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Sample records for arizona household graph

  1. Arizona

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona

  2. Arizona - Compare - U.S. Energy Information Administration (EIA)

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona Arizona

  3. Arizona - Rankings - U.S. Energy Information Administration (EIA)

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona Arizona

  4. Arizona - Search - U.S. Energy Information Administration (EIA)

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona Arizona

  5. Fact #565: April 6, 2009 Household Gasoline Expenditures by Income...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Household Gasoline Expenditures by Income Quintile Bar graph showing the household gasoline expenditures by income quintile in the years 1989, 1997, and 2007. For more detailed ...

  6. Gila County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Day, Arizona Central Heights-Midland City, Arizona Claypool, Arizona Gisela, Arizona Globe, Arizona Hayden, Arizona Miami, Arizona Payson, Arizona Peridot, Arizona Pine, Arizona...

  7. Pinal County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Chuichu, Arizona Coolidge, Arizona Dudleyville, Arizona Eloy, Arizona Florence, Arizona Gold Camp, Arizona Hayden, Arizona Kearny, Arizona Mammoth, Arizona Maricopa, Arizona...

  8. Navajo County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Mesa, Arizona Shongopovi, Arizona Shonto, Arizona Show Low, Arizona Snowflake, Arizona Taylor, Arizona Whiteriver, Arizona Winslow West, Arizona Winslow, Arizona Retrieved from...

  9. Pima County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona Flowing Wells, Arizona Green Valley, Arizona Littletown, Arizona Marana, Arizona Oro Valley, Arizona Picture Rocks, Arizona Pisinemo, Arizona Sahuarita, Arizona Santa Rosa,...

  10. Apache County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Houck, Arizona Lukachukai, Arizona Many Farms, Arizona McNary, Arizona Nazlini, Arizona Red Mesa, Arizona Rock Point, Arizona Rough Rock, Arizona Round Rock, Arizona Sawmill,...

  11. Yuma County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Solar Project Places in Yuma County, Arizona Fortuna Foothills, Arizona Gadsden, Arizona San Luis, Arizona Somerton, Arizona Tacna, Arizona Wellton, Arizona Yuma, Arizona...

  12. Cochise County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Douglas, Arizona Huachuca City, Arizona Naco, Arizona Pirtleville, Arizona Sierra Vista Southeast, Arizona Sierra Vista, Arizona St. David, Arizona Tombstone, Arizona...

  13. Coconino County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sedona, Arizona Supai, Arizona Tonalea, Arizona Tuba City, Arizona Tusayan, Arizona Williams, Arizona Winslow West, Arizona Retrieved from "http:en.openei.orgw...

  14. 2015 Arizona Housing Forum

    Broader source: Energy.gov [DOE]

    The 12th annual Arizona Housing Forum provides a platform for affordable housing professionals to network and share ideas to improve and create housing choices for Arizona. Registration is $350.

  15. Graph Theory

    SciTech Connect (OSTI)

    Sanfilippo, Antonio P.

    2005-12-27

    Graph theory is a branch of discrete combinatorial mathematics that studies the properties of graphs. The theory was pioneered by the Swiss mathematician Leonhard Euler in the 18th century, commenced its formal development during the second half of the 19th century, and has witnessed substantial growth during the last seventy years, with applications in areas as diverse as engineering, computer science, physics, sociology, chemistry and biology. Graph theory has also had a strong impact in computational linguistics by providing the foundations for the theory of features structures that has emerged as one of the most widely used frameworks for the representation of grammar formalisms.

  16. Utilization Graphs

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    that use data from the PDSF batch scheduler (SGE) to show the utilization of the cluster over the past 24 hours. The graphs were generated with RRDTool and are updated...

  17. Graham County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    B. Places in Graham County, Arizona Peridot, Arizona Pima, Arizona Safford, Arizona Swift Trail Junction, Arizona Thatcher, Arizona Retrieved from "http:en.openei.orgw...

  18. Mohave County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Creek, Arizona Mohave Valley, Arizona Mojave Ranch Estates, Arizona New Kingman-Butler, Arizona Peach Springs, Arizona Willow Valley, Arizona Retrieved from "http:...

  19. DOE - Office of Legacy Management -- Arizona

    Office of Legacy Management (LM)

    Arizona Arizona az_map Monument Valley Processing Site Tuba City Disposal

  20. Household magnets

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Household magnets Chances are very good that you have experimented with magnets. People have been fascinated with magnetism for thousands of years. As familiar to us as they may be, magnets still have some surprises for us. Here is a small collection of some of our favorite magnet experiments. What happens when we break a magnet in half? Radio Shack sells cheap ceramic magnets in several shapes. Get a ring shaped magnet and break it with pliers or a tap with a hammer. Try to put it back

  1. Benson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Act Smart Grid Projects in Benson, Arizona Southwest Transmission Cooperative, Inc. Smart Grid Project Registered Energy Companies in Benson, Arizona Arizona Electric Power...

  2. Arizona Electric Power Cooperative | Open Energy Information

    Open Energy Info (EERE)

    Arizona Electric Power Cooperative Jump to: navigation, search Name: Arizona Electric Power Cooperative Place: Benson, Arizona Zip: 85602 Product: AEPCO was originally founded in...

  3. Arizona Solar Center | Open Energy Information

    Open Energy Info (EERE)

    Center Jump to: navigation, search Logo: Arizona Solar Center Name: Arizona Solar Center Place: Mesa, Arizona Number of Employees: 1-10 Year Founded: 1999 Website:...

  4. Arizona/Transmission/Agency Links | Open Energy Information

    Open Energy Info (EERE)

    and Fish Department Arizona State Historic Preservation Office Arizona Department of Transportation Arizona Department of Agriculture Arizona Department of Water Resources Central...

  5. EIA - Household Transportation report: Household Vehicles Energy...

    U.S. Energy Information Administration (EIA) Indexed Site

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1994 August 1997 Release Next Update: EIA has discontinued this series....

  6. Arizona City, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    City, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.7558935, -111.6709584 Show Map Loading map... "minzoom":false,"mappingservice...

  7. Energy Exchange 2015: Phoenix, Arizona

    Broader source: Energy.gov [DOE]

    Presentations from Energy Exchange, a two-and-a-half day training scheduled for August 11-13, 2015, at the Phoenix Convention Center in Phoenix, Arizona.

  8. Methods of visualizing graphs

    DOE Patents [OSTI]

    Wong, Pak C.; Mackey, Patrick S.; Perrine, Kenneth A.; Foote, Harlan P.; Thomas, James J.

    2008-12-23

    Methods for visualizing a graph by automatically drawing elements of the graph as labels are disclosed. In one embodiment, the method comprises receiving node information and edge information from an input device and/or communication interface, constructing a graph layout based at least in part on that information, wherein the edges are automatically drawn as labels, and displaying the graph on a display device according to the graph layout. In some embodiments, the nodes are automatically drawn as labels instead of, or in addition to, the label-edges.

  9. Grecycle Arizona LLC | Open Energy Information

    Open Energy Info (EERE)

    to: navigation, search Name: Grecycle Arizona LLC Place: Tucson, Arizona Product: Biodiesel producer out of cooking oil that operates a 1.2m liter plant in Tucson, Arizona....

  10. Phoenix, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    is a stub. You can help OpenEI by expanding it. Phoenix is a city in Maricopa County, Arizona. It falls under Arizona's 2nd congressional district and Arizona's 3rd congressional...

  11. Geothermal energy in Arizona. Final report

    SciTech Connect (OSTI)

    Stone, C.; Witcher, J.C.

    1982-09-01

    Current knowledge and basic data on geothermal resources in Arizona are compiled. The following are covered: specific area investigations, thermal aspects of Arizona, and exploration methods. (MHR)

  12. Arizona State Land Department | Open Energy Information

    Open Energy Info (EERE)

    Department Jump to: navigation, search Logo: Arizona State Land Department Name: Arizona State Land Department Abbreviation: ASLD Address: 1616 W. Adams St. Place: Phoenix, AZ Zip:...

  13. Arizona State University | Open Energy Information

    Open Energy Info (EERE)

    University Jump to: navigation, search Name: Arizona State University Place: Tempe, Arizona Zip: 85287 Website: asu.edu Coordinates: 33.4183159, -111.9311939 Show Map Loading...

  14. Arizona/Incentives | Open Energy Information

    Open Energy Info (EERE)

    Incentive Incentive Type Active APS - Energy Efficiency Solutions for Business (Arizona) Utility Rebate Program Yes APS - GEOSmart Financing Program (Arizona) Utility Loan Program...

  15. Arizona Corporation Commission | Open Energy Information

    Open Energy Info (EERE)

    Commission Jump to: navigation, search Name: Arizona Corporation Commission Abbreviation: ACC Service Territory: Arizona Website: www.azcc.gov EIA Form 861 Data This article is a...

  16. Arizona Solar Tech | Open Energy Information

    Open Energy Info (EERE)

    Tech Jump to: navigation, search Name: Arizona Solar Tech Place: Phoenix, Arizona Zip: 85040 Sector: Solar, Vehicles Product: Designs and installs solar PV systems for vehicles,...

  17. Arizona Administrative Code | Open Energy Information

    Open Energy Info (EERE)

    Arizona Administrative Code Jump to: navigation, search OpenEI Reference LibraryAdd to library Legal Document- RegulationRegulation: Arizona Administrative CodeLegal Abstract This...

  18. mpiGraph

    Energy Science and Technology Software Center (OSTI)

    2007-05-22

    MpiGraph consists of an MPI application called mpiGraph written in C to measure message bandwidth and an associated crunch_mpiGraph script written in Perl to process the application output into an HTMO report. The mpiGraph application is designed to inspect the health and scalability of a high-performance interconnect while under heavy load. This is useful to detect hardware and software problems in a system, such as slow nodes, links, switches, or contention in switch routing. Itmore » is also useful to characterize how interconnect performance changes with different settings or how one interconnect type compares to another.« less

  19. Graph Generator Survey

    SciTech Connect (OSTI)

    Lothian, Josh; Powers, Sarah S; Sullivan, Blair D; Baker, Matthew B; Schrock, Jonathan; Poole, Stephen W

    2013-12-01

    The benchmarking effort within the Extreme Scale Systems Center at Oak Ridge National Laboratory seeks to provide High Performance Computing benchmarks and test suites of interest to the DoD sponsor. The work described in this report is a part of the effort focusing on graph generation. A previously developed benchmark, SystemBurn, allowed the emulation of dierent application behavior profiles within a single framework. To complement this effort, similar capabilities are desired for graph-centric problems. This report examines existing synthetic graph generator implementations in preparation for further study on the properties of their generated synthetic graphs.

  20. Graphs, matrices, and the GraphBLAS: Seven good reasons

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implementmore » a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.« less

  1. Graphs, matrices, and the GraphBLAS: Seven good reasons

    SciTech Connect (OSTI)

    Kepner, Jeremy; Bader, David; Buluç, Aydın; Gilbert, John; Mattson, Timothy; Meyerhenke, Henning

    2015-01-01

    The analysis of graphs has become increasingly important to a wide range of applications. Graph analysis presents a number of unique challenges in the areas of (1) software complexity, (2) data complexity, (3) security, (4) mathematical complexity, (5) theoretical analysis, (6) serial performance, and (7) parallel performance. Implementing graph algorithms using matrix-based approaches provides a number of promising solutions to these challenges. The GraphBLAS standard (istcbigdata.org/GraphBlas) is being developed to bring the potential of matrix based graph algorithms to the broadest possible audience. The GraphBLAS mathematically defines a core set of matrix-based graph operations that can be used to implement a wide class of graph algorithms in a wide range of programming environments. This paper provides an introduction to the GraphBLAS and describes how the GraphBLAS can be used to address many of the challenges associated with analysis of graphs.

  2. Arizona Oil and Gas Commission | Open Energy Information

    Open Energy Info (EERE)

    Commission Jump to: navigation, search Logo: Arizona Oil and Gas Commission Name: Arizona Oil and Gas Commission Address: 416 W. Congress Street, Suite 100 Place: Arizona Zip:...

  3. Arizona Department of Environmental Quality | Open Energy Information

    Open Energy Info (EERE)

    Arizona Department of Environmental Quality Name: Arizona Department of Environmental Quality Abbreviation: ADEQ Address: 1110 West Washington Street Phoenix, Arizona 85007 Place:...

  4. Arizona's 7th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    in Arizona's 7th congressional district Agenera, LLC Amereco Biofuels Corp Arizona Public Service Company APS Arizona Solar Tech EDGE Energy LLC EGreenIdeas Ecotality North...

  5. Arizona's 4th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    in Arizona's 4th congressional district Agenera, LLC Amereco Biofuels Corp Arizona Public Service Company APS Arizona Solar Tech EDGE Energy LLC EGreenIdeas Ecotality North...

  6. Arizona's 2nd congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    in Arizona's 2nd congressional district Agenera, LLC Amereco Biofuels Corp Arizona Public Service Company APS Arizona Solar Tech EDGE Energy LLC EGreenIdeas Ecotality North...

  7. Arizona Solar Energy Industries Association | Open Energy Information

    Open Energy Info (EERE)

    Arizona Solar Energy Industries Association Name: Arizona Solar Energy Industries Association Place: Arizona Website: www.arizonasolarindustry.org Coordinates: 34.0489281,...

  8. Sunshine Arizona Wind Energy LLC | Open Energy Information

    Open Energy Info (EERE)

    Sunshine Arizona Wind Energy LLC Jump to: navigation, search Name: Sunshine Arizona Wind Energy LLC Place: Flagstaff, Arizona Zip: 86001 Sector: Wind energy Product: Formed to...

  9. Arizona Regions | U.S. DOE Office of Science (SC)

    Office of Science (SC) Website

    is designated for your school's state, county, city, or district. For more information, please visit the High School Coach page. Arizona Region High School Regional Arizona Arizona...

  10. Arizona Regions | U.S. DOE Office of Science (SC)

    Office of Science (SC) Website

    for your school's state, county, city, or district. For more information, please visit the Middle School Coach page. Arizona Region Middle School Regional Arizona Arizona...

  11. Yavapai County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    EV Solar Products Energy Generation Facilities in Yavapai County, Arizona Prescott Airport Solar Plant Solar Power Plant Places in Yavapai County, Arizona Ash Fork, Arizona...

  12. EA-134-APS Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    EA-134-APS Arizona Public Service Company Order authorizing Arizona Public Service Company to export electric energy to Mexico. PDF icon EA-134-APS Arizona Public Service Company ...

  13. EA-108 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    EA-108 Arizona Public Service Company Order authorizing Arizona Public Service to export electric energy to Mexico. PDF icon EA-108 Arizona Public Service.pdf More Documents & ...

  14. Northern Arizona University Wind Projects | Open Energy Information

    Open Energy Info (EERE)

    Northern Arizona University Wind Projects (Redirected from Northern Arizona University Wind Project) Jump to: navigation, search Northern Arizona University ARD Wind Project...

  15. Arizona Map for Commercial Buildings

    U.S. Energy Information Administration (EIA) Indexed Site

    Home > Households, Buildings & Industry > Background Information on CBECS > 1979-1999 CBECS climate zone map Corrections Corrections to 1979-1999 CBECS Climate Zone Map, February...

  16. Arizona Map for Commercial Buildings

    U.S. Energy Information Administration (EIA) Indexed Site

    Documents%20and%20SettingsLPJEMEUstyleseiasitewideF.css" rel"stylesheet" type"textcss" > Home > Households, Buildings & Industry > Background Information on CBECS > 2003...

  17. PP-108 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    8 Arizona Public Service Company PP-108 Arizona Public Service Company Presidential Permit authorizing APSC to construct, operate and maintain electric transmission facitilites at the U.S. - Mexico Border. PDF icon PP-108 Arizona Public Service Company More Documents & Publications PP-107-1 Arizona Public Service Company PP-106 Arizona Public Service Company PP-107 Arizona Public Service

  18. Active mines in Arizona and Arizona exploration offices

    SciTech Connect (OSTI)

    Not Available

    1988-01-01

    This book is a directory that lists 91 mining operations and 107 sand and gravel operations. It lists the company name, address, key personnel, mine, mill, or smelter location, and a description of the operation. A map plotting the locations of all the active mines is also available ($2). Arizona Exploration Offices is a directory that lists 68 exploration companies in Arizona, 80% of whom list gold or silver as their principal exploration target. Other exploration companies are searching for industrial minerals, uranium, beryllium, rare earths, ferroalloys, and sulfur.

  19. Flagstaff, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    a stub. You can help OpenEI by expanding it. Flagstaff is a city in Coconino County, Arizona. It falls under Arizona's 1st congressional district.12 Contents 1 Registered...

  20. Arizona Power Authority | Open Energy Information

    Open Energy Info (EERE)

    Arizona Power Authority Place: Arizona Phone Number: 602-368-4265 Website: www.powerauthority.org Outage Hotline: 602-368-4265 References: EIA Form EIA-861 Final Data File for...

  1. Arizona: Building Smart from the Start

    SciTech Connect (OSTI)

    2003-06-01

    A fact sheet that describes Arizona's Housing Tax Credit Program, to make sure houses were built more efficiently.

  2. Energy Incentive Programs, Arizona | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Arizona Energy Incentive Programs, Arizona Updated February 2015 What public-purpose-funded energy efficiency programs are available in my state? Arizona's restructuring law provides for a systems benefits charge (SBC) to fund energy efficiency programs. The SBC is collected through a non-bypassable surcharge on electricity bills. Although some of these funds have been devoted to renewable energy programs, in 2013 Arizona utilities budgeted over $160 million to promote energy efficiency and load

  3. Energy Department, Arizona Utilities Announce Transmission Infrastructure

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Project Energization | Department of Energy Arizona Utilities Announce Transmission Infrastructure Project Energization Energy Department, Arizona Utilities Announce Transmission Infrastructure Project Energization February 12, 2015 - 2:30pm Addthis News Media Contact 202 586 4940 DOENews@hq.doe.gov Energy Department, Arizona Utilities Announce Transmission Infrastructure Project Energization Transmission Line Increases Reliability, Access to Affordable Energy in Southwest States WASHINGTON

  4. Subdominant pseudoultrametric on graphs

    SciTech Connect (OSTI)

    Dovgoshei, A A; Petrov, E A

    2013-08-31

    Let (G,w) be a weighted graph. We find necessary and sufficient conditions under which the weight w:E(G)?R{sup +} can be extended to a pseudoultrametric on V(G), and establish a criterion for the uniqueness of such an extension. We demonstrate that (G,w) is a complete k-partite graph, for k?2, if and only if for any weight that can be extended to a pseudoultrametric, among all such extensions one can find the least pseudoultrametric consistent with w. We give a structural characterization of graphs for which the subdominant pseudoultrametric is an ultrametric for any strictly positive weight that can be extended to a pseudoultrametric. Bibliography: 14 titles.

  5. Tribal Water in Arizona Conference

    Broader source: Energy.gov [DOE]

    The Law Seminars International is hosting the Tribal Water in Arizona: New Development for Indian Water Rights, Regulations, and Settlement Processes. The two-day conference will present an overview of the law governing tribal water rights and impacting the development of tribal water projects.

  6. Try This: Household Magnets

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Household Magnets Household Magnets Chances are very good that you have experimented with magnets. People have been fascinated with magnetism for thousands of years. As familiar to us as they may be, magnets still have some surprises for us. Here is a small collection of some of our favorite magnet experiments. What happens when we break a magnet in half? Radio Shack sells cheap ceramic magnets in several shapes. Get a ring shaped magnet and break it with pliers or a tap with a hammer. Try to

  7. GraphLib

    Energy Science and Technology Software Center (OSTI)

    2013-02-19

    This library is used in several LLNL projects, including STAT (the Stack Trace Analysis Tool for scalable debugging) and some modules in P^nMPI (a tool MPI tool infrastructure). It can also be used standalone for creating and manipulationg graphs, but its API is primarily tuned to support these other projects

  8. Simple and Flexible Scene Graph

    Energy Science and Technology Software Center (OSTI)

    2007-10-01

    The system implements a flexible and extensible scene graph for the visualization and analysis of scientific information.

  9. Temporal Representation in Semantic Graphs

    SciTech Connect (OSTI)

    Levandoski, J J; Abdulla, G M

    2007-08-07

    A wide range of knowledge discovery and analysis applications, ranging from business to biological, make use of semantic graphs when modeling relationships and concepts. Most of the semantic graphs used in these applications are assumed to be static pieces of information, meaning temporal evolution of concepts and relationships are not taken into account. Guided by the need for more advanced semantic graph queries involving temporal concepts, this paper surveys the existing work involving temporal representations in semantic graphs.

  10. Recursive Feature Extraction in Graphs

    Energy Science and Technology Software Center (OSTI)

    2014-08-14

    ReFeX extracts recursive topological features from graph data. The input is a graph as a csv file and the output is a csv file containing feature values for each node in the graph. The features are based on topological counts in the neighborhoods of each nodes, as well as recursive summaries of neighbors' features.

  11. A Clustering Graph Generator

    SciTech Connect (OSTI)

    Winlaw, Manda; De Sterck, Hans; Sanders, Geoffrey

    2015-10-26

    In very simple terms a network can be de ned as a collection of points joined together by lines. Thus, networks can be used to represent connections between entities in a wide variety of elds including engi- neering, science, medicine, and sociology. Many large real-world networks share a surprising number of properties, leading to a strong interest in model development research and techniques for building synthetic networks have been developed, that capture these similarities and replicate real-world graphs. Modeling these real-world networks serves two purposes. First, building models that mimic the patterns and prop- erties of real networks helps to understand the implications of these patterns and helps determine which patterns are important. If we develop a generative process to synthesize real networks we can also examine which growth processes are plausible and which are not. Secondly, high-quality, large-scale network data is often not available, because of economic, legal, technological, or other obstacles [7]. Thus, there are many instances where the systems of interest cannot be represented by a single exemplar network. As one example, consider the eld of cybersecurity, where systems require testing across diverse threat scenarios and validation across diverse network structures. In these cases, where there is no single exemplar network, the systems must instead be modeled as a collection of networks in which the variation among them may be just as important as their common features. By developing processes to build synthetic models, so-called graph generators, we can build synthetic networks that capture both the essential features of a system and realistic variability. Then we can use such synthetic graphs to perform tasks such as simulations, analysis, and decision making. We can also use synthetic graphs to performance test graph analysis algorithms, including clustering algorithms and anomaly detection algorithms.

  12. PP-106 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    6 Arizona Public Service Company PP-106 Arizona Public Service Company Presidential permit authorizing Arizona Public Service Company to construct, operate, and maintain electric transmission facilities at the U.S-Mexico border. PDF icon PP-106 Arizona Public Service Company More Documents & Publications PP-107-1 Arizona Public Service Company PP-107

  13. usage_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  14. housingunit_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  15. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  16. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ...

  17. ARIZONA RECOVERY ACT SNAPSHOT | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    Arizona has substantial natural resources, including coal, solar, and hydroelectric resources. The American Recovery & Reinvestment Act (ARRA) is making a meaningful down payment on the nation's energy and environmental future. The Recovery Act investments in Arizona reflect a broad range of clean energy projects, from energy efficiency and the smart grid to transportation, carbon capture and storage, and geothermal energy. Through these investments, Arizona's businesses, universities,

  18. Arizona Department of Environmental Quality's Individual Permits...

    Open Energy Info (EERE)

    search OpenEI Reference LibraryAdd to library Web Site: Arizona Department of Environmental Quality's Individual Permits Website Abstract This website contains information...

  19. Arizona Department of Environmental Quality's General Permits...

    Open Energy Info (EERE)

    search OpenEI Reference LibraryAdd to library Web Site: Arizona Department of Environmental Quality's General Permits Website Abstract This website provides information...

  20. Arizona Department of Environmental Quality's Application Forms...

    Open Energy Info (EERE)

    search OpenEI Reference LibraryAdd to library Web Site: Arizona Department of Environmental Quality's Application Forms and Guidance Website Abstract This site contains forms...

  1. Williams, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Williams, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2494566, -112.1910031 Show Map Loading map... "minzoom":false,"mappingser...

  2. Havasupai Indian Reservation, Supai Village, Arizona | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Havasupai Indian Reservation, Supai Village, Arizona Photo of Photovoltaic Energy System ... Three photovoltaic (PV) energy systems will supply up to 2 kilowatts of electrical power ...

  3. Energy Department, Arizona Utilities Announce Transmission Infrastruct...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    expand opportunity, and improve the competitiveness of the American economy. "This newly ... while improving opportunities for new renewable energy generation in Arizona," said ...

  4. Flagstaff, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona Wind Energy LLC References US Census Bureau Incorporated place and minor civil division population dataset (All States, all geography) US Census Bureau...

  5. Tucson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    in Tucson, Arizona Environmentally Protective Power Generation EPPG Ethanol Capital Management Expert Solar Systems General Plasma Inc Genesis Solar LLC GeoInnovation Global...

  6. Arizona/Wind Resources | Open Energy Information

    Open Energy Info (EERE)

    source History View New Pages Recent Changes All Special Pages Semantic SearchQuerying Get Involved Help Apps Datasets Community Login | Sign Up Search Page Edit History Arizona...

  7. Arizona Department of Environmental Quality's AZPDES Website...

    Open Energy Info (EERE)

    AZPDES Website Jump to: navigation, search OpenEI Reference LibraryAdd to library Web Site: Arizona Department of Environmental Quality's AZPDES Website Abstract This website...

  8. Phoenix, Arizona Data Dashboard | Department of Energy

    Broader source: Energy.gov (indexed) [DOE]

    The data dashboard for Phoenix, Arizona, a partner in the Better Buildings Neighborhood Program. File Phoenix Data Dashboard More Documents & Publications Austin Energy Data ...

  9. Prescott, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    district.12 Energy Generation Facilities in Prescott, Arizona Prescott Airport Solar Plant Solar Power Plant References US Census Bureau Incorporated place and...

  10. Arizona Teachers Prepare Students for Green Economy

    Broader source: Energy.gov [DOE]

    Students led by their building trades teacher , are wiring parts of the Raymond S. Kellis High School in Glendale, Arizona for solar power.

  11. Arizona Indian Gaming Association (AIGA) Expo

    Broader source: Energy.gov [DOE]

    This year’s EXPO will take place November 5-7, 2014 at the Radisson Fort McDowell Resort & Casino located in Scottsdale, Arizona.

  12. Burnside, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Burnside, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.7511228, -109.6245514 Show Map Loading map... "minzoom":false,"mappingser...

  13. Summit, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Summit, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.0670238, -110.9514796 Show Map Loading map... "minzoom":false,"mappingservi...

  14. Cameron, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.8758285, -111.4129207 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  15. Ganado, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Ganado, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.7114022, -109.5420492 Show Map Loading map... "minzoom":false,"mappingservi...

  16. Avondale, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Avondale, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4355977, -112.3496021 Show Map Loading map... "minzoom":false,"mappingser...

  17. Jerome, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.7489107, -112.1137716 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  18. Northern Arizona University | Open Energy Information

    Open Energy Info (EERE)

    University Jump to: navigation, search Name: Northern Arizona University Place: Flagstaff, AZ Zip: 86011 Phone Number: 928-523-0715 Website: nau.edu Coordinates: 35.1905403,...

  19. Littletown, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Littletown, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.1303561, -110.8728658 Show Map Loading map... "minzoom":false,"mappings...

  20. Peoria, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Peoria, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.5805955, -112.2373779 Show Map Loading map... "minzoom":false,"mappingservi...

  1. Springerville, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Springerville, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.1333799, -109.2859196 Show Map Loading map... "minzoom":false,"mappi...

  2. Surprise, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Surprise, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.6305938, -112.333216 Show Map Loading map... "minzoom":false,"mappingserv...

  3. Cottonwood, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.7391876, -112.0098791 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  4. Maricopa, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0581063, -112.0476423 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  5. Kaibab, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Kaibab, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.896652, -112.7407596 Show Map Loading map... "minzoom":false,"mappingservic...

  6. Coolidge, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Coolidge, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.977839, -111.517624 Show Map Loading map... "minzoom":false,"mappingservi...

  7. Gadsden, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Gadsden, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.5544974, -114.7849577 Show Map Loading map... "minzoom":false,"mappingserv...

  8. Whetstone, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Whetstone, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.701705, -110.340746 Show Map Loading map... "minzoom":false,"mappingserv...

  9. Chinle, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Chinle, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.1544483, -109.5526072 Show Map Loading map... "minzoom":false,"mappingservi...

  10. Blackwater, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Blackwater, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0311702, -111.582627 Show Map Loading map... "minzoom":false,"mappingse...

  11. Vail, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.0478583, -110.7120272 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  12. Cornville, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Cornville, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.7177989, -111.9215438 Show Map Loading map... "minzoom":false,"mappingse...

  13. Tsaile, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tsaile, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.303712, -109.214705 Show Map Loading map... "minzoom":false,"mappingservice...

  14. Wilhoit, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Wilhoit, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.4258586, -112.5868398 Show Map Loading map... "minzoom":false,"mappingserv...

  15. Mountainaire, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Mountainaire, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.0852924, -111.6659925 Show Map Loading map... "minzoom":false,"mappin...

  16. Kingman, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.189443, -114.0530065 Show Map Loading map... "minzoom":false,"mappingservice":"goog...

  17. Oracle, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Oracle, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.6109054, -110.7709348 Show Map Loading map... "minzoom":false,"mappingservi...

  18. Fredonia, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.945542, -112.5265889 Show Map Loading map... "minzoom":false,"mappingservice":"goog...

  19. Chuichu, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Chuichu, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.752002, -111.7831837 Show Map Loading map... "minzoom":false,"mappingservi...

  20. Sahuarita, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sahuarita, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.9575818, -110.955646 Show Map Loading map... "minzoom":false,"mappingser...

  1. Tortolita, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tortolita, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.4005302, -111.0400795 Show Map Loading map... "minzoom":false,"mappingse...

  2. Sacaton, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sacaton, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0767225, -111.7392993 Show Map Loading map... "minzoom":false,"mappingserv...

  3. Moenkopi, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Moenkopi, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.1111043, -111.2223624 Show Map Loading map... "minzoom":false,"mappingser...

  4. Paulden, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Paulden, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.8855756, -112.4682271 Show Map Loading map... "minzoom":false,"mappingserv...

  5. Parks, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Parks, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2605664, -111.9487743 Show Map Loading map... "minzoom":false,"mappingservic...

  6. Arizona Natural Gas Repressuring (Million Cubic Feet)

    U.S. Energy Information Administration (EIA) Indexed Site

    12312015 Next Release Date: 01292016 Referring Pages: Natural Gas Used for Repressuring Arizona Natural Gas Gross Withdrawals and Production Natural Gas Used for Repressuring...

  7. Tacna, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tacna, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.6975472, -113.9535427 Show Map Loading map... "minzoom":false,"mappingservic...

  8. Houck, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Houck, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2830803, -109.2070391 Show Map Loading map... "minzoom":false,"mappingservic...

  9. Tucson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tucson, Arizona: Energy Resources (Redirected from Tucson, AZ) Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.2217429, -110.926479 Show Map Loading map......

  10. Congress, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Congress, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.162526, -112.8507374 Show Map Loading map... "minzoom":false,"mappingserv...

  11. Supai, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Supai, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.2369265, -112.6890791 Show Map Loading map... "minzoom":false,"mappingservic...

  12. Superior, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Superior, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.293945, -111.0962305 Show Map Loading map... "minzoom":false,"mappingserv...

  13. Wellton, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Wellton, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.6728256, -114.1468821 Show Map Loading map... "minzoom":false,"mappingserv...

  14. Carefree, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Carefree, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.8222611, -111.918203 Show Map Loading map... "minzoom":false,"mappingserv...

  15. Willcox, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Willcox, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.2528519, -109.8320124 Show Map Loading map... "minzoom":false,"mappingserv...

  16. Chandler, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Chandler, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.3061605, -111.8412502 Show Map Loading map... "minzoom":false,"mappingser...

  17. Pirtleville, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Pirtleville, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.3570467, -109.561734 Show Map Loading map... "minzoom":false,"mappings...

  18. Dudleyville, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Dudleyville, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.914267, -110.733779 Show Map Loading map... "minzoom":false,"mappingse...

  19. Tonalea, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tonalea, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.3224923, -110.9634781 Show Map Loading map... "minzoom":false,"mappingserv...

  20. Mayer, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Mayer, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.3978054, -112.2362734 Show Map Loading map... "minzoom":false,"mappingservic...

  1. Ajo, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Ajo, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.3717248, -112.8607099 Show Map Loading map... "minzoom":false,"mappingservice"...

  2. Wickenburg, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Wickenburg, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.9686412, -112.729622 Show Map Loading map... "minzoom":false,"mappingse...

  3. Glendale, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.5386523, -112.1859866 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  4. Bisbee, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Bisbee, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.4481547, -109.9284084 Show Map Loading map... "minzoom":false,"mappingservi...

  5. Eloy, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Eloy, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.7558962, -111.554844 Show Map Loading map... "minzoom":false,"mappingservice"...

  6. Tolleson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tolleson, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4500425, -112.259321 Show Map Loading map... "minzoom":false,"mappingserv...

  7. Nazlini, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Nazlini, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.8963986, -109.4487147 Show Map Loading map... "minzoom":false,"mappingserv...

  8. Tombstone, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tombstone, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.7128683, -110.0675764 Show Map Loading map... "minzoom":false,"mappingse...

  9. Sedona, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sedona, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.8697395, -111.7609896 Show Map Loading map... "minzoom":false,"mappingservi...

  10. Sawmill, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sawmill, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.6181083, -110.3964911 Show Map Loading map... "minzoom":false,"mappingserv...

  11. Pisinemo, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Pisinemo, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.0378487, -112.3209689 Show Map Loading map... "minzoom":false,"mappingser...

  12. Sells, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sells, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.9120215, -111.881234 Show Map Loading map... "minzoom":false,"mappingservice...

  13. Hayden, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hayden, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0047878, -110.7853836 Show Map Loading map... "minzoom":false,"mappingservi...

  14. Kearny, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0570085, -110.9106656 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  15. Eagar, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Eagar, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.1111581, -109.291475 Show Map Loading map... "minzoom":false,"mappingservice...

  16. Stanfield, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Stanfield, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.8825531, -111.9620805 Show Map Loading map... "minzoom":false,"mappingse...

  17. Mammoth, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Mammoth, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.722568, -110.6406547 Show Map Loading map... "minzoom":false,"mappingservi...

  18. Lukachukai, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Lukachukai, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.416946, -109.2287125 Show Map Loading map... "minzoom":false,"mappingse...

  19. Florence, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.0314508, -111.3873431 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  20. Lechee, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Lechee, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.0322421, -110.7529145 Show Map Loading map... "minzoom":false,"mappingservi...

  1. Guadalupe, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Guadalupe, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.3708798, -111.9629216 Show Map Loading map... "minzoom":false,"mappingse...

  2. Dennehotso, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Dennehotso, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.479167, -111.2375 Show Map Loading map... "minzoom":false,"mappingservi...

  3. Naco, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Naco, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.3353801, -109.9481297 Show Map Loading map... "minzoom":false,"mappingservice...

  4. Marana, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Marana, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.414432, -111.172754 Show Map Loading map... "minzoom":false,"mappingservice...

  5. Winkelman, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Winkelman, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.9875659, -110.7709387 Show Map Loading map... "minzoom":false,"mappingse...

  6. Somerton, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Somerton, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.5964404, -114.709677 Show Map Loading map... "minzoom":false,"mappingserv...

  7. Williamson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Williamson, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.6900229, -112.5410052 Show Map Loading map... "minzoom":false,"mappings...

  8. Buckeye, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Buckeye, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.3703197, -112.5837766 Show Map Loading map... "minzoom":false,"mappingserv...

  9. Santan, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Santan, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.145476, -111.801546 Show Map Loading map... "minzoom":false,"mappingservice...

  10. Gilbert, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.3528264, -111.789027 Show Map Loading map... "minzoom":false,"mappingservice":"goog...

  11. Kaibito, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Kaibito, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.5972186, -111.0743114 Show Map Loading map... "minzoom":false,"mappingserv...

  12. Page, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Page, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.9147222, -111.4558333 Show Map Loading map... "minzoom":false,"mappingservice...

  13. Douglas, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Douglas, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.3445471, -109.5453447 Show Map Loading map... "minzoom":false,"mappingserv...

  14. Steamboat, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Steamboat, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.7513983, -109.8478915 Show Map Loading map... "minzoom":false,"mappingse...

  15. Phoenix, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Phoenix, Arizona: Energy Resources (Redirected from Phoenix, AZ) Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4483771, -112.0740373 Show Map Loading map......

  16. Leupp, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Leupp, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2980659, -111.0062528 Show Map Loading map... "minzoom":false,"mappingservic...

  17. Seligman, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Seligman, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.3255608, -112.8774057 Show Map Loading map... "minzoom":false,"mappingser...

  18. Tusayan, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tusayan, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.9735954, -112.1265569 Show Map Loading map... "minzoom":false,"mappingserv...

  19. Goodyear, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Goodyear, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4353199, -112.3582135 Show Map Loading map... "minzoom":false,"mappingser...

  20. Catalina, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Catalina, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.5000731, -110.9212146 Show Map Loading map... "minzoom":false,"mappingser...

  1. Yarnell, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Yarnell, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.2216927, -112.7474007 Show Map Loading map... "minzoom":false,"mappingserv...

  2. Yuma, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Yuma, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.7253248, -114.624397 Show Map Loading map... "minzoom":false,"mappingservice"...

  3. Mesa, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Inc. ETA Engineering Renegy Holdings Inc The Arizona Center for Algae Technology and Innovation References US Census Bureau Incorporated place and minor civil division...

  4. Arizona Center for Innovation | Open Energy Information

    Open Energy Info (EERE)

    Innovation Jump to: navigation, search Name: Arizona Center for Innovation Place: United States Sector: Services Product: General Financial & Legal Services ( Academic Research...

  5. BLM Arizona State Office | Open Energy Information

    Open Energy Info (EERE)

    Arizona Address: One North Central Avenue, Suite 800 Place: Phoenix, AZ Zip: 85004 Phone Number: 602-417-9200 ParentHolding Organization: Bureau of Land Management...

  6. Household energy consumption and expenditures 1987

    SciTech Connect (OSTI)

    Not Available

    1990-01-22

    This report is the third in the series of reports presenting data from the 1987 Residential Energy Consumption Survey (RECS). The 1987 RECS, seventh in a series of national surveys of households and their energy suppliers, provides baseline information on household energy use in the United States. Data from the seven RECS and its companion survey, the Residential Transportation Energy Consumption Survey (RTECS), are made available to the public in published reports such as this one, and on public use data files. This report presents data for the four Census regions and nine Census divisions on the consumption of and expenditures for electricity, natural gas, fuel oil and kerosene (as a single category), and liquefied petroleum gas (LPG). Data are also presented on consumption of wood at the Census region level. The emphasis in this report is on graphic depiction of the data. Data from previous RECS surveys are provided in the graphics, which indicate the regional trends in consumption, expenditures, and uses of energy. These graphs present data for the United States and each Census division. 12 figs., 71 tabs.

  7. EA-106 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    EA-106 Arizona Public Service Company Order authorizing Arizona Public Service Company to export electric energy to Mexico. PDF icon EA-106 Arizona Public Service (MX).pdf More ...

  8. The Future of Electric Vehicles and Arizona State University's MAIL

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Battery | Department of Energy The Future of Electric Vehicles and Arizona State University's MAIL Battery The Future of Electric Vehicles and Arizona State University's MAIL Battery August 11, 2010 - 4:26pm Addthis Cody Friesen and his team at Arizona State University | Photo Credit Arizona State University Cody Friesen and his team at Arizona State University | Photo Credit Arizona State University Andy Oare Andy Oare Former New Media Strategist, Office of Public Affairs What does this

  9. A Solar Win for Arizona | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    A Solar Win for Arizona A Solar Win for Arizona January 9, 2013 - 5:11pm Addthis The 150 megawatt Mesquite Solar 1 installation in Maricopa County, Arizona. | Photo courtesy of Sempra Energy. The 150 megawatt Mesquite Solar 1 installation in Maricopa County, Arizona. | Photo courtesy of Sempra Energy. The 150 megawatt Mesquite Solar 1 installation in Maricopa County, Arizona. | Photo courtesy of Sempra Energy. The 150 megawatt Mesquite Solar 1 installation in Maricopa County, Arizona. | Photo

  10. Graph Coarsening for Path Finding in Cybersecurity Graphs

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh

    2013-01-01

    n the pass-the-hash attack, hackers repeatedly steal password hashes and move through a computer network with the goal of reaching a computer with high level administrative privileges. In this paper we apply graph coarsening in network graphs for the purpose of detecting hackers using this attack or assessing the risk level of the network's current state. We repeatedly take graph minors, which preserve the existence of paths in the graph, and take powers of the adjacency matrix to count the paths. This allows us to detect the existence of paths as well as find paths that have high risk of being used by adversaries.

  11. Arizona's 3rd congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    congressional district Agenera, LLC Alchemix Corporation Amereco Biofuels Corp Arizona Public Service Company APS Arizona Solar Tech EDGE Energy LLC EGreenIdeas Ecotality North...

  12. Arizona Public Service Company APS | Open Energy Information

    Open Energy Info (EERE)

    Public Service Company APS Jump to: navigation, search Name: Arizona Public Service Company (APS) Place: Phoenix, Arizona Zip: 85004 Product: Generates, transmits and distributes...

  13. Arizona Const. Art.15 - The Corporation Commission | Open Energy...

    Open Energy Info (EERE)

    Arizona Const. Art.15 - The Corporation CommissionLegal Abstract This article sets forth the Constitutional provisions governing the Arizona Corporations Commission. Published...

  14. City of Williams - AZ, Arizona (Utility Company) | Open Energy...

    Open Energy Info (EERE)

    Williams - AZ, Arizona (Utility Company) Jump to: navigation, search Name: City of Williams - AZ Place: Arizona Phone Number: 928-635-2667 or 928-635-4451 Website:...

  15. Phoenix, Arizona Summary of Reported Data | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Summary of Reported Data Phoenix, Arizona Summary of Reported Data Summary of data reported by Better Buildings Neighborhood Program partner Phoenix, Arizona. PDF icon Phoenix, ...

  16. EERE Success Story-Arizona: Solar Panels Replace Inefficient...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    These projects typically have payback periods under five ... consumption of gasoline, diesel, propane, and electricity. Location Arizona Partners State of Arizona EERE Investment ...

  17. Northern Arizona University Wind Projects | Open Energy Information

    Open Energy Info (EERE)

    Wind Projects Jump to: navigation, search Northern Arizona University ARD Wind Project Northern Arizona University SHRM Wind Project Retrieved from "http:en.openei.orgw...

  18. Arizona Transmission Line Siting Committee | Open Energy Information

    Open Energy Info (EERE)

    Line Siting Committee Jump to: navigation, search Name: Arizona Transmission Line Siting Committee Abbreviation: TLSC Address: 1200 West Washington Street Place: Phoenix, Arizona...

  19. The Arizona Center for Algae Technology and Innovation | Open...

    Open Energy Info (EERE)

    Arizona Center for Algae Technology and Innovation Jump to: navigation, search Name: The Arizona Center for Algae Technology and Innovation Abbreviation: AzCATI Address: 7418 East...

  20. Arizona Nuclear Profile - Power Plants

    U.S. Energy Information Administration (EIA) Indexed Site

    nuclear power plants, summer capacity and net generation, 2010" "Plant name/total reactors","Summer capacity (mw)","Net generation (thousand mwh)","Share of State nuclear net generation (percent)","Owner" "Palo Verde Unit 1, Unit 2, Unit 3","3,937","31,200",100.0,"Arizona Public Service Co" "1 Plant 3 Reactors","3,937","31,200",100.0 "Note: Totals may not equal sum of

  1. Bisfuel links - Arizona State University

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Arizona State University http://chemistry.asu.edu/" target="_blank">ASU Department of Chemistry and Biochemistry http://sustainability.asu.edu/index.php" target="_blank">ASU Global Institute of Sustainability http://asulightworks.com/" target="_blank">ASU Lightworks http://sols.asu.edu/" target="_blank">ASU School of Life Sciences http://www.biodesign.asu.edu/" target="_blank">Biodesign Institute

  2. Northern Arizona University | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Northern Arizona University Northern Arizona University Team Roster: Anas Alkandari, Mechanical Engineering; Randon Allen, Electrical Engineering; Hashim Alramadhan, Mechanical Engineering; Jessica Bauer, Mechanical Engineering; Luke Baxter, Business Administration; Thomas Begay, Business Administration; Connor Campbell, Business Administration; Nathan Ceniceros, Mechanical Engineering; Norman Clark, Mechanical Engineering; Michael Coil, Business Administration; Jeremy Cook, Mechanical

  3. Phoenix, Arizona Data Dashboard | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Data Dashboard Phoenix, Arizona Data Dashboard The data dashboard for Phoenix, Arizona, a partner in the Better Buildings Neighborhood Program. File Phoenix Data Dashboard More Documents & Publications Austin Energy Data Dashboard Massachusetts -- SEP Data Dashboard Camden, New Jersey Data Dashboard

  4. Quantum Graph Analysis

    SciTech Connect (OSTI)

    Maunz, Peter Lukas Wilhelm; Sterk, Jonathan David; Lobser, Daniel; Parekh, Ojas D.; Ryan-Anderson, Ciaran

    2016-01-01

    In recent years, advanced network analytics have become increasingly important to na- tional security with applications ranging from cyber security to detection and disruption of ter- rorist networks. While classical computing solutions have received considerable investment, the development of quantum algorithms to address problems, such as data mining of attributed relational graphs, is a largely unexplored space. Recent theoretical work has shown that quan- tum algorithms for graph analysis can be more efficient than their classical counterparts. Here, we have implemented a trapped-ion-based two-qubit quantum information proces- sor to address these goals. Building on Sandia's microfabricated silicon surface ion traps, we have designed, realized and characterized a quantum information processor using the hyperfine qubits encoded in two 171 Yb + ions. We have implemented single qubit gates using resonant microwave radiation and have employed Gate set tomography (GST) to characterize the quan- tum process. For the first time, we were able to prove that the quantum process surpasses the fault tolerance thresholds of some quantum codes by demonstrating a diamond norm distance of less than 1 . 9 x 10 [?] 4 . We used Raman transitions in order to manipulate the trapped ions' motion and realize two-qubit gates. We characterized the implemented motion sensitive and insensitive single qubit processes and achieved a maximal process infidelity of 6 . 5 x 10 [?] 5 . We implemented the two-qubit gate proposed by Molmer and Sorensen and achieved a fidelity of more than 97 . 7%.

  5. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) Indexed Site

    or commercial trucks (See Table 1). Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 5 The 1991 RTECS count includes vehicles that were owned or used...

  6. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) Indexed Site

    logo printer-friendly version logo for Portable Document Format file Household Vehicles Energy Consumption 1991 December 1993 Release Next Update: August 1997. Based on the 1991...

  7. A Collection of Features for Semantic Graphs

    SciTech Connect (OSTI)

    Eliassi-Rad, T; Fodor, I K; Gallagher, B

    2007-05-02

    Semantic graphs are commonly used to represent data from one or more data sources. Such graphs extend traditional graphs by imposing types on both nodes and links. This type information defines permissible links among specified nodes and can be represented as a graph commonly referred to as an ontology or schema graph. Figure 1 depicts an ontology graph for data from National Association of Securities Dealers. Each node type and link type may also have a list of attributes. To capture the increased complexity of semantic graphs, concepts derived for standard graphs have to be extended. This document explains briefly features commonly used to characterize graphs, and their extensions to semantic graphs. This document is divided into two sections. Section 2 contains the feature descriptions for static graphs. Section 3 extends the features for semantic graphs that vary over time.

  8. Graph Partitioning and Sequencing Software

    Energy Science and Technology Software Center (OSTI)

    1995-09-19

    Graph partitioning is a fundemental problem in many scientific contexts. CHACO2.0 is a software package designed to partition and sequence graphs. CHACO2.0 allows for recursive application of several methods for finding small edge separators in weighted graphs. These methods include inertial, spectral, Kernighan Lin and multilevel methods in addition to several simpler strategies. Each of these approaches can be used to partition the graph into two, four, or eight pieces at each level of recursion.more » In addition, the Kernighan Lin method can be used to improve partitions generated by any of the other algorithms. CHACO2.0 can also be used to address various graph sequencing problems, with applications to scientific computing, database design, gene sequencing and other problems.« less

  9. PP-107 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Arizona Public Service Company PP-107 Arizona Public Service Company Presidential permit authorizing Arizona Public Service Company to construct, operate, and maintain electric transmission facilities at the U.S-Mexico border. PDF icon PP-107 Arizona Public Service Company More Documents & Publications PP-107-1

  10. Alternative Fuels Data Center: Arizona Transportation Data for Alternative

    Alternative Fuels and Advanced Vehicles Data Center [Office of Energy Efficiency and Renewable Energy (EERE)]

    Fuels and Vehicles Arizona Transportation Data for Alternative Fuels and Vehicles to someone by E-mail Share Alternative Fuels Data Center: Arizona Transportation Data for Alternative Fuels and Vehicles on Facebook Tweet about Alternative Fuels Data Center: Arizona Transportation Data for Alternative Fuels and Vehicles on Twitter Bookmark Alternative Fuels Data Center: Arizona Transportation Data for Alternative Fuels and Vehicles on Google Bookmark Alternative Fuels Data Center: Arizona

  11. ,"Arizona Natural Gas Gross Withdrawals and Production"

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,"(202) 586-8800",,,"4292016 6:48:23 AM" "Back to Contents","Data 1: Arizona Natural Gas Gross Withdrawals and Production" "Sourcekey","N9010AZ2","N9011AZ2","N9012AZ2","NGME...

  12. ,"Arizona Natural Gas Gross Withdrawals and Production"

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,"(202) 586-8800",,,"4292016 6:48:22 AM" "Back to Contents","Data 1: Arizona Natural Gas Gross Withdrawals and Production" "Sourcekey","N9010AZ2","N9011AZ2","N9012AZ2","NGME...

  13. ,"Arizona Natural Gas Consumption by End Use"

    U.S. Energy Information Administration (EIA) Indexed Site

    Consumption by End Use" ,"Click worksheet name or tab at bottom for data" ,"Worksheet Name","Description"," Of Series","Frequency","Latest Data for" ,"Data 1","Arizona Natural Gas ...

  14. EIS-0322: Sundance Energy Project, Arizona

    Broader source: Energy.gov [DOE]

    This EIS analyzes Western Area Power Administration (Western) decision to approve Sundance Energy LLC (Sundance) to interconnect a planned generator facility to Westerns transmission system in the vicinity of Coolidge, Arizona.

  15. Arizona Natural Gas Repressuring (Million Cubic Feet)

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Repressuring (Million Cubic Feet) Arizona Natural Gas Repressuring (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1970's 103...

  16. Next Generation Household Refrigerator | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Next Generation Household Refrigerator Next Generation Household Refrigerator Embraco's high efficiency, oil-free linear compressor.
    Credit: Whirlpool Embraco's high ...

  17. Strategies for Collecting Household Energy Data | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Collecting Household Energy Data Strategies for Collecting Household Energy Data Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for ...

  18. Household Vehicles Energy Use Cover Page

    U.S. Energy Information Administration (EIA) Indexed Site

    Energy Use Cover Page Glossary Home > Households, Buildings & Industry >Transportation Surveys > Household Vehicles Energy Use Cover Page Contact Us * Feedback * PrivacySecurity *...

  19. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    0a. Air Conditioning by Midwest Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 20.5 13.6 6.8 2.2 Air Conditioners Not Used ........................... 2.1 0.3 Q Q 27.5 Households Using Electric Air-Conditioning 1

  20. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    1a. Air Conditioning by South Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.8 1.2 1.3 1.4 Households With Electric Air-Conditioning Equipment ...................... 82.9 37.2 19.3 6.4 11.5 1.5 Air Conditioners Not Used ........................... 2.1 0.4 Q Q Q 28.2 Households Using Electric Air-Conditioning 1

  1. Khovanov homology of graph-links

    SciTech Connect (OSTI)

    Nikonov, Igor M

    2012-08-31

    Graph-links arise as the intersection graphs of turning chord diagrams of links. Speaking informally, graph-links provide a combinatorial description of links up to mutations. Many link invariants can be reformulated in the language of graph-links. Khovanov homology, a well-known and useful knot invariant, is defined for graph-links in this paper (in the case of the ground field of characteristic two). Bibliography: 14 titles.

  2. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    Total U.S. Northeast Census Region RSE Row Factors Total Census Division Middle Atlantic New England 0.5 1.0 1.2 1.8 Households With Electric Air-Conditioning Equipment ...

  3. char_household2001.pdf

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Contact: Stephanie J. Battles, Survey Manager (stephanie.battles@eia.doe.gov) World Wide Web: http:www.eia.doe.govemeuconsumption Table HC2-1a. Household Characteristics by ...

  4. char_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... Income Relative to Poverty Line Below 100 Percent ...... 15.0 13.2 1.8 Q ...

  5. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ... RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... 29.1 5.3 22.7 3.8 1 Below 150 percent of poverty line or 60 percent of median State ...

  6. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    107.0 7.1 12.3 7.7 6.3 NE Households Using Office Equipment ... NE RSE row factor not estimated because RSE's for all statistics in this row are between ...

  7. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ......... 107.0 24.5 17.1 7.4 NE Households Using Office Equipment ... NE RSE row factor not estimated because RSE's for all statistics in this row are between ...

  8. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    107.0 38.9 20.3 6.8 11.8 NE Households Using Office Equipment ... NE RSE row factor not estimated because RSE's for all statistics in this row are between ...

  9. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ......... 107.0 23.3 6.7 16.6 NE Households Using Office Equipment ... NE RSE row factor not estimated because RSE's for all statistics in this row are between ...

  10. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    ... location is over a period of one year, relative to a base temperature of 65 degrees Fahrenheit. A household is assigned to a climate zone according to the 30-year average annual ...

  11. Household Vehicles Energy Consumption 1991

    U.S. Energy Information Administration (EIA) Indexed Site

    16.8 17.4 18.6 18.9 1.7 2.2 0.6 1.5 Energy Information AdministrationHousehold Vehicles Energy Consumption 1991 15 Vehicle Miles Traveled per Vehicle (Thousand) . . . . . . . . ....

  12. ac_household2001.pdf

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. Four Most Populated ... New York California Texas Florida 0.4 1.1 1.7 1.2 1.2 Households With Electric Air-Conditi...

  13. ac_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    2a. Air Conditioning by Year of Construction, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total Year of Construction RSE Row Factors 1990 to ...

  14. ac_household2001.pdf

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    2a. Air Conditioning by West Census Region, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total U.S. West Census Region RSE Row Factors Total ...

  15. ac_household2001.pdf

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    8a. Air Conditioning by UrbanRural Location, Million U.S. Households, 2001 Air Conditioning Characteristics RSE Column Factor: Total UrbanRural Location 1 RSE Row Factors City ...

  16. Cover Page of Household Vehicles Energy Use: Latest Data & Trends

    Gasoline and Diesel Fuel Update (EIA)

    Household Vehicles Energy Use Cover Page Cover Page of Household Vehicles Energy Use: Latest Data & Trends...

  17. SOURCE PHENOMENOLOGY EXPERIMENTS IN ARIZONA

    SciTech Connect (OSTI)

    Jessie L. Bonner; Brian Stump; Mark Leidig; Heather Hooper; Xiaoning Yang; Rongmao Zhou; Tae Sung Kim; William R. Walter; Aaron Velasco; Chris Hayward; Diane Baker; C. L. Edwards; Steven Harder; Travis Glenn; Cleat Zeiler; James Britton; James F. Lewkowicz

    2005-09-30

    The Arizona Source Phenomenology Experiments (SPE) have resulted in an important dataset for the nuclear monitoring community. The 19 dedicated single-fired explosions and multiple delay-fired mining explosions were recorded by one of the most densely instrumented accelerometer and seismometer arrays ever fielded, and the data have already proven useful in quantifying confinement and excitation effects for the sources. It is very interesting to note that we have observed differences in the phenomenology of these two series of explosions resulting from the differences between the relatively slow (limestone) and fast (granodiorite) media. We observed differences at the two SPE sites in the way the rock failed during the explosions, how the S-waves were generated, and the amplitude behavior as a function of confinement. Our consortium's goal is to use the synergy of the multiple datasets collected during this experiment to unravel the phenomenological differences between the two emplacement media. The data suggest that the main difference between single-fired chemical and delay-fired mining explosion seismograms at regional distances is the increased surface wave energy for the latter source type. The effect of the delay-firing is to decrease the high-frequency P-wave amplitudes while increasing the surface wave energy because of the longer source duration and spall components. The results suggest that the single-fired explosions are surrogates for nuclear explosions in higher frequency bands (e.g., 6-8 Hz Pg/Lg discriminants). We have shown that the SPE shots, together with the mining explosions, are efficient sources of S-wave energy, and our next research stage is to postulate the possible sources contributing to the shear-wave energy.

  18. San Luis, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. San Luis is a city in Yuma County, Arizona. It falls under Arizona's 7th congressional...

  19. 1,"Palo Verde","Nuclear","Arizona Public Service Co",3937

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona" ,"Plant","Primary energy source","Operating company","Net summer capacity (MW)" 1,"Palo Verde","Nuclear","Arizona Public Service Co",3937 2,"Navajo","Coal","Salt River ...

  20. ,"Arizona Natural Gas Industrial Price (Dollars per Thousand...

    U.S. Energy Information Administration (EIA) Indexed Site

    586-8800",,,"1292016 12:15:22 AM" "Back to Contents","Data 1: Arizona Natural Gas Industrial Price (Dollars per Thousand Cubic Feet)" "Sourcekey","N3035AZ3" "Date","Arizona...

  1. Arizona Natural Gas Gross Withdrawals (Million Cubic Feet per...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Arizona Natural Gas Gross Withdrawals (Million Cubic Feet per Day) Arizona Natural Gas Gross Withdrawals (Million Cubic Feet per Day) Year Jan Feb Mar Apr May Jun Jul Aug Sep Oct...

  2. Alternative Fuels Data Center: Rolling Down the Arizona EV Highway

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Rolling Down the Arizona EV Highway to someone by E-mail Share Alternative Fuels Data Center: Rolling Down the Arizona EV Highway on Facebook Tweet about Alternative Fuels Data...

  3. Fort Defiance, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    OpenEI by expanding it. Fort Defiance is a census-designated place in Apache County, Arizona.1 US Recovery Act Smart Grid Projects in Fort Defiance, Arizona Navajo Tribal...

  4. Casa Grande, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Casa Grande is a city in Pinal County, Arizona. It falls under Arizona's 1st congressional...

  5. Graph modeling systems and methods

    SciTech Connect (OSTI)

    Neergaard, Mike

    2015-10-13

    An apparatus and a method for vulnerability and reliability modeling are provided. The method generally includes constructing a graph model of a physical network using a computer, the graph model including a plurality of terminating vertices to represent nodes in the physical network, a plurality of edges to represent transmission paths in the physical network, and a non-terminating vertex to represent a non-nodal vulnerability along a transmission path in the physical network. The method additionally includes evaluating the vulnerability and reliability of the physical network using the constructed graph model, wherein the vulnerability and reliability evaluation includes a determination of whether each terminating and non-terminating vertex represents a critical point of failure. The method can be utilized to evaluate wide variety of networks, including power grid infrastructures, communication network topologies, and fluid distribution systems.

  6. Graph Analytics for Signature Discovery

    SciTech Connect (OSTI)

    Hogan, Emilie A.; Johnson, John R.; Halappanavar, Mahantesh; Lo, Chaomei

    2013-06-01

    Within large amounts of seemingly unstructured data it can be diffcult to find signatures of events. In our work we transform unstructured data into a graph representation. By doing this we expose underlying structure in the data and can take advantage of existing graph analytics capabilities, as well as develop new capabilities. Currently we focus on applications in cybersecurity and communication domains. Within cybersecurity we aim to find signatures for perpetrators using the pass-the-hash attack, and in communications we look for emails or phone calls going up or down a chain of command. In both of these areas, and in many others, the signature we look for is a path with certain temporal properties. In this paper we discuss our methodology for finding these temporal paths within large graphs.

  7. Federal Correctional Institution - Phoenix, Arizona | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Federal Correctional Institution - Phoenix, Arizona Federal Correctional Institution - Phoenix, Arizona Photo of a Parabolic-Trough Solar Water-Heating System Installed at the Federal Correctional Institution Facility north of Phoenix, Arizona A parabolic-trough solar water-heating system was installed at the Federal Correctional Institution (FCI) facility north of Phoenix, Arizona. This medium security prison for males has a current population of about 1,200 inmates and uses an average of

  8. Final Report - Arizona Rooftop Solar Challenge | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Arizona Rooftop Solar Challenge Final Report - Arizona Rooftop Solar Challenge Awardee: Arizona Governor's Office of Energy Policy Location: Phoenix, AZ Subprogram: Soft Costs Funding Program: Rooftop Solar Challenge 1 The Arizona Rooftop Solar Challenge (ARC) is a regional partnership of the Rooftop Solar Challenge. Funded through the U.S. Department of Energy's SunShot Initiative, this program is focused on streamlining processes and reducing costs to make solar more affordable for the

  9. Arizona Recovery Act State Memo | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Arizona Recovery Act State Memo Arizona Recovery Act State Memo Arizona has substantial natural resources, including coal, solar, and hydroelectric resources. The American Recovery & Reinvestment Act (ARRA) is making a meaningful down payment on the nation's energy and environmental future. The Recovery Act investments in Arizona reflect a broad range of clean energy projects, from energy efficiency and the smart grid to transportation, carbon capture and storage, and geothermal energy.

  10. Phoenix, Arizona Summary of Reported Data | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Summary of Reported Data Phoenix, Arizona Summary of Reported Data Summary of data reported by Better Buildings Neighborhood Program partner Phoenix, Arizona. PDF icon Phoenix, Arizona Summary of Reported Data More Documents & Publications Virginia -- SEP Summary of Reported Data University Park Summary of Reported Data Alabama -- SEP Summary of Reported Data

  11. Dr.L: Distributed Recursive (Graph) Layout

    Energy Science and Technology Software Center (OSTI)

    2007-11-19

    Dr. L provides two-dimensional visualizations of very large abstract graph structures. it can be used for data mining applications including biology, scientific literature, and social network analysis. Dr. L is a graph layout program that uses a multilevel force-directed algorithm. A graph is input and drawn using a force-directed algorithm based on simulated annealing. The resulting layout is clustered using a single link algorithm. This clustering is used to produce a coarsened graph (fewer nodes)more » which is then re-drawn. this process is repeated until a sufficiently small graph is produced. The smallest graph is drawn and then used as a basis for drawing the original graph by refining the series of coarsened graphs that were produced. The layout engine can be run in serial or in parallel.« less

  12. Fact #748: October 8, 2012 Components of Household Expenditures...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Household Expenditures on Transportation, 1984-2010 Fact 748: October 8, 2012 Components of Household Expenditures on Transportation, 1984-2010 The overall share of annual household ...

  13. homeoffice_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    RSE Column Factor: Total 2001 Household Income Below Poverty Line Eli- gible for Fed- eral ... 29.1 5.3 22.7 3.8 1 Below 150 percent of poverty line or 60 percent of median State income

  14. Microsoft Word - Household Energy Use CA

    U.S. Energy Information Administration (EIA) Indexed Site

    US PAC CA Expenditures dollars ALL ENERGY average per household (excl. transportation) 0 ... households use 62 million Btu of energy per home, 31% less than the U.S. average. ...

  15. University of Arizona Compressed Air Energy Storage

    SciTech Connect (OSTI)

    Simmons, Joseph; Muralidharan, Krishna

    2012-12-31

    Boiled down to its essentials, the grant’s purpose was to develop and demonstrate the viability of compressed air energy storage (CAES) for use in renewable energy development. While everyone agrees that energy storage is the key component to enable widespread adoption of renewable energy sources, the development of a viable scalable technology has been missing. The Department of Energy has focused on expanded battery research and improved forecasting, and the utilities have deployed renewable energy resources only to the extent of satisfying Renewable Portfolio Standards. The lack of dispatchability of solar and wind-based electricity generation has drastically increased the cost of operation with these components. It is now clear that energy storage coupled with accurate solar and wind forecasting make up the only combination that can succeed in dispatchable renewable energy resources. Conventional batteries scale linearly in size, so the price becomes a barrier for large systems. Flow batteries scale sub-linearly and promise to be useful if their performance can be shown to provide sufficient support for solar and wind-base electricity generation resources. Compressed air energy storage provides the most desirable answer in terms of scalability and performance in all areas except efficiency. With the support of the DOE, Tucson Electric Power and Science Foundation Arizona, the Arizona Research Institute for Solar Energy (AzRISE) at the University of Arizona has had the opportunity to investigate CAES as a potential energy storage resource.

  16. DOE - Office of Legacy Management -- University of Arizona Southwest

    Office of Legacy Management (LM)

    Experiment Station Buildings - AZ 01 Arizona Southwest Experiment Station Buildings - AZ 01 FUSRAP Considered Sites Site: UNIVERSITY OF ARIZONA (SOUTHWEST EXPERIMENT STATION BUILDINGS) (AZ.01) Eliminated from consideration under FUSRAP Designated Name: Not Designated Alternate Name: U.S. Bureau of Mines AZ.01-1 Location: Tucson , Arizona AZ.01-1 Evaluation Year: 1987 AZ.01-2 AZ.01-3 Site Operations: Conducted research and development work on the processing of uranium ores. AZ.01-1 Site

  17. Fast generation of sparse random kernel graphs

    SciTech Connect (OSTI)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in time at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.

  18. Fast generation of sparse random kernel graphs

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Hagberg, Aric; Lemons, Nathan; Du, Wen -Bo

    2015-09-10

    The development of kernel-based inhomogeneous random graphs has provided models that are flexible enough to capture many observed characteristics of real networks, and that are also mathematically tractable. We specify a class of inhomogeneous random graph models, called random kernel graphs, that produces sparse graphs with tunable graph properties, and we develop an efficient generation algorithm to sample random instances from this model. As real-world networks are usually large, it is essential that the run-time of generation algorithms scales better than quadratically in the number of vertices n. We show that for many practical kernels our algorithm runs in timemore » at most ο(n(logn)²). As an example, we show how to generate samples of power-law degree distribution graphs with tunable assortativity.« less

  19. API Requirements for Dynamic Graph Prediction

    SciTech Connect (OSTI)

    Gallagher, B; Eliassi-Rad, T

    2006-10-13

    Given a large-scale time-evolving multi-modal and multi-relational complex network (a.k.a., a large-scale dynamic semantic graph), we want to implement algorithms that discover patterns of activities on the graph and learn predictive models of those discovered patterns. This document outlines the application programming interface (API) requirements for fast prototyping of feature extraction, learning, and prediction algorithms on large dynamic semantic graphs. Since our algorithms must operate on large-scale dynamic semantic graphs, we have chosen to use the graph API developed in the CASC Complex Networks Project. This API is supported on the back end by a semantic graph database (developed by Scott Kohn and his team). The advantages of using this API are (i) we have full-control of its development and (ii) the current API meets almost all of the requirements outlined in this document.

  20. Sun City, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Sun City, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.5975393, -112.2718239 Show Map Loading map... "minzoom":false,"mappingser...

  1. Big Park, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Park, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.780297, -111.7626535 Show Map Loading map... "minzoom":false,"mappingservice"...

  2. Munds Park, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Munds Park, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.945574, -111.6401551 Show Map Loading map... "minzoom":false,"mappingse...

  3. Litchfield Park, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Park, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4933743, -112.3579364 Show Map Loading map... "minzoom":false,"mappingservice...

  4. Arizona Electric Pwr Coop Inc | Open Energy Information

    Open Energy Info (EERE)

    Facebook: https:www.facebook.compagesArizonas-GT-Cooperatives347352335037?refts Outage Hotline: (520) 586-3631 References: EIA Form EIA-861 Final Data File for 2010...

  5. Prescott Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Page Edit with form History Prescott Valley, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.6100243, -112.315721 Show Map Loading...

  6. St. David, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    David, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.9042517, -110.2142399 Show Map Loading map... "minzoom":false,"mappingservic...

  7. Flowing Wells, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Wells, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.2939638, -111.0098178 Show Map Loading map... "minzoom":false,"mappingservic...

  8. La Paz County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Paz County, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.0061091, -113.9536466 Show Map Loading map... "minzoom":false,"mappings...

  9. Ash Fork, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Ash Fork, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2250114, -112.4840675 Show Map Loading map... "minzoom":false,"mappingser...

  10. UNIVERSITY OF ARIZONA HIGH ENERGY PHYSICS PROGRAM (Technical...

    Office of Scientific and Technical Information (OSTI)

    The High Energy Physics Group at the University of Arizona has conducted forefront research in elementary particle physics. Our theorists have developed new ideas in lattice QCD, ...

  11. Fort Defiance, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Act Smart Grid Projects in Fort Defiance, Arizona Navajo Tribal Utility Association Smart Grid Project References US Census Bureau 2005 Place to 2006 CBSA Retrieved from...

  12. Oro Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Oro Valley, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.3909071, -110.966488 Show Map Loading map... "minzoom":false,"mappingse...

  13. Geothermal-Exploration In Arizona | Open Energy Information

    Open Energy Info (EERE)

    In Arizona Authors C. Stone and W. R. Hahman Published Journal Transactions-American Geophysical Union, 1978 DOI Not Provided Check for DOI availability: http:...

  14. ,"Arizona Natural Gas Vehicle Fuel Price (Dollars per Thousand...

    U.S. Energy Information Administration (EIA) Indexed Site

    Of Series","Frequency","Latest Data for" ,"Data 1","Arizona Natural Gas Vehicle Fuel Price (Dollars per Thousand Cubic Feet)",1,"Annual",2012 ,"Release...

  15. Peach Springs, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Springs, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.5291589, -113.425491 Show Map Loading map... "minzoom":false,"mappingservi...

  16. Arizona State Historic Preservation Office | Open Energy Information

    Open Energy Info (EERE)

    Historic Preservation Office Jump to: navigation, search Name: Arizona State Historic Preservation Office Abbreviation: SHPO Address: 1300 West Washington Street Place: Phoenix,...

  17. Arizona State University TUV Rheinland JV | Open Energy Information

    Open Energy Info (EERE)

    University TUV Rheinland JV Jump to: navigation, search Name: Arizona State University & TUV Rheinland JV Sector: Solar Product: Solar JV formed for technology testing and...

  18. EIS-0441: Mohave County Wind Farm Project, Mohave County, Arizona...

    Office of Environmental Management (EM)

    as a cooperating agency, evaluated the environmental impacts of a proposed wind energy project on public lands in Mohave County, Arizona. Power generated by this project...

  19. Arizona State Land Department Applications and Permits Website...

    Open Energy Info (EERE)

    to: navigation, search OpenEI Reference LibraryAdd to library Web Site: Arizona State Land Department Applications and Permits Website Abstract This website contains supplemental...

  20. Sierra Vista, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Vista, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.5545394, -110.3036912 Show Map Loading map... "minzoom":false,"mappingservic...

  1. Sierra Vista Southeast, Arizona: Energy Resources | Open Energy...

    Open Energy Info (EERE)

    Vista Southeast, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.460592, -110.217428 Show Map Loading map... "minzoom":false,"mappi...

  2. RAPID/BulkTransmission/Arizona | Open Energy Information

    Open Energy Info (EERE)

    the Regional Entity responsible for coordinating and promoting Bulk Electric System reliability in the Western Interconnection, including Arizona. WECC also provides an...

  3. Arizona's 1st congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    System Solar Power Plant Retrieved from "http:en.openei.orgwindex.php?titleArizona%27s1stcongressionaldistrict&oldid175300" Feedback Contact needs updating Image needs...

  4. EIS-0474: Southline Transmission Line Project; Arizona and New...

    Broader source: Energy.gov (indexed) [DOE]

    consist of a new 225-mile transmission line between existing substations at Afton, New Mexico, and Apache, Arizona, and improvements to approximately 130 miles of existing...

  5. Green Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.8542511, -110.9937019 Show Map Loading map... "minzoom":false,"mappingservice":"goo...

  6. Camp Verde, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Verde, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.5636358, -111.8543178 Show Map Loading map... "minzoom":false,"mappingservic...

  7. Rio Verde, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Verde, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.7225429, -111.6756942 Show Map Loading map... "minzoom":false,"mappingservic...

  8. Tanque Verde, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tanque Verde, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.2517422, -110.7373056 Show Map Loading map... "minzoom":false,"mappin...

  9. Cottonwood-Verde Village, Arizona: Energy Resources | Open Energy...

    Open Energy Info (EERE)

    Cottonwood-Verde Village, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.6949847, -111.9820582 Show Map Loading map......

  10. Ajo Improvement Co (Arizona) EIA Revenue and Sales - April 2008...

    Open Energy Info (EERE)

    Ajo Improvement Co (Arizona) EIA Revenue and Sales - April 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ajo Improvement Co for April...

  11. Ajo Improvement Co (Arizona) EIA Revenue and Sales - October...

    Open Energy Info (EERE)

    Ajo Improvement Co (Arizona) EIA Revenue and Sales - October 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ajo Improvement Co for October...

  12. Arizona Online Environmental Review Tool | Open Energy Information

    Open Energy Info (EERE)

    Online Environmental Review ToolInfo GraphicMapChart Abstract The Arizona Game and Fish Department's Heritage Data Management System (HDMS) and Project Evaluation Program...

  13. New Kingman-Butler, Arizona: Energy Resources | Open Energy Informatio...

    Open Energy Info (EERE)

    Kingman-Butler, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.2593696, -114.0190671 Show Map Loading map... "minzoom":false,"mapp...

  14. Dewey-Humboldt, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Dewey-Humboldt, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.53, -112.2422222 Show Map Loading map... "minzoom":false,"mappingse...

  15. Phoenix Convention Center * Phoenix, Arizona Playing the Entire...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Phoenix, Arizona Playing the Entire Value Chain for Energy Storage Session 6: Innovation ... Critical infrastructure project - Davis-Monthan AFB, AZ AxionPower - one MW battery ...

  16. James Knox with the Arizona Department of Public Service performs...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Arizona, where more than 200 participants attended a three-hour panel discussion titled "Lessons Learned and Return to Operations Following 2014 Operational Incidents." The panel...

  17. Desert Hills, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Desert Hills, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.5538996, -114.3724569 Show Map Loading map... "minzoom":false,"mappin...

  18. St. Johns, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Johns, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.5058698, -109.3609327 Show Map Loading map... "minzoom":false,"mappingservic...

  19. Greenlee County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Greenlee County, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.2405598, -109.2831531 Show Map Loading map... "minzoom":false,"map...

  20. South Tucson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Tucson, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.199521, -110.968425 Show Map Loading map... "minzoom":false,"mappingservice...

  1. Winslow West, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    West, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.0322421, -110.7529145 Show Map Loading map... "minzoom":false,"mappingservice...

  2. Chino Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Valley, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.7575227, -112.4537809 Show Map Loading map... "minzoom":false,"mappingservi...

  3. Arizona Natural Gas Vented and Flared (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Date: 12312015 Next Release Date: 01292016 Referring Pages: Natural Gas Vented and Flared Arizona Natural Gas Gross Withdrawals and Production Natural Gas Vented and Flared...

  4. Apache Junction, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Junction, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.4150485, -111.5495777 Show Map Loading map... "minzoom":false,"mappingser...

  5. Queen Creek, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Creek, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.2486638, -111.6342993 Show Map Loading map... "minzoom":false,"mappingservic...

  6. McNary, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    McNary, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.0736564, -109.8570472 Show Map Loading map... "minzoom":false,"mappingservi...

  7. Bitter Springs, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Bitter Springs, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.6285991, -111.6543255 Show Map Loading map... "minzoom":false,"mapp...

  8. Bullhead City, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Bullhead City, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 35.1477774, -114.5682983 Show Map Loading map... "minzoom":false,"mappi...

  9. Mohave Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Valley, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.9330585, -114.5888533 Show Map Loading map... "minzoom":false,"mappingservi...

  10. Paradise Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Valley, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.5311541, -111.9426452 Show Map Loading map... "minzoom":false,"mappingservi...

  11. Drexel Heights, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Drexel Heights, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.1411888, -111.028427 Show Map Loading map... "minzoom":false,"mappi...

  12. Colorado City, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    City, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 36.9902621, -112.9757702 Show Map Loading map... "minzoom":false,"mappingservice...

  13. Huachuca City, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Huachuca City, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 31.6278703, -110.3339678 Show Map Loading map... "minzoom":false,"mappi...

  14. Cordes Lakes, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Lakes, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 34.3078074, -112.1034912 Show Map Loading map... "minzoom":false,"mappingservic...

  15. Gila Bend, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Gila Bend, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 32.9478236, -112.7168305 Show Map Loading map... "minzoom":false,"mappingse...

  16. Arizona's 5th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    Registered Energy Companies in Arizona's 5th congressional district AFV Solutions Inc AZ Biodiesel Advanced Energy Systems Inc AESI also Advanced Energy Inc AeroElektra...

  17. Arizona's 6th congressional district: Energy Resources | Open...

    Open Energy Info (EERE)

    ETA Engineering Renegy Holdings Inc The Arizona Center for Algae Technology and Innovation WindPower Innovations Inc Retrieved from "http:en.openei.orgw...

  18. Graph algorithms in the titan toolkit.

    SciTech Connect (OSTI)

    McLendon, William Clarence, III; Wylie, Brian Neil

    2009-10-01

    Graph algorithms are a key component in a wide variety of intelligence analysis activities. The Graph-Based Informatics for Non-Proliferation and Counter-Terrorism project addresses the critical need of making these graph algorithms accessible to Sandia analysts in a manner that is both intuitive and effective. Specifically we describe the design and implementation of an open source toolkit for doing graph analysis, informatics, and visualization that provides Sandia with novel analysis capability for non-proliferation and counter-terrorism.

  19. Analysis of MSE Cores Tuba City, Arizona, Site | Department of Energy

    Energy Savers [EERE]

    MSE Cores Tuba City, Arizona, Site Analysis of MSE Cores Tuba City, Arizona, Site Analysis of MSE Cores Tuba City, Arizona, Site PDF icon Analysis of MSE Cores Tuba City, Arizona, Site More Documents & Publications Analysis of Contaminant Rebound in Ground Water in Extraction Wells at the Tuba City, Arizona, Site Diffusion Multilayer Sampling of Ground Water in Five Wells at the Tuba City, Arizona, Site Third (March 2006) Coring and Analysis of Zero-Valent Iron Permeable Reactive Barrier,

  20. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    0a. Space Heating by Midwest Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. Midwest Census Region RSE Row Factors Total Census Division East North Central West North Central 0.5 1.0 1.2 1.6 Total .............................................................. 107.0 24.5 17.1 7.4 NE Heat Home .................................................... 106.0 24.5 17.1 7.4 NE Do Not Heat Home ....................................... 1.0 Q Q Q 19.8 No

  1. spaceheat_household2001.pdf

    U.S. Energy Information Administration (EIA) Indexed Site

    1a. Space Heating by South Census Region, Million U.S. Households, 2001 Space Heating Characteristics RSE Column Factor: Total U.S. South Census Region RSE Row Factors Total Census Division South Atlantic East South Central West South Central 0.5 0.9 1.2 1.4 1.3 Total .............................................................. 107.0 38.9 20.3 6.8 11.8 NE Heat Home .................................................... 106.0 38.8 20.2 6.8 11.8 NE Do Not Heat Home

  2. Microsoft Word - DOE-ID-13-056 Arizona State EC B3-6.doc

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Testing and Multiscale Simulation for Creep Fatigue Damage Analysis of Alloy 617 - Arizona State University SECTION B. Project Description Arizona State University proposes to...

  3. Enabling Graph Appliance for Genome Assembly

    SciTech Connect (OSTI)

    Singh, Rina; Graves, Jeffrey A; Lee, Sangkeun; Sukumar, Sreenivas R; Shankar, Mallikarjun

    2015-01-01

    In recent years, there has been a huge growth in the amount of genomic data available as reads generated from various genome sequencers. The number of reads generated can be huge, ranging from hundreds to billions of nucleotide, each varying in size. Assembling such large amounts of data is one of the challenging computational problems for both biomedical and data scientists. Most of the genome assemblers developed have used de Bruijn graph techniques. A de Bruijn graph represents a collection of read sequences by billions of vertices and edges, which require large amounts of memory and computational power to store and process. This is the major drawback to de Bruijn graph assembly. Massively parallel, multi-threaded, shared memory systems can be leveraged to overcome some of these issues. The objective of our research is to investigate the feasibility and scalability issues of de Bruijn graph assembly on Cray s Urika-GD system; Urika-GD is a high performance graph appliance with a large shared memory and massively multithreaded custom processor designed for executing SPARQL queries over large-scale RDF data sets. However, to the best of our knowledge, there is no research on representing a de Bruijn graph as an RDF graph or finding Eulerian paths in RDF graphs using SPARQL for potential genome discovery. In this paper, we address the issues involved in representing a de Bruin graphs as RDF graphs and propose an iterative querying approach for finding Eulerian paths in large RDF graphs. We evaluate the performance of our implementation on real world ebola genome datasets and illustrate how genome assembly can be accomplished with Urika-GD using iterative SPARQL queries.

  4. Household energy consumption and expenditures, 1990

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  5. Arizona Renewable Electric Power Industry Statistics

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona Primary Renewable Energy Capacity Source Hydro Conventional Primary Renewable Energy Generation Source Hydro Conventional Capacity (megawatts) Value Percent of State Total Total Net Summer Electricity Capacity 26,392 100.0 Total Net Summer Renewable Capacity 2,901 11.0 Geothermal - - Hydro Conventional 2,720 10.3 Solar 20 0.1 Wind 128 0.5 Wood/Wood Waste 29 0.1 MSW/Landfill Gas 4 * Other Biomass - - Generation (thousand megawatthours) Total Electricity Net Generation 111,751 100.0 Total

  6. Arizona Renewable Electric Power Industry Statistics

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona" "Primary Renewable Energy Capacity Source","Hydro Conventional" "Primary Renewable Energy Generation Source","Hydro Conventional" "Capacity (megawatts)","Value","Percent of State Total" "Total Net Summer Electricity Capacity",26392,100 "Total Net Summer Renewable Capacity",2901,11 " Geothermal","-","-" " Hydro Conventional",2720,10.3 "

  7. Storage opportunities in Arizona bedded evaporites

    SciTech Connect (OSTI)

    Neal, J.T.; Rauzi, S.L.

    1996-10-01

    Arizona is endowed with incredibly diverse natural beauty, and has also been blessed with at least seven discrete deposits of bedded salt. These deposits are dispersed around the state and cover some 2, 500 square miles; they currently contain 14 LPG storage caverns, with preliminary plans for more in the future. The areal extent and thickness of the deposits creates the opportunity for greatly expanded storage of LPG, natural gas, and compressed air energy storage (CAES). The location of salt deposits near Tucson and Phoenix may make CAES an attractive prospect in the future. The diversity of both locations and evaporate characteristics allows for much tailoring of individual operations to meet specific requirements.

  8. Tuba City, Arizona, Disposal Site Community Information

    Office of Legacy Management (LM)

    C O M M U N I T Y I N F O R M A T I O N Tuba City, Arizona, Disposal Site Tuba City Site Background 1954-1955 Tuba City mill is built. 1956-1966 Rare Metals Corporation and El Paso Natural Gas Company operate the uranium- and vanadium-ore processing mill. Chemicals from tailings piles and ponds leak into the soil and groundwater during milling operations. 1988 U.S. Department of Energy (DOE) cleans up materials from former milling operations. 1990 Mill tailings are placed in a disposal cell. A

  9. Graph Mining Meets the Semantic Web

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Lim, Seung-Hwan

    2015-01-01

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today, data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. We address that need through implementation of three popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, and PageRank). We implement these algorithms as SPARQL queries, wrapped within Python scripts. We evaluate the performance of our implementation on 6 real world data sets and show graph mining algorithms (that have a linear-algebra formulation) can indeed be unleashed on data represented as RDF graphs using the SPARQL query interface.

  10. GraphReduce: Large-Scale Graph Analytics on Accelerator-Based HPC Systems

    SciTech Connect (OSTI)

    Sengupta, Dipanjan; Agarwal, Kapil; Song, Shuaiwen; Schwan, Karsten

    2015-09-30

    Recent work on real-world graph analytics has sought to leverage the massive amount of parallelism offered by GPU devices, but challenges remain due to the inherent irregularity of graph algorithms and limitations in GPU-resident memory for storing large graphs. We present GraphReduce, a highly efficient and scalable GPU-based framework that operates on graphs that exceed the device’s internal memory capacity. GraphReduce adopts a combination of both edge- and vertex-centric implementations of the Gather-Apply-Scatter programming model and operates on multiple asynchronous GPU streams to fully exploit the high degrees of parallelism in GPUs with efficient graph data movement between the host and the device.

  11. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existingmore » graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.« less

  12. Enabling Graph Mining in RDF Triplestores using SPARQL for Holistic In-situ Graph Analysis

    SciTech Connect (OSTI)

    Lee, Sangkeun; Sukumar, Sreenivas R; Hong, Seokyong; Lim, Seung-Hwan

    2016-01-01

    The graph analysis is now considered as a promising technique to discover useful knowledge in data with a new perspective. We envi- sion that there are two dimensions of graph analysis: OnLine Graph Analytic Processing (OLGAP) and Graph Mining (GM) where each respectively focuses on subgraph pattern matching and automatic knowledge discovery in graph. Moreover, as these two dimensions aim to complementarily solve complex problems, holistic in-situ graph analysis which covers both OLGAP and GM in a single system is critical for minimizing the burdens of operating multiple graph systems and transferring intermediate result-sets between those systems. Nevertheless, most existing graph analysis systems are only capable of one dimension of graph analysis. In this work, we take an approach to enabling GM capabilities (e.g., PageRank, connected-component analysis, node eccentricity, etc.) in RDF triplestores, which are originally developed to store RDF datasets and provide OLGAP capability. More specifically, to achieve our goal, we implemented six representative graph mining algorithms using SPARQL. The approach allows a wide range of available RDF data sets directly applicable for holistic graph analysis within a system. For validation of our approach, we evaluate performance of our implementations with nine real-world datasets and three different computing environments - a laptop computer, an Amazon EC2 instance, and a shared-memory Cray XMT2 URIKA-GD graph-processing appliance. The experimen- tal results show that our implementation can provide promising and scalable performance for real world graph analysis in all tested environments. The developed software is publicly available in an open-source project that we initiated.

  13. PP-107-1 Arizona Public Service Company | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    -1 Arizona Public Service Company PP-107-1 Arizona Public Service Company Presidential permit authorizing Arizona Public Service Company to construct, operate, and maintain electric transmission facilities at the U.S-Mexico border. PDF icon PP-107-1 Arizona Public Service Company More Documents & Publications PP-107

  14. The MultiThreaded Graph Library (MTGL)

    Energy Science and Technology Software Center (OSTI)

    2008-07-17

    The MultiThreaded Graph Library (MTGL) is a set of header files that implement graph algorithm in such a way that they can run on massively multithreaded architectures. It is based upon the Boost Graph Library, but doesn’t use Boost since the latter doesn’t run well on these architectures.

  15. Plant-Wide Energy Efficiency Assessment at the Arizona Portland Cement Plant in Rillito, Arizona

    SciTech Connect (OSTI)

    Stephen J. Coppinger, P.E.; Bruce Colburn, Ph.D., P.E., CEM

    2007-05-17

    A Department of Energy Plant-wide Assessment was undertaken by Arizona Portland Cement (APC) beginning in May 2005. The assessment was performed at APC’s cement production facility in Rillito, Arizona. The assessment included a compressed air evaluation along with a detailed process audit of plant operations and equipment. The purpose of this Energy Survey was to identify a series of energy cost savings opportunities at the Plant, and provide preliminary cost and savings estimates for the work. The assessment was successful in identifying projects that could provide annual savings of over $2.7 million at an estimated capital cost of $4.3 million. If implemented, these projects could amount to a savings of over 4.9 million kWh/yr and 384,420 MMBtu/year.

  16. Household Response To Dynamic Pricing Of Electricity: A Survey...

    Open Energy Info (EERE)

    Household Response To Dynamic Pricing Of Electricity: A Survey Of The Experimental Evidence Jump to: navigation, search Tool Summary LAUNCH TOOL Name: Household Response To Dynamic...

  17. Loan Programs for Low- and Moderate-Income Households | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Programs for Low- and Moderate-Income Households Loan Programs for Low- and Moderate-Income Households Better Buildings Residential Network Multifamily and Low-Income Housing Peer ...

  18. Kingston Creek Hydro Project Powers 100 Households | Department...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Kingston Creek Hydro Project Powers 100 Households Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting firm Nevada ...

  19. Energy Information Administration/Household Vehicles Energy Consumptio...

    U.S. Energy Information Administration (EIA) Indexed Site

    , Energy Information AdministrationHousehold Vehicles Energy Consumption 1994 ix Household Vehicles Energy Consumption 1994 presents statistics about energy-related...

  20. ASSESSMENT OF HOUSEHOLD CARBON FOOTPRINT REDUCTION POTENTIALS

    SciTech Connect (OSTI)

    Kramer, Klaas Jan; Homan, Greg; Brown, Rich; Worrell, Ernst; Masanet, Eric

    2009-04-15

    The term ?household carbon footprint? refers to the total annual carbon emissions associated with household consumption of energy, goods, and services. In this project, Lawrence Berkeley National Laboratory developed a carbon footprint modeling framework that characterizes the key underlying technologies and processes that contribute to household carbon footprints in California and the United States. The approach breaks down the carbon footprint by 35 different household fuel end uses and 32 different supply chain fuel end uses. This level of end use detail allows energy and policy analysts to better understand the underlying technologies and processes contributing to the carbon footprint of California households. The modeling framework was applied to estimate the annual home energy and supply chain carbon footprints of a prototypical California household. A preliminary assessment of parameter uncertainty associated with key model input data was also conducted. To illustrate the policy-relevance of this modeling framework, a case study was conducted that analyzed the achievable carbon footprint reductions associated with the adoption of energy efficient household and supply chain technologies.

  1. Bipartite graph partitioning and data clustering

    SciTech Connect (OSTI)

    Zha, Hongyuan; He, Xiaofeng; Ding, Chris; Gu, Ming; Simon, Horst D.

    2001-05-07

    Many data types arising from data mining applications can be modeled as bipartite graphs, examples include terms and documents in a text corpus, customers and purchasing items in market basket analysis and reviewers and movies in a movie recommender system. In this paper, the authors propose a new data clustering method based on partitioning the underlying biopartite graph. The partition is constructed by minimizing a normalized sum of edge weights between unmatched pairs of vertices of the bipartite graph. They show that an approximate solution to the minimization problem can be obtained by computing a partial singular value decomposition (SVD) of the associated edge weight matrix of the bipartite graph. They point out the connection of their clustering algorithm to correspondence analysis used in multivariate analysis. They also briefly discuss the issue of assigning data objects to multiple clusters. In the experimental results, they apply their clustering algorithm to the problem of document clustering to illustrate its effectiveness and efficiency.

  2. Fault-tolerant dynamic task graph scheduling

    SciTech Connect (OSTI)

    Kurt, Mehmet C.; Krishnamoorthy, Sriram; Agrawal, Kunal; Agrawal, Gagan

    2014-11-16

    In this paper, we present an approach to fault tolerant execution of dynamic task graphs scheduled using work stealing. In particular, we focus on selective and localized recovery of tasks in the presence of soft faults. We elicit from the user the basic task graph structure in terms of successor and predecessor relationships. The work stealing-based algorithm to schedule such a task graph is augmented to enable recovery when the data and meta-data associated with a task get corrupted. We use this redundancy, and the knowledge of the task graph structure, to selectively recover from faults with low space and time overheads. We show that the fault tolerant design retains the essential properties of the underlying work stealing-based task scheduling algorithm, and that the fault tolerant execution is asymptotically optimal when task re-execution is taken into account. Experimental evaluation demonstrates the low cost of recovery under various fault scenarios.

  3. Visualization Graph | OpenEI Community

    Open Energy Info (EERE)

    8 August, 2012 - 12:37 New Gapminder Visualizations Added EIA Energy data Gapminder OECD OpenEI SEDS Visualization Graph OpenEI now features some cool new Gapminder...

  4. Bayati Kim Saberi random graph sampler

    Energy Science and Technology Software Center (OSTI)

    2012-06-05

    This software package implements the algorithm from a paper by Bayati, Kim, and Saberi (first reference below) to generate a uniformly random sample of a graph with a prescribed degree distribution.

  5. Accelerating semantic graph databases on commodity clusters

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Haglin, David J.; Feo, John T.; Weaver, Jesse R.; Tumeo, Antonino; Villa, Oreste

    2013-10-06

    We are developing a full software system for accelerating semantic graph databases on commodity cluster that scales to hundreds of nodes while maintaining constant query throughput. Our framework comprises a SPARQL to C++ compiler, a library of parallel graph methods and a custom multithreaded runtime layer, which provides a Partitioned Global Address Space (PGAS) programming model with fork/join parallelism and automatic load balancing over a commodity clusters. We present preliminary results for the compiler and for the runtime.

  6. Graph representation of protein free energy landscape

    SciTech Connect (OSTI)

    Li, Minghai; Duan, Mojie; Fan, Jue; Huo, Shuanghong; Han, Li

    2013-11-14

    The thermodynamics and kinetics of protein folding and protein conformational changes are governed by the underlying free energy landscape. However, the multidimensional nature of the free energy landscape makes it difficult to describe. We propose to use a weighted-graph approach to depict the free energy landscape with the nodes on the graph representing the conformational states and the edge weights reflecting the free energy barriers between the states. Our graph is constructed from a molecular dynamics trajectory and does not involve projecting the multi-dimensional free energy landscape onto a low-dimensional space defined by a few order parameters. The calculation of free energy barriers was based on transition-path theory using the MSMBuilder2 package. We compare our graph with the widely used transition disconnectivity graph (TRDG) which is constructed from the same trajectory and show that our approach gives more accurate description of the free energy landscape than the TRDG approach even though the latter can be organized into a simple tree representation. The weighted-graph is a general approach and can be used on any complex system.

  7. Town of Wickenburg, Arizona (Utility Company) | Open Energy Informatio...

    Open Energy Info (EERE)

    Name: Town of Wickenburg Place: Arizona Phone Number: (928) 684-5451 x1520 Website: www.ci.wickenburg.az.us694Ut Outage Hotline: 928-684-5411 References: EIA Form EIA-861 Final...

  8. Garkane Energy Coop, Inc (Arizona) | Open Energy Information

    Open Energy Info (EERE)

    Garkane Energy Coop, Inc Place: Arizona Phone Number: Kanab Office: (888)644-5026 -- Loa Office (800) 747-5403 -- Hatch Office(888)735-4288 -- Hildale Office(435) 874-2810 Website:...

  9. Valencia West, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Valencia West is a census-designated place in Pima County, Arizona.1 References US...

  10. Santa Cruz County, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Santa Cruz County is a county in Arizona. Its FIPS County Code is 023. It is classified as...

  11. Santa Rosa, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Santa Rosa is a census-designated place in Pima County, Arizona.1 References US Census...

  12. DOI Approves Three Renewable Energy Projects in Arizona and Nevada

    Broader source: Energy.gov [DOE]

    The U.S. Department of the Interior (DOI) on June 3 announced the approval of three major renewable energy projects in Arizona and Nevada that are expected to deliver up to 520 megawatts to the electricity grid.

  13. DOI Approves Three Renewable Energy Projects in Arizona and Nevada...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    The 100-megawatt Quartzsite Solar Energy Project, located on 1,600 acres of BLM-managed lands in La Paz County, Arizona, will use concentrating solar power (CSP) "power tower" ...

  14. Corona de Tucson, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Corona de Tucson is a census-designated place in Pima County, Arizona.1 References US...

  15. ,"Arizona Natural Gas Price Sold to Electric Power Consumers...

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,"(202) 586-8800",,,"1292016 12:16:41 AM" "Back to Contents","Data 1: Arizona Natural Gas Price Sold to Electric Power Consumers (Dollars per Thousand Cubic Feet)"...

  16. Spring Valley, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Spring Valley is a census-designated place in Yavapai County, Arizona.1 References US...

  17. Red Mesa, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Red Mesa is a census-designated place in Apache County, Arizona.1 References US...

  18. DOE - Office of Legacy Management -- University of Arizona Southwest...

    Office of Legacy Management (LM)

    of the University of Arizona under FUSRAP; October 13, 1987 AZ.01-4 - DOE Letter; Bauer to Liverman; Past Operations and a Survey by Messrs, Jascewsky, and Smith; February 7, 1978

  19. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - April 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  20. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - November 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  1. Ajo Improvement Co (Arizona) EIA Revenue and Sales - June 2008...

    Open Energy Info (EERE)

    Ajo Improvement Co (Arizona) EIA Revenue and Sales - June 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ajo Improvement Co for June 2008....

  2. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - May 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  3. Ajo Improvement Co (Arizona) EIA Revenue and Sales - July 2008...

    Open Energy Info (EERE)

    Ajo Improvement Co (Arizona) EIA Revenue and Sales - July 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ajo Improvement Co for July 2008....

  4. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - February 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  5. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - June 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  6. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - February 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  7. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - January 2009 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  8. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - March 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin Electric...

  9. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - October 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  10. Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and...

    Open Energy Info (EERE)

    Ak-Chin Electric Utility Authority (Arizona) EIA Revenue and Sales - January 2008 Jump to: navigation, search EIA Monthly Electric Utility Sales and Revenue Data for Ak-Chin...

  11. Arizona Right-of-Way Instruction Sheet | Open Energy Information

    Open Energy Info (EERE)

    Right-of-Way Instruction Sheet Jump to: navigation, search OpenEI Reference LibraryAdd to library PermittingRegulatory Guidance - Instructions: Arizona Right-of-Way Instruction...

  12. Arizona Natural Gas Lease and Plant Fuel Consumption (Million...

    U.S. Energy Information Administration (EIA) Indexed Site

    and Plant Fuel Consumption (Million Cubic Feet) Arizona Natural Gas Lease and Plant Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6...

  13. Arizona Natural Gas Lease Fuel Consumption (Million Cubic Feet...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Fuel Consumption (Million Cubic Feet) Arizona Natural Gas Lease Fuel Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  14. Arizona Natural Gas Total Consumption (Million Cubic Feet)

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Total Consumption (Million Cubic Feet) Arizona Natural Gas Total Consumption (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  15. Arizona Natural Gas Vented and Flared (Million Cubic Feet)

    Gasoline and Diesel Fuel Update (EIA)

    Vented and Flared (Million Cubic Feet) Arizona Natural Gas Vented and Flared (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9...

  16. Arizona Natural Gas Number of Residential Consumers (Number of...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Residential Consumers (Number of Elements) Arizona Natural Gas Number of Residential Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  17. Arizona Natural Gas Input Supplemental Fuels (Million Cubic Feet...

    Gasoline and Diesel Fuel Update (EIA)

    Input Supplemental Fuels (Million Cubic Feet) Arizona Natural Gas Input Supplemental Fuels (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  18. Arizona Natural Gas Exports (No Intransit Deliveries) (Million...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Exports (No Intransit Deliveries) (Million Cubic Feet) Arizona Natural Gas Exports (No Intransit Deliveries) (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  19. Arizona Natural Gas % of Total Residential Deliveries (Percent...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    % of Total Residential Deliveries (Percent) Arizona Natural Gas % of Total Residential Deliveries (Percent) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8...

  20. Arizona Natural Gas Number of Commercial Consumers (Number of...

    U.S. Energy Information Administration (EIA) Indexed Site

    Commercial Consumers (Number of Elements) Arizona Natural Gas Number of Commercial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7...

  1. Arizona Price of Natural Gas Sold to Commercial Consumers (Dollars...

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Sold to Commercial Consumers (Dollars per Thousand Cubic Feet) Arizona Price of Natural Gas Sold to Commercial Consumers (Dollars per Thousand Cubic Feet) Year Jan Feb Mar Apr May...

  2. ,"Arizona Dry Natural Gas Production (Million Cubic Feet)"

    U.S. Energy Information Administration (EIA) Indexed Site

    ,,"(202) 586-8800",,,"01042016 7:36:54 AM" "Back to Contents","Data 1: Arizona Dry Natural Gas Production (Million Cubic Feet)" "Sourcekey","NA1160SAZ2"...

  3. EECBG Success Story: Energy Upgrades to Save Small Arizona Town...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    of Oro Valley Energy Upgrades to Save Small Arizona Town Big Money Workers demonstrate the nitrogen tank used to inflate tires in St. Peters, MO. | Courtesy of the City of St. ...

  4. Gold Camp, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Hide Map This article is a stub. You can help OpenEI by expanding it. Gold Camp is a census-designated place in Pinal County, Arizona.1 References US Census...

  5. Continuous-time quantum walks on star graphs

    SciTech Connect (OSTI)

    Salimi, S.

    2009-06-15

    In this paper, we investigate continuous-time quantum walk on star graphs. It is shown that quantum central limit theorem for a continuous-time quantum walk on star graphs for N-fold star power graph, which are invariant under the quantum component of adjacency matrix, converges to continuous-time quantum walk on K{sub 2} graphs (complete graph with two vertices) and the probability of observing walk tends to the uniform distribution.

  6. EIS-0474: Southline Transmission Line Project; Arizona and New Mexico |

    Energy Savers [EERE]

    Department of Energy 474: Southline Transmission Line Project; Arizona and New Mexico EIS-0474: Southline Transmission Line Project; Arizona and New Mexico Summary The Bureau of Land Management and Western Area Power Administration are preparing an EIS as joint lead agencies to evaluate the potential environmental impacts of the proposed Southline Transmission Project and address associated potential land use plan amendments. The project would consist of a new 225-mile transmission line

  7. EIS-0427: Grapevine Canyon Wind Project, Coconino County, Arizona

    Broader source: Energy.gov [DOE]

    This EIS evaluates the environmental impacts of a proposed wind energy generation project in Coconino County, Arizona, on privately owned ranch lands and trust lands administered by the Arizona State Land Department. The proposed project includes a new transmission tie-line that would cross lands administered by Coconino National Forest and interconnect with DOE’s Western Area Power Administration’s existing Glen Canyon-Pinnacle Peak transmission lines.

  8. DOE-University of Arizona Faculty Development Project. Final report

    SciTech Connect (OSTI)

    Fillerup, Joseph M.

    1980-09-08

    The DOE-University of Arizona Faculty Development Project on Energy successfully completed a faculty development program. There were three phases of the program consisting of: a three week energy workshop for teachers, participation and cooperation with Students for Safe Energy in presentation of an Alternative Energy Festival at the University of Arizona, and workshops for teachers conducted at Flowing Wells School District. Each of these is described. Attendees are listed and a director's evaluation of the workshop is given.

  9. Arizona - Natural Gas 2014 Million Cu. Feet Percent of

    U.S. Energy Information Administration (EIA) Indexed Site

    4 Arizona - Natural Gas 2014 Million Cu. Feet Percent of National Total Million Cu. Feet Percent of National Total Total Net Movements: - Industrial: Dry Production: Vehicle Fuel: Deliveries to Consumers: Residential: Electric Power: Commercial: Total Delivered: Table S3. Summary statistics for natural gas - Arizona, 2010-2014 2010 2011 2012 2013 2014 Number of Producing Gas Wells at End of Year 5 5 5 5 5 Production (million cubic feet) Gross Withdrawals From Gas Wells 183 168 117 72 106 From

  10. Household energy consumption and expenditures 1993

    SciTech Connect (OSTI)

    1995-10-05

    This presents information about household end-use consumption of energy and expenditures for that energy. These data were collected in the 1993 Residential Energy Consumption Survey; more than 7,000 households were surveyed for information on their housing units, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information represents all households nationwide (97 million). Key findings: National residential energy consumption was 10.0 quadrillion Btu in 1993, a 9% increase over 1990. Weather has a significant effect on energy consumption. Consumption of electricity for appliances is increasing. Houses that use electricity for space heating have lower overall energy expenditures than households that heat with other fuels. RECS collected data for the 4 most populous states: CA, FL, NY, TX.

  11. Active mines in Arizona - 1993. Directory 40

    SciTech Connect (OSTI)

    Phillips, K.A.; Niemuth, N.J.; Bain, D.R.

    1992-01-01

    A directory of the active mines in Arizona is presented. The directory was compiled in November, 1992 from field visits and information received by the Department's technical staff. For the purpose of this directory, an active mine is defined as a mine in continuous operation, either in production or under full-time development for production. Custom milling operations that are active or available on a full-time basis are also included in the directory. It is acknowledged that there are additional mines not listed that are in an exploration, evaluation, or part-time development phase. There are others where production is on an intermittent basis that are not listed. The report is dependent on the cooperation of government agencies, private industry, and individuals who voluntarily provide information on their projects and activities. The directory is arranged alphabetically by company name. Each listing includes corporate addresses, mine name and location, operation description, and key personnel. The listing for the sand and gravel operations include name, address, and phone number.

  12. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2016-04-12

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  13. Dynamic graph system for a semantic database

    DOE Patents [OSTI]

    Mizell, David

    2015-01-27

    A method and system in a computer system for dynamically providing a graphical representation of a data store of entries via a matrix interface is disclosed. A dynamic graph system provides a matrix interface that exposes to an application program a graphical representation of data stored in a data store such as a semantic database storing triples. To the application program, the matrix interface represents the graph as a sparse adjacency matrix that is stored in compressed form. Each entry of the data store is considered to represent a link between nodes of the graph. Each entry has a first field and a second field identifying the nodes connected by the link and a third field with a value for the link that connects the identified nodes. The first, second, and third fields represent the rows, column, and elements of the adjacency matrix.

  14. Oil and gas exploration and development in Arizona

    SciTech Connect (OSTI)

    Nations, D.; Doss, A.K.; Ubarra, R.

    1984-07-01

    Recent oil and gas exploration activity has been widespread throughout Arizona. Development drilling has continued in the Dineh-bi-keyah and Teec-nos-Pos fields in the northeastern corner, and exploratory drilling continues to test potential Paleozoic reservoirs elsewhere on the plateau. Several shallow wells north of the Grand Canyon encountered shows and limited recoveries of oil from Permian and Triassic rocks. The greatest activity has occurred along the Overthrust trend from northwestern to southeastern Arizona. Several million acres were leased and eight exploratory wells drilled along this trend. None were discoveries, but the presence of a Laramide thrust fault in the vicinity of Tombstone was established. The other tests have neither proved nor disproved the concept of the Overthrust belt in southern Arizona. Recent discoveries in the nonmarine Tertiary and marine Paleozoic of southern Nevada have stimulated interest in the oil potential of similar rocks and structures in the Basin and Range province of Arizona, which are coincident with the Overthrust trend. Reported gas discoveries by Pemex in Miocene marine sediments of the Gulf of California have stimulated leasing in the Yuma area, where one uncompleted well is reported to be a potential producer. The Pedregosa basin of extreme southeastern Arizona remains an area of great interest to explorationists because of the presence of a 25,000-ft (7600-m) sequence of Paleozoic marine sediments similar to those of the Permian basin, and Cretaceous marine rocks, including coral-rudist reefs, similar to those that produce in Texas and Mexico.

  15. Integrated solid waste management of Scottsdale, Arizona

    SciTech Connect (OSTI)

    1995-11-01

    The subject document reports the results of an in-depth investigation of the fiscal year 1992 cost of the city of Scottsdale, Arizona, integrated municipal solid waste management (IMSWM) system, the energy consumed to operate the system, and the environmental performance requirements for each of the system`s waste-processing and disposal facilities. The document reports actual data from records kept by participants. Every effort was made to minimize the use of assumptions, and no attempt is made to interpret the data reported. Analytical approaches are documented so that interested analysts may per-form manipulation or further analysis of the data. As such, the report is a reference document for municipal solid waste (MSW) management professionals who are interested in the actual costs and energy consumption, for a 1-year period, of an operating IMSWM system. The report is organized into two main parts. The first part is the executive summary and case study portion of the report. The executive summary provides a basic description of the study area and selected economic and energy information. Within the case study are detailed descriptions of each component operating during the study period; the quantities of solid waste collected, processed, and marketed within the study boundaries; the cost of MSW in Scottsdale; an energy usage analysis; a review of federal, state, and local environmental requirement compliance; a reference section; and a glossary of terms. The second part of the report focuses on a more detailed discourse on the above topics. In addition, the methodology used to determine the economic costs and energy consumption of the system components is found in the second portion of this report. The methodology created for this project will be helpful for those professionals who wish to break out the costs of their own integrated systems.

  16. Apache County, Arizona ASHRAE 169-2006 Climate Zone | Open Energy...

    Open Energy Info (EERE)

    Apache County, Arizona ASHRAE 169-2006 Climate Zone Jump to: navigation, search County Climate Zone Place Apache County, Arizona ASHRAE Standard ASHRAE 169-2006 Climate Zone Number...

  17. SWTC v. Arizona Corp. Comn, 142 P3d 1240 (2006) | Open Energy...

    Open Energy Info (EERE)

    SWTC v. Arizona Corp. Comn, 142 P3d 1240 (2006) Jump to: navigation, search OpenEI Reference LibraryAdd to library Legal CaseHearing: SWTC v. Arizona Corp. Comn, 142 P3d 1240...

  18. Communication Graph Generator for Parallel Programs

    Energy Science and Technology Software Center (OSTI)

    2014-04-08

    Graphator is a collection of relatively simple sequential programs that generate communication graphs/matrices for commonly occurring patterns in parallel programs. Currently, there is support for five communication patterns: two-dimensional 4-point stencil, four-dimensional 8-point stencil, all-to-alls over sub-communicators, random near-neighbor communication, and near-neighbor communication.

  19. GraphPrints: Towards a Graph Analytic Method for Network Anomaly Detection

    SciTech Connect (OSTI)

    Harshaw, Chris R; Bridges, Robert A; Iannacone, Michael D; Reed, Joel W; Goodall, John R

    2016-01-01

    This paper introduces a novel graph-analytic approach for detecting anomalies in network flow data called \\textit{GraphPrints}. Building on foundational network-mining techniques, our method represents time slices of traffic as a graph, then counts graphlets\\textemdash small induced subgraphs that describe local topology. By performing outlier detection on the sequence of graphlet counts, anomalous intervals of traffic are identified, and furthermore, individual IPs experiencing abnormal behavior are singled-out. Initial testing of GraphPrints is performed on real network data with an implanted anomaly. Evaluation shows false positive rates bounded by 2.84\\% at the time-interval level, and 0.05\\% at the IP-level with 100\\% true positive rates at both.

  20. Independent Confirmatory Survey Report for the University of Arizona Nuclear Reactor Laboratory, Tucson, Arizona

    SciTech Connect (OSTI)

    Nick A. Altic

    2011-11-11

    The University of Arizona (University) research reactor is a TRIGA swimming pool type reactor designed by General Atomics and constructed at the University in 1958. The reactor first went into operation in December of 1958 under U.S. Nuclear Regulatory Commission (NRC) license R-52 until final shut down on May 18, 2010. Initial site characterization activities were conducted in February 2009 during ongoing reactor operations to assess the radiological status of the Nuclear Reactor Laboratory (NRL) excluding the reactor tank, associated components, and operating systems. Additional post-shutdown characterization activities were performed to complete characterization activities as well as verify assumptions made in the Decommissioning Plan (DP) that were based on a separate activation analysis (ESI 2009 and WMG 2009). Final status survey (FSS) activities began shortly after the issuance of the FSS plan in May 2011. The contractor completed measurement and sampling activities during the week of August 29, 2011.

  1. August 2015 Groundwater and Surface Water Sampling at the Tuba City, Arizona, Disposal Site

    Office of Legacy Management (LM)

    and Surface Water Sampling at the Tuba City, Arizona, Disposal Site November 2015 LMS/TUB/S00815 This page intentionally left blank U.S. Department of Energy DVP-August 2015, Tuba City, Arizona, Disposal Site November 2015 RIN 15087262 Page i Contents Sampling Event Summary ...............................................................................................................1 Tuba City, Arizona, Disposal Site, Sample Location Map

  2. Modular Environment for Graph Research and Analysis with a Persistent

    Energy Science and Technology Software Center (OSTI)

    2009-11-18

    The MEGRAPHS software package provides a front-end to graphs and vectors residing on special-purpose computing resources. It allows these data objects to be instantiated, destroyed, and manipulated. A variety of primitives needed for typical graph analyses are provided. An example program illustrating how MEGRAPHS can be used to implement a PageRank computation is included in the distribution.The MEGRAPHS software package is targeted towards developers of graph algorithms. Programmers using MEGRAPHS would write graph analysis programsmore » in terms of high-level graph and vector operations. These computations are transparently executed on the Cray XMT compute nodes.« less

  3. StreamWorks - A system for Dynamic Graph Search

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Ray, Abhik; Beus, Sherman J.; Feo, John T.

    2013-06-11

    Acting on time-critical events by processing ever growing social media, news or cyber data streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Mining and searching for subgraph patterns in a continuous setting requires an efficient approach to incremental graph search. The goal of our work is to enable real-time search capabilities for graph databases. This demonstration will present a dynamic graph query system that leverages the structural and semantic characteristics of the underlying multi-relational graph.

  4. Determinants of Household Use of Selected Energy Star Appliances - Energy

    U.S. Energy Information Administration (EIA) Indexed Site

    Information Administration Determinants of Household Use of Selected Energy Star Appliances Release date: May 25, 2016 Introduction According to the 2009 Residential Energy Consumption Survey (RECS), household appliances1accounted for 35% of U.S. household energy consumption, up from 24% in 1993. Thus, improvements in the energy performance of residential appliances as well as increases in the use of more efficient appliances can be effective in reducing household energy consumption and

  5. Frequent Subgraph Discovery in Large Attributed Streaming Graphs

    SciTech Connect (OSTI)

    Ray, Abhik; Holder, Larry; Choudhury, Sutanay

    2014-08-13

    The problem of finding frequent subgraphs in large dynamic graphs has so far only consid- ered a dynamic graph as being represented by a series of static snapshots taken at various points in time. This representation of a dynamic graph does not lend itself well to real time processing of real world graphs like social networks or internet traffic which consist of a stream of nodes and edges. In this paper we propose an algorithm that discovers the frequent subgraphs present in a graph represented by a stream of labeled nodes and edges. Our algorithm is efficient and consists of tunable parameters that can be tuned by the user to get interesting patterns from various kinds of graph data. In our model updates to the graph arrive in the form of batches which contain new nodes and edges. Our algorithm con- tinuously reports the frequent subgraphs that are estimated to be found in the entire graph as each batch arrives. We evaluate our system using 5 large dynamic graph datasets: the Hetrec 2011 challenge data, Twitter, DBLP and two synthetic. We evaluate our approach against two popular large graph miners, i.e., SUBDUE and GERM. Our experimental re- sults show that we can find the same frequent subgraphs as a non-incremental approach applied to snapshot graphs, and in less time.

  6. Graph processing platforms at scale: practices and experiences

    SciTech Connect (OSTI)

    Lim, Seung-Hwan; Lee, Sangkeun; Brown, Tyler C; Sukumar, Sreenivas R; Ganesh, Gautam

    2015-01-01

    Graph analysis unveils hidden associations of data in many phenomena and artifacts, such as road network, social networks, genomic information, and scientific collaboration. Unfortunately, a wide diversity in the characteristics of graphs and graph operations make it challenging to find a right combination of tools and implementation of algorithms to discover desired knowledge from the target data set. This study presents an extensive empirical study of three representative graph processing platforms: Pegasus, GraphX, and Urika. Each system represents a combination of options in data model, processing paradigm, and infrastructure. We benchmarked each platform using three popular graph operations, degree distribution, connected components, and PageRank over a variety of real-world graphs. Our experiments show that each graph processing platform shows different strength, depending the type of graph operations. While Urika performs the best in non-iterative operations like degree distribution, GraphX outputforms iterative operations like connected components and PageRank. In addition, we discuss challenges to optimize the performance of each platform over large scale real world graphs.

  7. Knowledge Representation Issues in Semantic Graphs for Relationship Detection

    SciTech Connect (OSTI)

    Barthelemy, M; Chow, E; Eliassi-Rad, T

    2005-02-02

    An important task for Homeland Security is the prediction of threat vulnerabilities, such as through the detection of relationships between seemingly disjoint entities. A structure used for this task is a ''semantic graph'', also known as a ''relational data graph'' or an ''attributed relational graph''. These graphs encode relationships as typed links between a pair of typed nodes. Indeed, semantic graphs are very similar to semantic networks used in AI. The node and link types are related through an ontology graph (also known as a schema). Furthermore, each node has a set of attributes associated with it (e.g., ''age'' may be an attribute of a node of type ''person''). Unfortunately, the selection of types and attributes for both nodes and links depends on human expertise and is somewhat subjective and even arbitrary. This subjectiveness introduces biases into any algorithm that operates on semantic graphs. Here, we raise some knowledge representation issues for semantic graphs and provide some possible solutions using recently developed ideas in the field of complex networks. In particular, we use the concept of transitivity to evaluate the relevance of individual links in the semantic graph for detecting relationships. We also propose new statistical measures for semantic graphs and illustrate these semantic measures on graphs constructed from movies and terrorism data.

  8. EIS-0417: South Mountain Freeway (Loop 202); Phoenix, Arizona

    Broader source: Energy.gov [DOE]

    Federal Highway Administration and Arizona Department of Transportation, with Western Area Power Administration as a cooperating agency, prepared an EIS that analyzes the potential environmental impacts of the proposed South Mountain Freeway (Loop 202) project in the Greater Metropolitan Phoenix Area.

  9. EA-1989: Cliffrose Solar Energy Interconnection Project, Mohave County, Arizona

    Broader source: Energy.gov [DOE]

    DOE’s Western Area Power Administration (Western) is preparing an EA that will assess the potential environmental impacts of interconnecting the proposed Cliffrose Solar Energy Project in Mohave County, Arizona, to Western’s transmission system at the existing Griffith Substation. Additional information is available at http://www.wapa.gov/dsw/environment/CliffroseSolarEnergyProject.html.

  10. EIS-0441: Mohave County Wind Farm Project, Mohave County, Arizona

    Broader source: Energy.gov [DOE]

    This EIS, prepared by the Bureau of Land Management with DOE’s Western Area Power Administration as a cooperating agency, evaluated the environmental impacts of a proposed wind energy project on public lands in Mohave County, Arizona. Power generated by this project would tie to the electrical power grid through an interconnection to one of Western’s transmission lines.

  11. EIS-0297: Griffith Energy Project, Mohave County, Arizona

    Broader source: Energy.gov [DOE]

    Western Area Power Administration (Western) intends to prepare an environmental impact statement (EIS) regarding the proposal by Griffith Energy (GE), LLC, to construct an electric generating facility on private property and to interconnect this facility with Western’s system in the vicinity of Kingman, Arizona.

  12. Strategies for Collecting Household Energy Data | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Collecting Household Energy Data Strategies for Collecting Household Energy Data Better Buildings Neighborhood Program Data and Evaluation Peer Exchange Call: Strategies for Collecting Household Energy Data, Call Slides and Discussion Summary, July 19, 2012. PDF icon Call Slides and Discussion Summary More Documents & Publications Homeowner and Contractor Surveys Mastermind: Jim Mikel, Spirit Foundation Generating Energy Efficiency Project Leads and Allocating Leads to Contractors

  13. Jargon and Graph Modularity on Twitter

    SciTech Connect (OSTI)

    Dowling, Chase P.; Corley, Courtney D.; Farber, Robert M.; Reynolds, William

    2013-09-01

    The language of conversation is just as dependent upon word choice as it is on who is taking part. Twitter provides an excellent test-bed in which to conduct experiments not only on language usage but on who is using what language with whom. To this end, we combine large scale graph analytical techniques with known socio-linguistic methods. In this article we leverage both expert curated vocabularies and naive mathematical graph analyses to determine if network behavior on Twitter corroborates with the current understanding of language usage. The results reported indicate that, based on networks constructed from user to user communication and communities identified using the Clauset- Newman greedy modularity algorithm we find that more prolific users of these curated vocabularies are concentrated in distinct network communities.

  14. Household Energy Consumption Segmentation Using Hourly Data

    SciTech Connect (OSTI)

    Kwac, J; Flora, J; Rajagopal, R

    2014-01-01

    The increasing US deployment of residential advanced metering infrastructure (AMI) has made hourly energy consumption data widely available. Using CA smart meter data, we investigate a household electricity segmentation methodology that uses an encoding system with a pre-processed load shape dictionary. Structured approaches using features derived from the encoded data drive five sample program and policy relevant energy lifestyle segmentation strategies. We also ensure that the methodologies developed scale to large data sets.

  15. Household energy consumption and expenditures, 1987

    SciTech Connect (OSTI)

    Not Available

    1989-10-10

    Household Energy Consumption and Expenditures 1987, Part 1: National Data is the second publication in a series from the 1987 Residential Energy Consumption Survey (RECS). It is prepared by the Energy End Use Division (EEUD) of the Office of Energy Markets and End Use (EMEU), Energy Information Administration (EIA). The EIA collects and publishes comprehensive data on energy consumption in occupied housing units in the residential sector through the RECS. 15 figs., 50 tabs.

  16. Household and environmental characteristics related to household energy-consumption change: A human ecological approach

    SciTech Connect (OSTI)

    Guerin, D.A.

    1988-01-01

    This study focused on the family household as an organism and on its interaction with the three environments of the human ecosystem (natural, behavioral, and constructed) as these influence energy consumption and energy-consumption change. A secondary statistical analysis of data from the US Department of Energy Residential Energy Consumption Surveys (RECS) was completed. The 1980 and 1983 RECS were used as the data base. Longitudinal data, including household, environmental, and energy-consumption measures, were available for over 800 households. The households were selected from a national sample of owner-occupied housing units surveyed in both years. Results showed a significant( p = <.05) relationship between the dependent-variable energy-consumption change and the predictor variables heating degree days, addition of insulation, addition of a wood-burning stove, year the housing unit was built, and weighted number of appliances. A significant (p = <.05) relationship was found between the criterion variable energy-consumption change and the discriminating variables of age of the head of the household, cooling degree days, heating degree days, year the housing unit was built, and number of stories in the housing unit.

  17. Useful Graphs and Charts - Ion Beams - Radiation Effects Facility /

    Broader source: All U.S. Department of Energy (DOE) Office Webpages (Extended Search)

    Cyclotron Institute / Texas A&M University Ion Beams Available Beams / Beam Change Times / Measurements / Useful Graphs Useful Graphs and Charts LET vs. Range in Si Graphs: 15 MeV/u Beams 24.8 MeV/u Beams 40 MeV/u Beams Beam energy, Let and range in si at various air-gaps from rear foil: 15 MeV/u Beams 24.8 and 40 MeV/u Beams Quick Links Beam Characterization and verification Beam List Beam Change Times 15 MeV/u LET vs Range Graph 25 MeV/u LET vs Range Graph 40 Mev/u LET vs Range Graph

  18. Sequoia supercomputer tops Graph 500 | National Nuclear Security

    National Nuclear Security Administration (NNSA)

    Administration Sequoia supercomputer tops Graph 500 Wednesday, November 19, 2014 - 11:34am Lawrence Livermore National Laboratory scientists' search for new ways to solve large complex national security problems led to the top ranking on Graph 500 and new techniques for solving large graph problems on small high performance computing (HPC) systems, all the way down to a single server. Lawrence Livermore's Sequoia supercomputer, a 20-petaflop IBM Blue Gene/Q system, achieved the world's best

  19. The effects of indoor pollution on Arizona children

    SciTech Connect (OSTI)

    Dodge, R.

    1982-05-01

    The respiratory health of a large group of Arizona school children who have been exposed to indoor pollutants-tobacco smoke and home cooking fumes-is reported. A significant relationship was found between parental smoking and symptoms of cough, wheeze, and sputum production. Also, children in homes where gas cooking fuel was used had higher rates of cough than children in homes where electricity was used. No differences in pulmonary function or yearly lung growth rates occurred among subjects grouped by exposure to tobacco smoke or cooking fuel. Thus, parental smoking and home cooking fuel affected cross-sectional respiratory symptom rates in a large group of Arizona school children. Study of pulmonary function, however, revealed no lung function or lung growth effects during 4 yr of study.

  20. Two linear time, low overhead algorithms for graph layout

    Energy Science and Technology Software Center (OSTI)

    2008-01-10

    The software comprises two algorithms designed to perform a 2D layout of a graph structure in time linear with respect to the vertices and edges in the graph, whereas most other layout algorithms have a running time that is quadratic with respect to the number of vertices or greater. Although these layout algorithms run in a fraction of the time as their competitors, they provide competitive results when applied to most real-world graphs. These algorithmsmore » also have a low constant running time and small memory footprint, making them useful for small to large graphs.« less

  1. EA-2023: Crossman Peak Communications Facility; Mohave County, Arizona

    Broader source: Energy.gov [DOE]

    Western Area Power Administration is preparing an EA that assesses the potential environmental impacts of a proposed new microwave communication facility to be located adjacent to a privately-owned one near Crossman Peak, east of Lake Havasu City in Mohave County, Arizona. The proposal would consist of a microwave communication facility, an access road, and an approximately 8-mile electrical service distribution line across private land and land administered by the Bureau of Land Management.

  2. International energy indicators. [Statistical tables and graphs

    SciTech Connect (OSTI)

    Bauer, E.K.

    1980-05-01

    International statistical tables and graphs are given for the following: (1) Iran - Crude Oil Capacity, Production and Shut-in, June 1974-April 1980; (2) Saudi Arabia - Crude Oil Capacity, Production, and Shut-in, March 1974-Apr 1980; (3) OPEC (Ex-Iran and Saudi Arabia) - Capacity, Production and Shut-in, June 1974-March 1980; (4) Non-OPEC Free World and US Production of Crude Oil, January 1973-February 1980; (5) Oil Stocks - Free World, US, Japan, and Europe (Landed, 1973-1st Quarter, 1980); (6) Petroleum Consumption by Industrial Countries, January 1973-December 1979; (7) USSR Crude Oil Production and Exports, January 1974-April 1980; and (8) Free World and US Nuclear Generation Capacity, January 1973-March 1980. Similar statistical tables and graphs included for the United States include: (1) Imports of Crude Oil and Products, January 1973-April 1980; (2) Landed Cost of Saudi Oil in Current and 1974 Dollars, April 1974-January 1980; (3) US Trade in Coal, January 1973-March 1980; (4) Summary of US Merchandise Trade, 1976-March 1980; and (5) US Energy/GNP Ratio, 1947 to 1979.

  3. Could Gila Bend, Arizona, Become the Solar Capital of the World?

    Broader source: Energy.gov [DOE]

    Serving approximately 9,000 homes with clean renewable energy, the Paloma and Cotton Center solar plants highlight the rapidly rising solar corridor in Gila Bend, Arizona.

  4. Top-of-the-World, Arizona: Energy Resources | Open Energy Information

    Open Energy Info (EERE)

    Top-of-the-World, Arizona: Energy Resources Jump to: navigation, search Equivalent URI DBpedia Coordinates 33.3494997, -110.9926154 Show Map Loading map......

  5. Delivering Energy Efficiency to Middle Income Single Family Households

    SciTech Connect (OSTI)

    none,

    2011-12-01

    Provides state and local policymakers with information on successful approaches to the design and implementation of residential efficiency programs for households ineligible for low-income programs.

  6. Barriers to household investment in residential energy conservation: preliminary assessment

    SciTech Connect (OSTI)

    Hoffman, W.L.

    1982-12-01

    A general assessment of the range of barriers which impede household investments in weatherization and other energy efficiency improvements for their homes is provided. The relationship of similar factors to households' interest in receiving a free energy audits examined. Rates of return that underly household investments in major conservation improvements are assessed. A special analysis of household knowledge of economically attractive investments is provided that compares high payback improvements specified by the energy audit with the list of needed or desirable conservation improvements identified by respondents. (LEW)

  7. Household energy consumption and expenditures, 1990. [Contains Glossary

    SciTech Connect (OSTI)

    Not Available

    1993-03-02

    This report, Household Energy Consumption and Expenditures 1990, is based upon data from the 1990 Residential Energy Consumption Survey (RECS). Focusing on energy end-use consumption and expenditures of households, the 1990 RECS is the eighth in a series conducted since 1978 by the Energy Information Administration (EIA). Over 5,000 households were surveyed, providing information on their housing units, housing characteristics, energy consumption and expenditures, stock of energy-consuming appliances, and energy-related behavior. The information provided represents the characteristics and energy consumption of 94 million households nationwide.

  8. Loan Programs for Low- and Moderate-Income Households

    Broader source: Energy.gov [DOE]

    Better Buildings Residential Network Multifamily and Low-Income Housing Peer Exchange Call Series: Loan Programs for Low- and Moderate-Income Households, March 13, 2014.

  9. Scaling Semantic Graph Databases in Size and Performance

    SciTech Connect (OSTI)

    Morari, Alessandro; Castellana, Vito G.; Villa, Oreste; Tumeo, Antonino; Weaver, Jesse R.; Haglin, David J.; Choudhury, Sutanay; Feo, John T.

    2014-08-06

    In this paper we present SGEM, a full software system for accelerating large-scale semantic graph databases on commodity clusters. Unlike current approaches, SGEM addresses semantic graph databases by only employing graph methods at all the levels of the stack. On one hand, this allows exploiting the space efficiency of graph data structures and the inherent parallelism of graph algorithms. These features adapt well to the increasing system memory and core counts of modern commodity clusters. On the other hand, however, these systems are optimized for regular computation and batched data transfers, while graph methods usually are irregular and generate fine-grained data accesses with poor spatial and temporal locality. Our framework comprises a SPARQL to data parallel C compiler, a library of parallel graph methods and a custom, multithreaded runtime system. We introduce our stack, motivate its advantages with respect to other solutions and show how we solved the challenges posed by irregular behaviors. We present the result of our software stack on the Berlin SPARQL benchmarks with datasets up to 10 billion triples (a triple corresponds to a graph edge), demonstrating scaling in dataset size and in performance as more nodes are added to the cluster.

  10. Query optimization for graph analytics on linked data using SPARQL

    SciTech Connect (OSTI)

    Hong, Seokyong; Lee, Sangkeun; Lim, Seung -Hwan; Sukumar, Sreenivas R.; Vatsavai, Ranga Raju

    2015-07-01

    Triplestores that support query languages such as SPARQL are emerging as the preferred and scalable solution to represent data and meta-data as massive heterogeneous graphs using Semantic Web standards. With increasing adoption, the desire to conduct graph-theoretic mining and exploratory analysis has also increased. Addressing that desire, this paper presents a solution that is the marriage of Graph Theory and the Semantic Web. We present software that can analyze Linked Data using graph operations such as counting triangles, finding eccentricity, testing connectedness, and computing PageRank directly on triple stores via the SPARQL interface. We describe the process of optimizing performance of the SPARQL-based implementation of such popular graph algorithms by reducing the space-overhead, simplifying iterative complexity and removing redundant computations by understanding query plans. Our optimized approach shows significant performance gains on triplestores hosted on stand-alone workstations as well as hardware-optimized scalable supercomputers such as the Cray XMT.

  11. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    SciTech Connect (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graph mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'

  12. An Experiment on Graph Analysis Methodologies for Scenarios

    SciTech Connect (OSTI)

    Brothers, Alan J.; Whitney, Paul D.; Wolf, Katherine E.; Kuchar, Olga A.; Chin, George

    2005-09-30

    Visual graph representations are increasingly used to represent, display, and explore scenarios and the structure of organizations. The graph representations of scenarios are readily understood, and commercial software is available to create and manage these representations. The purpose of the research presented in this paper is to explore whether these graph representations support quantitative assessments of the underlying scenarios. The underlying structure of the scenarios is the information that is being targeted in the experiment and the extent to which the scenarios are similar in content. An experiment was designed that incorporated both the contents of the scenarios and analysts’ graph representations of the scenarios. The scenarios’ content was represented graphically by analysts, and both the structure and the semantics of the graph representation were attempted to be used to understand the content. The structure information was not found to be discriminating for the content of the scenarios in this experiment; but, the semantic information was discriminating.

  13. Graph facilitates tracking water and gas influx

    SciTech Connect (OSTI)

    Gruy, H.J. )

    1990-03-26

    Graphing the vertical distribution of reservoir volume is an easy method for estimating the acre-ft remaining to be exploited in reservoirs with water or gas encroachment. To evaluate reservoir performance and estimate oil and gas reserves in water-drive reservoirs or oil reservoirs with a gas cap, it is necessary to determine the magnitude of the movement of oil-water and gas-oil contact surfaces. In reviewing reserve estimates and reservoir studies done by others, the authors have found that very few reservoir engineers or geologists have an easy method for tracking the movement of these surfaces and estimating the volumes of oil displaced water encroachment, gas cap expansion, or the volumes of oil lost by wetting the gas cap. The following method evolved from the author's studies of the East Texas field starting in 1942, and it took this form in the early 1950s.

  14. Arizona Natural Gas Number of Industrial Consumers (Number of Elements)

    U.S. Energy Information Administration (EIA) Indexed Site

    Industrial Consumers (Number of Elements) Arizona Natural Gas Number of Industrial Consumers (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 358 344 354 1990's 526 532 532 526 519 530 534 480 514 555 2000's 526 504 488 450 414 425 439 395 383 390 2010's 368 371 379 383 386 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 4/29/2016 Next Release Date:

  15. Arizona Quantity of Production Associated with Reported Wellhead Value

    U.S. Energy Information Administration (EIA) Indexed Site

    (Million Cubic Feet) Quantity of Production Associated with Reported Wellhead Value (Million Cubic Feet) Arizona Quantity of Production Associated with Reported Wellhead Value (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1980's 26 10 0 0 0 0 1,360 1990's 2,125 1,225 730 548 691 500 405 401 411 439 2000's 332 266 243 426 306 211 588 634 503 695 2010's 165 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid

  16. Arizona Total Electric Power Industry Net Generation, by Energy Source

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona" "Energy Source",2006,2007,2008,2009,2010 "Fossil",73385,79794,82715,74509,73386 " Coal",40443,41275,43840,39707,43644 " Petroleum",73,49,52,63,66 " Natural Gas",32869,38469,38822,34739,29676 " Other Gases","-","-","-","-","-" "Nuclear",24012,26782,29250,30662,31200 "Renewables",6846,6639,7400,6630,6941 "Pumped Storage",149,125,95,169,209

  17. GATEWAY Demonstrations: Trial Demonstration of Area Lighting Retrofit, Yuma Border Patrol, Yuma, Arizona

    SciTech Connect (OSTI)

    Wilkerson, A. M.; McCullough, J. J.

    2014-12-31

    Along the Yuma Sector Border Patrol Area in Yuma, Arizona, the GATEWAY program conducted a trial demonstration in which the incumbent quartz metal halide area lighting was replaced with LED at three pole locations at the Yuma Sector Border Patrol Area in Yuma, Arizona. The retrofit was documented to better understand LED technology performance in high-temperature environments.

  18. Composing Data Parallel Code for a SPARQL Graph Engine

    SciTech Connect (OSTI)

    Castellana, Vito G.; Tumeo, Antonino; Villa, Oreste; Haglin, David J.; Feo, John

    2013-09-08

    Big data analytics process large amount of data to extract knowledge from them. Semantic databases are big data applications that adopt the Resource Description Framework (RDF) to structure metadata through a graph-based representation. The graph based representation provides several benefits, such as the possibility to perform in memory processing with large amounts of parallelism. SPARQL is a language used to perform queries on RDF-structured data through graph matching. In this paper we present a tool that automatically translates SPARQL queries to parallel graph crawling and graph matching operations. The tool also supports complex SPARQL constructs, which requires more than basic graph matching for their implementation. The tool generates parallel code annotated with OpenMP pragmas for x86 Shared-memory Multiprocessors (SMPs). With respect to commercial database systems such as Virtuoso, our approach reduces memory occupation due to join operations and provides higher performance. We show the scaling of the automatically generated graph-matching code on a 48-core SMP.

  19. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    per Household and Other Demographic Statistics Fact 618: April 12, 2010 Vehicles per Household and Other Demographic Statistics Since 1969, the number of vehicles per ...

  20. Reconstructing householder vectors from Tall-Skinny QR

    DOE Public Access Gateway for Energy & Science Beta (PAGES Beta)

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstratemore » the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.« less

  1. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-01-01

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  2. Projecting household energy consumption within a conditional demand framework

    SciTech Connect (OSTI)

    Teotia, A.; Poyer, D.

    1991-12-31

    Few models attempt to assess and project household energy consumption and expenditure by taking into account differential household choices correlated with such variables as race, ethnicity, income, and geographic location. The Minority Energy Assessment Model (MEAM), developed by Argonne National Laboratory (ANL) for the US Department of Energy (DOE), provides a framework to forecast the energy consumption and expenditure of majority, black, Hispanic, poor, and nonpoor households. Among other variables, household energy demand for each of these population groups in MEAM is affected by housing factors (such as home age, home ownership, home type, type of heating fuel, and installed central air conditioning unit), demographic factors (such as household members and urban/rural location), and climate factors (such as heating degree days and cooling degree days). The welfare implications of the revealed consumption patterns by households are also forecast. The paper provides an overview of the model methodology and its application in projecting household energy consumption under alternative energy scenarios developed by Data Resources, Inc., (DRI).

  3. Reconstructing householder vectors from Tall-Skinny QR

    SciTech Connect (OSTI)

    Ballard, Grey Malone; Demmel, James; Grigori, Laura; Jacquelin, Mathias; Knight, Nicholas; Nguyen, Hong Diep

    2015-08-05

    The Tall-Skinny QR (TSQR) algorithm is more communication efficient than the standard Householder algorithm for QR decomposition of matrices with many more rows than columns. However, TSQR produces a different representation of the orthogonal factor and therefore requires more software development to support the new representation. Further, implicitly applying the orthogonal factor to the trailing matrix in the context of factoring a square matrix is more complicated and costly than with the Householder representation. We show how to perform TSQR and then reconstruct the Householder vector representation with the same asymptotic communication efficiency and little extra computational cost. We demonstrate the high performance and numerical stability of this algorithm both theoretically and empirically. The new Householder reconstruction algorithm allows us to design more efficient parallel QR algorithms, with significantly lower latency cost compared to Householder QR and lower bandwidth and latency costs compared with Communication-Avoiding QR (CAQR) algorithm. Experiments on supercomputers demonstrate the benefits of the communication cost improvements: in particular, our experiments show substantial improvements over tuned library implementations for tall-and-skinny matrices. Furthermore, we also provide algorithmic improvements to the Householder QR and CAQR algorithms, and we investigate several alternatives to the Householder reconstruction algorithm that sacrifice guarantees on numerical stability in some cases in order to obtain higher performance.

  4. EAGLE: 'EAGLE'Is an' Algorithmic Graph Library for Exploration

    Energy Science and Technology Software Center (OSTI)

    2015-01-16

    The Resource Description Framework (RDF) and SPARQL Protocol and RDF Query Language (SPARQL) were introduced about a decade ago to enable flexible schema-free data interchange on the Semantic Web. Today data scientists use the framework as a scalable graph representation for integrating, querying, exploring and analyzing data sets hosted at different sources. With increasing adoption, the need for graph mining capabilities for the Semantic Web has emerged. Today there is no tools to conduct "graphmore » mining" on RDF standard data sets. We address that need through implementation of popular iterative Graph Mining algorithms (Triangle count, Connected component analysis, degree distribution, diversity degree, PageRank, etc.). We implement these algorithms as SPARQL queries, wrapped within Python scripts and call our software tool as EAGLE. In RDF style, EAGLE stands for "EAGLE 'Is an' algorithmic graph library for exploration. EAGLE is like 'MATLAB' for 'Linked Data.'« less

  5. Must all charting and graphing code be written in javascript...

    Open Energy Info (EERE)

    Must all charting and graphing code be written in javascript? Home > Groups > Databus In the documentation chapter entitled Developing charts using 3rd party api, we are told that...

  6. Parallel Algorithms for Graph Optimization using Tree Decompositions

    SciTech Connect (OSTI)

    Sullivan, Blair D; Weerapurage, Dinesh P; Groer, Christopher S

    2012-06-01

    Although many $\\cal{NP}$-hard graph optimization problems can be solved in polynomial time on graphs of bounded tree-width, the adoption of these techniques into mainstream scientific computation has been limited due to the high memory requirements of the necessary dynamic programming tables and excessive runtimes of sequential implementations. This work addresses both challenges by proposing a set of new parallel algorithms for all steps of a tree decomposition-based approach to solve the maximum weighted independent set problem. A hybrid OpenMP/MPI implementation includes a highly scalable parallel dynamic programming algorithm leveraging the MADNESS task-based runtime, and computational results demonstrate scaling. This work enables a significant expansion of the scale of graphs on which exact solutions to maximum weighted independent set can be obtained, and forms a framework for solving additional graph optimization problems with similar techniques.

  7. Highly Asynchronous VisitOr Queue Graph Toolkit

    Energy Science and Technology Software Center (OSTI)

    2012-10-01

    HAVOQGT is a C++ framework that can be used to create highly parallel graph traversal algorithms. The framework stores the graph and algorithmic data structures on external memory that is typically mapped to high performance locally attached NAND FLASH arrays. The framework supports a vertex-centered visitor programming model. The frameworkd has been used to implement breadth first search, connected components, and single source shortest path.

  8. Mining Graphs for Understanding Time-Varying Volumetric Data (Journal

    Office of Scientific and Technical Information (OSTI)

    Article) | SciTech Connect SciTech Connect Search Results Journal Article: Mining Graphs for Understanding Time-Varying Volumetric Data Citation Details In-Document Search Title: Mining Graphs for Understanding Time-Varying Volumetric Data Authors: Gu, Yi ; Wang, Chaoli ; Peterka, Tom ; Jacob, Robert ; Kim, Seung Hyun Publication Date: 2016-01-01 OSTI Identifier: 1249546 DOE Contract Number: AC02-06CH11357 Resource Type: Journal Article Resource Relation: Journal Name: IEEE Transactions on

  9. TIFF Image Writer patch for OpenSceneGraph

    Energy Science and Technology Software Center (OSTI)

    2012-01-05

    This software consists of code modifications to the open-source OpenSceneGraph software package to enable the creation of TlFF images containing 16 bit unsigned data. They also allow the user to disable compression and set the DPI tags in the resulting TIFF Images. Some image analysis programs require uncompressed, 16 bit unsigned input data. These code modifications allow programs based on OpenSceneGraph to write out such images, improving connectivity between applications.

  10. The peculiar phase structure of random graph bisection

    SciTech Connect (OSTI)

    Percus, Allon G; Istrate, Gabriel; Goncalves, Bruno T; Sumi, Robert Z

    2008-01-01

    The mincut graph bisection problem involves partitioning the n vertices of a graph into disjoint subsets, each containing exactly n/2 vertices, while minimizing the number of 'cut' edges with an endpoint in each subset. When considered over sparse random graphs, the phase structure of the graph bisection problem displays certain familiar properties, but also some surprises. It is known that when the mean degree is below the critical value of 2 log 2, the cutsize is zero with high probability. We study how the minimum cutsize increases with mean degree above this critical threshold, finding a new analytical upper bound that improves considerably upon previous bounds. Combined with recent results on expander graphs, our bound suggests the unusual scenario that random graph bisection is replica symmetric up to and beyond the critical threshold, with a replica symmetry breaking transition possibly taking place above the threshold. An intriguing algorithmic consequence is that although the problem is NP-hard, we can find near-optimal cutsizes (whose ratio to the optimal value approaches 1 asymptotically) in polynomial time for typical instances near the phase transition.

  11. Table 2. Percent of Households with Vehicles, Selected Survey...

    U.S. Energy Information Administration (EIA) Indexed Site

    Percent of Households with Vehicles, Selected Survey Years " ,"Survey Years" ,1983,1985,1988,1991,1994,2001 "Total",85.5450237,89.00343643,88.75545852,89.42917548,87.25590956,92.08...

  12. Fact #614: March 15, 2010 Average Age of Household Vehicles

    Broader source: Energy.gov [DOE]

    The average age of household vehicles has increased from 6.6 years in 1977 to 9.2 years in 2009. Pickup trucks have the oldest average age in every year listed. Sport utility vehicles (SUVs), first...

  13. Household heating bills expected to be lower this winter

    U.S. Energy Information Administration (EIA) Indexed Site

    In its new forecast, the U.S. Energy Information Administration said households that rely on heating oil which are mainly located in the Northeast will pay the lowest heating ...

  14. Arizona Natural Gas Pipeline and Distribution Use (Million Cubic Feet)

    U.S. Energy Information Administration (EIA) Indexed Site

    (Million Cubic Feet) Arizona Natural Gas Pipeline and Distribution Use (Million Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1990's 18,597 19,585 18,570 2000's 20,657 22,158 20,183 18,183 15,850 17,558 20,617 20,397 22,207 20,846 2010's 15,447 13,158 12,372 12,619 13,484 - = No Data Reported; -- = Not Applicable; NA = Not Available; W = Withheld to avoid disclosure of individual company data. Release Date: 4/29/2016 Next Release Date: 5/31/2016

  15. U.S. hydropower resource assessment for Arizona

    SciTech Connect (OSTI)

    Conner, A.M.; Francfort, J.E.

    1997-10-01

    The US Department of Energy is developing an estimate of the undeveloped hydropower potential in the US. The Hydropower Evaluation Software (HES) is a computer model that was developed by the Idaho National Engineering Laboratory for this purpose. HES measures the undeveloped hydropower resources available in the US, using uniform criteria for measurement. The software was developed and tested using hydropower information and data provided by the Southwestern Power Administration. It is a menu-driven program that allows the personal computer user to assign environmental attributes to potential hydropower sites, calculate development suitability factors for each site based on the environmental attributes present, and generate reports based on these suitability factors. This report describes the resource assessment results for the State of Arizona.

  16. Arizona Renewable Electric Power Industry Net Generation, by Energy Source

    U.S. Energy Information Administration (EIA) Indexed Site

    Arizona" "Energy Source",2006,2007,2008,2009,2010 "Geothermal","-","-","-","-","-" "Hydro Conventional",6793,6598,7286,6427,6622 "Solar",13,9,15,14,16 "Wind","-","-","-",30,135 "Wood/Wood Waste",8,"-",76,137,140 "MSW Biogenic/Landfill Gas",28,29,19,18,24 "Other Biomass",4,4,4,4,4 "Total",6846,6639,7400,6630,694

  17. National uranium resource evaluation, Marble Canyon Quadrangle, Arizona and Utah

    SciTech Connect (OSTI)

    Field, M T; Blauvelt, R P

    1982-05-01

    The Marble Canyon Quadrangle (2/sup 0/), northeast Arizona, was evaluated to a depth of 1500 m for uranium favorability using National Uranium Resource Evaluation criteria. Known mines and prospects were examined; field reconnaissance was done in selected areas of the quadrangle; and a ground-water geochemical survey was made in the southeast third of the quadrangle. The Shinarump and Petrified Forest Members of the Triassic Chinle Formation, which is exposed in the western and northeastern parts of the quadrangle and is present beneath the surface of much of the quadrangle, were found favorable for channel-sandstone uranium deposits. A portion of the Cretaceous Toreva Formation in the southeast part of the quadrangle was found favorable for peneconcordant-sandstone uranium deposits. The western part of the quadrangle was found favorable for uranium concentrations in breccia pipes.

  18. UMTRA project water sampling and analysis plan, Tuba City, Arizona

    SciTech Connect (OSTI)

    1996-02-01

    Planned, routine ground water sampling activities at the U.S. Department of Energy (DOE) Uranium Mill Tailings Remedial Action (UMTRA) Project site in Tuba City, Arizona, are described in the following sections of this water sampling and analysis plan (WSAP). This plan identifies and justifies the sampling locations, analytical parameters, detection limits, and sampling frequency for the stations routinely monitored at the site. The ground water data are used for site characterization and risk assessment. The regulatory basis for routine ground water monitoring at UMTRA Project sites is derived from the U.S. Environmental Protection Agency (EPA) regulations in 40 CFR Part 192 (1994) and the final EPA standards of 1995 (60 FR 2854). Sampling procedures are guided by the UMTRA Project standard operating procedures (SOP) (JEG, n.d.), and the most effective technical approach for the site.

  19. Transferring 2001 National Household Travel Survey

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim; Schmoyer, Richard L; Chin, Shih-Miao

    2007-05-01

    Policy makers rely on transportation statistics, including data on personal travel behavior, to formulate strategic transportation policies, and to improve the safety and efficiency of the U.S. transportation system. Data on personal travel trends are needed to examine the reliability, efficiency, capacity, and flexibility of the Nation's transportation system to meet current demands and to accommodate future demand. These data are also needed to assess the feasibility and efficiency of alternative congestion-mitigating technologies (e.g., high-speed rail, magnetically levitated trains, and intelligent vehicle and highway systems); to evaluate the merits of alternative transportation investment programs; and to assess the energy-use and air-quality impacts of various policies. To address these data needs, the U.S. Department of Transportation (USDOT) initiated an effort in 1969 to collect detailed data on personal travel. The 1969 survey was the first Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990, 1995, and 2001. Data on daily travel were collected in 1969, 1977, 1983, 1990 and 1995. In 2001, the survey was renamed the National Household Travel Survey (NHTS) and it collected both daily and long-distance trips. The 2001 survey was sponsored by three USDOT agencies: Federal Highway Administration (FHWA), Bureau of Transportation Statistics (BTS), and National Highway Traffic Safety Administration (NHTSA). The primary objective of the survey was to collect trip-based data on the nature and characteristics of personal travel so that the relationships between the characteristics of personal travel and the demographics of the traveler can be established. Commercial and institutional travel were not part of the survey. Due to the survey's design, data in the NHTS survey series were not recommended for estimating travel statistics for categories smaller than the combination of Census division (e.g., New England, Middle Atlantic, and Pacific), MSA size, and the availability of rail. Extrapolating NHTS data within small geographic areas could risk developing and subsequently using unreliable estimates. For example, if a planning agency in City X of State Y estimates travel rates and other travel characteristics based on survey data collected from NHTS sample households that were located in City X of State Y, then the agency could risk developing and using unreliable estimates for their planning process. Typically, this limitation significantly increases as the size of an area decreases. That said, the NHTS contains a wealth of information that could allow statistical inferences about small geographic areas, with a pre-determined level of statistical certainty. The question then becomes whether a method can be developed that integrates the NHTS data and other data to estimate key travel characteristics for small geographic areas such as Census tract and transportation analysis zone, and whether this method can outperform other, competing methods.

  20. Determinants of Household Use of Selected Energy Star Appliances

    U.S. Energy Information Administration (EIA) Indexed Site

    Determinants of Household Use of Selected Energy Star Appliances May 2016 Independent Statistics & Analysis www.eia.gov U.S. Department of Energy Washington, DC 20585 U.S. Energy Information Administration | Determinants of Household Use of Selected Energy Star Appliances i This report was prepared by the U.S. Energy Information Administration (EIA), the statistical and analytical agency within the U.S. Department of Energy. By law, EIA's data, analyses, and forecasts are independent of

  1. Algorithms and architectures for high performance analysis of semantic graphs.

    SciTech Connect (OSTI)

    Hendrickson, Bruce Alan

    2005-09-01

    Semantic graphs offer one promising avenue for intelligence analysis in homeland security. They provide a mechanism for describing a wide variety of relationships between entities of potential interest. The vertices are nouns of various types, e.g. people, organizations, events, etc. Edges in the graph represent different types of relationships between entities, e.g. 'is friends with', 'belongs-to', etc. Semantic graphs offer a number of potential advantages as a knowledge representation system. They allow information of different kinds, and collected in differing ways, to be combined in a seamless manner. A semantic graph is a very compressed representation of some of relationship information. It has been reported that the semantic graph can be two orders of magnitude smaller than the processed intelligence data. This allows for much larger portions of the data universe to be resident in computer memory. Many intelligence queries that are relevant to the terrorist threat are naturally expressed in the language of semantic graphs. One example is the search for 'interesting' relationships between two individuals or between an individual and an event, which can be phrased as a search for short paths in the graph. Another example is the search for an analyst-specified threat pattern, which can be cast as an instance of subgraph isomorphism. It is important to note than many kinds of analysis are not relationship based, so these are not good candidates for semantic graphs. Thus, a semantic graph should always be used in conjunction with traditional knowledge representation and interface methods. Operations that involve looking for chains of relationships (e.g. friend of a friend) are not efficiently executable in a traditional relational database. However, the semantic graph can be thought of as a pre-join of the database, and it is ideally suited for these kinds of operations. Researchers at Sandia National Laboratories are working to facilitate semantic graph analysis. Since intelligence datasets can be extremely large, the focus of this work is on the use of parallel computers. We have been working to develop scalable parallel algorithms that will be at the core of a semantic graph analysis infrastructure. Our work has involved two different thrusts, corresponding to two different computer architectures. The first architecture of interest is distributed memory, message passing computers. These machines are ubiquitous and affordable, but they are challenging targets for graph algorithms. Much of our distributed-memory work to date has been collaborative with researchers at Lawrence Livermore National Laboratory and has focused on finding short paths on distributed memory parallel machines. Our implementation on 32K processors of BlueGene/Light finds shortest paths between two specified vertices in just over a second for random graphs with 4 billion vertices.

  2. Builders Challenge High Performance Builder Spotlight: Yavapai College, Chino Valley, Arizona

    SciTech Connect (OSTI)

    2009-12-22

    Building America Builders Challenge fact sheet on Yavapai College of Chino Valley, Arizona. These college students built a Building America Builders Challenge house that achieved the remarkably low HERS score of -3 and achieved a tight building envelope.

  3. FIA-12-0053- In the Matter of Arizona Nevada District Organization

    Broader source: Energy.gov [DOE]

    On October 11, 2012, the Department of Energy’s (DOE) Office of Hearings and Appeals (OHA) denied a Freedom of Information Act (FOIA) Appeal filed by the California Arizona Nevada District...

  4. FIA-12-0054- In the Matter of California-Arizona-Nevada District Organization Contract Compliance

    Broader source: Energy.gov [DOE]

    On September 14, 2012, California-Arizona-Nevada District Organization Contract Compliance (CANDO) filed an appeal from a final determination issued by the Loan Guarantee Program Office (LGPO) of...

  5. Arizona State Land Department Rights-of-Way Website | Open Energy...

    Open Energy Info (EERE)

    to: navigation, search OpenEI Reference LibraryAdd to library Web Site: Arizona State Land Department Rights-of-Way Website Abstract This website provides general information...

  6. 49 A.R.S. 255 et seq.: Arizona Pollutant Discharge Elimination...

    Open Energy Info (EERE)

    search OpenEI Reference LibraryAdd to library Legal Document- StatuteStatute: 49 A.R.S. 255 et seq.: Arizona Pollutant Discharge Elimination System ProgramLegal Abstract...

  7. FIA-12-0059- In the Matter of California Arizona Nevada District Organization

    Broader source: Energy.gov [DOE]

    On October 31, 2012, the Department of Energy’s (DOE) Office of Hearings and Appeals (OHA) denied a Freedom of Information Act (FOIA) Appeal filed by the California Arizona Nevada District...

  8. NPDES compliance monitoring report: Silver bell mine, Pima County, Arizona. Final report

    SciTech Connect (OSTI)

    Ganter, W.

    1992-10-01

    This presents the findings of a compliance evaluation inspection of the Silver Bell Mine in Pima County, Arizona, conducted on August 19, 1992. It is part of a series of inspections of uncontrolled discharges of mine drainage.

  9. NPDES compliance monitoring report: Paloverde decline, Pima County, Arizona. Final report

    SciTech Connect (OSTI)

    Ganter, W.

    1992-10-07

    This presents the findings of a compliance evaluation inspection of the Paloverde Decline in Pima County, Arizona, conducted on August 21, 1992. It is part of a series of inspections of uncontrolled discharges of mine drainage.

  10. Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number...

    Annual Energy Outlook [U.S. Energy Information Administration (EIA)]

    Gas and Gas Condensate Wells (Number of Elements) Arizona Natural Gas Number of Gas and Gas Condensate Wells (Number of Elements) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5...

  11. NPDES compliance monitoring report: Oracle Ridge Mine, San Manuel, Arizona. Draft report

    SciTech Connect (OSTI)

    Stevens, J.

    1992-11-03

    This presents the findings of a compliance evaluation inspection of the Oracle Ridge Copper Mine near San Manuel, Arizona, conducted on August 17, 1992. It is part of a series of inspections of uncontrolled discharges of mine drainage.

  12. Tuba City, Arizona, Disposal Site Groundwater Compliance Path Forward Fact Sheet

    Office of Legacy Management (LM)

    Tuba City, Arizona, Disposal Site Groundwater Compliance Path Forward Fact Sheet Fact Sheet The U.S. Department of Energy Office of Legacy Management is responsible for site management and for ensuring that the selected groundwater compliance strategy at the Tuba City, Arizona, Disposal Site continues to be protective of human health and the environment. Southwesterly view of Tuba City mill in operation, circa 1966. Tuba City site, 2010. Tuba City Site background The Tuba City uranium mill

  13. On the mixing time of geographical threshold graphs

    SciTech Connect (OSTI)

    Bradonjic, Milan

    2009-01-01

    In this paper, we study the mixing time of random graphs generated by the geographical threshold graph (GTG) model, a generalization of random geometric graphs (RGG). In a GTG, nodes are distributed in a Euclidean space, and edges are assigned according to a threshold function involving the distance between nodes as well as randomly chosen node weights. The motivation for analyzing this model is that many real networks (e.g., wireless networks, the Internet, etc.) need to be studied by using a 'richer' stochastic model (which in this case includes both a distance between nodes and weights on the nodes). We specifically study the mixing times of random walks on 2-dimensional GTGs near the connectivity threshold. We provide a set of criteria on the distribution of vertex weights that guarantees that the mixing time is {Theta}(n log n).

  14. Mining Large Heterogeneous Graphs using Cray s Urika

    SciTech Connect (OSTI)

    Sukumar, Sreenivas R; Bond, Nathaniel A

    2013-01-01

    Pattern discovery and predictive modeling from seemingly related Big Data represented as massive, ad-hoc, heterogeneous networks (e.g., extremely large graphs with complex, possibly unknown structure) is an outstanding problem in many application domains. To address this problem, we are designing graph-mining algorithms capable of discovering relationship-patterns from such data and using those discovered patterns as features for classification and predictive modeling. Specifically, we are: (i) exploring statistical properties, mechanics and generative models of behavior patterns in heterogeneous information networks, (ii) developing novel, automated and scalable graph-pattern discovery algorithms and (iii) applying our relationship-analytics (data science + network science) expertise to domains spanning healthcare to homeland security.

  15. Fast Search for Dynamic Multi-Relational Graphs

    SciTech Connect (OSTI)

    Choudhury, Sutanay; Holder, Larry; Chin, George; Feo, John T.

    2013-06-23

    Acting on time-critical events by processing ever growing social media or news streams is a major technical challenge. Many of these data sources can be modeled as multi-relational graphs. Continuous queries or techniques to search for rare events that typically arise in monitoring applications have been studied extensively for relational databases. This work is dedicated to answer the question that emerges naturally: how can we efficiently execute a continuous query on a dynamic graph? This paper presents an exact subgraph search algorithm that exploits the temporal characteristics of representative queries for online news or social media monitoring. The algorithm is based on a novel data structure called the that leverages the structural and semantic characteristics of the underlying multi-relational graph. The paper concludes with extensive experimentation on several real-world datasets that demonstrates the validity of this approach.

  16. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, J.D.; Liu, Xiaomin; McMahon, J.E.

    1996-11-01

    This report presents a detailed model of hot water use patterns in individual household. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies. 21 refs., 3 figs., 10 tabs.

  17. Modeling patterns of hot water use in households

    SciTech Connect (OSTI)

    Lutz, James D.; Liu, Xiaomin; McMahon, James E.; Dunham, Camilla; Shown, Leslie J.; McCure, Quandra T.

    1996-01-01

    This report presents a detailed model of hot water use patterns in individual households. The model improves upon an existing model by including the effects of four conditions that were previously unaccounted for: the absence of a clothes washer; the absence of a dishwasher; a household consisting of seniors only; and a household that does not pay for its own hot water use. Although these four conditions can significantly affect residential hot water use, and have been noted in other studies, this is the first time that they have been incorporated into a detailed model. This model allows detailed evaluation of the impact of potential efficiency standards for water heaters and other market transformation policies.

  18. Arizona Natural Gas Pipeline and Distribution Use Price (Dollars per

    U.S. Energy Information Administration (EIA) Indexed Site

    Thousand Cubic Feet) Price (Dollars per Thousand Cubic Feet) Arizona Natural Gas Pipeline and Distribution Use Price (Dollars per Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0.15 0.15 0.15 1970's 0.17 0.17 0.19 0.22 0.28 0.36 0.44 0.64 0.75 1.29 1980's 1.62 2.22 2.86 3.16 2.83 2.79 2.22 1.49 1.79 1.50 1990's 1.65 1.26 1.25 1.68 1.28 1.19 1.80 2.20 1.90 2.08 2000's 3.61 3.96 NA -- -- -- - = No Data Reported; -- = Not Applicable; NA

  19. Socioeconomic impact of photovoltaic power at Schuchulik, Arizona. Final report

    SciTech Connect (OSTI)

    Bahr, D.; Garrett, B.G.; Chrisman, C.

    1980-10-01

    Schuchuli, a small remote village on the Papago Indian Reservation in southwest Arizona, is 27 kilometers (17 miles) from the nearest available utility power. In some respects, Schuchuli resembles many of the rural villages in other parts of the world. For example, it's relatively small in size (about 60 residents), composed of a number of extended family groupings, and remotely situated relative to major population centers (190 km, or 120 miles, from Tucson). Its lack of conventional power is due to the prohibitive cost of supplying a small electrical load with a long-distance distribution line. Furthermore, alternate energy sources are expensive and place a burden on the resources of the villagers. On December 16, 1978, as part of a federally funded project, a solar cell power system was put into operation at Schuchuli. The system powers the village water pump, lighting for homes ad other village buildings, family refrigerators and a communal washing machine and sewing machine. The project, managed for the US Department of Energy by the NASA Lewis Research Center, provided for a one-year socio-economic study to assess the impact of a relatively small amount of electricity on the basic living environment of the villagers. The results of that study are presented, including village history, group life, energy use in general and the use of the photovoltaic-powered appliances. No significant impacts due to the photovoltaic power system were observed.

  20. Arizona Natural Gas Wellhead Price (Dollars per Thousand Cubic Feet)

    U.S. Energy Information Administration (EIA) Indexed Site

    Wellhead Price (Dollars per Thousand Cubic Feet) Arizona Natural Gas Wellhead Price (Dollars per Thousand Cubic Feet) Decade Year-0 Year-1 Year-2 Year-3 Year-4 Year-5 Year-6 Year-7 Year-8 Year-9 1960's 0.15 0.16 0.18 1970's 0.17 0.18 0.18 0.18 0.20 0.28 0.28 0.33 0.37 0.41 1980's 2.59 3.08 2.90 1.80 1990's 1.20 1.50 1.85 1.30 1.40 1.20 1.65 2.40 1.88 2.08 2000's 3.50 4.12 2.60 4.33 5.12 6.86 5.70 5.98 7.09 3.19 2010's 4.11 - = No Data Reported; -- = Not Applicable; NA = Not Available; W =

  1. UMTRA project water sampling and analysis plan, Monument Valley, Arizona

    SciTech Connect (OSTI)

    Not Available

    1994-04-01

    The Monument Valley Uranium Mill Tailings Remedial Action (UMTRA) Project site in Cane Valley is a former uranium mill that has undergone surface remediation in the form of tailings and contaminated materials removal. Contaminated materials from the Monument Valley (Arizona) UMTRA Project site have been transported to the Mexican Hat (Utah) UMTRA Project site for consolidation with the Mexican Hat tailings. Tailings removal was completed in February 1994. Three geologic units at the site contain water: the unconsolidated eolian and alluvial deposits (alluvial aquifer), the Shinarump Conglomerate (Shinarump Member), and the De Chelly Sandstone. Water quality analyses indicate the contaminant plume has migrated north of the site and is mainly in the alluvial aquifer. An upward hydraulic gradient in the De Chelly Sandstone provides some protection to that aquifer. This water sampling and analysis plan recommends sampling domestic wells, monitor wells, and surface water in April and September 1994. The purpose of sampling is to continue periodic monitoring for the surface program, evaluate changes to water quality for site characterization, and provide data for the baseline risk assessment. Samples taken in April will be representative of high ground water levels and samples taken in September will be representative of low ground water levels. Filtered and nonfiltered samples will be analyzed for plume indicator parameters and baseline risk assessment parameters.

  2. Evaluation of geothermal energy in Arizona. Arizona geothermal planning/commercialization team. Quarterly topical progress report, July 1-September 30, 1980

    SciTech Connect (OSTI)

    White, D.H.; Mancini, F.; Goldstone, L.A.; Malysa, L.

    1980-01-01

    Progress is reviewed on the following: area development plans, evaluation of geothermal applications, continued evaluation of geothermal resources, engineering and economic analyses, technical assistance in the state of Arizona, the impact of various growth patterns upon geothermal energy development, and the outreach program. (MHR)

  3. A Glance at China’s Household Consumption

    SciTech Connect (OSTI)

    Shui, Bin

    2009-10-22

    Known for its scale, China is the most populous country with the world’s third largest economy. In the context of rising living standards, a relatively lower share of household consumption in its GDP, a strong domestic market and globalization, China is witnessing an unavoidable increase in household consumption, related energy consumption and carbon emissions. Chinese policy decision makers and researchers are well aware of these challenges and keen to promote green lifestyles. China has developed a series of energy policies and programs, and launched a wide‐range social marketing activities to promote energy conservation.

  4. New York Household Travel Patterns: A Comparison Analysis

    SciTech Connect (OSTI)

    Hu, Patricia S; Reuscher, Tim

    2007-05-01

    In 1969, the U. S. Department of Transportation began collecting detailed data on personal travel to address various transportation planning issues. These issues range from assessing transportation investment programs to developing new technologies to alleviate congestion. This 1969 survey was the birth of the Nationwide Personal Transportation Survey (NPTS). The survey was conducted again in 1977, 1983, 1990 and 1995. Longer-distance travel was collected in 1977 and 1995. In 2001, the survey was renamed to the National Household Travel Survey (NHTS) and collected both daily and longer-distance trips in one survey. In addition to the number of sample households that the national NPTS/NHTS survey allotted to New York State (NYS), the state procured an additional sample of households in both the 1995 and 2001 surveys. In the 1995 survey, NYS procured an addition sample of more than 9,000 households, increasing the final NY NPTS sample size to a total of 11,004 households. Again in 2001, NYS procured 12,000 additional sample households, increasing the final New York NHTS sample size to a total of 13,423 households with usable data. These additional sample households allowed NYS to address transportation planning issues pertinent to geographic areas significantly smaller than for what the national NPTS and NHTS data are intended. Specifically, these larger sample sizes enable detailed analysis of twelve individual Metropolitan Planning Organizations (MPOs). Furthermore, they allowed NYS to address trends in travel behavior over time. In this report, travel data for the entire NYS were compared to those of the rest of the country with respect to personal travel behavior and key travel determinants. The influence of New York City (NYC) data on the comparisons of the state of New York to the rest of the country was also examined. Moreover, the analysis examined the relationship between population density and travel patterns, and the similarities and differences among New York MPOs. The 1995 and 2001 survey data make it possible to examine and identify travel trends over time. This report does not address, however, the causes of the differences and/or trends.

  5. STRUCTURAL ANNOTATION OF EM IMAGES BY GRAPH CUT

    SciTech Connect (OSTI)

    Chang, Hang; Auer, Manfred; Parvin, Bahram

    2009-05-08

    Biological images have the potential to reveal complex signatures that may not be amenable to morphological modeling in terms of shape, location, texture, and color. An effective analytical method is to characterize the composition of a specimen based on user-defined patterns of texture and contrast formation. However, such a simple requirement demands an improved model for stability and robustness. Here, an interactive computational model is introduced for learning patterns of interest by example. The learned patterns bound an active contour model in which the traditional gradient descent optimization is replaced by the more efficient optimization of the graph cut methods. First, the energy function is defined according to the curve evolution. Next, a graph is constructed with weighted edges on the energy function and is optimized with the graph cut algorithm. As a result, the method combines the advantages of the level set method and graph cut algorithm, i.e.,"topological" invariance and computational efficiency. The technique is extended to the multi-phase segmentation problem; the method is validated on synthetic images and then applied to specimens imaged by transmission electron microscopy(TEM).

  6. In-Memory Graph Databases for Web-Scale Data

    SciTech Connect (OSTI)

    Castellana, Vito G.; Morari, Alessandro; Weaver, Jesse R.; Tumeo, Antonino; Haglin, David J.; Villa, Oreste; Feo, John

    2015-03-01

    RDF databases have emerged as one of the most relevant way for organizing, integrating, and managing expo- nentially growing, often heterogeneous, and not rigidly structured data for a variety of scientific and commercial fields. In this paper we discuss the solutions integrated in GEMS (Graph database Engine for Multithreaded Systems), a software framework for implementing RDF databases on commodity, distributed-memory high-performance clusters. Unlike the majority of current RDF databases, GEMS has been designed from the ground up to primarily employ graph-based methods. This is reflected in all the layers of its stack. The GEMS framework is composed of: a SPARQL-to-C++ compiler, a library of data structures and related methods to access and modify them, and a custom runtime providing lightweight software multithreading, network messages aggregation and a partitioned global address space. We provide an overview of the framework, detailing its component and how they have been closely designed and customized to address issues of graph methods applied to large-scale datasets on clusters. We discuss in details the principles that enable automatic translation of the queries (expressed in SPARQL, the query language of choice for RDF databases) to graph methods, and identify differences with respect to other RDF databases.

  7. Integrated Network Decompositions and Dynamic Programming for Graph Optimization (INDDGO)

    Energy Science and Technology Software Center (OSTI)

    2012-05-31

    The INDDGO software package offers a set of tools for finding exact solutions to graph optimization problems via tree decompositions and dynamic programming algorithms. Currently the framework offers serial and parallel (distributed memory) algorithms for finding tree decompositions and solving the maximum weighted independent set problem. The parallel dynamic programming algorithm is implemented on top of the MADNESS task-based runtime.

  8. Cyber Graph Queries for Geographically Distributed Data Centers

    SciTech Connect (OSTI)

    Berry, Jonathan W.; Collins, Michael; Kearns, Aaron; Phillips, Cynthia A.; Saia, Jared

    2015-05-01

    We present new algorithms for a distributed model for graph computations motivated by limited information sharing we first discussed in [20]. Two or more independent entities have collected large social graphs. They wish to compute the result of running graph algorithms on the entire set of relationships. Because the information is sensitive or economically valuable, they do not wish to simply combine the information in a single location. We consider two models for computing the solution to graph algorithms in this setting: 1) limited-sharing: the two entities can share only a polylogarithmic size subgraph; 2) low-trust: the entities must not reveal any information beyond the query answer, assuming they are all honest but curious. We believe this model captures realistic constraints on cooperating autonomous data centers. We have algorithms in both setting for s - t connectivity in both models. We also give an algorithm in the low-communication model for finding a planted clique. This is an anomaly- detection problem, finding a subgraph that is larger and denser than expected. For both the low- communication algorithms, we exploit structural properties of social networks to prove perfor- mance bounds better than what is possible for general graphs. For s - t connectivity, we use known properties. For planted clique, we propose a new property: bounded number of triangles per node. This property is based upon evidence from the social science literature. We found that classic examples of social networks do not have the bounded-triangles property. This is because many social networks contain elements that are non-human, such as accounts for a business, or other automated accounts. We describe some initial attempts to distinguish human nodes from automated nodes in social networks based only on topological properties.

  9. EERE Success Story-Kingston Creek Hydro Project Powers 100 Households...

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Kingston Creek Hydro Project Powers 100 Households EERE Success Story-Kingston Creek Hydro Project Powers 100 Households August 21, 2013 - 12:00am Addthis Nevada-based contracting ...

  10. Fact #727: May 14, 2012 Nearly Twenty Percent of Households Own Three or More Vehicles

    Broader source: Energy.gov [DOE]

    Household vehicle ownership has changed over the last six decades. In 1960, over twenty percent of households did not own a vehicle, but by 2010, that number fell to less than 10%. The number of...

  11. Fact #747: October 1, 2012 Behind Housing, Transportation is the Top Household Expenditure

    Broader source: Energy.gov [DOE]

    Except for housing, transportation was the largest single expenditure for the average American household in 2010. The average household spends more on transportation in a year than on food. Vehicle...

  12. Fact #729: May 28, 2012 Secondary Household Vehicles Travel Fewer Miles

    Broader source: Energy.gov [DOE]

    When a household has more than one vehicle, the secondary vehicles travel fewer miles than the primary vehicle. In a two-vehicle household, the second vehicle travels less than half of the miles...

  13. Heating oil and propane households bills to be lower this winter...

    U.S. Energy Information Administration (EIA) Indexed Site

    Heating oil and propane households bills to be lower this winter despite recent cold spell Despite the recent cold weather, households that use heating oil or propane as their main ...

  14. Fact #618: April 12, 2010 Vehicles per Household and Other Demographic Statistics

    Broader source: Energy.gov [DOE]

    Since 1969, the number of vehicles per household has increased by 66% and the number of vehicles per licensed driver has increased by 47%. The number of workers per household has changed the least...

  15. LDRD final report : first application of geospatial semantic graphs to SAR image data.

    SciTech Connect (OSTI)

    Brost, Randolph C.; McLendon, William Clarence,

    2013-01-01

    Modeling geospatial information with semantic graphs enables search for sites of interest based on relationships between features, without requiring strong a priori models of feature shape or other intrinsic properties. Geospatial semantic graphs can be constructed from raw sensor data with suitable preprocessing to obtain a discretized representation. This report describes initial work toward extending geospatial semantic graphs to include temporal information, and initial results applying semantic graph techniques to SAR image data. We describe an efficient graph structure that includes geospatial and temporal information, which is designed to support simultaneous spatial and temporal search queries. We also report a preliminary implementation of feature recognition, semantic graph modeling, and graph search based on input SAR data. The report concludes with lessons learned and suggestions for future improvements.

  16. Final Technical Report for the BOOST2013 Workshop. Hosted by the University of Arizona

    SciTech Connect (OSTI)

    Johns, Kenneth

    2015-02-20

    BOOST 2013 was the 5th International Joint Theory/Experiment Workshop on Phenomenology, Reconstruction and Searches for Boosted Objects in High Energy Hadron Collisions. It was locally organized and hosted by the Experimental High Energy Physics Group at the University of Arizona and held at Flagstaff, Arizona on August 12-16, 2013. The workshop provided a forum for theorists and experimentalists to present and discuss the latest findings related to the reconstruction of boosted objects in high energy hadron collisions and their use in searches for new physics. This report gives the outcomes of the BOOST 2013 Workshop.

  17. Light Shines on Better Budget for Glendale, Arizona | Department of Energy

    Office of Energy Efficiency and Renewable Energy (EERE) Indexed Site

    Shines on Better Budget for Glendale, Arizona Light Shines on Better Budget for Glendale, Arizona July 20, 2010 - 10:00am Addthis A LED light bulb is installed in one of Glendale, Ariz.'s traffic signals. | Photo courtesy of Glendale A LED light bulb is installed in one of Glendale, Ariz.'s traffic signals. | Photo courtesy of Glendale Glendale, Ariz., like many other cities, was facing several problems: a tight budget and aging buildings using outdated lighting - making repairs difficult and

  18. EA-1948: Gila-North Gila Transmission Line Rebuild and Upgrade Project, Yuma County, Arizona

    Broader source: Energy.gov [DOE]

    DOE’s Western Area Power Administration (Western) prepared this EA to analyze the potential environmental impacts of a proposal to rebuild and upgrade two parallel 4.8-mile transmission lines between the Gila and North Gila Substations and take actions in support of portions of Arizona Public Service’s construction of a new, 12.8 mile 230-kV transmission line between North Gila and a proposed substation in Yuma County, Arizona. The U.S. Bureau of Reclamation and U.S. Army Corps of Engineers are cooperating agencies.

  19. A Graph Analytic Metric for Mitigating Advanced Persistent Threat

    SciTech Connect (OSTI)

    Johnson, John R.; Hogan, Emilie A.

    2013-06-04

    This paper introduces a novel graph analytic metric that can be used to measure the potential vulnerability of a cyber network to specific types of attacks that use lateral movement and privilege escalation such as the well known Pass The Hash, (PTH). The metric is computed from an oriented subgraph of the underlying cyber network induced by selecting only those edges for which a given property holds between the two vertices of the edge. The metric with respect to a select node on the subgraph is defined as the likelihood that the select node is reachable from another arbitrary node in the graph. This metric can be calculated dynamically from the authorization and auditing layers during the network security authorization phase and will potentially enable predictive deterrence against attacks such as PTH.

  20. Codesign Lessons Learned from Implementing Graph Matching on Multithreaded Architectures

    SciTech Connect (OSTI)

    Halappanavar, Mahantesh; Pothen, Alex; Azad, Md Ariful; Manne, Fredrik; Langguth, Johannes; Khan, Arif

    2015-08-12

    Co-design of algorithms and architectures is an effective way to address the performance of irregular applications on multithreaded architectures. We explore the interplay between algorithm design and architectural features using graph matching as a case study. We present the key lessons that we have learnt as a means to influence co-design of algorithms and architecture for execution of data-intensive irregular workloads.