yankee Environmental Systems, Inc. (YES) collector obtained samples of significantly higher volumes and analyte concentrations than the standard NADP collector.

Automatic transducer switching provides accurate wide range measurement of pressure differential

NASA Technical Reports Server (NTRS)

Yoder, S. K.

1967-01-01

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

Automatic Detection of Landslides at Stromboli Volcano

NASA Astrophysics Data System (ADS)

Giudicepietro, F.; Esposito, A. M.; D'Auria, L.; Peluso, R.; Martini, M.

2011-12-01

In this work we present an automatic system for the landslide detection at Stromboli volcano that has proved to be effective both during the 2007 effusive eruption and in the recent (2 August 2011) volcanic activity. The study of the landslides at Stromboli is important because they could be considered short-term precursors of effusive eruptions, as seen during the 2007 eruption, and in addition they allow to improve the monitoring of the gravitational instabilities of the Sciara del Fuoco flank. The proposed system uses a two-class MLP (Multi Layer Perceptron) neural network in order to discriminate the landslides from other seismic signals usually recorded at Stromboli, such as explosion-quakes and volcanic tremor. To train and test the network we used a dataset of 537 signals, including 267 landslides and 270 other events (130 explosion-quakes and 140 tremor signals). The net performance is of 98.7%, if averaged over different net configurations, and of 99.5% for the best net performance. Based on the neural network response, the automatic system calculates a Landslide Percentage Index (LPI) defined on the number of signals identified as landslides by the net on a given temporal interval in order to recognize anomalies in the landslide rate. This system was sensitive to the signals produced by the flow of lava front during a recent mild effusive episode on the "La Sciara del Fuoco" slope.

The USA National Phenology Network: A national observatory for assessment of biotic response to environmental variation

NASA Astrophysics Data System (ADS)

Weltzin, J. F.; USA National Phenology Network National Coordinating Office

2011-12-01

The USA National Phenology Network (USA-NPN; www.usanpn.org), established in 2007, is a national science and monitoring initiative focused on phenology as a tool to understand how plants, animals and landscapes respond to climatic variability and change. Core functions of the National Coordinating Office (NCO) of USA-NPN are to provide a national information management system including databases, develop and implement internationally standardized phenology monitoring protocols, create partnerships with a variety of organizations including field stations for implementation, facilitate research and the development of decision support tools, and promote education and outreach activities related to phenology and climate change. This presentation will describe programs, tools and materials developed by USA-NPN to facilitate science, management and education related to phenology of plants, animals and landscapes within protected areas at local, regional and national scales. Particular emphasis will be placed on the on-line integrated animal and plant monitoring program, Nature's Notebook, which provides standardized protocols for phenological status monitoring and data management for over 500 animal and plant species. The monitoring system facilitates collection of sampling intensity, absence data, considerable metadata (from site to observation). We recently added functionality for recording estimates of animal abundance and plant canopy development. Real-time raw data for plants (from 2009 to present) and animals (from 2010 to present), including FGDC-compliant metadata and documented methodology, are now available for download from the website. A new data exploration tool premiered in spring 2010 allows sophisticated graphical visualization of integrated phenological and meteorological data. The network seeks to develop partnerships with other organizations interested in (1) implementing vetted, standardized protocols for phenological or ecological monitoring, and (2

From Caprio's lilacs to the USA National Phenology Network

USGS Publications Warehouse

Schwartz, Mark D.; Betancourt, Julio L.; Weltzin, Jake F.

2012-01-01

Continental-scale monitoring is vital for understanding and adapting to temporal changes in seasonal climate and associated phenological responses. The success of monitoring programs will depend on recruiting, retaining, and managing members of the public to routinely collect phenological observations according to standardized protocols. Here, we trace the development of infrastructure for phenological monitoring in the US, culminating in the USA National Phenology Network, a program that engages scientists and volunteers.

Automatic Large-Scalae 3d Building Shape Refinement Using Conditional Generative Adversarial Networks

NASA Astrophysics Data System (ADS)

Bittner, K.; d'Angelo, P.; Körner, M.; Reinartz, P.

2018-05-01

Three-dimensional building reconstruction from remote sensing imagery is one of the most difficult and important 3D modeling problems for complex urban environments. The main data sources provided the digital representation of the Earths surface and related natural, cultural, and man-made objects of the urban areas in remote sensing are the digital surface models (DSMs). The DSMs can be obtained either by light detection and ranging (LIDAR), SAR interferometry or from stereo images. Our approach relies on automatic global 3D building shape refinement from stereo DSMs using deep learning techniques. This refinement is necessary as the DSMs, which are extracted from image matching point clouds, suffer from occlusions, outliers, and noise. Though most previous works have shown promising results for building modeling, this topic remains an open research area. We present a new methodology which not only generates images with continuous values representing the elevation models but, at the same time, enhances the 3D object shapes, buildings in our case. Mainly, we train a conditional generative adversarial network (cGAN) to generate accurate LIDAR-like DSM height images from the noisy stereo DSM input. The obtained results demonstrate the strong potential of creating large areas remote sensing depth images where the buildings exhibit better-quality shapes and roof forms.

Development of a Coordinated National Soil Moisture Network: A Pilot Study

NASA Astrophysics Data System (ADS)

Lucido, J. M.; Quiring, S. M.; Verdin, J. P.; Pulwarty, R. S.; Baker, B.; Cosgrove, B.; Escobar, V. M.; Strobel, M.

2014-12-01

Soil moisture data is critical for accurate drought prediction, flood forecasting, climate modeling, prediction of crop yields and water budgeting. However, soil moisture data are collected by many agencies and organizations in the United States using a variety of instruments and methods for varying applications. These data are often distributed and represented in disparate formats, posing significant challenges for use. In recognition of these challenges, the President's Climate Action Plan articulated the need for a coordinated national soil moisture network. In response to this action plan, a team led by the National Integrated Drought Information System has begun to develop a framework for this network and has instituted a proof-of-concept pilot study. This pilot is located in the south-central plains of the US, and will serve as a reference architecture for the requisite data systems and inform the design of the national network. The pilot comprises both in-situ and modeled soil moisture datasets (historical and real-time) and will serve the following use cases: operational drought monitoring, experimental land surface modeling, and operational hydrological modeling. The pilot will be implemented using a distributed network design in order to serve dispersed data in real-time directly from data providers. Standard service protocols will be used to enable future integration with external clients. The pilot network will additionally contain a catalog of data sets and web service endpoints, which will be used to broker web service calls. A mediation and aggregation service will then intelligently request, compile, and transform the distributed datasets from their native formats into a standardized output. This mediation framework allows data to be hosted and maintained locally by the data owners while simplifying access through a single service interface. These data services will then be used to create visualizations, for example, views of the current soil

winderosionnetwork.org – Portal to the National Wind Erosion Research Network

USDA-ARS?s Scientific Manuscript database

The National Wind Erosion Research Network was established in 2014 as a collaborative effort led by the USDA Agricultural Research Service and Natural Resources Conservation Service, and USDI Bureau of Land Management, to address the need for standardized measurements of wind erosion and its control...

The National Stream Quality Accounting Network (NASQAN) - Some questions and answers

USGS Publications Warehouse

Ficke, John F.; Hawkinson, Richard O.

1975-01-01

One of the major new efforts of the U.S. Geological Survey is the National Stream Quality Accounting Network (NASQAN). This circular is intended to answer some of the frequently asked questions concerning concepts used in establishing NASQAN, its purposes, design, value, and future plans.

External quality-assurance results for the National Atmospheric Deposition Program and the National Trends Network during 1986

USGS Publications Warehouse

See, Randolph B.; Schroder, LeRoy J.; Willoughby, Timothy C.

1988-01-01

During 1986, the U.S. Geological Survey operated three programs to provide external quality-assurance monitoring of the National Atmospheric Deposition Program and National Trends Network. An intersite-comparison program was used to assess the accuracy of onsite pH and specific-conductance determinations at quarterly intervals. The blind-audit program was used to assess the effect of routine sample handling on the precision and bias of program and network wet-deposition data. Analytical results from four laboratories, which routinely analyze wet-deposition samples, were examined to determine if differences existed between laboratory analytical results and to provide estimates of the analytical precision of each laboratory. An average of 78 and 89 percent of the site operators participating in the intersite-comparison met the network goals for pH and specific conductance. A comparison of analytical values versus actual values for samples submitted as part of the blind-audit program indicated that analytical values were slightly but significantly (a = 0.01) larger than actual values for pH, magnesium, sodium, and sulfate; analytical values for specific conductance were slightly less than actual values. The decreased precision in the analyses of blind-audit samples when compared to interlaboratory studies indicates that a large amount of uncertainty in network deposition data may be a result of routine field operations. The results of the interlaboratory comparison study indicated that the magnitude of the difference between laboratory analyses was small for all analytes. Analyses of deionized, distilled water blanks by participating laboratories indicated that the laboratories had difficulty measuring analyte concentrations near their reported detection limits. (USGS)

Automatic optimisation of gamma dose rate sensor networks: The DETECT Optimisation Tool

NASA Astrophysics Data System (ADS)

Helle, K. B.; Müller, T. O.; Astrup, P.; Dyve, J. E.

2014-05-01

Fast delivery of comprehensive information on the radiological situation is essential for decision-making in nuclear emergencies. Most national radiological agencies in Europe employ gamma dose rate sensor networks to monitor radioactive pollution of the atmosphere. Sensor locations were often chosen using regular grids or according to administrative constraints. Nowadays, however, the choice can be based on more realistic risk assessment, as it is possible to simulate potential radioactive plumes. To support sensor planning, we developed the DETECT Optimisation Tool (DOT) within the scope of the EU FP 7 project DETECT. It evaluates the gamma dose rates that a proposed set of sensors might measure in an emergency and uses this information to optimise the sensor locations. The gamma dose rates are taken from a comprehensive library of simulations of atmospheric radioactive plumes from 64 source locations. These simulations cover the whole European Union, so the DOT allows evaluation and optimisation of sensor networks for all EU countries, as well as evaluation of fencing sensors around possible sources. Users can choose from seven cost functions to evaluate the capability of a given monitoring network for early detection of radioactive plumes or for the creation of dose maps. The DOT is implemented as a stand-alone easy-to-use JAVA-based application with a graphical user interface and an R backend. Users can run evaluations and optimisations, and display, store and download the results. The DOT runs on a server and can be accessed via common web browsers; it can also be installed locally.

Wireless Sensors Network (Sensornet)

NASA Technical Reports Server (NTRS)

Perotti, J.

2003-01-01

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

Social networks and alcohol use disorders: findings from a nationally representative sample

PubMed Central

Mowbray, Orion; Quinn, Adam; Cranford, James A.

2014-01-01

Background While some argue that social network ties of individuals with alcohol use disorders (AUD) are robust, there is evidence to suggest that individuals with AUDs have few social network ties, which are a known risk factor for health and wellness. Objectives Social network ties to friends, family, co-workers and communities of individuals are compared among individuals with a past-year diagnosis of alcohol dependence or alcohol abuse to individuals with no lifetime diagnosis of AUD. Method Respondents from Wave 2 of the National Epidemiologic Survey on Alcohol Related Conditions (NESARC) were assessed for the presence of past-year alcohol dependence or past-year alcohol abuse, social network ties, sociodemographics and clinical characteristics. Results Bivariate analyses showed that both social network size and social network diversity was significantly smaller among individuals with alcohol dependence, compared to individuals with alcohol abuse or no AUD. When social and clinical factors related to AUD status were controlled, multinomial logistic models showed that social network diversity remained a significant predictor of AUD status, while social network size did not differ among AUD groups. Conclusion Social networks of individuals with AUD may be different than individuals with no AUD, but this claim is dependent on specific AUD diagnosis and how social networks are measured. PMID:24405256

Performance of a wireless sensor network for crop monitoring and irrigation control

USDA-ARS?s Scientific Manuscript database

Robust automatic irrigation scheduling has been demonstrated using wired sensors and sensor network systems with subsurface drip and moving irrigation systems. However, there are limited studies that report on crop yield and water use efficiency resulting from the use of wireless networks to automat...

The Emergence Of The National Research And Education Network (NREN) And Its Implications For American Telecommunications

NASA Astrophysics Data System (ADS)

Maloff, Joel H.

1990-01-01

"The nation which most completely assimilates high performance computing into its economy will very likely emerge as the dominant intellectual, economic, and technological force in the next century", Senator Albert Gore, Jr., May 18, 1989, while introducing Senate Bill 1067, "The National High Performance Computer Technology Act of 1989". A national network designed to link supercomputers, particle accelerators, researchers, educators, government, and industry is beginning to emerge. The degree to which the United States can mobilize the resources inherent within our academic, industrial and government sectors towards the establishment of such a network infrastructure will have direct bearing on the economic and political stature of this country in the next century. This program will have significant impact on all forms of information transfer, and peripheral benefits to all walks of life similar to those experienced from the moon landing program of the 1960's. The key to our success is the involvement of scientists, librarians, network designers, and bureaucrats in the planning stages. Collectively, the resources resident within the United States are awesome; individually, their impact is somewhat more limited. The engineers, technicians, business people, and educators participating in this conference have a vital role to play in the success of the National Research and Education Network (NREN).

Automatic Publication of a MIS Product to GeoNetwork: Case of the AIS Indexer

DTIC Science & Technology

2012-11-01

installation and configuration The following instructions are for installing and configuring the software packages Java 1.6 and MySQL 5.5 which are...An Automatic Identification System (AIS) reception indexer Java application was developed in the summer of 2011, based on the work of Lapinski and...release; distribution unlimited 13. SUPPLEMENTARY NOTES 14. ABSTRACT An Automatic Identification System (AIS) reception indexer Java application was

Automatic pedicles detection using convolutional neural network in a 3D spine reconstruction from biplanar radiographs

NASA Astrophysics Data System (ADS)

Bakhous, Christine; Aubert, Benjamin; Vazquez, Carlos; Cresson, Thierry; Parent, Stefan; De Guise, Jacques

2018-02-01

The 3D analysis of the spine deformities (scoliosis) has a high potential in its clinical diagnosis and treatment. In a biplanar radiographs context, a 3D analysis requires a 3D reconstruction from a pair of 2D X-rays. Whether being fully-/semiautomatic or manual, this task is complex because of the noise, the structure superimposition and partial information due to a limited projections number. Being involved in the axial vertebra rotation (AVR), which is a fundamental clinical parameter for scoliosis diagnosis, pedicles are important landmarks for the 3D spine modeling and pre-operative planning. In this paper, we focus on the extension of a fully-automatic 3D spine reconstruction method where the Vertebral Body Centers (VBCs) are automatically detected using Convolutional Neural Network (CNN) and then regularized using a Statistical Shape Model (SSM) framework. In this global process, pedicles are inferred statistically during the SSM regularization. Our contribution is to add a CNN-based regression model for pedicle detection allowing a better pedicle localization and improving the clinical parameters estimation (e.g. AVR, Cobb angle). Having 476 datasets including healthy patients and Adolescent Idiopathic Scoliosis (AIS) cases with different scoliosis grades (Cobb angles up to 116°), we used 380 for training, 48 for testing and 48 for validation. Adding the local CNN-based pedicle detection decreases the mean absolute error of the AVR by 10%. The 3D mean Euclidian distance error between detected pedicles and ground truth decreases by 17% and the maximum error by 19%. Moreover, a general improvement is observed in the 3D spine reconstruction and reflected in lower errors on the Cobb angle estimation.

Toxicity of soluble film automatic dishwashing products as reported to the United Kingdom National Poisons Information Service 2008-2015.

PubMed

Day, Rachael; Eddleston, Michael; Thomas, Simon H L; Thompson, John P; Vale, J Allister

2016-11-01

Soluble film automatic dishwashing tablets, unlike their traditional counterparts, require no removal from an outer protective wrapper prior to use. Instead, the tablets are enclosed by a water-soluble polyvinyl alcohol film and are loaded straight into the dishwashing machine. They most commonly contain a source of hydrogen peroxide (often as sodium percarbonate) and non-ionic surfactants. Other constituents in some formulations include sodium carbonate, sodium tripolyphosphate and sodium silicate, which reduce water hardness. The pH once dissolved in water is alkaline. To determine the toxicity from exposure to soluble film automatic dishwashing tablets. Telephone enquiries to the United Kingdom National Poisons Information Service regarding soluble film automatic dishwashing products were analysed retrospectively for the period January 2008 to December 2015. There were 498 enquiries relating to 488 patients. Almost all exposures occurred in the home (98.4%) and involved children aged ≤5 years (92.8%). Exposure occurred mainly as a result of ingestion alone (n = 470, 96.3%); eye contact alone (n = 9, 1.8%) and exposures involving multiple routes (ingestion with skin or eye contact; n = 9, 1.8%) made up the remaining cases. The majority of patients were asymptomatic following exposure (n = 325, 67.4%). The most common feature following ingestion was vomiting which occurred in 121 of 474 cases (25.5%) where clinical data were available. Nausea (n = 8, 1.7%) and coughing (n = 6, 1.3%) were also reported; three patients developed stomatitis and another five developed a rash where ingestion alone was considered to be the sole route of exposure. Ocular exposure to the tablet contents resulted in blurred vision, eye pain or conjunctivitis in seven of ten patients. Ingestion of a soluble film automatic dishwashing tablet rarely resulted in clinically significant symptoms, which is surprising given the potential hazard of the ingredients. Hence

A case analysis of INFOMED: the Cuban national health care telecommunications network and portal.

PubMed

Séror, Ann C

2006-01-27

The Internet and telecommunications technologies contribute to national health care system infrastructures and extend global health care services markets. The Cuban national health care system offers a model to show how a national information portal can contribute to system integration, including research, education, and service delivery as well as international trade in products and services. The objectives of this paper are (1) to present the context of the Cuban national health care system since the revolution in 1959, (2) to identify virtual institutional infrastructures of the system associated with the Cuban National Health Care Telecommunications Network and Portal (INFOMED), and (3) to show how they contribute to Cuban trade in international health care service markets. Qualitative case research methods were used to identify the integrated virtual infrastructure of INFOMED and to show how it reflects socialist ideology. Virtual institutional infrastructures include electronic medical and information services and the structure of national networks linking such services. Analysis of INFOMED infrastructures shows integration of health care information, research, and education as well as the interface between Cuban national information networks and the global Internet. System control mechanisms include horizontal integration and coordination through virtual institutions linked through INFOMED, and vertical control through the Ministry of Public Health and the government hierarchy. Telecommunications technology serves as a foundation for a dual market structure differentiating domestic services from international trade. INFOMED is a model of interest for integrating health care information, research, education, and services. The virtual infrastructures linked through INFOMED support the diffusion of Cuban health care products and services in global markets. Transferability of this model is contingent upon ideology and interpretation of values such as individual

A Case Analysis of INFOMED: The Cuban National Health Care Telecommunications Network and Portal

PubMed Central

2006-01-01

Background The Internet and telecommunications technologies contribute to national health care system infrastructures and extend global health care services markets. The Cuban national health care system offers a model to show how a national information portal can contribute to system integration, including research, education, and service delivery as well as international trade in products and services. Objective The objectives of this paper are (1) to present the context of the Cuban national health care system since the revolution in 1959, (2) to identify virtual institutional infrastructures of the system associated with the Cuban National Health Care Telecommunications Network and Portal (INFOMED), and (3) to show how they contribute to Cuban trade in international health care service markets. Methods Qualitative case research methods were used to identify the integrated virtual infrastructure of INFOMED and to show how it reflects socialist ideology. Virtual institutional infrastructures include electronic medical and information services and the structure of national networks linking such services. Results Analysis of INFOMED infrastructures shows integration of health care information, research, and education as well as the interface between Cuban national information networks and the global Internet. System control mechanisms include horizontal integration and coordination through virtual institutions linked through INFOMED, and vertical control through the Ministry of Public Health and the government hierarchy. Telecommunications technology serves as a foundation for a dual market structure differentiating domestic services from international trade. Conclusions INFOMED is a model of interest for integrating health care information, research, education, and services. The virtual infrastructures linked through INFOMED support the diffusion of Cuban health care products and services in global markets. Transferability of this model is contingent upon ideology

Educational Policies. National Dropout Prevention Center/Network Newsletter. Volume 19, Number 2, Spring 2007

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2007-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Policy Matters; (2) A Conversation With A State Policymaker (Stephen Canessa); (3) Policy Matters at the School Level (Steven W. Edwards); (4) EEDA: Promise or Peril? (Sam…

A National Network of Neurotechnology Centers for the BRAIN Initiative

PubMed Central

Alivisatos, A. Paul; Chun, Miyoung; Church, George M.; Greenspan, Ralph J.; Roukes, Michael L.; Yuste, Rafael

2017-01-01

We propose the creation of a national network of neurotechnology centers to enhance and accelerate the BRAIN Initiative and optimally leverage the effort and creativity of individual laboratories involved in it. As “brain observatories,” these centers could provide the critical interdisciplinary environment both for realizing ambitious and complex technologies and for providing individual investigators with access to them. PMID:26481036

National Geographic Society Kids Network: Report on 1994 teacher participants

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

NONE

In 1994, National Geographic Society Kids Network, a computer/telecommunications-based science curriculum, was presented to elementary and middle school teachers through summer programs sponsored by NGS and US DOE. The network program assists teachers in understanding the process of doing science; understanding the role of computers and telecommunications in the study of science, math, and engineering; and utilizing computers and telecommunications appropriately in the classroom. The program enables teacher to integrate science, math, and technology with other subjects with the ultimate goal of encouraging students of all abilities to pursue careers in science/math/engineering. This report assesses the impact of the networkmore » program on participating teachers.« less

Social Networks and Risk for Depressive Symptoms in a National Sample of Sexual Minority Youth

PubMed Central

Hatzenbuehler, Mark L.; McLaughlin, Katie A.; Xuan, Ziming

2012-01-01

The aim of the study was to examine the social networks of sexual minority youths and to determine the associations between social networks and depressive symptoms. Data were obtained from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative cohort study of American adolescents (N=14,212). Wave 1 (1994–1995) collected extensive information about the social networks of participants through peer nomination inventories, as well as measures of sexual minority status and depressive symptoms. Using social network data, we examined three characteristics of adolescents’ social relationships: (1) social isolation; (2) degree of connectedness; and (3) social status. Sexual minority youths, particularly females, were more isolated, less connected, and had lower social status in peer networks than opposite-sex attracted youths. Among sexual minority male (but not female) youths, greater isolation as well as lower connectedness and status within a network were associated with greater depressive symptoms. Moreover, greater isolation in social networks partially explained the association between sexual minority status and depressive symptoms among males. Finally, a significant 3-way interaction indicated that the association between social isolation and depression was stronger for sexual minority male youths than non-minority youths and sexual minority females. These results suggest that the social networks in which sexual minority male youths are embedded may confer risk for depressive symptoms, underscoring the importance of considering peer networks in both research and interventions targeting sexual minority male adolescents. PMID:22771037

Social networks and risk for depressive symptoms in a national sample of sexual minority youth.

PubMed

Hatzenbuehler, Mark L; McLaughlin, Katie A; Xuan, Ziming

2012-10-01

The aim of the study was to examine the social networks of sexual minority youths and to determine the associations between social networks and depressive symptoms. Data were obtained from the National Longitudinal Study of Adolescent Health (Add Health), a nationally representative cohort study of American adolescents (N = 14,212). Wave 1 (1994-1995) collected extensive information about the social networks of participants through peer nomination inventories, as well as measures of sexual minority status and depressive symptoms. Using social network data, we examined three characteristics of adolescents' social relationships: (1) social isolation; (2) degree of connectedness; and (3) social status. Sexual minority youths, particularly females, were more isolated, less connected, and had lower social status in peer networks than opposite-sex attracted youths. Among sexual minority male (but not female) youths, greater isolation as well as lower connectedness and status within a network were associated with greater depressive symptoms. Moreover, greater isolation in social networks partially explained the association between sexual minority status and depressive symptoms among males. Finally, a significant 3-way interaction indicated that the association between social isolation and depression was stronger for sexual minority male youths than non-minority youths and sexual minority females. These results suggest that the social networks in which sexual minority male youths are embedded may confer risk for depressive symptoms, underscoring the importance of considering peer networks in both research and interventions targeting sexual minority male adolescents. Copyright © 2012 Elsevier Ltd. All rights reserved.

Network capability estimation. Vela network evaluation and automatic processing research. Technical report. [NETWORTH

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

Snell, N.S.

1976-09-24

NETWORTH is a computer program which calculates the detection and location capability of seismic networks. A modified version of NETWORTH has been developed. This program has been used to evaluate the effect of station 'downtime', the signal amplitude variance, and the station detection threshold upon network detection capability. In this version all parameters may be changed separately for individual stations. The capability of using signal amplitude corrections has been added. The function of amplitude corrections is to remove possible bias in the magnitude estimate due to inhomogeneous signal attenuation. These corrections may be applied to individual stations, individual epicenters, ormore » individual station/epicenter combinations. An option has been added to calculate the effect of station 'downtime' upon network capability. This study indicates that, if capability loss due to detection errors can be minimized, then station detection threshold and station reliability will be the fundamental limits to network performance. A baseline network of thirteen stations has been performed. These stations are as follows: Alaskan Long Period Array, (ALPA); Ankara, (ANK); Chiang Mai, (CHG); Korean Seismic Research Station, (KSRS); Large Aperture Seismic Array, (LASA); Mashhad, (MSH); Mundaring, (MUN); Norwegian Seismic Array, (NORSAR); New Delhi, (NWDEL); Red Knife, Ontario, (RK-ON); Shillong, (SHL); Taipei, (TAP); and White Horse, Yukon, (WH-YK).« less

National Earthquake Information Center Seismic Event Detections on Multiple Scales

NASA Astrophysics Data System (ADS)

Patton, J.; Yeck, W. L.; Benz, H.; Earle, P. S.; Soto-Cordero, L.; Johnson, C. E.

2017-12-01

The U.S. Geological Survey National Earthquake Information Center (NEIC) monitors seismicity on local, regional, and global scales using automatic picks from more than 2,000 near-real time seismic stations. This presents unique challenges in automated event detection due to the high variability in data quality, network geometries and density, and distance-dependent variability in observed seismic signals. To lower the overall detection threshold while minimizing false detection rates, NEIC has begun to test the incorporation of new detection and picking algorithms, including multiband (Lomax et al., 2012) and kurtosis (Baillard et al., 2014) pickers, and a new bayesian associator (Glass 3.0). The Glass 3.0 associator allows for simultaneous processing of variably scaled detection grids, each with a unique set of nucleation criteria (e.g., nucleation threshold, minimum associated picks, nucleation phases) to meet specific monitoring goals. We test the efficacy of these new tools on event detection in networks of various scales and geometries, compare our results with previous catalogs, and discuss lessons learned. For example, we find that on local and regional scales, rapid nucleation of small events may require event nucleation with both P and higher-amplitude secondary phases (e.g., S or Lg). We provide examples of the implementation of a scale-independent associator for an induced seismicity sequence (local-scale), a large aftershock sequence (regional-scale), and for monitoring global seismicity. Baillard, C., Crawford, W. C., Ballu, V., Hibert, C., & Mangeney, A. (2014). An automatic kurtosis-based P-and S-phase picker designed for local seismic networks. Bulletin of the Seismological Society of America, 104(1), 394-409. Lomax, A., Satriano, C., & Vassallo, M. (2012). Automatic picker developments and optimization: FilterPicker - a robust, broadband picker for real-time seismic monitoring and earthquake early-warning, Seism. Res. Lett. , 83, 531-540, doi: 10

77 FR 26509 - Request for Information on Proposed New Program: National Network for Manufacturing Innovation...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-05-04

...): Refining standards, materials, and equipment for additive manufacturing to enable low- cost, low-volume...-01] Request for Information on Proposed New Program: National Network for Manufacturing Innovation...: Request for information. SUMMARY: The NIST-hosted Advanced Manufacturing National Program Office (AMNPO...

Career and Technical Education. National Dropout Prevention Center/Network Newsletter. Volume 20, Number 3

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2008-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) Why Do I Have to Learn This?; (2) 2008 NDPN Crystal Star Winners; (3) Effective Freshman Transition for School Improvement (David Livingston, John Greene, and Lindy Stahlman); (4)…

Middle College. National Dropout Prevention Center/Network Newsletter. Volume 17, Number 4, Fall 2005

ERIC Educational Resources Information Center

Duckenfield, Marty, Ed.

2005-01-01

The "National Dropout Prevention Newsletter" is published quarterly by the National Dropout Prevention Center/Network. This issue contains the following articles: (1) College As A Bridge to High School Graduation (Terry Cash); (2) 2005 NDPN Crystal Star Awards of Excellence; (3) Mott Middle College (Chery S. Wagonlander); (4) Gateway to…

Complex Networks Analysis of Manual and Machine Translations

NASA Astrophysics Data System (ADS)

Amancio, Diego R.; Antiqueira, Lucas; Pardo, Thiago A. S.; da F. Costa, Luciano; Oliveira, Osvaldo N.; Nunes, Maria G. V.

Complex networks have been increasingly used in text analysis, including in connection with natural language processing tools, as important text features appear to be captured by the topology and dynamics of the networks. Following previous works that apply complex networks concepts to text quality measurement, summary evaluation, and author characterization, we now focus on machine translation (MT). In this paper we assess the possible representation of texts as complex networks to evaluate cross-linguistic issues inherent in manual and machine translation. We show that different quality translations generated by MT tools can be distinguished from their manual counterparts by means of metrics such as in- (ID) and out-degrees (OD), clustering coefficient (CC), and shortest paths (SP). For instance, we demonstrate that the average OD in networks of automatic translations consistently exceeds the values obtained for manual ones, and that the CC values of source texts are not preserved for manual translations, but are for good automatic translations. This probably reflects the text rearrangements humans perform during manual translation. We envisage that such findings could lead to better MT tools and automatic evaluation metrics.

Improving Schools' Partnership Programs in the National Network of Partnership Schools

ERIC Educational Resources Information Center

Sanders, Mavis; Sheldon, Steven; Epstein, Joyce

2005-01-01

The current wave of educational reform includes an emphasis on family and community involvement as a strategy for school improvement. Yet, to effectively engage families and communities in the educational process, educators need assistance and support. In 1996, the National Network of Partnership Schools (NNPS) was established to build the…

Improved passive optical network architectures to support local area network emulation and protection

NASA Astrophysics Data System (ADS)

Wong, Elaine; Nadarajah, Nishaanthan; Chae, Chang-Joon; Nirmalathas, Ampalavanapillai; Attygalle, Sanjeewa M.

2006-01-01

We describe two optical layer schemes which simultaneously facilitate local area network emulation and automatic protection switching against distribution fiber breaks in passive optical networks. One scheme employs a narrowband fiber Bragg grating placed close to the star coupler in the feeder fiber of the passive optical network, while the other uses an additional short length distribution fiber from the star coupler to each customer for the redirection of the customer traffic. Both schemes use RF subcarrier multiplexed transmission for intercommunication between customers in conjunction with upstream access to the central office at baseband. Failure detection and automatic protection switching are performed independently by each optical network unit that is located at the customer premises in a distributed manner. The restoration of traffic transported between the central office and an optical network unit in the event of the distribution fiber break is performed by interconnecting adjacent optical network units and carrying out signal transmissions via an independent but interconnected optical network unit. Such a protection mechanism enables multiple adjacent optical network units to be simultaneously protected by a single optical network unit utilizing its maximum available bandwidth. We experimentally verify the feasibility of both schemes with 1.25 Gb/s upstream baseband transmission to the central office and 155 Mb/s local area network data transmission on a RF subcarrier frequency. The experimental results obtained from both schemes are compared, and the power budgets are calculated to analyze the scalability of each scheme.

Ganges-Brahmaputra-Meghna Delta Connectivity Analysis Using New Tools for the Automatic Extraction of Channel Networks from Remotely Sensed Imagery

NASA Astrophysics Data System (ADS)

Jarriel, T. M.; Isikdogan, F.; Passalacqua, P.; Bovik, A.

2017-12-01

River deltas are one of the environmental ecosystems most threatened by climate change and anthropogenic activity. While their low elevation gradients and fertile soil have made them optimal for human inhabitation and diverse ecologic growth, it also makes them susceptible to adverse effects of sea level rise, flooding, subsidence, and manmade structures such as dams, levees, and dikes. One particularly large and threatened delta that is the focus area of this study, is the Ganges-Brahmaputra-Meghna Delta (GBMD) on the southern coast of Bangladesh/West Bengal India. In this study we analyze the GBMD channel network, identify areas of maximum change of the network, and use this information to predict how the network will respond under future scenarios. Landsat images of the delta from 1973 to 2017 are analyzed using new tools for the automatic extraction of channel networks from remotely sensed imagery [Isikdogan et al., 2017a, Isikdogan et al., 2017b]. The tools return channel width and channel centerline location at the resolution of the input imagery (30 m). Channel location variance over time is computed using the combined data from 1973 to 2017 and, based on this information, zones of highest change in the system are identified (Figure 1). Network metrics measuring characteristics of the delta's channels and islands are calculated for each year of the study and compared to the variance results in order to identify what metrics capture this change. These results provide both a method to identify zones of the GBMD that are currently experiencing the most change, as well as a means to predict what areas of the delta will experience network changes in the future. This information will be useful for informing coastal sustainability decisions about what areas of such a large and complex network should be the focus of remediation and mitigation efforts. Isikdogan, F., A. Bovik, P. Passalacqua (2017a), RivaMap: An Automated River Analysis and Mapping Engine, Remote

A new matrix for scoring the functionality of national laboratory networks in Africa: introducing the LABNET scorecard

PubMed Central

Datema, Tjeerd; Keita-Sow, Mah-Sere; Ndihokubwayo, Jean-Bosco; Isadore, Jocelyn; Oskam, Linda; Nkengasong, John; Lewis, Kim

2016-01-01

Background Functional national laboratory networks and systems are indispensable to the achievement of global health security targets according to the International Health Regulations. The lack of indicators to measure the functionality of national laboratory network has limited the efficiency of past and current interventions to enhance laboratory capacity in resource-limited-settings. Scorecard for laboratory networks We have developed a matrix for the assessment of national laboratory network functionality and progress thereof, with support from the African Society of Laboratory Medicine and the Association of Public Health Laboratories. The laboratory network (LABNET) scorecard was designed to: (1) Measure the status of nine overarching core capabilities of laboratory network required to achieve global health security targets, as recommended by the main normative standards; (2) Complement the World Health Organization joint external evaluation tool for the assessment of health system preparedness to International Health Regulations (2005) by providing detailed information on laboratory systems; and (3) Serve as a clear roadmap to guide the stepwise implementation of laboratory capability to prevent, detect and act upon infectious threats. Conclusions The application of the LABNET scorecard under the coordination of the African Society of Laboratory Medicine and the Association of Public Health Laboratories could contribute to the design, monitoring and evaluation of upcoming Global Health Security Agenda-supported laboratory capacity building programmes in sub Saharan-Africa and other resource-limited settings, and inform the development of national laboratory policies and strategic plans. Endorsement by the World Health Organization Regional Office for Africa is foreseen. PMID:28879141

Establishment of National Gravity Base Network of Iran

NASA Astrophysics Data System (ADS)

Hatam Chavari, Y.; Bayer, R.; Hinderer, J.; Ghazavi, K.; Sedighi, M.; Luck, B.; Djamour, Y.; Le Moign, N.; Saadat, R.; Cheraghi, H.

2009-04-01

A gravity base network is supposed to be a set of benchmarks uniformly distributed across the country and the absolute gravity values at the benchmarks are known to the best accessible accuracy. The gravity at the benchmark stations are either measured directly with absolute devices or transferred by gravity difference measurements by gravimeters from known stations. To decrease the accumulation of random measuring errors arising from these transfers, the number of base stations distributed across the country should be as small as possible. This is feasible if the stations are selected near to the national airports long distances apart but faster accessible and measurable by a gravimeter carried in an airplane between the stations. To realize the importance of such a network, various applications of a gravity base network are firstly reviewed. A gravity base network is the required reference frame for establishing 1st , 2nd and 3rd order gravity networks. Such a gravity network is used for the following purposes: a. Mapping of the structure of upper crust in geology maps. The required accuracy for the measured gravity values is about 0.2 to 0.4 mGal. b. Oil and mineral explorations. The required accuracy for the measured gravity values is about 5 µGal. c. Geotechnical studies in mining areas for exploring the underground cavities as well as archeological studies. The required accuracy is about 5 µGal and better. d. Subsurface water resource explorations and mapping crustal layers which absorb it. An accuracy of the same level of previous applications is required here too. e. Studying the tectonics of the Earth's crust. Repeated precise gravity measurements at the gravity network stations can assist us in identifying systematic height changes. The accuracy of the order of 5 µGal and more is required. f. Studying volcanoes and their evolution. Repeated precise gravity measurements at the gravity network stations can provide valuable information on the gradual

A National Network of Neurotechnology Centers for the BRAIN Initiative.

PubMed

Alivisatos, A Paul; Chun, Miyoung; Church, George M; Greenspan, Ralph J; Roukes, Michael L; Yuste, Rafael

2015-11-04

We propose the creation of a national network of neurotechnology centers to enhance and accelerate the BRAIN Initiative and optimally leverage the effort and creativity of individual laboratories involved in it. As "brain observatories," these centers could provide the critical interdisciplinary environment both for realizing ambitious and complex technologies and for providing individual investigators with access to them. Copyright © 2015 Elsevier Inc. All rights reserved.

The guitar chord-generating algorithm based on complex network

NASA Astrophysics Data System (ADS)

Ren, Tao; Wang, Yi-fan; Du, Dan; Liu, Miao-miao; Siddiqi, Awais

2016-02-01

This paper aims to generate chords for popular songs automatically based on complex network. Firstly, according to the characteristics of guitar tablature, six chord networks of popular songs by six pop singers are constructed and the properties of all networks are concluded. By analyzing the diverse chord networks, the accompaniment regulations and features are shown, with which the chords can be generated automatically. Secondly, in terms of the characteristics of popular songs, a two-tiered network containing a verse network and a chorus network is constructed. With this network, the verse and chorus can be composed respectively with the random walk algorithm. Thirdly, the musical motif is considered for generating chords, with which the bad chord progressions can be revised. This method can make the accompaniments sound more melodious. Finally, a popular song is chosen for generating chords and the new generated accompaniment sounds better than those done by the composers.

Neural network model for automatic traffic incident detection : final report, August 2001.

DOT National Transportation Integrated Search

2001-08-01

Automatic freeway incident detection is an important component of advanced transportation management systems (ATMS) that provides information for emergency relief and traffic control and management purposes. In this research, a multi-paradigm intelli...

A National Climate Change Adaptation Network for Protecting Water Security

NASA Astrophysics Data System (ADS)

Weaver, A.; Sauchyn, D.; Byrne, J. M.

2009-12-01

Water security and resource-dependent community-survival are being increasingly challenged as a consequence of climate change, and it is urgent that we plan now for the security of our water supplies which support our lives and livelihoods. However, the range of impacts of climate change on water availability, and the consequent environmental and human adaptations that are required, is so complex and serious that it will take the combined work of natural, health and social scientists working with industries and communities to solve them. Networks are needed that will identify crucial water issues under climate change at a range of scales in order to provide regionally-sensitive, solutions-oriented research and adaptation. We suggest national and supra-national water availability and community sustainability issues must be addressed by multidisciplinary research and adaptation networks. The work must be driven by a bottom-up research paradigm — science in the service of community and governance. We suggest that interdisciplinary teams of researchers, in partnership with community decision makers and local industries, are the best means to develop solutions as communities attempt to address future water demands, protect their homes from infrastructure damage, and meet their food, drinking water, and other essential resource requirements. The intention is to cover: the impact of climate change on Canadian natural resources, both marine and terrestrial; issues of long-term sustainability and resilience in human communities and the environments in which they are embedded; the making and moving of knowledge, be that between members of Indigenous and non-Indigenous communities, researchers of different disciplines, communities, industry, policymakers and the academy and the crucial involvement of the various orders of government in the response to water problems, under conditions of heightened uncertainty. Such an adaptation network must include a national

Network design and quality checks in automatic orientation of close-range photogrammetric blocks.

PubMed

Dall'Asta, Elisa; Thoeni, Klaus; Santise, Marina; Forlani, Gianfranco; Giacomini, Anna; Roncella, Riccardo

2015-04-03

Due to the recent improvements of automatic measurement procedures in photogrammetry, multi-view 3D reconstruction technologies are becoming a favourite survey tool. Rapidly widening structure-from-motion (SfM) software packages offer significantly easier image processing workflows than traditional photogrammetry packages. However, while most orientation and surface reconstruction strategies will almost always succeed in any given task, estimating the quality of the result is, to some extent, still an open issue. An assessment of the precision and reliability of block orientation is necessary and should be included in every processing pipeline. Such a need was clearly felt from the results of close-range photogrammetric surveys of in situ full-scale and laboratory-scale experiments. In order to study the impact of the block control and the camera network design on the block orientation accuracy, a series of Monte Carlo simulations was performed. Two image block configurations were investigated: a single pseudo-normal strip and a circular highly-convergent block. The influence of surveying and data processing choices, such as the number and accuracy of the ground control points, autofocus and camera calibration was investigated. The research highlights the most significant aspects and processes to be taken into account for adequate in situ and laboratory surveys, when modern SfM software packages are used, and evaluates their effect on the quality of the results of the surface reconstruction.

First-year Progress and Future Directions of the USA National Phenology Network

NASA Astrophysics Data System (ADS)

Weltzin, J. F.; Losleben, M. V.

2008-12-01

Background Periodic plant and animal cycles driven by seasonal variations in climate (i.e., phenology) set the stage for dynamics of ecosystem processes, determine land surface properties, control biosphere-atmosphere interactions, and affect food production, health, conservation, and recreation. Phenological data and models have applications related to scientific research, education and outreach, as well as to stakeholders interested in agriculture, tourism and recreation, human health, and natural resource conservation and management. The predictive potential of phenology requires a new data resource-a national network of integrated phenological observations and the tools to access and analyze them at multiple scales. The USA National Phenology Network (USA-NPN) is an emerging and exciting partnership between federal agencies, the academic community, and the general public to monitor and understand the influence of seasonal cycles on the Nation's resources. The USA-NPN will establish a wall-to-wall science and monitoring initiative focused on phenology as a tool to understand how plants, animals and landscapes respond to climate variation, and as a tool to facilitate human adaptation to ongoing and potential future climate change. Results The National Coordinating Office of the USA-NPN began operation in August 2007 at the University of Arizona, Tucson, AZ. This first year of operation produced many new phenology products and venues for phenology research and citizen involvement, as well as identification of future directions for the USA NPN. Products include a new web-site (www.usanpn.org) that went live in June 2008; the web-site includes a tool for on-line data entry, and serves as a clearinghouse for products and information to facilitate research and communication related to phenology. The new core Plant Phenology Program includes profiles for 185 vetted local, regional, and national plant species with descriptions and monitoring protocols, as well as

Mammal Inventory of the Mojave Network Parks-Death Valley and Joshua Tree National Parks, Lake Mead National Recreation Area, Manzanar National Historic Site, and Mojave National Preserve

USGS Publications Warehouse

Drost, Charles A.; Hart, Jan

2008-01-01

This report describes the results of a mammal inventory study of National Park Service units in the Mojave Desert Network, including Death Valley National Park, Joshua Tree National Park, Lake Mead National Recreation Area, Manzanar National Historic Site, and Mojave National Preserve. Fieldwork for the inventory focused on small mammals, primarily rodents and bats. Fieldwork for terrestrial small mammals used trapping with Sherman and Tomahawk small- and medium-sized mammal traps, along with visual surveys for diurnal species. The majority of sampling for terrestrial small mammals was carried out in 2002 and 2003. Methods used in field surveys for bats included mist-netting at tanks and other water bodies, along with acoustic surveys using Anabat. Most of the bat survey work was conducted in 2003. Because of extremely dry conditions in the first two survey years (and associated low mammal numbers), we extended field sampling into 2004, following a relatively wet winter. In addition to field sampling, we also reviewed, evaluated, and summarized museum and literature records of mammal species for all of the Park units. We documented a total of 59 mammal species as present at Death Valley National Park, with an additional five species that we consider of probable occurrence. At Joshua Tree, we also documented 50 species, and an additional four 'probable' species. At Lake Mead National Recreation Area, 57 mammal species have been positively documented, with 10 additional probable species. Manzanar National Historic Site had not been previously surveyed. We documented 19 mammal species at Manzanar, with an additional 11 probable species. Mojave National Preserve had not had a comprehensive list previously, either. There are now a total of 50 mammal species documented at Mojave, with three additional probable species. Of these totals, 23 occurrences are new at individual park units (positively documented for the first time), with most of these being at Manzanar

Global and national laboratory networks support high quality surveillance for measles and rubella.

PubMed

Xu, Wenbo; Zhang, Yan; Wang, Huiling; Zhu, Zhen; Mao, Naiying; Mulders, Mick N; Rota, Paul A

2017-05-01

Laboratory networks are an essential component of disease surveillance systems because they provide accurate and timely confirmation of infection. WHO coordinates global laboratory surveillance of vaccine preventable diseases, including measles and rubella. The more than 700 laboratories within the WHO Global Measles and Rubella Laboratory Network (GMRLN) supports surveillance for measles, rubella and congenial rubella syndrome in 191 counties. This paper describes the overall structure and function of the GMRLN and highlights the largest of the national laboratory networks, the China Measles and Rubella Laboratory Network. Published by Oxford University Press on behalf of Royal Society of Tropical Medicine and Hygiene 2017. This work is written by (a) US Government employee(s) and is in the public domain in the US.

Compliance with National Comprehensive Cancer Network anti-emesis guidelines in a Community Hospital Cancer Center.

PubMed

Daniel, Divya; Waddell, Aubrey

2016-02-01

Nausea and vomiting are common adverse events exhibited by patients receiving chemotherapy. Prophylactic use of anti-emetic agents has been shown to reduce chemotherapy-induced nausea and vomiting. Compliance with the National Comprehensive Cancer Network anti-emesis guidelines (Version 1.2013) by practitioners in a community out-patient hospital (Blount Memorial Hospital) has been reviewed and the results are presented herein. Retrospective study of patients receiving their first cycle of chemotherapy. A total of 487 patients were reviewed from January 2005 to July 2012. In total, 70 patients were categorized in the high-risk category, 292 patients were categorized in the moderate-risk category, 60 patients were categorized in the low-risk category, and 65 patients were categorized in the minimal-risk category as per the National Comprehensive Cancer Network guidelines. Included patients were being administered the first cycle of their first treatment at Blount Memorial Hospital. Data were collected retrospectively from patient chemotherapy dispensing folders. In all, 63% of the patients received appropriate anti-emetic prophylaxis medications as per the National Comprehensive Cancer Network guidelines. Post-comparison between outcomes based on the risk category showed that patients in the moderate-risk category were most likely (91%) and patients in the low-risk category were least likely (6.67%) to receive appropriate anti-emetic prophylaxis as per the National Comprehensive Cancer Network guidelines. Overall compliance with guidelines is acceptable. Patients in the moderate risk category are most likely to receive appropriate anti-emetic prophylaxis. © The Author(s) 2014.

Automatic classification of DMSA scans using an artificial neural network

NASA Astrophysics Data System (ADS)

Wright, J. W.; Duguid, R.; Mckiddie, F.; Staff, R. T.

2014-04-01

DMSA imaging is carried out in nuclear medicine to assess the level of functional renal tissue in patients. This study investigated the use of an artificial neural network to perform diagnostic classification of these scans. Using the radiological report as the gold standard, the network was trained to classify DMSA scans as positive or negative for defects using a representative sample of 257 previously reported images. The trained network was then independently tested using a further 193 scans and achieved a binary classification accuracy of 95.9%. The performance of the network was compared with three qualified expert observers who were asked to grade each scan in the 193 image testing set on a six point defect scale, from ‘definitely normal’ to ‘definitely abnormal’. A receiver operating characteristic analysis comparison between a consensus operator, generated from the scores of the three expert observers, and the network revealed a statistically significant increase (α < 0.05) in performance between the network and operators. A further result from this work was that when suitably optimized, a negative predictive value of 100% for renal defects was achieved by the network, while still managing to identify 93% of the negative cases in the dataset. These results are encouraging for application of such a network as a screening tool or quality assurance assistant in clinical practice.

A new matrix for scoring the functionality of national laboratory networks in Africa: introducing the LABNET scorecard.

PubMed

Ondoa, Pascale; Datema, Tjeerd; Keita-Sow, Mah-Sere; Ndihokubwayo, Jean-Bosco; Isadore, Jocelyn; Oskam, Linda; Nkengasong, John; Lewis, Kim

2016-01-01

Functional national laboratory networks and systems are indispensable to the achievement of global health security targets according to the International Health Regulations. The lack of indicators to measure the functionality of national laboratory network has limited the efficiency of past and current interventions to enhance laboratory capacity in resource-limited-settings. We have developed a matrix for the assessment of national laboratory network functionality and progress thereof, with support from the African Society of Laboratory Medicine and the Association of Public Health Laboratories. The laboratory network (LABNET) scorecard was designed to: (1) Measure the status of nine overarching core capabilities of laboratory network required to achieve global health security targets, as recommended by the main normative standards; (2) Complement the World Health Organization joint external evaluation tool for the assessment of health system preparedness to International Health Regulations (2005) by providing detailed information on laboratory systems; and (3) Serve as a clear roadmap to guide the stepwise implementation of laboratory capability to prevent, detect and act upon infectious threats. The application of the LABNET scorecard under the coordination of the African Society of Laboratory Medicine and the Association of Public Health Laboratories could contribute to the design, monitoring and evaluation of upcoming Global Health Security Agenda-supported laboratory capacity building programmes in sub Saharan-Africa and other resource-limited settings, and inform the development of national laboratory policies and strategic plans. Endorsement by the World Health Organization Regional Office for Africa is foreseen.

The National Research and Education Network (NREN): Research and Policy Perspectives.

ERIC Educational Resources Information Center

McClure, Charles R.; And Others

This book provides an overview and status report on the progress made in developing the National Research and Education Network (NREN) as of early 1991. It reports on a number of investigations that provide a research and policy perspective on the NREN and computer-mediated communication (CMC), and brings together key source documents that have…

Automatic Imitation

ERIC Educational Resources Information Center

Heyes, Cecilia

2011-01-01

"Automatic imitation" is a type of stimulus-response compatibility effect in which the topographical features of task-irrelevant action stimuli facilitate similar, and interfere with dissimilar, responses. This article reviews behavioral, neurophysiological, and neuroimaging research on automatic imitation, asking in what sense it is "automatic"…

Automatic Tool for Local Assembly Structures

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

Whole community shotgun sequencing of total DNA (i.e. metagenomics) and total RNA (i.e. metatranscriptomics) has provided a wealth of information in the microbial community structure, predicted functions, metabolic networks, and is even able to reconstruct complete genomes directly. Here we present ATLAS (Automatic Tool for Local Assembly Structures) a comprehensive pipeline for assembly, annotation, genomic binning of metagenomic and metatranscriptomic data with an integrated framework for Multi-Omics. This will provide an open source tool for the Multi-Omic community at large.

What Information Does Your EHR Contain? Automatic Generation of a Clinical Metadata Warehouse (CMDW) to Support Identification and Data Access Within Distributed Clinical Research Networks.

PubMed

Bruland, Philipp; Doods, Justin; Storck, Michael; Dugas, Martin

2017-01-01

Data dictionaries provide structural meta-information about data definitions in health information technology (HIT) systems. In this regard, reusing healthcare data for secondary purposes offers several advantages (e.g. reduce documentation times or increased data quality). Prerequisites for data reuse are its quality, availability and identical meaning of data. In diverse projects, research data warehouses serve as core components between heterogeneous clinical databases and various research applications. Given the complexity (high number of data elements) and dynamics (regular updates) of electronic health record (EHR) data structures, we propose a clinical metadata warehouse (CMDW) based on a metadata registry standard. Metadata of two large hospitals were automatically inserted into two CMDWs containing 16,230 forms and 310,519 data elements. Automatic updates of metadata are possible as well as semantic annotations. A CMDW allows metadata discovery, data quality assessment and similarity analyses. Common data models for distributed research networks can be established based on similarity analyses.

Commutated automatic gain control system

NASA Technical Reports Server (NTRS)

Yost, S. R.

1982-01-01

A commutated automatic gain control (AGC) system was designed and built for a prototype Loran C receiver. The receiver uses a microcomputer to control a memory aided phase-locked loop (MAPLL). The microcomputer also controls the input/output, latitude/longitude conversion, and the recently added AGC system. The circuit designed for the AGC is described, and bench and flight test results are presented. The AGC circuit described actually samples starting at a point 40 microseconds after a zero crossing determined by the software lock pulse ultimately generated by a 30 microsecond delay and add network in the receiver front end envelope detector.

Bidirectional automatic release of reserve for low voltage network made with low capacity PLCs

NASA Astrophysics Data System (ADS)

Popa, I.; Popa, G. N.; Diniş, C. M.; Deaconu, S. I.

2018-01-01

The article presents the design of a bidirectional automatic release of reserve made on two types low capacity programmable logic controllers: PS-3 from Klöckner-Moeller and Zelio from Schneider. It analyses the electronic timing circuits that can be used for making the bidirectional automatic release of reserve: time-on delay circuit and time-off delay circuit (two types). In the paper are present the sequences code for timing performed on the PS-3 PLC, the logical functions for the bidirectional automatic release of reserve, the classical control electrical diagram (with contacts, relays, and time relays), the electronic control diagram (with logical gates and timing circuits), the code (in IL language) made for the PS-3 PLC, and the code (in FBD language) made for Zelio PLC. A comparative analysis will be carried out on the use of the two types of PLC and will be present the advantages of using PLCs.

Automatic cerebrospinal fluid segmentation in non-contrast CT images using a 3D convolutional network

NASA Astrophysics Data System (ADS)

Patel, Ajay; van de Leemput, Sil C.; Prokop, Mathias; van Ginneken, Bram; Manniesing, Rashindra

2017-03-01

Segmentation of anatomical structures is fundamental in the development of computer aided diagnosis systems for cerebral pathologies. Manual annotations are laborious, time consuming and subject to human error and observer variability. Accurate quantification of cerebrospinal fluid (CSF) can be employed as a morphometric measure for diagnosis and patient outcome prediction. However, segmenting CSF in non-contrast CT images is complicated by low soft tissue contrast and image noise. In this paper we propose a state-of-the-art method using a multi-scale three-dimensional (3D) fully convolutional neural network (CNN) to automatically segment all CSF within the cranial cavity. The method is trained on a small dataset comprised of four manually annotated cerebral CT images. Quantitative evaluation of a separate test dataset of four images shows a mean Dice similarity coefficient of 0.87 +/- 0.01 and mean absolute volume difference of 4.77 +/- 2.70 %. The average prediction time was 68 seconds. Our method allows for fast and fully automated 3D segmentation of cerebral CSF in non-contrast CT, and shows promising results despite a limited amount of training data.

A corticostriatal deficit promotes temporal distortion of automatic action in ageing

PubMed Central

Matamales, Miriam; Skrbis, Zala; Bailey, Matthew R; Balsam, Peter D; Balleine, Bernard W; Götz, Jürgen

2017-01-01

The acquisition of motor skills involves implementing action sequences that increase task efficiency while reducing cognitive loads. This learning capacity depends on specific cortico-basal ganglia circuits that are affected by normal ageing. Here, combining a series of novel behavioural tasks with extensive neuronal mapping and targeted cell manipulations in mice, we explored how ageing of cortico-basal ganglia networks alters the microstructure of action throughout sequence learning. We found that, after extended training, aged mice produced shorter actions and displayed squeezed automatic behaviours characterised by ultrafast oligomeric action chunks that correlated with deficient reorganisation of corticostriatal activity. Chemogenetic disruption of a striatal subcircuit in young mice reproduced age-related within-sequence features, and the introduction of an action-related feedback cue temporarily restored normal sequence structure in aged mice. Our results reveal static properties of aged cortico-basal ganglia networks that introduce temporal limits to action automaticity, something that can compromise procedural learning in ageing. PMID:29058672

Toward next-generation optical networks: a network operator perspective based on experimental tests and economic analysis

NASA Astrophysics Data System (ADS)

Xiao, Xiaojun; Du, Chunsheng; Zhou, Rongsheng

2004-04-01

As a result of data traffic"s exponential growth, network is currently evolving from fixed circuit switched services to dynamic packet switched services, which has brought unprecedented changes to the existing transport infrastructure. It is generally agreed that automatic switched optical network (ASON) is one of the promising solutions for the next generation optical networks. In this paper, we present the results of our experimental tests and economic analysis on ASON. The intention of this paper is to present our perspective, in terms of evolution strategy toward ASON, on next generation optical networks. It is shown through experimental tests that the performance of current Pre-standard ASON enabled equipments satisfies the basic requirements of network operators and is ready for initial deployment. The results of the economic analysis show that network operators can be benefit from the deployment of ASON from three sides. Firstly, ASON can reduce the CAPEX for network expanding by integrating multiple ADM & DCS into one box. Secondly, ASON can reduce the OPEX for network operation by introducing automatic resource control scheme. Finally, ASON can increase margin revenue by providing new optical network services such as Bandwidth on Demand, optical VPN etc. Finally, the evolution strategy is proposed as our perspective toward next generation optical networks. We hope the evolution strategy introduced may be helpful for the network operators to gracefully migrate their fixed ring based legacy networks to next generation dynamic mesh based network.

Avoiding Accountability: How Charter Operators Evade Ohio's Automatic Closure Law. K-12 Education

ERIC Educational Resources Information Center

DePaoli, Jennifer; van Lier, Piet

2013-01-01

Ohio's charter-closure law is touted as one of the toughest in the nation because it requires the automatic closure of charter schools that consistently fail to meet academic standards. Ohio's charter-closure law, which became effective in 2008 and was revised in 2011, calls for automatic closure of schools rated in Academic Emergency for at least…

Journal Article: the National Dioxin Air Monitoring Network ...

EPA Pesticide Factsheets

In June, 1998, the U.S. EPA established the National Dioxin Air Monitoring Network (NDAMN). The primary goal of NDAMN is determine the temporal and geographical variability of atmospheric CDDs, CDFs, and coplanar PCBs at rural and nonimpacted locations throughout the United States. Currently operating at 32 sampling stations, NDAMN has three primary purposes: (1) to determine the atmospheric levels and occurrences of dioxin-like compounds in rural and agricultural areas where livestock, poultry and animal feed crops are grown; (2) to provide measurements of atmospheric levels of dioxin-like compounds in different geographic regions of the U.S.; and (3) to provide information regarding the long-range transport of dioxin-like compounds in air over the U.S. At Dioxin 2000, we reported on the preliminary results of monitoring at 9 rural locations from June 1998 through June 1999. By the end of 1999, NDAMN had expanded to 21 sampling stations. Then, at Dioxin 2001, we reported the results of the first 18 months of operation of NDAMN at 15 rural and 6 National Park stations in the United States. The following is intended to be an update to this national monitoring effort. We are reporting the air monitoring results of 17 rural and 8 National Park NDAMN stations operational over 4 sampling moments during calendar year 2000. Two stations located in suburban Washington DC and San Francisco, CA are more urban in character and serve as an indicator of CDD/F and cop

Automatic detection of anatomical regions in frontal x-ray images: comparing convolutional neural networks to random forest

NASA Astrophysics Data System (ADS)

Olory Agomma, R.; Vázquez, C.; Cresson, T.; De Guise, J.

2018-02-01

Most algorithms to detect and identify anatomical structures in medical images require either to be initialized close to the target structure, or to know that the structure is present in the image, or to be trained on a homogeneous database (e.g. all full body or all lower limbs). Detecting these structures when there is no guarantee that the structure is present in the image, or when the image database is heterogeneous (mixed configurations), is a challenge for automatic algorithms. In this work we compared two state-of-the-art machine learning techniques in order to determine which one is the most appropriate for predicting targets locations based on image patches. By knowing the position of thirteen landmarks points, labelled by an expert in EOS frontal radiography, we learn the displacement between salient points detected in the image and these thirteen landmarks. The learning step is carried out with a machine learning approach by exploring two methods: Convolutional Neural Network (CNN) and Random Forest (RF). The automatic detection of the thirteen landmarks points in a new image is then obtained by averaging the positions of each one of these thirteen landmarks estimated from all the salient points in the new image. We respectively obtain for CNN and RF, an average prediction error (both mean and standard deviation in mm) of 29 +/-18 and 30 +/- 21 for the thirteen landmarks points, indicating the approximate location of anatomical regions. On the other hand, the learning time is 9 days for CNN versus 80 minutes for RF. We provide a comparison of the results between the two machine learning approaches.

The HOME network: an Australian national initiative for home therapies.

PubMed

Chow, Josephine; Fortnum, Debbie; Moodie, Jo-Anne; Simmonds, Rosemary; Tomlins, Melinda

2013-01-01

Longer, more frequent dialysis at home can improve life expectancy for patients with chronic kidney disease. Increased use of home dialysis therapies also benefits the hospital system, allowing for more efficient allocation of clinic resources. However, the Australian and New Zealand Data Registry statistics highlight the low uptake of home haemodialysis and peritoneal dialysis across Australia. In August 2009, the Australia's HOME Network was established as a national initiative to engage and empower healthcare professionals working in the home dialysis specialty. The aim was to develop solutions to advocate for and ultimately increase the use of home therapies. This paper describes the development, achievement and future plan of the Australian HOME Network. Achievements to date include: a survey of HOME Network members to assess the current state of patient and healthcare professional-targeted education resources; development of two patient case studies and activities addressing how to overcome the financial burden experienced by patients on home dialysis. Future projects aim to improve patient and healthcare professional education, and advocacy for home dialysis therapies. The HOME Network is supporting healthcare professionals working in the home dialysis specialty to develop solutions and tools that will help to facilitate greater utilisation of home dialysis therapies. © 2013 European Dialysis and Transplant Nurses Association/European Renal Care Association.

A TDMA Broadcast Satellite/Ground Architecture for the Aeronautical Telecommunications Network

NASA Technical Reports Server (NTRS)

Shamma, Mohammed A.; Raghavan, Rajesh S.

2003-01-01

An initial evaluation of a TDMA satellite broadcast architecture with an integrated ground network is proposed in this study as one option for the Aeronautical Telecommunications Network (ATN). The architecture proposed consists of a ground based network that is dedicated to the reception and transmissions of Automatic Dependent Surveillance Broadcast (ADS-B) messages from Mode-S or UAT type systems, along with tracks from primary and secondary surveillance radars. Additionally, the ground network could contain VHF Digital Link Mode 2, 3 or 4 transceivers for the reception and transmissions of Controller-Pilot Data Link Communications (CPDLC) messages and for voice. The second part of the ATN network consists of a broadcast satellite based system that is mainly dedicated for the transmission of surveillance data as well as En-route Flight Information Service Broadcast (FIS-B) to all aircraft. The system proposed integrates those two network to provide a nation wide comprehensive service utilizing near term or existing technologies and hence keeping the economic factor in prospective. The next few sections include a background introduction, the ground subnetwork, the satellite subnetwork, modeling and simulations, and conclusion and recommendations.

Neural network applications in telecommunications

NASA Technical Reports Server (NTRS)

Alspector, Joshua

1994-01-01

Neural network capabilities include automatic and organized handling of complex information, quick adaptation to continuously changing environments, nonlinear modeling, and parallel implementation. This viewgraph presentation presents Bellcore work on applications, learning chip computational function, learning system block diagram, neural network equalization, broadband access control, calling-card fraud detection, software reliability prediction, and conclusions.

Adaptive Neural Networks for Automatic Negotiation

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

Sakas, D. P.; Vlachos, D. S.; Simos, T. E.

The use of fuzzy logic and fuzzy neural networks has been found effective for the modelling of the uncertain relations between the parameters of a negotiation procedure. The problem with these configurations is that they are static, that is, any new knowledge from theory or experiment lead to the construction of entirely new models. To overcome this difficulty, we apply in this work, an adaptive neural topology to model the negotiation process. Finally a simple simulation is carried in order to test the new method.

Automating the Presentation of Computer Networks

DTIC Science & Technology

2006-12-01

software to overlay operational state information. Other network management tools like Computer Associates Unicenter [6,7] generate internal network...and required manual placement assistance. A number of software libraries [20] offer a wealth of automatic layout algorithms and presentation...FX010857971033.aspx [2] Microsoft (2005) Visio 2003 Product Demo, http://www.microsoft.com/office/visio/prodinfo/demo.mspx [3] Smartdraw (2005) Network

Quality Inservice Education: Final Report of the National Inservice Network, 1978-1981.

ERIC Educational Resources Information Center

Burrello, Leonard C.; And Others

The document comprises the final report of the National Inservice Network (NIN), a program to describe and distribute regular education inservice (REGI) project abstracts, products, and lessons aimed at more effectively working with handicapped students. Initial sections contain an executive summary and an overview explaining the NIN as a…

Enhancement of the national strong-motion network in Turkey

USGS Publications Warehouse

Gulkan, Polat; Ceken, U.; Colakoglu, Z.; Ugras, T.; Kuru, T.; Apak, A.; Anderson, J.G.; Sucuoglu, H.; Celebi, M.; Akkar, D.S.; Yazgan, U.; Denizlioglu, A.Z.

2007-01-01

Two arrays comprising 20 strong-motion sensors were established in western Turkey. The 14 stations of BYTNet follow a N-S trending line about 65 km in length, normal to strands of the North Anatolian fault that runs between the cities of Bursa and Yalova. Here the dominant character of the potential fault movement is a right-lateral transform slip. The DATNet array, comprising a total of eight stations, is arranged along a 110-km-long E-W trending direction along the Menderes River valley between Denizli and Aydin. (Two stations in this array were incorporated from the existing Turkish national strong-motion network.) This is an extensional tectonic environment, and the network mornitors potential large normal-faulting earthquakes on the faults in the valley. The installation of the arrays was supported by the North Atlantic Treaty Organization (NATO) under its Science for Peace Program. Maintenance and calibration is performed by the General Directorate of Disaster Affairs (GDDA) according to a protocol between Middle East Technical University (METU) and GDDA. Many young engineers and scientists have been trained in network operation and evaluation during the course of the project, and an international workshop dealing with strong-motion instrumentation has been organized as part of the project activities.

Engineering of Sensor Network Structure for Dependable Fusion

DTIC Science & Technology

2014-08-15

Lossy Wireless Sensor Networks , IEEE/ACM Transactions on Networking , (04 2013): 0. doi: 10.1109/TNET.2013.2256795 Soumik Sarkar, Kushal Mukherjee...Phoha, Bharat B. Madan, Asok Ray. Distributed Network Control for Mobile Multi-Modal Wireless Sensor Networks , Journal of Parallel and Distributed...Deadline Constraints, IEEE Transactions on Automatic Control special issue on Wireless Sensor and Actuator Networks , (01 2011): 1. doi: Eric Keller

Comparison Of Semi-Automatic And Automatic Slick Detection Algorithms For Jiyeh Power Station Oil Spill, Lebanon

NASA Astrophysics Data System (ADS)

Osmanoglu, B.; Ozkan, C.; Sunar, F.

2013-10-01

After air strikes on July 14 and 15, 2006 the Jiyeh Power Station started leaking oil into the eastern Mediterranean Sea. The power station is located about 30 km south of Beirut and the slick covered about 170 km of coastline threatening the neighboring countries Turkey and Cyprus. Due to the ongoing conflict between Israel and Lebanon, cleaning efforts could not start immediately resulting in 12 000 to 15 000 tons of fuel oil leaking into the sea. In this paper we compare results from automatic and semi-automatic slick detection algorithms. The automatic detection method combines the probabilities calculated for each pixel from each image to obtain a joint probability, minimizing the adverse effects of atmosphere on oil spill detection. The method can readily utilize X-, C- and L-band data where available. Furthermore wind and wave speed observations can be used for a more accurate analysis. For this study, we utilize Envisat ASAR ScanSAR data. A probability map is generated based on the radar backscatter, effect of wind and dampening value. The semi-automatic algorithm is based on supervised classification. As a classifier, Artificial Neural Network Multilayer Perceptron (ANN MLP) classifier is used since it is more flexible and efficient than conventional maximum likelihood classifier for multisource and multi-temporal data. The learning algorithm for ANN MLP is chosen as the Levenberg-Marquardt (LM). Training and test data for supervised classification are composed from the textural information created from SAR images. This approach is semiautomatic because tuning the parameters of classifier and composing training data need a human interaction. We point out the similarities and differences between the two methods and their results as well as underlining their advantages and disadvantages. Due to the lack of ground truth data, we compare obtained results to each other, as well as other published oil slick area assessments.

Deployment of the National Transparent Optical Network around the San Francisco Bay Area

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

McCammon, K.; Haigh, R.; Armstrong, G.

1996-06-01

We report on the deployment and initial operation of the National Transparent Optical Network, an experimental WDM network testbed around the San Francisco Bay Area, during the Optical Fiber Conference (OFC`96) held in San Jose, CA. The deployment aspects of the physical plant, optical and SONET layers are examined along with a discussion of broadband applications which utilized the network during the OFC`96 demonstration. The network features dense WDM technology, transparent optical routing technology using acousto- optic tunable filter based switches, and network modules with add/drop, multicast, and wavelength translation capabilities. The physical layer consisted of over 300 km ofmore » Sprint and Pacific Bell conventional single mode fiber which was amplified with I I optical amplifiers deployed in pre-amp, post-amp, and line amp configurations. An out-of-band control network provided datacom channels from remote equipment sites to the SONET network manager deployed at the San Jose Convention Center for the conference. Data transport over five wavelengths was achieved in the 1550 nm window using a variety of signal formats including analog and digital signal transmission on different wavelengths on the same fiber. The network operated throughout the week of OFC`96 and is still in operation today.« less

SA-SOM algorithm for detecting communities in complex networks

NASA Astrophysics Data System (ADS)

Chen, Luogeng; Wang, Yanran; Huang, Xiaoming; Hu, Mengyu; Hu, Fang

2017-10-01

Currently, community detection is a hot topic. This paper, based on the self-organizing map (SOM) algorithm, introduced the idea of self-adaptation (SA) that the number of communities can be identified automatically, a novel algorithm SA-SOM of detecting communities in complex networks is proposed. Several representative real-world networks and a set of computer-generated networks by LFR-benchmark are utilized to verify the accuracy and the efficiency of this algorithm. The experimental findings demonstrate that this algorithm can identify the communities automatically, accurately and efficiently. Furthermore, this algorithm can also acquire higher values of modularity, NMI and density than the SOM algorithm does.

Adaptive Self-Tuning Networks

NASA Astrophysics Data System (ADS)

Knox, H. A.; Draelos, T.; Young, C. J.; Lawry, B.; Chael, E. P.; Faust, A.; Peterson, M. G.

2015-12-01

The quality of automatic detections from seismic sensor networks depends on a large number of data processing parameters that interact in complex ways. The largely manual process of identifying effective parameters is painstaking and does not guarantee that the resulting controls are the optimal configuration settings. Yet, achieving superior automatic detection of seismic events is closely related to these parameters. We present an automated sensor tuning (AST) system that learns near-optimal parameter settings for each event type using neuro-dynamic programming (reinforcement learning) trained with historic data. AST learns to test the raw signal against all event-settings and automatically self-tunes to an emerging event in real-time. The overall goal is to reduce the number of missed legitimate event detections and the number of false event detections. Reducing false alarms early in the seismic pipeline processing will have a significant impact on this goal. Applicable both for existing sensor performance boosting and new sensor deployment, this system provides an important new method to automatically tune complex remote sensing systems. Systems tuned in this way will achieve better performance than is currently possible by manual tuning, and with much less time and effort devoted to the tuning process. With ground truth on detections in seismic waveforms from a network of stations, we show that AST increases the probability of detection while decreasing false alarms.

Data from selected U.S. Geological Survey national stream water-quality monitoring networks (WQN) on CD-ROM

USGS Publications Warehouse

Alexander, R.B.; Ludtke, A.S.; Fitzgerald, K.K.; Schertz, T.L.

1996-01-01

Data from two U.S. Geological Survey (USGS) national stream water-quality monitoring networks, the National Stream Quality Accounting Network (NASQAN) and the Hydrologic Benchmark Network (HBN), are now available in a two CD-ROM set. These data on CD-ROM are collectively referred to as WQN, water-quality networks. Data from these networks have been used at the national, regional, and local levels to estimate the rates of chemical flux from watersheds, quantify changes in stream water quality for periods during the past 30 years, and investigate relations between water quality and streamflow as well as the relations of water quality to pollution sources and various physical characteristics of watersheds. The networks include 679 monitoring stations in watersheds that represent diverse climatic, physiographic, and cultural characteristics. The HBN includes 63 stations in relatively small, minimally disturbed basins ranging in size from 2 to 2,000 square miles with a median drainage basin size of 57 square miles. NASQAN includes 618 stations in larger, more culturally-influenced drainage basins ranging in size from one square mile to 1.2 million square miles with a median drainage basin size of about 4,000 square miles. The CD-ROMs contain data for 63 physical, chemical, and biological properties of water (122 total constituents including analyses of dissolved and water suspended-sediment samples) collected during more than 60,000 site visits. These data approximately span the periods 1962-95 for HBN and 1973-95 for NASQAN. The data reflect sampling over a wide range of streamflow conditions and the use of relatively consistent sampling and analytical methods. The CD-ROMs provide ancillary information and data-retrieval tools to allow the national network data to be properly and efficiently used. Ancillary information includes the following: descriptions of the network objectives and history, characteristics of the network stations and water-quality data, historical

Conversion of KEGG metabolic pathways to SBGN maps including automatic layout

PubMed Central

2013-01-01

Background Biologists make frequent use of databases containing large and complex biological networks. One popular database is the Kyoto Encyclopedia of Genes and Genomes (KEGG) which uses its own graphical representation and manual layout for pathways. While some general drawing conventions exist for biological networks, arbitrary graphical representations are very common. Recently, a new standard has been established for displaying biological processes, the Systems Biology Graphical Notation (SBGN), which aims to unify the look of such maps. Ideally, online repositories such as KEGG would automatically provide networks in a variety of notations including SBGN. Unfortunately, this is non‐trivial, since converting between notations may add, remove or otherwise alter map elements so that the existing layout cannot be simply reused. Results Here we describe a methodology for automatic translation of KEGG metabolic pathways into the SBGN format. We infer important properties of the KEGG layout and treat these as layout constraints that are maintained during the conversion to SBGN maps. Conclusions This allows for the drawing and layout conventions of SBGN to be followed while creating maps that are still recognizably the original KEGG pathways. This article details the steps in this process and provides examples of the final result. PMID:23953132

Performance of wavelet analysis and neural networks for pathological voices identification

NASA Astrophysics Data System (ADS)

Salhi, Lotfi; Talbi, Mourad; Abid, Sabeur; Cherif, Adnane

2011-09-01

Within the medical environment, diverse techniques exist to assess the state of the voice of the patient. The inspection technique is inconvenient for a number of reasons, such as its high cost, the duration of the inspection, and above all, the fact that it is an invasive technique. This study focuses on a robust, rapid and accurate system for automatic identification of pathological voices. This system employs non-invasive, non-expensive and fully automated method based on hybrid approach: wavelet transform analysis and neural network classifier. First, we present the results obtained in our previous study while using classic feature parameters. These results allow visual identification of pathological voices. Second, quantified parameters drifting from the wavelet analysis are proposed to characterise the speech sample. On the other hand, a system of multilayer neural networks (MNNs) has been developed which carries out the automatic detection of pathological voices. The developed method was evaluated using voice database composed of recorded voice samples (continuous speech) from normophonic or dysphonic speakers. The dysphonic speakers were patients of a National Hospital 'RABTA' of Tunis Tunisia and a University Hospital in Brussels, Belgium. Experimental results indicate a success rate ranging between 75% and 98.61% for discrimination of normal and pathological voices using the proposed parameters and neural network classifier. We also compared the average classification rate based on the MNN, Gaussian mixture model and support vector machines.

Fully automatic time-window selection using machine learning for global adjoint tomography

NASA Astrophysics Data System (ADS)

Chen, Y.; Hill, J.; Lei, W.; Lefebvre, M. P.; Bozdag, E.; Komatitsch, D.; Tromp, J.

2017-12-01

Selecting time windows from seismograms such that the synthetic measurements (from simulations) and measured observations are sufficiently close is indispensable in a global adjoint tomography framework. The increasing amount of seismic data collected everyday around the world demands "intelligent" algorithms for seismic window selection. While the traditional FLEXWIN algorithm can be "automatic" to some extent, it still requires both human input and human knowledge or experience, and thus is not deemed to be fully automatic. The goal of intelligent window selection is to automatically select windows based on a learnt engine that is built upon a huge number of existing windows generated through the adjoint tomography project. We have formulated the automatic window selection problem as a classification problem. All possible misfit calculation windows are classified as either usable or unusable. Given a large number of windows with a known selection mode (select or not select), we train a neural network to predict the selection mode of an arbitrary input window. Currently, the five features we extract from the windows are its cross-correlation value, cross-correlation time lag, amplitude ratio between observed and synthetic data, window length, and minimum STA/LTA value. More features can be included in the future. We use these features to characterize each window for training a multilayer perceptron neural network (MPNN). Training the MPNN is equivalent to solve a non-linear optimization problem. We use backward propagation to derive the gradient of the loss function with respect to the weighting matrices and bias vectors and use the mini-batch stochastic gradient method to iteratively optimize the MPNN. Numerical tests show that with a careful selection of the training data and a sufficient amount of training data, we are able to train a robust neural network that is capable of detecting the waveforms in an arbitrary earthquake data with negligible detection error

Evaluating the stability of DSM-5 PTSD symptom network structure in a national sample of U.S. military veterans.

PubMed

von Stockert, Sophia H H; Fried, Eiko I; Armour, Cherie; Pietrzak, Robert H

2018-03-15

Previous studies have used network models to investigate how PTSD symptoms associate with each other. However, analyses examining the degree to which these networks are stable over time, which are critical to identifying symptoms that may contribute to the chronicity of this disorder, are scarce. In the current study, we evaluated the temporal stability of DSM-5 PTSD symptom networks over a three-year period in a nationally representative sample of trauma-exposed U.S. military veterans. Data were analyzed from 611 trauma-exposed U.S. military veterans who participated in the National Health and Resilience in Veterans Study (NHRVS). We estimated regularized partial correlation networks of DSM-5 PTSD symptoms at baseline (Time 1) and at three-year follow-up (Time 2), and examined their temporal stability. Evaluation of the network structure of PTSD symptoms at Time 1 and Time 2 using a formal network comparison indicated that the Time 1 network did not differ significantly from the Time 2 network with regard to network structure (p = 0.12) or global strength (sum of all absolute associations, i.e. connectivity; p = 0.25). Centrality estimates of both networks (r = 0.86) and adjacency matrices (r = 0.69) were highly correlated. In both networks, avoidance, intrusive, and negative cognition and mood symptoms were among the more central nodes. This study is limited by the use of a self-report instrument to assess PTSD symptoms and recruitment of a relatively homogeneous sample of predominantly older, Caucasian veterans. Results of this study demonstrate the three-year stability of DSM-5 PTSD symptom network structure in a nationally representative sample of trauma-exposed U.S. military veterans. They further suggest that trauma-related avoidance, intrusive, and dysphoric symptoms may contribute to the chronicity of PTSD symptoms in this population. Published by Elsevier B.V.

Using Antelope and Seiscomp in the framework of the Romanian Seismic Network

NASA Astrophysics Data System (ADS)

Marius Craiu, George; Craiu, Andreea; Marmureanu, Alexandru; Neagoe, Cristian

2014-05-01

The National Institute for Earth Physics (NIEP) operates a real-time seismic network designed to monitor the seismic activity on the Romania territory, dominated by the Vrancea intermediate-depth (60-200 km) earthquakes. The NIEP real-time network currently consists of 102 stations and two seismic arrays equipped with different high quality digitizers (Kinemetrics K2, Quanterra Q330, Quanterra Q330HR, PS6-26, Basalt), broadband and short period seismometers (CMG3ESP, CMG40T, KS2000, KS54000, KS2000, CMG3T, STS2, SH-1, S13, Mark l4c, Ranger, Gs21, Mark 22) and acceleration sensors (Episensor Kinemetrics). The primary goal of the real-time seismic network is to provide earthquake parameters from more broad-band stations with a high dynamic range, for more rapid and accurate computation of the locations and magnitudes of earthquakes. The Seedlink and AntelopeTM program packages are completely automated Antelope seismological system is run at the Data Center in Măgurele. The Antelope data acquisition and processing software is running for real-time processing and post processing. The Antelope real-time system provides automatic event detection, arrival picking, event location, and magnitude calculation. It also provides graphical displays and automatic location within near real time after a local, regional or teleseismic event has occurred SeisComP 3 is another automated system that is run at the NIEP and which provides the following features: data acquisition, data quality control, real-time data exchange and processing, network status monitoring, issuing event alerts, waveform archiving and data distribution, automatic event detection and location, easy access to relevant information about stations, waveforms, and recent earthquakes. The main goal of this paper is to compare both of these data acquisitions systems in order to improve their detection capabilities, location accuracy, magnitude and depth determination and reduce the RMS and other location errors.

Cascaded deep decision networks for classification of endoscopic images

NASA Astrophysics Data System (ADS)

Murthy, Venkatesh N.; Singh, Vivek; Sun, Shanhui; Bhattacharya, Subhabrata; Chen, Terrence; Comaniciu, Dorin

2017-02-01

Both traditional and wireless capsule endoscopes can generate tens of thousands of images for each patient. It is desirable to have the majority of irrelevant images filtered out by automatic algorithms during an offline review process or to have automatic indication for highly suspicious areas during an online guidance. This also applies to the newly invented endomicroscopy, where online indication of tumor classification plays a significant role. Image classification is a standard pattern recognition problem and is well studied in the literature. However, performance on the challenging endoscopic images still has room for improvement. In this paper, we present a novel Cascaded Deep Decision Network (CDDN) to improve image classification performance over standard Deep neural network based methods. During the learning phase, CDDN automatically builds a network which discards samples that are classified with high confidence scores by a previously trained network and concentrates only on the challenging samples which would be handled by the subsequent expert shallow networks. We validate CDDN using two different types of endoscopic imaging, which includes a polyp classification dataset and a tumor classification dataset. From both datasets we show that CDDN can outperform other methods by about 10%. In addition, CDDN can also be applied to other image classification problems.

National High Frequency Radar Network (hfrnet) and Pacific Research Efforts

NASA Astrophysics Data System (ADS)

Hazard, L.; Terrill, E. J.; Cook, T.; de Paolo, T.; Otero, M. P.; Rogowski, P.; Schramek, T. A.

2016-12-01

The U.S. High Frequency Radar Network (HFRNet) has been in operation for over ten years with representation from 31 organizations spanning academic institutions, state and local government agencies, and private organizations. HFRNet currently holds a collection from over 130 radar installations totaling over 10 million records of surface ocean velocity measurements. HFRNet is a primary example of inter-agency and inter-institutional partnerships for improving oceanographic research and operations. HF radar derived surface currents have been used in several societal applications including coastal search and rescue, oil spill response, water quality monitoring and marine navigation. Central to the operational success of the large scale network is an efficient data management, storage, access, and delivery system. The networking of surface current mapping systems is characterized by a tiered structure that extends from the individual field installations to local regional operations maintaining multiple sites and on to centralized locations aggregating data from all regions. The data system development effort focuses on building robust data communications from remote field locations (sites) for ingestion into the data system via data on-ramps (Portals or Site Aggregators) to centralized data repositories (Nodes). Centralized surface current data enables the aggregation of national surface current grids and allows for ingestion into displays, management tools, and models. The Coastal Observing Research and Development Center has been involved in international relationships and research in the Philippines, Palau, and Vietnam. CORDC extends this IT architecture of surface current mapping data systems leveraging existing developments and furthering standardization of data services for seamless integration of higher level applications. Collaborations include the Philippine Atmospheric Geophysical and Astronomical Services Administration (PAGASA), The Coral Reef Research

Advances of FishNet towards a fully automatic monitoring system for fish migration

NASA Astrophysics Data System (ADS)

Kratzert, Frederik; Mader, Helmut

2017-04-01

Restoring the continuum of river networks, affected by anthropogenic constructions, is one of the main objectives of the Water Framework Directive. Regarding fish migration, fish passes are a widely used measure. Often the functionality of these fish passes needs to be assessed by monitoring. Over the last years, we developed a new semi-automatic monitoring system (FishCam) which allows the contact free observation of fish migration in fish passes through videos. The system consists of a detection tunnel, equipped with a camera, a motion sensor and artificial light sources, as well as a software (FishNet), which helps to analyze the video data. In its latest version, the software is capable of detecting and tracking objects in the videos as well as classifying them into "fish" and "no-fish" objects. This allows filtering out the videos containing at least one fish (approx. 5 % of all grabbed videos) and reduces the manual labor to the analysis of these videos. In this state the entire system has already been used in over 20 different fish passes across Austria for a total of over 140 months of monitoring resulting in more than 1.4 million analyzed videos. As a next step towards a fully automatic monitoring system, a key feature is the automatized classification of the detected fish into their species, which is still an unsolved task in a fully automatic monitoring environment. Recent advances in the field of machine learning, especially image classification with deep convolutional neural networks, sound promising in order to solve this problem. In this study, different approaches for the fish species classification are tested. Besides an image-only based classification approach using deep convolutional neural networks, various methods that combine the power of convolutional neural networks as image descriptors with additional features, such as the fish length and the time of appearance, are explored. To facilitate the development and testing phase of this approach

A new methodology for automatic detection of reference points in 3D cephalometry: A pilot study.

PubMed

Ed-Dhahraouy, Mohammed; Riri, Hicham; Ezzahmouly, Manal; Bourzgui, Farid; El Moutaoukkil, Abdelmajid

2018-04-05

The aim of this study was to develop a new method for an automatic detection of reference points in 3D cephalometry to overcome the limits of 2D cephalometric analyses. A specific application was designed using the C++ language for automatic and manual identification of 21 (reference) points on the craniofacial structures. Our algorithm is based on the implementation of an anatomical and geometrical network adapted to the craniofacial structure. This network was constructed based on the anatomical knowledge of the 3D cephalometric (reference) points. The proposed algorithm was tested on five CBCT images. The proposed approach for the automatic 3D cephalometric identification was able to detect 21 points with a mean error of 2.32mm. In this pilot study, we propose an automated methodology for the identification of the 3D cephalometric (reference) points. A larger sample will be implemented in the future to assess the method validity and reliability. Copyright © 2018 CEO. Published by Elsevier Masson SAS. All rights reserved.

Holding-based network of nations based on listed energy companies: An empirical study on two-mode affiliation network of two sets of actors

NASA Astrophysics Data System (ADS)

Li, Huajiao; Fang, Wei; An, Haizhong; Gao, Xiangyun; Yan, Lili

2016-05-01

Economic networks in the real world are not homogeneous; therefore, it is important to study economic networks with heterogeneous nodes and edges to simulate a real network more precisely. In this paper, we present an empirical study of the one-mode derivative holding-based network constructed by the two-mode affiliation network of two sets of actors using the data of worldwide listed energy companies and their shareholders. First, we identify the primitive relationship in the two-mode affiliation network of the two sets of actors. Then, we present the method used to construct the derivative network based on the shareholding relationship between two sets of actors and the affiliation relationship between actors and events. After constructing the derivative network, we analyze different topological features on the node level, edge level and entire network level and explain the meanings of the different values of the topological features combining the empirical data. This study is helpful for expanding the usage of complex networks to heterogeneous economic networks. For empirical research on the worldwide listed energy stock market, this study is useful for discovering the inner relationships between the nations and regions from a new perspective.

Precision and bias of selected analytes reported by the National Atmospheric Deposition Program and National Trends Network, 1983; and January 1980 through September 1984

USGS Publications Warehouse

Schroder, L.J.; Bricker, A.W.; Willoughby, T.C.

1985-01-01

Blind-audit samples with known analyte concentrations have been prepared by the U.S. Geological Survey and distributed to the National Atmospheric Deposition Program 's Central Analytical Laboratory. The difference between the National Atmospheric Deposition Program and National Trends Network reported analyte concentrations and known analyte concentrations have been calculated, and the bias has been determined. Calcium, magnesium , sodium, and chloride were biased at the 99-percent confidence limit; potassium and sulfate were unbiased at the 99-percent confidence limit, for 1983 results. Relative-percent differences between the measured and known analyte concentration for calcium , magnesium, sodium, potassium, chloride, and sulfate have been calculated for 1983. The median relative percent difference for calcium was 17.0; magnesium was 6.4; sodium was 10.8; potassium was 6.4; chloride was 17.2; and sulfate was -5.3. These relative percent differences should be used to correct the 1983 data before user-analysis of the data. Variances have been calculated for calcium, magnesium, sodium, potassium, chloride, and sulfate determinations. These variances should be applicable to natural-sample analyte concentrations reported by the National Atmospheric Deposition Program and National Trends Network for calendar year 1983. (USGS)

12 CFR 19.244 - Automatic removal, suspension, and debarment.

Code of Federal Regulations, 2014 CFR

2014-01-01

... OF PRACTICE AND PROCEDURE Removal, Suspension, and Debarment of Accountants From Performing Audit Services § 19.244 Automatic removal, suspension, and debarment. (a) An independent public accountant or accounting firm may not perform audit services for insured national banks if the accountant or firm: (1) Is...

12 CFR 19.244 - Automatic removal, suspension, and debarment.

Code of Federal Regulations, 2011 CFR

2011-01-01

... OF PRACTICE AND PROCEDURE Removal, Suspension, and Debarment of Accountants From Performing Audit Services § 19.244 Automatic removal, suspension, and debarment. (a) An independent public accountant or accounting firm may not perform audit services for insured national banks if the accountant or firm: (1) Is...

12 CFR 19.244 - Automatic removal, suspension, and debarment.

Code of Federal Regulations, 2012 CFR

2012-01-01

... OF PRACTICE AND PROCEDURE Removal, Suspension, and Debarment of Accountants From Performing Audit Services § 19.244 Automatic removal, suspension, and debarment. (a) An independent public accountant or accounting firm may not perform audit services for insured national banks if the accountant or firm: (1) Is...

12 CFR 19.244 - Automatic removal, suspension, and debarment.

Code of Federal Regulations, 2013 CFR

2013-01-01

... OF PRACTICE AND PROCEDURE Removal, Suspension, and Debarment of Accountants From Performing Audit Services § 19.244 Automatic removal, suspension, and debarment. (a) An independent public accountant or accounting firm may not perform audit services for insured national banks if the accountant or firm: (1) Is...

Automatic Earthquake Detection and Location by Waveform coherency in Alentejo (South Portugal) Using CatchPy

NASA Astrophysics Data System (ADS)

Custodio, S.; Matos, C.; Grigoli, F.; Cesca, S.; Heimann, S.; Rio, I.

2015-12-01

Seismic data processing is currently undergoing a step change, benefitting from high-volume datasets and advanced computer power. In the last decade, a permanent seismic network of 30 broadband stations, complemented by dense temporary deployments, covered mainland Portugal. This outstanding regional coverage currently enables the computation of a high-resolution image of the seismicity of Portugal, which contributes to fitting together the pieces of the regional seismo-tectonic puzzle. Although traditional manual inspections are valuable to refine automatic results they are impracticable with the big data volumes now available. When conducted alone they are also less objective since the criteria is defined by the analyst. In this work we present CatchPy, a scanning algorithm to detect earthquakes in continuous datasets. Our main goal is to implement an automatic earthquake detection and location routine in order to have a tool to quickly process large data sets, while at the same time detecting low magnitude earthquakes (i.e. lowering the detection threshold). CatchPY is designed to produce an event database that could be easily located using existing location codes (e.g.: Grigoli et al. 2013, 2014). We use CatchPy to perform automatic detection and location of earthquakes that occurred in Alentejo region (South Portugal), taking advantage of a dense seismic network deployed in the region for two years during the DOCTAR experiment. Results show that our automatic procedure is particularly suitable for small aperture networks. The event detection is performed by continuously computing the short-term-average/long-term-average of two different characteristic functions (CFs). For the P phases we used a CF based on the vertical energy trace while for S phases we used a CF based on the maximum eigenvalue of the instantaneous covariance matrix (Vidale 1991). Seismic event location is performed by waveform coherence analysis, scanning different hypocentral coordinates

An automatic aerosol classification for earlinet: application and results

NASA Astrophysics Data System (ADS)

Papagiannopoulos, Nikolaos; Mona, Lucia; Amiridis, Vassilis; Binietoglou, Ioannis; D'Amico, Giuseppe; Guma-Claramunt, P.; Schwarz, Anja; Alados-Arboledas, Lucas; Amodeo, Aldo; Apituley, Arnoud; Baars, Holger; Bortoli, Daniele; Comeron, Adolfo; Guerrero-Rascado, Juan Luis; Kokkalis, Panos; Nicolae, Doina; Papayannis, Alex; Pappalardo, Gelsomina; Wandinger, Ulla; Wiegner, Matthias

2018-04-01

Aerosol typing is essential for understanding the impact of the different aerosol sources on climate, weather system and air quality. An aerosol classification method for EARLINET (European Aerosol Research Lidar Network) measurements is introduced which makes use the Mahalanobis distance classifier. The performance of the automatic classification is tested against manually classified EARLINET data. Results of the application of the method to an extensive aerosol dataset will be presented.

Report: Results of Technical Network Vulnerability Assessment: EPA’s Radiation and Indoor Environments National Laboratory

EPA Pesticide Factsheets

Report #09-P-0053, December 9, 2008. Vulnerability testing of EPA’s Radiation and Indoor Environments National Laboratory (R&IEN) network identified Internet Protocol addresses with medium-risk vulnerabilities.

METHODS INTERCOMPARISON OF SAMPLERS FOR EPA'S NATIONAL PM 2.5 CHEMICAL SPECIATION NETWORK

EPA Science Inventory

The objective of this sampler intercomparison field study is to determine the performance characteristics for the collection of the chemical components of PM2.5 by the chemical speciation monitors developed for the national PM2.5 network relative to each other, to the Federal R...

Automatic detection and recognition of traffic signs in stereo images based on features and probabilistic neural networks

NASA Astrophysics Data System (ADS)

Sheng, Yehua; Zhang, Ka; Ye, Chun; Liang, Cheng; Li, Jian

2008-04-01

Considering the problem of automatic traffic sign detection and recognition in stereo images captured under motion conditions, a new algorithm for traffic sign detection and recognition based on features and probabilistic neural networks (PNN) is proposed in this paper. Firstly, global statistical color features of left image are computed based on statistics theory. Then for red, yellow and blue traffic signs, left image is segmented to three binary images by self-adaptive color segmentation method. Secondly, gray-value projection and shape analysis are used to confirm traffic sign regions in left image. Then stereo image matching is used to locate the homonymy traffic signs in right image. Thirdly, self-adaptive image segmentation is used to extract binary inner core shapes of detected traffic signs. One-dimensional feature vectors of inner core shapes are computed by central projection transformation. Fourthly, these vectors are input to the trained probabilistic neural networks for traffic sign recognition. Lastly, recognition results in left image are compared with recognition results in right image. If results in stereo images are identical, these results are confirmed as final recognition results. The new algorithm is applied to 220 real images of natural scenes taken by the vehicle-borne mobile photogrammetry system in Nanjing at different time. Experimental results show a detection and recognition rate of over 92%. So the algorithm is not only simple, but also reliable and high-speed on real traffic sign detection and recognition. Furthermore, it can obtain geometrical information of traffic signs at the same time of recognizing their types.

Adaptive Neural Network Algorithm for Power Control in Nuclear Power Plants

NASA Astrophysics Data System (ADS)

Masri Husam Fayiz, Al

2017-01-01

The aim of this paper is to design, test and evaluate a prototype of an adaptive neural network algorithm for the power controlling system of a nuclear power plant. The task of power control in nuclear reactors is one of the fundamental tasks in this field. Therefore, researches are constantly conducted to ameliorate the power reactor control process. Currently, in the Department of Automation in the National Research Nuclear University (NRNU) MEPhI, numerous studies are utilizing various methodologies of artificial intelligence (expert systems, neural networks, fuzzy systems and genetic algorithms) to enhance the performance, safety, efficiency and reliability of nuclear power plants. In particular, a study of an adaptive artificial intelligent power regulator in the control systems of nuclear power reactors is being undertaken to enhance performance and to minimize the output error of the Automatic Power Controller (APC) on the grounds of a multifunctional computer analyzer (simulator) of the Water-Water Energetic Reactor known as Vodo-Vodyanoi Energetichesky Reaktor (VVER) in Russian. In this paper, a block diagram of an adaptive reactor power controller was built on the basis of an intelligent control algorithm. When implementing intelligent neural network principles, it is possible to improve the quality and dynamic of any control system in accordance with the principles of adaptive control. It is common knowledge that an adaptive control system permits adjusting the controller’s parameters according to the transitions in the characteristics of the control object or external disturbances. In this project, it is demonstrated that the propitious options for an automatic power controller in nuclear power plants is a control system constructed on intelligent neural network algorithms.

Ghana's experience in the establishment of a national digital seismic network observatory

NASA Astrophysics Data System (ADS)

Ahulu, Sylvanus; Danuor, Sylvester Kojo

2015-07-01

The Government of Ghana has established a National Digital Seismic Network Observatory in Ghana with the aim of monitoring events such as earthquakes, blasts from mining and quarrying, nuclear tests, etc. The Digital Observatory was commissioned on 19 December 2012, and was dedicated to Geosciences in Ghana. Previously Ghana did not have any operational, digital seismic network acquisition system with the capability of monitoring and analysing data for planning and research purposes. The Ghana Geological Survey has been monitoring seismic events with an analogue system which was not efficient and does not deliver real-time data. Hence, the importance of setting up the National Digital Seismic Network System which would enable the Geological Survey to constantly monitor, manage and coordinate both natural and man-made seismic activities in the country and around the globe, to some extent on real-time basis. The Network System is made up of six remote digital stations that transmit data via satellite to the central observatory. Sensors used are 3× Trillium Compact and 3× Trillium 120PA with Trident digitizers. The department has also acquired strong motion equipment: Titan accelerometers with Taurus digitizers from Nanometrics. Three of each of these instruments have been installed at the Akosombo and Kpong hydrodams, and also at the Weija water supply dam. These instruments are used to monitor dams. The peak ground acceleration (PGA) values established from the analysed data from the accelerometers will be used to retrofit or carry out maintenance work of the dam structures to avoid collapse. Apart from these, the observatory also assesses and analyses seismic waveforms relevant to its needs from the Global Seismographic Network (GSN) system operated by the US Geological Survey. The Ghana Geological Survey, through its Seismic Network Observatory makes data available to its stakeholder institutions for earthquake disaster mitigation; reports on all aspects of

Integrating an Automatic Judge into an Open Source LMS

ERIC Educational Resources Information Center

Georgouli, Katerina; Guerreiro, Pedro

2011-01-01

This paper presents the successful integration of the evaluation engine of Mooshak into the open source learning management system Claroline. Mooshak is an open source online automatic judge that has been used for international and national programming competitions. although it was originally designed for programming competitions, Mooshak has also…

Enhancing Outreach using Social Networks at the National Seismological Network of Costa Rica

NASA Astrophysics Data System (ADS)

Linkimer, L.; Lücke, O. H.

2014-12-01

Costa Rica has a very high seismicity rate and geological processes are part of everyday life. Traditionally, information about these processes has been provided by conventional mass media (television and radio). However, due to the new trends in information flow a new approach towards Science Education is necessary for transmitting knowledge from scientific research for the general public in Costa Rica. Since 1973, the National Seismological Network of Costa Rica (RSN: UCR-ICE) studies the seismicity and volcanic activity in the country. In this study, we describe the different channels to report earthquake information that the RSN is currently using: email, social networks, and a website, as well as the development of a smartphone application. Since the RSN started actively participating in Social Networks, an increase in awareness in the general public has been noticed particularly regarding felt earthquakes. Based on this trend, we have focused on enhancing public outreach through Social Media. We analyze the demographics and geographic distribution of the RSN Facebook Page, the growth of followers, and the significance of their feedback for reporting intensity data. We observe that certain regions of the country have more Facebook activity, although those regions are not the most populated nor have a high Internet connectivity index. We interpret this pattern as the result of a higher awareness to geological hazards in those specific areas. We noticed that the growth of RSN users on Facebook has a strong correlation with the seismic events as opposed to Twitter that displays a steady growth with no clear correlations with specific seismic events. We see the Social Networks as opportunities to engage non-science audiences and encourage the population to participate in reporting seismic observations, thus providing intensity data. With the increasing access to Internet from mobile phones in Costa Rica, we see this approach to science education as an opportunity

Completeness of Methicillin-Resistant Staphylococcus aureus Bloodstream Infection Reporting From Outpatient Hemodialysis Facilities to the National Healthcare Safety Network, 2013.

PubMed

Nguyen, Duc B; See, Isaac; Gualandi, Nicole; Shugart, Alicia; Lines, Christi; Bamberg, Wendy; Dumyati, Ghinwa; Harrison, Lee H; Lesher, Lindsey; Nadle, Joelle; Petit, Susan; Ray, Susan M; Schaffner, William; Townes, John; Njord, Levi; Sievert, Dawn; Thompson, Nicola D; Patel, Priti R

2016-02-01

Reports of bloodstream infections caused by methicillin-resistant Staphylococcus aureus among chronic hemodialysis patients to 2 Centers for Disease Control and Prevention surveillance systems (National Healthcare Safety Network Dialysis Event and Emerging Infections Program) were compared to evaluate completeness of reporting. Many methicillin-resistant S. aureus bloodstream infections identified in hospitals were not reported to National Healthcare Safety Network Dialysis Event.

Automatic Detection of Storm Damages Using High-Altitude Photogrammetric Imaging

NASA Astrophysics Data System (ADS)

Litkey, P.; Nurminen, K.; Honkavaara, E.

2013-05-01

The risks of storms that cause damage in forests are increasing due to climate change. Quickly detecting fallen trees, assessing the amount of fallen trees and efficiently collecting them are of great importance for economic and environmental reasons. Visually detecting and delineating storm damage is a laborious and error-prone process; thus, it is important to develop cost-efficient and highly automated methods. Objective of our research project is to investigate and develop a reliable and efficient method for automatic storm damage detection, which is based on airborne imagery that is collected after a storm. The requirements for the method are the before-storm and after-storm surface models. A difference surface is calculated using two DSMs and the locations where significant changes have appeared are automatically detected. In our previous research we used four-year old airborne laser scanning surface model as the before-storm surface. The after-storm DSM was provided from the photogrammetric images using the Next Generation Automatic Terrain Extraction (NGATE) algorithm of Socet Set software. We obtained 100% accuracy in detection of major storm damages. In this investigation we will further evaluate the sensitivity of the storm-damage detection process. We will investigate the potential of national airborne photography, that is collected at no-leaf season, to automatically produce a before-storm DSM using image matching. We will also compare impact of the terrain extraction algorithm to the results. Our results will also promote the potential of national open source data sets in the management of natural disasters.

Teaching with technology: automatically receiving information from the internet and web.

PubMed

Wink, Diane M

2010-01-01

In this bimonthly series, the author examines how nurse educators can use the Internet and Web-based computer technologies such as search, communication, and collaborative writing tools, social networking and social bookmarking sites, virtual worlds, and Web-based teaching and learning programs. This article presents information and tools related to automatically receiving information from the Internet and Web.

An historical overview of the National Network of Libraries of Medicine, 1985–2015

PubMed Central

Speaker, Susan L.

2018-01-01

The National Network of Libraries of Medicine (NNLM), established as the Regional Medical Library Program in 1965, has a rich and remarkable history. The network’s first twenty years were documented in a detailed 1987 history by Alison Bunting, AHIP, FMLA. This article traces the major trends in the network’s development since then: reconceiving the Regional Medical Library staff as a “field force” for developing, marketing, and distributing a growing number of National Library of Medicine (NLM) products and services; subsequent expansion of outreach to health professionals who are unaffiliated with academic medical centers, particularly those in public health; the advent of the Internet during the 1990s, which brought the migration of NLM and NNLM resources and services to the World Wide Web, and a mandate to encourage and facilitate Internet connectivity in the network; and the further expansion of the NLM and NNLM mission to include providing consumer health resources to satisfy growing public demand. The concluding section discusses the many challenges that NNLM staff faced as they transformed the network from a system that served mainly academic medical researchers to a larger, denser organization that offers health information resources to everyone. PMID:29632439

Avoiding Accountability: How Charter Operators Evade Ohio's Automatic Closure Law. K-12 Education. Executive Summary

ERIC Educational Resources Information Center

DePaoli, Jennifer; van Lier, Piet

2013-01-01

Ohio's charter-closure law is touted as one of the toughest in the nation because it requires the automatic closure of charter schools that consistently fail to meet academic standards. Ohio's charter-closure law, which became effective in 2008 and was revised in 2011, calls for automatic closure of schools rated in Academic Emergency for at least…

A statistical intercomparison between "urban" and "rural" precipitation chemistry data from greater Manchester and two nearby secondary national network sites in the United Kingdom

NASA Astrophysics Data System (ADS)

Lee, David S.; Longhurst, James W. S.

Precipitation chemistry data from a dense urban monitoring network in Greater Manchester, northwest England, were compared with interpolated values from the U.K. secondary national acid deposition monitoring network for the year 1988. Differences were found to be small. However, when data from individual sites from the Greater Manchester network were compared with data from the two nearest secondary national network sites, significant differences were found using simple and complex statistical analyses. Precipitation chemistry at rural sites could be similar to that at urban sites, but the sources of some ions were thought to be different. The synoptic-scale gradients of precipitation chemistry, as shown by the secondary national network, also accounted for some of the differences.

Journal Article: the National Dioxin Air Monitoring Network ...

EPA Pesticide Factsheets

The U.S. EPA has established a National Dioxin Air Monitoring Network (NDAMN) to determine the temporal and geographical variability of atmospheric CDDs, CDFs and coplanar PCBs at rural and nonimpacted locations throughout the United States. Currently operating at 32 sampling stations, NDAMN has three primary purposes: (1) to determine the atmospheric levels and occurrences of dioxin-like compounds in rural and agricultural areas where livestock, poultry and animal feed crops are grown; (2) to provide measurements of atmospheric levels of dioxin-like compounds in different geographic regions of the U.S.; and (3) to provide information regarding the long-range transport of dioxin-like compounds in air over the U.S. Designed in 1997, NDAMN has been implemented in phases, with the first phase consisting of 9 monitoring stations. Previously EPA has reported on the preliminary results of monitoring at 9 rural locations from June1998 through June 19991. The one-year measurement at the 9 stations indicated an annual mean TEQDF–WHO98 air concentration of 12 fg m-3. Since this reporting, NDAMN has been extended to include additional stations. The following is intended to be an update to this national monitoring effort. We are reporting the air monitoring results of 22 NDAMN stations operational over 9 sampling moments from June 1998 to December 1999. Fifteen stations are in rural areas, and 6 are located in National Parks. One station is located in suburban Wa

A deep-learning based automatic pulmonary nodule detection system

NASA Astrophysics Data System (ADS)

Zhao, Yiyuan; Zhao, Liang; Yan, Zhennan; Wolf, Matthias; Zhan, Yiqiang

2018-02-01

Lung cancer is the deadliest cancer worldwide. Early detection of lung cancer is a promising way to lower the risk of dying. Accurate pulmonary nodule detection in computed tomography (CT) images is crucial for early diagnosis of lung cancer. The development of computer-aided detection (CAD) system of pulmonary nodules contributes to making the CT analysis more accurate and with more efficiency. Recent studies from other groups have been focusing on lung cancer diagnosis CAD system by detecting medium to large nodules. However, to fully investigate the relevance between nodule features and cancer diagnosis, a CAD that is capable of detecting nodules with all sizes is needed. In this paper, we present a deep-learning based automatic all size pulmonary nodule detection system by cascading two artificial neural networks. We firstly use a U-net like 3D network to generate nodule candidates from CT images. Then, we use another 3D neural network to refine the locations of the nodule candidates generated from the previous subsystem. With the second sub-system, we bring the nodule candidates closer to the center of the ground truth nodule locations. We evaluate our system on a public CT dataset provided by the Lung Nodule Analysis (LUNA) 2016 grand challenge. The performance on the testing dataset shows that our system achieves 90% sensitivity with an average of 4 false positives per scan. This indicates that our system can be an aid for automatic nodule detection, which is beneficial for lung cancer diagnosis.

Automatic coronary artery calcium scoring in cardiac CT angiography using paired convolutional neural networks.

PubMed

Wolterink, Jelmer M; Leiner, Tim; de Vos, Bob D; van Hamersvelt, Robbert W; Viergever, Max A; Išgum, Ivana

2016-12-01

The amount of coronary artery calcification (CAC) is a strong and independent predictor of cardiovascular events. CAC is clinically quantified in cardiac calcium scoring CT (CSCT), but it has been shown that cardiac CT angiography (CCTA) may also be used for this purpose. We present a method for automatic CAC quantification in CCTA. This method uses supervised learning to directly identify and quantify CAC without a need for coronary artery extraction commonly used in existing methods. The study included cardiac CT exams of 250 patients for whom both a CCTA and a CSCT scan were available. To restrict the volume-of-interest for analysis, a bounding box around the heart is automatically determined. The bounding box detection algorithm employs a combination of three ConvNets, where each detects the heart in a different orthogonal plane (axial, sagittal, coronal). These ConvNets were trained using 50 cardiac CT exams. In the remaining 200 exams, a reference standard for CAC was defined in CSCT and CCTA. Out of these, 100 CCTA scans were used for training, and the remaining 100 for evaluation of a voxel classification method for CAC identification. The method uses ConvPairs, pairs of convolutional neural networks (ConvNets). The first ConvNet in a pair identifies voxels likely to be CAC, thereby discarding the majority of non-CAC-like voxels such as lung and fatty tissue. The identified CAC-like voxels are further classified by the second ConvNet in the pair, which distinguishes between CAC and CAC-like negatives. Given the different task of each ConvNet, they share their architecture, but not their weights. Input patches are either 2.5D or 3D. The ConvNets are purely convolutional, i.e. no pooling layers are present and fully connected layers are implemented as convolutions, thereby allowing efficient voxel classification. The performance of individual 2.5D and 3D ConvPairs with input sizes of 15 and 25 voxels, as well as the performance of ensembles of these Conv

IMNN: Information Maximizing Neural Networks

NASA Astrophysics Data System (ADS)

Charnock, Tom; Lavaux, Guilhem; Wandelt, Benjamin D.

2018-04-01

This software trains artificial neural networks to find non-linear functionals of data that maximize Fisher information: information maximizing neural networks (IMNNs). As compressing large data sets vastly simplifies both frequentist and Bayesian inference, important information may be inadvertently missed. Likelihood-free inference based on automatically derived IMNN summaries produces summaries that are good approximations to sufficient statistics. IMNNs are robustly capable of automatically finding optimal, non-linear summaries of the data even in cases where linear compression fails: inferring the variance of Gaussian signal in the presence of noise, inferring cosmological parameters from mock simulations of the Lyman-α forest in quasar spectra, and inferring frequency-domain parameters from LISA-like detections of gravitational waveforms. In this final case, the IMNN summary outperforms linear data compression by avoiding the introduction of spurious likelihood maxima.

Social Networks and Welfare in Future Animal Management

PubMed Central

Koene, Paul; Ipema, Bert

2014-01-01

Simple Summary Living in a stable social environment is important to animals. Animal species have developed social behaviors and rules of approach and avoidance of conspecifics in order to co-exist. Animal species are kept or domesticated without explicit regard for their inherent social behavior and rules. Examples of social structures are provided for four species kept and managed by humans. This information is important for the welfare management of these species. In the near future, automatic measurement of social structures will provide a tool for daily welfare management together with nearest neighbor information. Abstract It may become advantageous to keep human-managed animals in the social network groups to which they have adapted. Data concerning the social networks of farm animal species and their ancestors are scarce but essential to establishing the importance of a natural social network for farmed animal species. Social Network Analysis (SNA) facilitates the characterization of social networking at group, subgroup and individual levels. SNA is currently used for modeling the social behavior and management of wild animals and social welfare of zoo animals. It has been recognized for use with farm animals but has yet to be applied for management purposes. Currently, the main focus is on cattle, because in large groups (poultry), recording of individuals is expensive and the existence of social networks is uncertain due to on-farm restrictions. However, in many cases, a stable social network might be important to individual animal fitness, survival and welfare. For instance, when laying hens are not too densely housed, simple networks may be established. We describe here small social networks in horses, brown bears, laying hens and veal calves to illustrate the importance of measuring social networks among animals managed by humans. Emphasis is placed on the automatic measurement of identity, location, nearest neighbors and nearest neighbor distance for

Best Practices in Service Learning: Building a National Community College Network, 1994-1997. AACC Project Brief.

ERIC Educational Resources Information Center

Robinson, Gail; Barnett, Lynn

As part of the Learn and Serve America Program of the Corporation for National Service, the American Association of Community Colleges (AACC) has helped develop campus-based programs that have instigated a growing community college service learning network. Ten colleges, selected in a national competition for grants ranging from $2,000 to $12,000…

Automatic theory generation from analyst text files using coherence networks

NASA Astrophysics Data System (ADS)

Shaffer, Steven C.

2014-05-01

This paper describes a three-phase process of extracting knowledge from analyst textual reports. Phase 1 involves performing natural language processing on the source text to extract subject-predicate-object triples. In phase 2, these triples are then fed into a coherence network analysis process, using a genetic algorithm optimization. Finally, the highest-value sub networks are processed into a semantic network graph for display. Initial work on a well- known data set (a Wikipedia article on Abraham Lincoln) has shown excellent results without any specific tuning. Next, we ran the process on the SYNthetic Counter-INsurgency (SYNCOIN) data set, developed at Penn State, yielding interesting and potentially useful results.

Automatic Computer Mapping of Terrain

NASA Technical Reports Server (NTRS)

Smedes, H. W.

1971-01-01

Computer processing of 17 wavelength bands of visible, reflective infrared, and thermal infrared scanner spectrometer data, and of three wavelength bands derived from color aerial film has resulted in successful automatic computer mapping of eight or more terrain classes in a Yellowstone National Park test site. The tests involved: (1) supervised and non-supervised computer programs; (2) special preprocessing of the scanner data to reduce computer processing time and cost, and improve the accuracy; and (3) studies of the effectiveness of the proposed Earth Resources Technology Satellite (ERTS) data channels in the automatic mapping of the same terrain, based on simulations, using the same set of scanner data. The following terrain classes have been mapped with greater than 80 percent accuracy in a 12-square-mile area with 1,800 feet of relief; (1) bedrock exposures, (2) vegetated rock rubble, (3) talus, (4) glacial kame meadow, (5) glacial till meadow, (6) forest, (7) bog, and (8) water. In addition, shadows of clouds and cliffs are depicted, but were greatly reduced by using preprocessing techniques.

Bi-national Social Networks and Assimilation: A Test of the Importance of Transnationalism

PubMed Central

Mouw, Ted; Chavez, Sergio; Edelblute, Heather; Verdery, Ashton

2015-01-01

While the concept of transnationalism has gained widespread popularity among scholars as a way to describe immigrants’ long-term maintenance of cross-border ties to their origin communities, critics have argued that the overall proportion of immigrants who engage in transnational behavior is low and that, as a result, transnationalism has little sustained effect on the process of immigrant adaptation and assimilation. In this paper, we argue that a key shortcoming in the current empirical debate on transnationalism is the lack of data on the social networks that connect migrants to each other and to non-migrants in communities of origin. To address this shortcoming, our analysis uses unique bi-national data on the social network connecting an immigrant sending community in Guanajuato, Mexico, to two destination areas in the United States. We test for the effect of respondents’ positions in cross-border networks on their migration intentions and attitudes towards the United States using data on the opinions of their peers, their participation in cross border and local communication networks, and their structural position in the network. The results indicate qualified empirical support for a network-based model of transnationalism; in the U.S. sample we find evidence of network clustering consistent with peer effects, while in the Mexican sample we find evidence of the importance of cross-border communication with friends. PMID:25750462

Social Network Type and Subjective Well-being in a National Sample of Older Americans

PubMed Central

Litwin, Howard; Shiovitz-Ezra, Sharon

2011-01-01

Purpose: The study considers the social networks of older Americans, a population for whom there have been few studies of social network type. It also examines associations between network types and well-being indicators: loneliness, anxiety, and happiness. Design and Methods: A subsample of persons aged 65 years and older from the first wave of the National Social Life, Health, and Aging Project was employed (N = 1,462). We applied K-means cluster analysis to derive social network types using 7 criterion variables. In the multivariate stage, the well-being outcomes were regressed on the network type construct and on background and health characteristics by means of logistic regression. Results: Five social network types were derived: “diverse,” “friend,” “congregant,” “family,” and “restricted.” Social network type was found to be associated with each of the well-being indicators after adjusting for demographic and health confounders. Respondents embedded in network types characterized by greater social capital tended to exhibit better well-being in terms of less loneliness, less anxiety, and greater happiness. Implications: Knowledge about differing network types should make gerontological practitioners more aware of the varying interpersonal milieus in which older people function. Adopting network type assessment as an integral part of intake procedures and tracing network shifts over time can serve as a basis for risk assessment as well as a means for determining the efficacy of interventions. PMID:21097553

Evaluating the Potential Benefits of Advanced Automatic Crash Notification.

PubMed

Plevin, Rebecca E; Kaufman, Robert; Fraade-Blanar, Laura; Bulger, Eileen M

2017-04-01

Advanced Automatic Collision Notification (AACN) services in passenger vehicles capture crash data during collisions that could be transferred to Emergency Medical Services (EMS) providers. This study explored how EMS response times and other crash factors impacted the odds of fatality. The goal was to determine if information transmitted by AACN could help decrease mortality by allowing EMS providers to be better prepared upon arrival at the scene of a collision. The Crash Injury Research and Engineering Network (CIREN) database of the US Department of Transportation/National Highway Traffic Safety Administration (USDOT/NHTSA; Washington DC, USA) was searched for all fatal crashes between 1996 and 2012. The CIREN database also was searched for illustrative cases. The NHTSA's Fatal Analysis Reporting System (FARS) and National Automotive Sampling System Crashworthiness Data System (NASS CDS) databases were queried for all fatal crashes between 2000 and 2011 that involved a passenger vehicle. Detailed EMS time data were divided into prehospital time segments and analyzed descriptively as well as via multiple logistic regression models. The CIREN data showed that longer times from the collision to notification of EMS providers were associated with more frequent invasive interventions within the first three hours of hospital admission and more transfers from a regional hospital to a trauma center. The NASS CDS and FARS data showed that rural collisions with crash-notification times >30 minutes were more likely to be fatal than collisions with similar crash-notification times occurring in urban environments. The majority of a patient's prehospital time occurred between the arrival of EMS providers on-scene and arrival at a hospital. The need for extrication increased the on-scene time segment as well as total prehospital time. An AACN may help decrease mortality following a motor vehicle collision (MVC) by alerting EMS providers earlier and helping them discern when

Journal Article: EPA's National Dioxin Air Monitoring Network (Ndamn): Design, Implementation, and Final Results

EPA Science Inventory

The U.S. Environmental Protection Agency (U.S. EPA) established the National Dioxin Air Monitoring Network (NDAMN) in June of 1998, and operated it until November of 2004. The objective of NDAMN was to determine background air concentrations of polychlorinated dibenzo-p-dioxins (...

Automatic Organ Segmentation for CT Scans Based on Super-Pixel and Convolutional Neural Networks.

PubMed

Liu, Xiaoming; Guo, Shuxu; Yang, Bingtao; Ma, Shuzhi; Zhang, Huimao; Li, Jing; Sun, Changjian; Jin, Lanyi; Li, Xueyan; Yang, Qi; Fu, Yu

2018-04-20

Accurate segmentation of specific organ from computed tomography (CT) scans is a basic and crucial task for accurate diagnosis and treatment. To avoid time-consuming manual optimization and to help physicians distinguish diseases, an automatic organ segmentation framework is presented. The framework utilized convolution neural networks (CNN) to classify pixels. To reduce the redundant inputs, the simple linear iterative clustering (SLIC) of super-pixels and the support vector machine (SVM) classifier are introduced. To establish the perfect boundary of organs in one-pixel-level, the pixels need to be classified step-by-step. First, the SLIC is used to cut an image into grids and extract respective digital signatures. Next, the signature is classified by the SVM, and the rough edges are acquired. Finally, a precise boundary is obtained by the CNN, which is based on patches around each pixel-point. The framework is applied to abdominal CT scans of livers and high-resolution computed tomography (HRCT) scans of lungs. The experimental CT scans are derived from two public datasets (Sliver 07 and a Chinese local dataset). Experimental results show that the proposed method can precisely and efficiently detect the organs. This method consumes 38 s/slice for liver segmentation. The Dice coefficient of the liver segmentation results reaches to 97.43%. For lung segmentation, the Dice coefficient is 97.93%. This finding demonstrates that the proposed framework is a favorable method for lung segmentation of HRCT scans.

Acoustic Sensor Planning for Gunshot Location in National Parks: A Pareto Front Approach

PubMed Central

González-Castaño, Francisco Javier; Alonso, Javier Vales; Costa-Montenegro, Enrique; López-Matencio, Pablo; Vicente-Carrasco, Francisco; Parrado-García, Francisco J.; Gil-Castiñeira, Felipe; Costas-Rodríguez, Sergio

2009-01-01

In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabaneros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario. PMID:22303135

Acoustic sensor planning for gunshot location in national parks: a pareto front approach.

PubMed

González-Castaño, Francisco Javier; Alonso, Javier Vales; Costa-Montenegro, Enrique; López-Matencio, Pablo; Vicente-Carrasco, Francisco; Parrado-García, Francisco J; Gil-Castiñeira, Felipe; Costas-Rodríguez, Sergio

2009-01-01

In this paper, we propose a solution for gunshot location in national parks. In Spain there are agencies such as SEPRONA that fight against poaching with considerable success. The DiANa project, which is endorsed by Cabaneros National Park and the SEPRONA service, proposes a system to automatically detect and locate gunshots. This work presents its technical aspects related to network design and planning. The system consists of a network of acoustic sensors that locate gunshots by hyperbolic multi-lateration estimation. The differences in sound time arrivals allow the computation of a low error estimator of gunshot location. The accuracy of this method depends on tight sensor clock synchronization, which an ad-hoc time synchronization protocol provides. On the other hand, since the areas under surveillance are wide, and electric power is scarce, it is necessary to maximize detection coverage and minimize system cost at the same time. Therefore, sensor network planning has two targets, i.e., coverage and cost. We model planning as an unconstrained problem with two objective functions. We determine a set of candidate solutions of interest by combining a derivative-free descent method we have recently proposed with a Pareto front approach. The results are clearly superior to random seeding in a realistic simulation scenario.

Yuma proving grounds automatic UXO detection using biomorphic robots

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

Tilden, M.W.

1996-07-01

The current variety and dispersion of Unexploded Ordnance (UXO) is a daunting technological problem for current sensory and extraction techniques. The bottom line is that the only way to insure a live UXO has been found and removed is to step on it. As this is an upsetting proposition for biological organisms like animals, farmers, or Yuma field personnel, this paper details a non-biological approach to developing inexpensive, automatic machines that will find, tag, and may eventually remove UXO from a variety of terrains by several proposed methods. The Yuma proving grounds (Arizona) has been pelted with bombs, mines, missiles,more » and shells since the 1940s. The idea of automatic machines that can clean up after such testing is an old one but as yet unrealized because of the daunting cost, power and complexity requirements of capable robot mechanisms. A researcher at Los Alamos National Laboratory has invented and developed a new variety of living robots that are solar powered, legged, autonomous, adaptive to massive damage, and very inexpensive. This technology, called Nervous Networks (Nv), allows for the creation of capable walking mechanisms (known as Biomorphic robots, or Biomechs for short) that rather than work from task principles use instead a survival-based design philosophy. This allows Nv based machines to continue doing work even after multiple limbs and sensors have been removed or damaged, and to dynamically negotiate complex terrains as an emergent property of their operation (fighting to proceed, as it were). They are not programmed, and indeed, the twelve transistor Nv controller keeps their electronic cost well below that of most pocket radios. It is suspected that advanced forms of these machines in huge numbers may be an interesting, capable solution to the problem of general and specific UXO identification, tagging, and removal.« less

FIELD EVALUATION OF SAMPLERS FOR EPA'S NATIONAL PM 2.5 CHEMICAL SPECIATION NETWORK-PRELIMINARY RESULTS FROM ATLANTA

EPA Science Inventory

The US EPA bas established a national network at nearly 1100 sites to monitor PM2.5 mass for testing compliance with the PM2.5 National Ambient Air Quality Standards. The objective of the field evaluation is to determine the performance characteristics for the collection of the...

Features and perspectives of automatized construction crane-manipulators

NASA Astrophysics Data System (ADS)

Stepanov, Mikhail A.; Ilukhin, Peter A.

2018-03-01

Modern construction industry still has a high percentage of manual labor, and the greatest prospects of improving the construction process are lying in the field of automatization. In this article automatized construction manipulator-cranes are being studied in order to achieve the most rational design scheme. This is done through formulating a list of general conditions necessary for such cranes and a set of specialized kinematical conditions. A variety of kinematical schemes is evaluated via these conditions, and some are taken for further dynamical analisys. The comparative dynamical analisys of taken schemes was made and the most rational scheme was defined. Therefore a basis for a more complex and practical research of manipulator-cranes design is given and ways to implement them on practical level can now be calculated properly. Also, the perspectives of implementation of automated control systems and informational networks on construction sites in order to boost the quality of construction works, safety of labour and ecological safety are shown.

[Alexithymia and automatic activation of emotional-evaluative information].

PubMed

Suslow, T; Arolt, V; Junghanns, K

1998-05-01

The emotional valence of stimuli seems to be stored in the associative network and is automatically activated on the mere observation of a stimulus. A principal characteristic of alexithymia represents the difficulty to symbolize emotions verbally. The present study examines the relationship between the dimensions of the alexithymia construct and emotional priming effects in a word-word paradigma. The 20-Item Toronto Alexithymia Scale was administered to 32 subjects along with two word reading tasks as measures of emotional and semantic priming effects. The subscale "difficulty describing feelings" correlated as expected negatively with the negative inhibition effect. The subscale "externally oriented thinking" tended to correlate negatively with the negative facilitation effect. Thus, these dimensions of alexithymia are inversely related to the degree of automatic emotional priming. In summary, there is evidence for an impaired structural integration of emotion and language in persons with difficulties in describing feelings. Poor "symbolization" of emotions in alexithymia is discussed from a cognitive perspective.

Method for automatic detection of wheezing in lung sounds.

PubMed

Riella, R J; Nohama, P; Maia, J M

2009-07-01

The present report describes the development of a technique for automatic wheezing recognition in digitally recorded lung sounds. This method is based on the extraction and processing of spectral information from the respiratory cycle and the use of these data for user feedback and automatic recognition. The respiratory cycle is first pre-processed, in order to normalize its spectral information, and its spectrogram is then computed. After this procedure, the spectrogram image is processed by a two-dimensional convolution filter and a half-threshold in order to increase the contrast and isolate its highest amplitude components, respectively. Thus, in order to generate more compressed data to automatic recognition, the spectral projection from the processed spectrogram is computed and stored as an array. The higher magnitude values of the array and its respective spectral values are then located and used as inputs to a multi-layer perceptron artificial neural network, which results an automatic indication about the presence of wheezes. For validation of the methodology, lung sounds recorded from three different repositories were used. The results show that the proposed technique achieves 84.82% accuracy in the detection of wheezing for an isolated respiratory cycle and 92.86% accuracy for the detection of wheezes when detection is carried out using groups of respiratory cycles obtained from the same person. Also, the system presents the original recorded sound and the post-processed spectrogram image for the user to draw his own conclusions from the data.

Automatic Design of Digital Synthetic Gene Circuits

PubMed Central

Marchisio, Mario A.; Stelling, Jörg

2011-01-01

De novo computational design of synthetic gene circuits that achieve well-defined target functions is a hard task. Existing, brute-force approaches run optimization algorithms on the structure and on the kinetic parameter values of the network. However, more direct rational methods for automatic circuit design are lacking. Focusing on digital synthetic gene circuits, we developed a methodology and a corresponding tool for in silico automatic design. For a given truth table that specifies a circuit's input–output relations, our algorithm generates and ranks several possible circuit schemes without the need for any optimization. Logic behavior is reproduced by the action of regulatory factors and chemicals on the promoters and on the ribosome binding sites of biological Boolean gates. Simulations of circuits with up to four inputs show a faithful and unequivocal truth table representation, even under parametric perturbations and stochastic noise. A comparison with already implemented circuits, in addition, reveals the potential for simpler designs with the same function. Therefore, we expect the method to help both in devising new circuits and in simplifying existing solutions. PMID:21399700

The National Network of State Perinatal Quality Collaboratives: A Growing Movement to Improve Maternal and Infant Health.

PubMed

Henderson, Zsakeba T; Ernst, Kelly; Simpson, Kathleen Rice; Berns, Scott D; Suchdev, Danielle B; Main, Elliott; McCaffrey, Martin; Lee, Karyn; Rouse, Tara Bristol; Olson, Christine K

2018-02-01

State Perinatal Quality Collaboratives (PQCs) are networks of multidisciplinary teams working to improve maternal and infant health outcomes. To address the shared needs across state PQCs and enable collaboration, Centers for Disease Control and Prevention, in partnership with March of Dimes and perinatal quality improvement experts from across the country, supported the development and launch of the National Network of PQCs National Network of Perinatal Quality Collaboratives (NNPQC). This process included assessing the status of PQCs in this country and identifying the needs and resources that would be most useful to support PQC development. National representatives from 48 states gathered for the first meeting of the NNPQC to share best practices for making measurable improvements in maternal and infant health. The number of state PQCs has grown considerably over the past decade, with an active PQC or a PQC in development in almost every state. However, PQCs have some common challenges that need to be addressed. After its successful launch, the NNPQC is positioned to ensure that every state PQC has access to key tools and resources that build capacity to actively improve maternal and infant health outcomes and healthcare quality.

Network of Internet-Controlled HF Receivers for Ionospheric Researches

NASA Astrophysics Data System (ADS)

Koloskov, A. V.; Yampolski, Y. M.; Zalizovski, A. V.; Galushko, V. G.; Kascheev, A. S.; La Hoz, C.; Brekke, A.; Beley, V. S.; Rietveld, M. T.

2014-12-01

A network of HF receivers intended for multi-position monitoring of the ionosphere is described. At present, it includes nine observation sites located at high, middle and low latitudes in both hemispheres of the Earth. The basic element of the network is a small- size receiving and measuring units designed at the Institute of Radio Astronomy (IRA) of the National Academy of Sciences of Ukraine (NASU) on the basis of a personal computer equipped with commercial digital receiving modules. Software packages developed by the authors make it possible to remotely control the facilities via the Internet network. The received emissions are HF signals from special transmitters and broadcast radio stations. These are processed using Doppler and pulse selection algorithms. In the Internet-controlled mode, the observation results are transferred to the main server in real time to be automatically processed and visualized at the website of the IRA NASU’s Department of Radiophysics of Geospace. Several examples of using the observation results obtained with the HF receiver network for diagnostics of dynamic processes in the near-Earth plasma are presented. The advantages of the multiposition mode of observations are discussed. The possibility of upgrading the HF facilities to provide measuring angles of arrival of signals is considered.

Intelligentization: an efficient means to get more from optical networking

NASA Astrophysics Data System (ADS)

Chen, Zhi Yun

2001-10-01

Infocom is a term used to describe the merger of Information and Communications and is used to show the radical changes in today's network traffic. The continuous growth of Infocom traffic, especially that of Internet, is driving Infocom networks to expand rapidly. To service providers, the traffic is consuming the bandwidth of their network. Simultaneously, users are complaining too slow, the net never stopped in China. It is the reality faced by both the service providers and equipment vendors. Demands from both the customers and competition in market call for an efficient network infrastructure. What should a Service Provider do? This paper will first analyze the development trends of optical networking and the formation of the concepts of Intelligent Optical Network (ION) and Automatic Switched Optical Network (ASON) as a solution to this problem. Next it will look at the ways to bring intelligence into optical networks, discussing the benefits to service providers by showing some application examples. Finally, it concludes that the development of optical networking has arrived at a point of introducing intelligence into optical networks. The intelligent optical networks and Automatic Switched Optical Networks will immediately bring a wide range of benefit to service providers, equipment vendors, and, of course, the end users.

Automatic fluid dispenser

NASA Technical Reports Server (NTRS)

Sakellaris, P. C. (Inventor)

1977-01-01

Fluid automatically flows to individual dispensing units at predetermined times from a fluid supply and is available only for a predetermined interval of time after which an automatic control causes the fluid to drain from the individual dispensing units. Fluid deprivation continues until the beginning of a new cycle when the fluid is once again automatically made available at the individual dispensing units.

Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection.

PubMed

Nguyen, Thanh; Bui, Vy; Lam, Van; Raub, Christopher B; Chang, Lin-Ching; Nehmetallah, George

2017-06-26

We propose a fully automatic technique to obtain aberration free quantitative phase imaging in digital holographic microscopy (DHM) based on deep learning. The traditional DHM solves the phase aberration compensation problem by manually detecting the background for quantitative measurement. This would be a drawback in real time implementation and for dynamic processes such as cell migration phenomena. A recent automatic aberration compensation approach using principle component analysis (PCA) in DHM avoids human intervention regardless of the cells' motion. However, it corrects spherical/elliptical aberration only and disregards the higher order aberrations. Traditional image segmentation techniques can be employed to spatially detect cell locations. Ideally, automatic image segmentation techniques make real time measurement possible. However, existing automatic unsupervised segmentation techniques have poor performance when applied to DHM phase images because of aberrations and speckle noise. In this paper, we propose a novel method that combines a supervised deep learning technique with convolutional neural network (CNN) and Zernike polynomial fitting (ZPF). The deep learning CNN is implemented to perform automatic background region detection that allows for ZPF to compute the self-conjugated phase to compensate for most aberrations.

Modulation Classification of Satellite Communication Signals Using Cumulants and Neural Networks

NASA Technical Reports Server (NTRS)

Smith, Aaron; Evans, Michael; Downey, Joseph

2017-01-01

National Aeronautics and Space Administration (NASA)'s future communication architecture is evaluating cognitive technologies and increased system intelligence. These technologies are expected to reduce the operational complexity of the network, increase science data return, and reduce interference to self and others. In order to increase situational awareness, signal classification algorithms could be applied to identify users and distinguish sources of interference. A significant amount of previous work has been done in the area of automatic signal classification for military and commercial applications. As a preliminary step, we seek to develop a system with the ability to discern signals typically encountered in satellite communication. Proposed is an automatic modulation classifier which utilizes higher order statistics (cumulants) and an estimate of the signal-to-noise ratio. These features are extracted from baseband symbols and then processed by a neural network for classification. The modulation types considered are phase-shift keying (PSK), amplitude and phase-shift keying (APSK),and quadrature amplitude modulation (QAM). Physical layer properties specific to the Digital Video Broadcasting - Satellite- Second Generation (DVB-S2) standard, such as pilots and variable ring ratios, are also considered. This paper will provide simulation results of a candidate modulation classifier, and performance will be evaluated over a range of signal-to-noise ratios, frequency offsets, and nonlinear amplifier distortions.

Toward a national animal telemetry network for aquatic observations in the United States

USGS Publications Warehouse

Block, Barbara A.; Holbrook, Christopher; Simmons, Samantha E; Holland, Kim N; Ault, Jerald S.; Costa, Daniel P.; Mate, Bruce R; Seitz, Andrew C.; Arendt, Michael D.; Payne, John; Mahmoudi, Behzad; Moore, Peter L.; Price, James; J. J. Levenson,; Wilson, Doug; Kochevar, Randall E

2016-01-01

Animal telemetry is the science of elucidating the movements and behavior of animals in relation to their environment or habitat. Here, we focus on telemetry of aquatic species (marine mammals, sharks, fish, sea birds and turtles) and so are concerned with animal movements and behavior as they move through and above the world’s oceans, coastal rivers, estuaries and great lakes. Animal telemetry devices (“tags”) yield detailed data regarding animal responses to the coupled ocean–atmosphere and physical environment through which they are moving. Animal telemetry has matured and we describe a developing US Animal Telemetry Network (ATN) observing system that monitors aquatic life on a range of temporal and spatial scales that will yield both short- and long-term benefits, fill oceanographic observing and knowledge gaps and advance many of the U.S. National Ocean Policy Priority Objectives. ATN has the potential to create a huge impact for the ocean observing activities undertaken by the U.S. Integrated Ocean Observing System (IOOS) and become a model for establishing additional national-level telemetry networks worldwide.

Extending gene ontology with gene association networks.

PubMed

Peng, Jiajie; Wang, Tao; Wang, Jixuan; Wang, Yadong; Chen, Jin

2016-04-15

Gene ontology (GO) is a widely used resource to describe the attributes for gene products. However, automatic GO maintenance remains to be difficult because of the complex logical reasoning and the need of biological knowledge that are not explicitly represented in the GO. The existing studies either construct whole GO based on network data or only infer the relations between existing GO terms. None is purposed to add new terms automatically to the existing GO. We proposed a new algorithm 'GOExtender' to efficiently identify all the connected gene pairs labeled by the same parent GO terms. GOExtender is used to predict new GO terms with biological network data, and connect them to the existing GO. Evaluation tests on biological process and cellular component categories of different GO releases showed that GOExtender can extend new GO terms automatically based on the biological network. Furthermore, we applied GOExtender to the recent release of GO and discovered new GO terms with strong support from literature. Software and supplementary document are available at www.msu.edu/%7Ejinchen/GOExtender jinchen@msu.edu or ydwang@hit.edu.cn Supplementary data are available at Bioinformatics online. © The Author 2015. Published by Oxford University Press. All rights reserved. For Permissions, please e-mail: journals.permissions@oup.com.

Effects of specimen preparation on the electromagnetic property measurements of solid materials with an automatic network analyzer

NASA Technical Reports Server (NTRS)

Long, E. R., Jr.

1986-01-01

Effects of specimen preparation on measured values of an acrylic's electomagnetic properties at X-band microwave frequencies, TE sub 1,0 mode, utilizing an automatic network analyzer have been studied. For 1 percent or less error, a gap between the specimen edge and the 0.901-in. wall of the specimen holder was the most significant parameter. The gap had to be less than 0.002 in. The thickness variation and alignment errors in the direction parallel to the 0.901-in. wall were equally second most significant and had to be less than 1 degree. Errors in the measurement f the thickness were third most significant. They had to be less than 3 percent. The following parameters caused errors of 1 percent or less: ratios of specimen-holder thicknesses of more than 15 percent, gaps between the specimen edge and the 0.401-in. wall less than 0.045 in., position errors less than 15 percent, surface roughness, hickness variation in the direction parallel to the 0.401-in. wall less than 35 percent, and specimen alignment in the direction parallel to the 0.401-in. wall mass than 5 degrees.

An inventory of terrestrial mammals at national parks in the Northeast Temperate Network and Sagamore Hill National Historic Site

USGS Publications Warehouse

Gilbert, Andrew T.; O'Connell, Allan F.; Annand, Elizabeth M.; Talancy, Neil W.; Sauer, John R.; Nichols, James D.

2008-01-01

An inventory of mammals was conducted during 2004 at nine national park sites in the Northeast Temperate Network (NETN): Acadia National Park (NP), Marsh-Billings-Rockefeller National Historical Park (NHP), Minute Man NHP, Morristown NHP, Roosevelt-Vanderbilt National Historic Site (NHS), Saint-Gaudens NHS, Saugus Iron Works NHS, Saratoga NHP, and Weir Farm NHS. Sagamore Hill NHS, part of the Northeast Coastal and Barrier Network (NCBN), was also surveyed. Each park except Acadia NP was sampled twice, once in the winter/spring and again in the summer/fall. During the winter/spring visit, indirect measure (IM) sampling arrays were employed at 2 to 16 stations and included sampling by remote cameras, cubby boxes (covered trackplates), and hair traps. IM stations were established and re-used during the summer/fall sampling period. Trapping was conducted at 2 to 12 stations at all parks except Acadia NP during the summer/fall period and consisted of arrays of small-mammal traps, squirrel-sized live traps, and some fox-sized live traps. We used estimation-based procedures and probabilistic sampling techniques to design this inventory. A total of 38 species was detected by IM sampling, trapping, and field observations. Species diversity (number of species) varied among parks, ranging from 8 to 24, with Minute Man NHP having the most species detected. Raccoon (Procyon lotor), Virginia Opossum (Didelphis virginiana), Fisher (Martes pennanti), and Domestic Cat (Felis silvestris) were the most common medium-sized mammals detected in this study and White-footed Mouse (Peromyscus leucopus), Northern Short-tailed Shrew (Blarina brevicauda), Deer Mouse (P. maniculatus), and Meadow Vole (Microtus pennsylvanicus) the most common small mammals detected. All species detected are considered fairly common throughout their range including the Fisher, which has been reintroduced in several New England states. We did not detect any state or federal endangered or threatened species.

How does the 'rest-self overlap' mediate the qualitative and automatic features of self-reference?

PubMed

Northoff, Georg

2016-01-01

The target article points out the qualitative and automatic features of self-reference while leaving open the underlying neural mechanisms. Based on empirical evidence about rest-self overlap and rest-stimulus interaction being special for self-related stimuli, I postulate that the resting state shows self-specific organization. The resting state's self-specific organization may be encoded by activity balances between different networks which in turn predispose the qualitative features of subsequent self-related stimulus-induced activity in, for instance, SAN as well as the automatic features of self-reference effects.

Installation and Testing Instructions for the Sandia Automatic Report Generator (ARG).

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

Clay, Robert L.

Robert L. CLAY Sandia National Laboratories P.O. Box 969 Livermore, CA 94551, U.S.A. rlclay@sandia.gov In this report, we provide detailed and reproducible installation instructions of the Automatic Report Generator (ARG), for both Linux and macOS target platforms.

Inventory of montane-nesting birds in the Arctic Network of National Parks, Alaska

USGS Publications Warehouse

Tibbitts, T.L.; Ruthrauff, D.R.; Gill, Robert E.; Handel, Colleen M.

2006-01-01

The Alaska Science Center of the U.S. Geological Survey conducted an inventory of birds in montane areas of the four northern parks in the Arctic Network of National Parks, Alaska. This effort represents the first comprehensive assessment of breeding range and habitat associations for the majority of avian species in the Arctic Network. Ultimately, these data provide a framework upon which to design future monitoring programs.A stratified random sampling design was used to select sample plots (n = 73 plots) that were allocated in proportion to the availability of ecological subsections. Point counts (n = 1,652) were conducted to quantify abundance, distribution, and habitat associations of birds. Field work occurred over three years (2001 to 2003) during two-week-long sessions in late May through early June that coincided with peak courtship activity of breeding birds.Totals of 53 species were recorded in Cape Krusenstern National Monument, 91 in Noatak National Preserve, 57 in Kobuk Valley National Park, and 96 in Gates of the Arctic National Park and Preserve. Substantial proportions of species in individual parks are considered species of conservation concern (18 to 26%) or species of stewardship responsibility of the land managers in the region (8 to 18%). The most commonly detected passerines on point counts included Redpoll spp. (Carduelis flammea and C. hornemanni), Savannah Sparrow (Passerculus sandwichensis), and American Tree Sparrow (Spizella arborea). The most numerous shorebirds were American Golden-Plover (Pluvialis dominica), Wilson’s Snipe (Gallinago delicata), and Whimbrel (Numenius phaeopus). Most species were detected at low rates, reflecting the low breeding densities (and/or low detectabilities) of birds in the montane Arctic. Suites of species were associated with particular ranges of elevation and showed strong associations with particular habitat types.

Water quality success stories: Integrated assessments from the IOOS regional associations and national water quality monitoring network

USGS Publications Warehouse

Ragsdale, Rob; Vowinkel, Eric; Porter, Dwayne; Hamilton, Pixie; Morrison, Ru; Kohut, Josh; Connell, Bob; Kelsey, Heath; Trowbridge, Phil

2011-01-01

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

Quality-control design for surface-water sampling in the National Water-Quality Network

USGS Publications Warehouse

Riskin, Melissa L.; Reutter, David C.; Martin, Jeffrey D.; Mueller, David K.

2018-04-10

The data-quality objectives for samples collected at surface-water sites in the National Water-Quality Network include estimating the extent to which contamination, matrix effects, and measurement variability affect interpretation of environmental conditions. Quality-control samples provide insight into how well the samples collected at surface-water sites represent the true environmental conditions. Quality-control samples used in this program include field blanks, replicates, and field matrix spikes. This report describes the design for collection of these quality-control samples and the data management needed to properly identify these samples in the U.S. Geological Survey’s national database.

Geophysical phenomena classification by artificial neural networks

NASA Technical Reports Server (NTRS)

Gough, M. P.; Bruckner, J. R.

1995-01-01

Space science information systems involve accessing vast data bases. There is a need for an automatic process by which properties of the whole data set can be assimilated and presented to the user. Where data are in the form of spectrograms, phenomena can be detected by pattern recognition techniques. Presented are the first results obtained by applying unsupervised Artificial Neural Networks (ANN's) to the classification of magnetospheric wave spectra. The networks used here were a simple unsupervised Hamming network run on a PC and a more sophisticated CALM network run on a Sparc workstation. The ANN's were compared in their geophysical data recognition performance. CALM networks offer such qualities as fast learning, superiority in generalizing, the ability to continuously adapt to changes in the pattern set, and the possibility to modularize the network to allow the inter-relation between phenomena and data sets. This work is the first step toward an information system interface being developed at Sussex, the Whole Information System Expert (WISE). Phenomena in the data are automatically identified and provided to the user in the form of a data occurrence morphology, the Whole Information System Data Occurrence Morphology (WISDOM), along with relationships to other parameters and phenomena.

The role of the P3 and CNV components in voluntary and automatic temporal orienting: A high spatial-resolution ERP study.

PubMed

Mento, Giovanni

2017-12-01

A main distinction has been proposed between voluntary and automatic mechanisms underlying temporal orienting (TO) of selective attention. Voluntary TO implies the endogenous directing of attention induced by symbolic cues. Conversely, automatic TO is exogenously instantiated by the physical properties of stimuli. A well-known example of automatic TO is sequential effects (SEs), which refer to the adjustments in participants' behavioral performance as a function of the trial-by-trial sequential distribution of the foreperiod between two stimuli. In this study a group of healthy adults underwent a cued reaction time task purposely designed to assess both voluntary and automatic TO. During the task, both post-cue and post-target event-related potentials (ERPs) were recorded by means of a high spatial resolution EEG system. In the results of the post-cue analysis, the P3a and P3b were identified as two distinct ERP markers showing distinguishable spatiotemporal features and reflecting automatic and voluntary a priori expectancy generation, respectively. The brain source reconstruction further revealed that distinct cortical circuits supported these two temporally dissociable components. Namely, the voluntary P3b was supported by a left sensorimotor network, while the automatic P3a was generated by a more distributed frontoparietal circuit. Additionally, post-cue contingent negative variation (CNV) and post-target P3 modulations were observed as common markers of voluntary and automatic expectancy implementation and response selection, although partially dissociable neural networks subserved these two mechanisms. Overall, these results provide new electrophysiological evidence suggesting that distinct neural substrates can be recruited depending on the voluntary or automatic cognitive nature of the cognitive mechanisms subserving TO. Copyright © 2017 Elsevier Ltd. All rights reserved.

A novel design of an automatic lighting control system for a wireless sensor network with increased sensor lifetime and reduced sensor numbers.

PubMed

Mohamaddoust, Reza; Haghighat, Abolfazl Toroghi; Sharif, Mohamad Javad Motahari; Capanni, Niccolo

2011-01-01

Wireless sensor networks (WSN) are currently being applied to energy conservation applications such as light control. We propose a design for such a system called a lighting automatic control system (LACS). The LACS system contains a centralized or distributed architecture determined by application requirements and space usage. The system optimizes the calculations and communications for lighting intensity, incorporates user illumination requirements according to their activities and performs adjustments based on external lighting effects in external sensor and external sensor-less architectures. Methods are proposed for reducing the number of sensors required and increasing the lifetime of those used, for considerably reduced energy consumption. Additionally we suggest methods for improving uniformity of illuminance distribution on a workplane's surface, which improves user satisfaction. Finally simulation results are presented to verify the effectiveness of our design.

Automatic extraction of road features in urban environments using dense ALS data

NASA Astrophysics Data System (ADS)

Soilán, Mario; Truong-Hong, Linh; Riveiro, Belén; Laefer, Debra

2018-02-01

This paper describes a methodology that automatically extracts semantic information from urban ALS data for urban parameterization and road network definition. First, building façades are segmented from the ground surface by combining knowledge-based information with both voxel and raster data. Next, heuristic rules and unsupervised learning are applied to the ground surface data to distinguish sidewalk and pavement points as a means for curb detection. Then radiometric information was employed for road marking extraction. Using high-density ALS data from Dublin, Ireland, this fully automatic workflow was able to generate a F-score close to 95% for pavement and sidewalk identification with a resolution of 20 cm and better than 80% for road marking detection.

Frederick National Lab and the Pancreatic Cancer Action Network Award Fellowships for KRAS Research | Poster

Cancer.gov

By Nancy Parrish, Staff Writer The Frederick National Laboratory for Cancer Research (FNLCR) recently formed a partnership with the Pancreatic Cancer Action Network (PanCAN) to award a one-year fellowship to two scientists whose research will help lead to new therapies for pancreatic cancer. The scientists will focus on KRAS, a gene in the RAS family that is mutated in 95 percent of pancreatic cancers, according to the National Cancer Institute (NCI).

Hardware Neural Network for a Visual Inspection System

NASA Astrophysics Data System (ADS)

Chun, Seungwoo; Hayakawa, Yoshihiro; Nakajima, Koji

The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.

Protecting against cyber threats in networked information systems

NASA Astrophysics Data System (ADS)

Ertoz, Levent; Lazarevic, Aleksandar; Eilertson, Eric; Tan, Pang-Ning; Dokas, Paul; Kumar, Vipin; Srivastava, Jaideep

2003-07-01

This paper provides an overview of our efforts in detecting cyber attacks in networked information systems. Traditional signature based techniques for detecting cyber attacks can only detect previously known intrusions and are useless against novel attacks and emerging threats. Our current research at the University of Minnesota is focused on developing data mining techniques to automatically detect attacks against computer networks and systems. This research is being conducted as a part of MINDS (Minnesota Intrusion Detection System) project at the University of Minnesota. Experimental results on live network traffic at the University of Minnesota show that the new techniques show great promise in detecting novel intrusions. In particular, during the past few months our techniques have been successful in automatically identifying several novel intrusions that could not be detected using state-of-the-art tools such as SNORT.

Automatic building identification under bomb damage conditions

NASA Astrophysics Data System (ADS)

Woodley, Robert; Noll, Warren; Barker, Joseph; Wunsch, Donald C., II

2009-05-01

Given the vast amount of image intelligence utilized in support of planning and executing military operations, a passive automated image processing capability for target identification is urgently required. Furthermore, transmitting large image streams from remote locations would quickly use available band width (BW) precipitating the need for processing to occur at the sensor location. This paper addresses the problem of automatic target recognition for battle damage assessment (BDA). We utilize an Adaptive Resonance Theory approach to cluster templates of target buildings. The results show that the network successfully classifies targets from non-targets in a virtual test bed environment.

Social networks, mental health problems, and mental health service utilization in OEF/OIF National Guard veterans.

PubMed

Sripada, Rebecca K; Bohnert, Amy S B; Teo, Alan R; Levine, Debra S; Pfeiffer, Paul N; Bowersox, Nicholas W; Mizruchi, Mark S; Chermack, Stephen T; Ganoczy, Dara; Walters, Heather; Valenstein, Marcia

2015-09-01

Low social support and small social network size have been associated with a variety of negative mental health outcomes, while their impact on mental health services use is less clear. To date, few studies have examined these associations in National Guard service members, where frequency of mental health problems is high, social support may come from military as well as other sources, and services use may be suboptimal. Surveys were administered to 1448 recently returned National Guard members. Multivariable regression models assessed the associations between social support characteristics, probable mental health conditions, and service utilization. In bivariate analyses, large social network size, high social network diversity, high perceived social support, and high military unit support were each associated with lower likelihood of having a probable mental health condition (p < .001). In adjusted analyses, high perceived social support (OR .90, CI .88-.92) and high unit support (OR .96, CI .94-.97) continued to be significantly associated with lower likelihood of mental health conditions. Two social support measures were associated with lower likelihood of receiving mental health services in bivariate analyses, but were not significant in adjusted models. General social support and military-specific support were robustly associated with reduced mental health symptoms in National Guard members. Policy makers, military leaders, and clinicians should attend to service members' level of support from both the community and their units and continue efforts to bolster these supports. Other strategies, such as focused outreach, may be needed to bring National Guard members with need into mental health care.

Automaticity of phonological and semantic processing during visual word recognition.

PubMed

Pattamadilok, Chotiga; Chanoine, Valérie; Pallier, Christophe; Anton, Jean-Luc; Nazarian, Bruno; Belin, Pascal; Ziegler, Johannes C

2017-04-01

Reading involves activation of phonological and semantic knowledge. Yet, the automaticity of the activation of these representations remains subject to debate. The present study addressed this issue by examining how different brain areas involved in language processing responded to a manipulation of bottom-up (level of visibility) and top-down information (task demands) applied to written words. The analyses showed that the same brain areas were activated in response to written words whether the task was symbol detection, rime detection, or semantic judgment. This network included posterior, temporal and prefrontal regions, which clearly suggests the involvement of orthographic, semantic and phonological/articulatory processing in all tasks. However, we also found interactions between task and stimulus visibility, which reflected the fact that the strength of the neural responses to written words in several high-level language areas varied across tasks. Together, our findings suggest that the involvement of phonological and semantic processing in reading is supported by two complementary mechanisms. First, an automatic mechanism that results from a task-independent spread of activation throughout a network in which orthography is linked to phonology and semantics. Second, a mechanism that further fine-tunes the sensitivity of high-level language areas to the sensory input in a task-dependent manner. Copyright © 2017 Elsevier Inc. All rights reserved.

Social Networks and Welfare in Future Animal Management.

PubMed

Koene, Paul; Ipema, Bert

2014-03-17

It may become advantageous to keep human-managed animals in the social network groups to which they have adapted. Data concerning the social networks of farm animal species and their ancestors are scarce but essential to establishing the importance of a natural social network for farmed animal species. Social Network Analysis (SNA) facilitates the characterization of social networking at group, subgroup and individual levels. SNA is currently used for modeling the social behavior and management of wild animals and social welfare of zoo animals. It has been recognized for use with farm animals but has yet to be applied for management purposes. Currently, the main focus is on cattle, because in large groups (poultry), recording of individuals is expensive and the existence of social networks is uncertain due to on-farm restrictions. However, in many cases, a stable social network might be important to individual animal fitness, survival and welfare. For instance, when laying hens are not too densely housed, simple networks may be established. We describe here small social networks in horses, brown bears, laying hens and veal calves to illustrate the importance of measuring social networks among animals managed by humans. Emphasis is placed on the automatic measurement of identity, location, nearest neighbors and nearest neighbor distance for management purposes. It is concluded that social networks are important to the welfare of human-managed animal species and that welfare management based on automatic recordings will become available in the near future.

LTAR linkages with other research networks: Capitalizing on network interconnections

USDA-ARS?s Scientific Manuscript database

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

A network approach to policy framing: A case study of the National Aboriginal and Torres Strait Islander Health Plan.

PubMed

Browne, Jennifer; de Leeuw, Evelyne; Gleeson, Deborah; Adams, Karen; Atkinson, Petah; Hayes, Rick

2017-01-01

Aboriginal health policy in Australia represents a unique policy subsystem comprising a diverse network of Aboriginal-specific and "mainstream" organisations, often with competing interests. This paper describes the network structure of organisations attempting to influence national Aboriginal health policy and examines how the different subgroups within the network approached the policy discourse. Public submissions made as part of a policy development process for the National Aboriginal and Torres Strait Islander Health Plan were analysed using a novel combination of network analysis and qualitative framing analysis. Other organisational actors in the network in each submission were identified, and relationships between them determined; these were used to generate a network map depicting the ties between actors. A qualitative framing analysis was undertaken, using inductive coding of the policy discourses in the submissions. The frames were overlaid with the network map to identify the relationship between the structure of the network and the way in which organisations framed Aboriginal health problems. Aboriginal organisations were central to the network and strongly connected with each other. The network consisted of several densely connected subgroups, whose central nodes were closely connected to one another. Each subgroup deployed a particular policy frame, with a frame of "system dysfunction" also adopted by all but one subgroup. Analysis of submissions revealed that many of the stakeholders in Aboriginal health policy actors are connected to one another. These connections help to drive the policy discourse. The combination of network and framing analysis illuminates competing interests within a network, and can assist advocacy organisations to identify which network members are most influential. Copyright © 2016 Elsevier Ltd. All rights reserved.

Network Randomization and Dynamic Defense for Critical Infrastructure Systems

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

Chavez, Adrian R.; Martin, Mitchell Tyler; Hamlet, Jason

2015-04-01

Critical Infrastructure control systems continue to foster predictable communication paths, static configurations, and unpatched systems that allow easy access to our nation's most critical assets. This makes them attractive targets for cyber intrusion. We seek to address these attack vectors by automatically randomizing network settings, randomizing applications on the end devices themselves, and dynamically defending these systems against active attacks. Applying these protective measures will convert control systems into moving targets that proactively defend themselves against attack. Sandia National Laboratories has led this effort by gathering operational and technical requirements from Tennessee Valley Authority (TVA) and performing research and developmentmore » to create a proof-of-concept solution. Our proof-of-concept has been tested in a laboratory environment with over 300 nodes. The vision of this project is to enhance control system security by converting existing control systems into moving targets and building these security measures into future systems while meeting the unique constraints that control systems face.« less

A new approach to mentoring for research careers: the National Research Mentoring Network.

PubMed

Sorkness, Christine A; Pfund, Christine; Ofili, Elizabeth O; Okuyemi, Kolawole S; Vishwanatha, Jamboor K; Zavala, Maria Elena; Pesavento, Theresa; Fernandez, Mary; Tissera, Anthony; Deveci, Alp; Javier, Damaris; Short, Alexis; Cooper, Paige; Jones, Harlan; Manson, Spero; Buchwald, Dedra; Eide, Kristin; Gouldy, Andrea; Kelly, Erin; Langford, Nicole; McGee, Richard; Steer, Clifford; Unold, Thad; Weber-Main, Anne Marie; Báez, Adriana; Stiles, Jonathan; Pemu, Priscilla; Thompson, Winston; Gwathmey, Judith; Lawson, Kimberly; Johnson, Japera; Hall, Meldra; Paulsen, Douglas; Fouad, Mona; Smith, Ann; Luna, Rafael; Wilson, Donald; Adelsberger, Greg; Simenson, Drew; Cook, Abby; Feliu-Mojer, Monica; Harwood, Eileen; Jones, Amy; Branchaw, Janet; Thomas, Stephen; Butz, Amanda; Byars-Winston, Angela; House, Stephanie; McDaniels, Melissa; Quinn, Sandra; Rogers, Jenna; Spencer, Kim; Utzerath, Emily; Duplicate Of Weber-Main; Womack, Veronica

2017-01-01

Effective mentorship is critical to the success of early stage investigators, and has been linked to enhanced mentee productivity, self-efficacy, and career satisfaction. The mission of the National Research Mentoring Network (NRMN) is to provide all trainees across the biomedical, behavioral, clinical, and social sciences with evidence-based mentorship and professional development programming that emphasizes the benefits and challenges of diversity, inclusivity, and culture within mentoring relationships, and more broadly the research workforce. The purpose of this paper is to describe the structure and activities of NRMN. NRMN serves as a national training hub for mentors and mentees striving to improve their relationships by better aligning expectations, promoting professional development, maintaining effective communication, addressing equity and inclusion, assessing understanding, fostering independence, and cultivating ethical behavior. Training is offered in-person at institutions, regional training, or national meetings, as well as via synchronous and asynchronous platforms; the growing training demand is being met by a cadre of NRMN Master Facilitators. NRMN offers career stage-focused coaching models for grant writing, and other professional development programs. NRMN partners with diverse stakeholders from the NIH-sponsored Diversity Program Consortium (DPC), as well as organizations outside the DPC to work synergistically towards common diversity goals. NRMN offers a virtual portal to the Network and all NRMN program offerings for mentees and mentors across career development stages. NRMNet provides access to a wide array of mentoring experiences and resources including MyNRMN, Guided Virtual Mentorship Program, news, training calendar, videos, and workshops. National scale and sustainability are being addressed by NRMN "Coaches-in-Training" offerings for more senior researchers to implement coaching models across the nation. "Shark Tanks" provide

A study on ship automatic berthing with assistance of auxiliary devices

NASA Astrophysics Data System (ADS)

Tran, Van Luong; Im, Namkyun

2012-09-01

The recent researches on the automatic berthing control problems have used various kinds of tools as a control method such as expert system, fuzzy logic controllers and artificial neural network (ANN). Among them, ANN has proved to be one of the most effective and attractive options. In a marine context, the berthing maneuver is a complicated procedure in which both human experience and intensive control operations are involved. Nowadays, in most cases of berthing operation, auxiliary devices are used to make the schedule safer and faster but none of above researches has taken into account. In this study, ANN is applied to design the controllers for automatic ship berthing using assistant devices such as bow thruster and tug. Using back-propagation algorithm, we trained ANN with set of teaching data to get a minimal error between output values and desired values of four control outputs including rudder, propeller revolution, bow thruster and tug. Then, computer simulations of automatic berthing were carried out to verify the effecttiveness of the system. The results of the simulations showed good performance for the proposed berthing control system.

Accelerating Innovation that Enhances Resource Recovery in the Wastewater Sector: Advancing a National Testbed Network.

PubMed

Mihelcic, James R; Ren, Zhiyong Jason; Cornejo, Pablo K; Fisher, Aaron; Simon, A J; Snyder, Seth W; Zhang, Qiong; Rosso, Diego; Huggins, Tyler M; Cooper, William; Moeller, Jeff; Rose, Bob; Schottel, Brandi L; Turgeon, Jason

2017-07-18

This Feature examines significant challenges and opportunities to spur innovation and accelerate adoption of reliable technologies that enhance integrated resource recovery in the wastewater sector through the creation of a national testbed network. The network is a virtual entity that connects appropriate physical testing facilities, and other components needed for a testbed network, with researchers, investors, technology providers, utilities, regulators, and other stakeholders to accelerate the adoption of innovative technologies and processes that are needed for the water resource recovery facility of the future. Here we summarize and extract key issues and developments, to provide a strategy for the wastewater sector to accelerate a path forward that leads to new sustainable water infrastructures.

Evaluating automatic attentional capture by self-relevant information.

PubMed

Ocampo, Brenda; Kahan, Todd A

2016-01-01

Our everyday decisions and memories are inadvertently influenced by self-relevant information. For example, we are faster and more accurate at making perceptual judgments about stimuli associated with ourselves, such as our own face or name, as compared with familiar non-self-relevant stimuli. Humphreys and Sui propose a "self-attention network" to account for these effects, wherein self-relevant stimuli automatically capture our attention and subsequently enhance the perceptual processing of self-relevant information. We propose that the masked priming paradigm and continuous flash suppression represent two ways to experimentally examine these controversial claims.

A discrete optimization approach for locating automatic vehicle identification readers for the provision of roadway travel times

DOT National Transportation Integrated Search

2002-11-01

This paper develops an algorithm for optimally locating surveillance technologies with an emphasis on Automatic Vehicle Identification tag readers by maximizing the benefit that would accrue from measuring travel times on a transportation network. Th...

Optimizing the real-time automatic location of the events produced in Romania using an advanced processing system

NASA Astrophysics Data System (ADS)

Neagoe, Cristian; Grecu, Bogdan; Manea, Liviu

2016-04-01

National Institute for Earth Physics (NIEP) operates a real time seismic network which is designed to monitor the seismic activity on the Romanian territory, which is dominated by the intermediate earthquakes (60-200 km) from Vrancea area. The ability to reduce the impact of earthquakes on society depends on the existence of a large number of high-quality observational data. The development of the network in recent years and an advanced seismic acquisition are crucial to achieving this objective. The software package used to perform the automatic real-time locations is Seiscomp3. An accurate choice of the Seiscomp3 setting parameters is necessary to ensure the best performance of the real-time system i.e., the most accurate location for the earthquakes and avoiding any false events. The aim of this study is to optimize the algorithms of the real-time system that detect and locate the earthquakes in the monitored area. This goal is pursued by testing different parameters (e.g., STA/LTA, filters applied to the waveforms) on a data set of representative earthquakes of the local seismicity. The results are compared with the locations from the Romanian Catalogue ROMPLUS.

Higher-order neural network software for distortion invariant object recognition

NASA Technical Reports Server (NTRS)

Reid, Max B.; Spirkovska, Lilly

1991-01-01

The state-of-the-art in pattern recognition for such applications as automatic target recognition and industrial robotic vision relies on digital image processing. We present a higher-order neural network model and software which performs the complete feature extraction-pattern classification paradigm required for automatic pattern recognition. Using a third-order neural network, we demonstrate complete, 100 percent accurate invariance to distortions of scale, position, and in-plate rotation. In a higher-order neural network, feature extraction is built into the network, and does not have to be learned. Only the relatively simple classification step must be learned. This is key to achieving very rapid training. The training set is much smaller than with standard neural network software because the higher-order network only has to be shown one view of each object to be learned, not every possible view. The software and graphical user interface run on any Sun workstation. Results of the use of the neural software in autonomous robotic vision systems are presented. Such a system could have extensive application in robotic manufacturing.

75 FR 50987 - Privacy Act System of Records; National Animal Health Laboratory Network (NAHLN)

Federal Register 2010, 2011, 2012, 2013, 2014

2010-08-18

...] Privacy Act System of Records; National Animal Health Laboratory Network (NAHLN) AGENCY: Animal and Plant Health Inspection Service, USDA. ACTION: Notice of a proposed new system of records; request for comment. SUMMARY: The U.S. Department of Agriculture (USDA) proposes to add a new Privacy Act system of records to...

Automatic detection of invasive ductal carcinoma in whole slide images with convolutional neural networks

NASA Astrophysics Data System (ADS)

Cruz-Roa, Angel; Basavanhally, Ajay; González, Fabio; Gilmore, Hannah; Feldman, Michael; Ganesan, Shridar; Shih, Natalie; Tomaszewski, John; Madabhushi, Anant

2014-03-01

This paper presents a deep learning approach for automatic detection and visual analysis of invasive ductal carcinoma (IDC) tissue regions in whole slide images (WSI) of breast cancer (BCa). Deep learning approaches are learn-from-data methods involving computational modeling of the learning process. This approach is similar to how human brain works using different interpretation levels or layers of most representative and useful features resulting into a hierarchical learned representation. These methods have been shown to outpace traditional approaches of most challenging problems in several areas such as speech recognition and object detection. Invasive breast cancer detection is a time consuming and challenging task primarily because it involves a pathologist scanning large swathes of benign regions to ultimately identify the areas of malignancy. Precise delineation of IDC in WSI is crucial to the subsequent estimation of grading tumor aggressiveness and predicting patient outcome. DL approaches are particularly adept at handling these types of problems, especially if a large number of samples are available for training, which would also ensure the generalizability of the learned features and classifier. The DL framework in this paper extends a number of convolutional neural networks (CNN) for visual semantic analysis of tumor regions for diagnosis support. The CNN is trained over a large amount of image patches (tissue regions) from WSI to learn a hierarchical part-based representation. The method was evaluated over a WSI dataset from 162 patients diagnosed with IDC. 113 slides were selected for training and 49 slides were held out for independent testing. Ground truth for quantitative evaluation was provided via expert delineation of the region of cancer by an expert pathologist on the digitized slides. The experimental evaluation was designed to measure classifier accuracy in detecting IDC tissue regions in WSI. Our method yielded the best quantitative

[Wearable Automatic External Defibrillators].

PubMed

Luo, Huajie; Luo, Zhangyuan; Jin, Xun; Zhang, Leilei; Wang, Changjin; Zhang, Wenzan; Tu, Quan

2015-11-01

Defibrillation is the most effective method of treating ventricular fibrillation(VF), this paper introduces wearable automatic external defibrillators based on embedded system which includes EGG measurements, bioelectrical impedance measurement, discharge defibrillation module, which can automatic identify VF signal, biphasic exponential waveform defibrillation discharge. After verified by animal tests, the device can realize EGG acquisition and automatic identification. After identifying the ventricular fibrillation signal, it can automatic defibrillate to abort ventricular fibrillation and to realize the cardiac electrical cardioversion.

German MedicalTeachingNetwork (MDN) implementing national standards for teacher training.

PubMed

Lammerding-Koeppel, M; Ebert, T; Goerlitz, A; Karsten, G; Nounla, C; Schmidt, S; Stosch, C; Dieter, P

2016-01-01

An increasing demand for proof of professionalism in higher education strives for quality assurance (QA) and improvement in medical education. A wide range of teacher trainings is available to medical staff in Germany. Cross-institutional approval of individual certificates is usually a difficult and time consuming task for institutions. In case of non-acceptance it may hinder medical teachers in their professional mobility. The faculties of medicine aimed to develop a comprehensive national framework, to promote standards for formal faculty development programmes across institutions and to foster professionalization of medical teaching. Addressing the above challenges in a joint approach, the faculties set up the national MedicalTeacherNetwork (MDN). Great importance is attributed to work out nationally concerted standards for faculty development and an agreed-upon quality control process across Germany. Medical teachers benefit from these advantages due to portability of faculty development credentials from one faculty of medicine to another within the MDN system. The report outlines the process of setting up the MDN and the national faculty development programme in Germany. Success factors, strengths and limitations are discussed from an institutional, individual and general perspective. Faculties engaged in similar developments might be encouraged to transfer the MDN concept to their countries.

Integrated Seismological Network of Brazil: Key developments in technology.

NASA Astrophysics Data System (ADS)

Pirchiner, Marlon; Assumpção, Marcelo; Ferreira, Joaquim; França, George

2010-05-01

The Integrated Seismological Network of Brazil - BRASIS - will integrate the main Brazilian seismology groups in an extensive permanent broadband network with a (near) real-time acquisition system and automatic preliminary processing of epicenters and magnitudes. About 60 stations will cover the whole country to continuously monitor the seismic activity. Most stations will be operating by the end of 2010. Data will be received from remote stations at each research group and redistributed to all other groups. In addition to issuing a national catalog of earthquakes, each institution will make its own analysis and issue their own bulletins taking into account local and regional lithospheric structure. We chose the SEED format, seedlink and SeisComP as standard data format, exchange protocol and software framework for the network management, respectively. All different existing equipment (eg, Guralp/Scream, Geotech/CD1.1, RefTek/RTP, Quanterra/seedlink) will be integrated into the same system. We plan to provide: 1) improved station management through remote control, and indexes for quality control of acquired data, sending alerts to operators in critical cases. 2) automatic processing: picking, location with local and regional models and determination of magnitudes, issuing newsletters and alerts. 3) maintainence of an earthquakes catalog, and a waveforms database. 4) query tools and access to metadata, catalogs and waveform available to all researchers. In addition, the catalog of earthquakes and other seismological data will be available as layers in a Spatial Data Infrastructure with open source standards, providing new analysis capabilities to all geoscientists. Seiscomp3 has already been installed in two centers (UFRN and USP) with successful tests of Q330, Guralp, RefTek and Geotech plug-ins to the seedlink protocol. We will discuss the main difficulties of our project and the solutions adopted to improve the Brazilian seismological infrastructure.

77 FR 71399 - Notice of Public Workshop: Blueprint for Action: Workshop on the Design of the National Network...

Federal Register 2010, 2011, 2012, 2013, 2014

2012-11-30

...).'' This workshop series provides a forum for the AMNPO to present the proposed design of the National...: Blueprint for Action: Workshop on the Design of the National Network for Manufacturing Innovation (NNMI... Standards and Technology (NIST), announces the first workshop in a new series of public workshops entitled...

10 CFR 95.49 - Security of automatic data processing (ADP) systems.

Code of Federal Regulations, 2010 CFR

2010-01-01

... 10 Energy 2 2010-01-01 2010-01-01 false Security of automatic data processing (ADP) systems. 95.49 Section 95.49 Energy NUCLEAR REGULATORY COMMISSION (CONTINUED) FACILITY SECURITY CLEARANCE AND SAFEGUARDING OF NATIONAL SECURITY INFORMATION AND RESTRICTED DATA Control of Information § 95.49 Security of...