993 resultados para macro-network


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EXTRACT (SEE PDF FOR FULL ABSTRACT): The U.S. Geological Survey is working to define a hydroclimatic data network. The Geological Survey collects stream discharge data at more than 7000 sites throughout the United States. Many of these stations are operated to supply information about specific activities such as flood control, irrigation projects, or hydropower generation. As a beginning, the Geological Survey will attempt to identify stations that represent natural streamflow. Several lists of stations representing "natural" streamflow have been complied in the past. While there is some overlap among these lists, a consistent compilation is preferred. The present effort is to produce one list identifying those stations having periods of record which would be suitable for mesoscale climatic analyses.

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The liquid-crystal light valve (LCLV) is a useful component for performing integration, thresholding, and gain functions in optical neural networks. Integration of the neural activation channels is implemented by pixelation of the LCLV, with use of a structured metallic layer between the photoconductor and the liquid-crystal layer. Measurements are presented for this type of valve, examples of which were prepared for two specific neural network implementations. The valve fabrication and measurement were carried out at the State Optical Institute, St. Petersburg, Russia, and the modeling and system applications were investigated at the Institute of Microtechnology, Neuchâtel, Switzerland.

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In this paper we present an unsupervised neural network which exhibits competition between units via inhibitory feedback. The operation is such as to minimize reconstruction error, both for individual patterns, and over the entire training set. A key difference from networks which perform principal components analysis, or one of its variants, is the ability to converge to non-orthogonal weight values. We discuss the network's operation in relation to the twin goals of maximizing information transfer and minimizing code entropy, and show how the assignment of prior probabilities to network outputs can help to reduce entropy. We present results from two binary coding problems, and from experiments with image coding.

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Reef fishes are conspicuous and essential components of coral reef ecosystems and economies of southern Florida and the United States Virgin Islands (USVI). Throughout Florida and the USVI, reef fish are under threat from a variety of anthropogenic and natural stressors including overfishing, habitat loss, and environmental changes. The South Florida/Caribbean Network (SFCN), a unit of the National Park Service (NPS), is charged with monitoring reef fishes, among other natural and cultural resources, within six parks in the South Florida - Caribbean region (Biscayne National Park, BISC; Buck Island Reef National Monument, BUIS; Dry Tortugas National Park, DRTO; Everglades National Park, EVER; Salt River Bay National Historic Park and Ecological Preserve, SARI; Virgin Islands National Park, VIIS). Monitoring data is intended for park managers who are and will continue to be asked to make decisions to balance environmental protection, fishery sustainability and park use by visitors. The range and complexity of the issues outlined above, and the need for NPS to invest in a strategy of monitoring, modeling, and management to ensure the sustainability of its precious assets, will require strategic investment in long-term, high-precision, multispecies reef fish data that increases inherent system knowledge and reduces uncertainty. The goal of this guide is to provide the framework for park managers and researchers to create or enhance a reef fish monitoring program within areas monitored by the SFCN. The framework is expected to be applicable to other areas as well, including the Florida Keys National Marine Sanctuary and Virgin Islands Coral Reef National Monument. The favored approach is characterized by an iterative process of data collection, dataset integration, sampling design analysis, and population and community assessment that evaluates resource risks associated with management policies. Using this model, a monitoring program can adapt its survey methods to increase accuracy and precision of survey estimates as new information becomes available, and adapt to the evolving needs and broadening responsibilities of park management.

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The National Oceanic and Atmospheric Administration (NOAA), in cooperation with the New Jersey Marine Sciences Consortium (NJMSC), hosted a workshop at Rutgers University on 19-21 September 2005 to explore ways to link the U.S. Integrated Ocean Observing System (IOOS) to the emerging infrastructure of the National Water Quality Monitoring Network (NWQMN). Participating partners included the Mid-Atlantic Coastal Ocean Observing Regional Association, U.S. Geological Survey, Rutgers University Coastal Ocean Observing Laboratory, and the New Jersey Sea Grant College. The workshop was designed to highlight the importance of ecological and human health linkages in the movement of materials, nutrients, organisms and contaminants along the Delaware Bay watershed-estuary-coastal waters gradient (hereinafter, the “Delaware Bay Ecosystem [DBE]”), and to address specific water quality issues in the mid-Atlantic region, especially the area comprising the Delaware River drainage and near-shore waters. Attendees included federal, state and municipal officials, coastal managers, members of academic and research institutions, and industry representatives. The primary goal of the effort was to identify key management issues and related scientific questions that could be addressed by a comprehensive IOOS-NWQMN infrastructure (US Commission on Ocean Policy 2004; U.S. Ocean Action Plan 2004). At a minimum, cooperative efforts among the three federal agencies (NOAA, USGS and EPA) involved in water quality monitoring were required. Further and recommended by the U.S. Commission on Ocean Policy, outreach to states, regional organizations, and tribes was necessary to develop an efficient system of data gathering, quality assurance and quality control protocols, product development, and information dissemination.

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The Virginia Aquarium & Marine Science Center Foundation’s Stranding Response Program (VAQS) was awarded a grant in 2008 to conduct life history analysis on over 10 years of Tursiops truncatus teeth and gonad samples from stranded animals in Virginia. A major part of this collaborative grant included a workshop involving life historians from Hubbs-Sea World Research Institute (HSWRI), NOS, Texas A & M University (TAMU), and University of North Carolina Wilmington (UNCW). The workshop was held at the NOAA Center for Coastal Environmental Health & Biomolecular Research in Charleston, SC on 7-9 July 2009. The workshop convened to 1) address current practices among the groups conducting life history analysis, 2) decide on protocols to follow for the collaborative Prescott grant between VAQS and HSWRI, 3) demonstrate tissue preparation techniques and discuss shortcuts and pitfalls, 4) demonstrate data collection from prepared testes, ovaries, and teeth, and 5) discuss data analysis and prepare an outline and timeline for a future manuscript. The workshop concluded with discussions concerning the current collaborative Tursiops Life History Prescott grant award and the beginnings of a collaborative Prescott proposal with members of the Alliance of Marine Mammal Parks and Aquariums to further clarify reproductive analyses. This technical memorandum serves as a record of this workshop.

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We have recently exchanged and integrated into a single database tag detections for conch, teleost and elasmobranch fish from four separately maintained arrays in the U.S. Virgin Islands including the NMFS queen conch array (St. John nearshore), NOAA’s Biogeography Branch array (St. John nearshore & midshelf reef); UVI shelf edge arrays (Marine Conservation District, Grammanik & other shelf edge); NOAA NMFS Apex Predator array COASTSPAN (St. John nearshore). The integrated database has over 7.5 million hits. Data is shared only with consent of partners and full acknowledgements. Thus, the summary of integrated data here uses data from NOAA and UVI arrays under a cooperative agreement. The benefits of combining and sharing data have included increasing the total area of detection resulting in an understanding of broader scale connectivity than would have been possible with a single array. Partnering has also been cost-effectiveness through sharing of field work, staff time and equipment and exchanges of knowledge and experience across the network. Use of multiple arrays has also helped in optimizing the design of arrays when additional receivers are deployed. The combined arrays have made the USVI network one of the most extensive acoustic arrays in the world with a total of 150+ receivers available, although not necessarily all deployed at all times. Currently, two UVI graduate student projects are using acoustic array data.