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Robust Optimal Fragmentation and Dispersion of Near-Earth Objects Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:31:30.000Z<p> During the past 2 decades, various concepts for mitigating the impact threats from NEOs have been proposed, but many of these concepts were impractical and not technically credible. In particular, all non-nuclear techniques require mission lead times larger than 10 years. However, for the most probable impact threat with a warning time less than 10 years, the use of high-energy nuclear explosives in space becomes inevitable for proper fragmentation and dispersion of an NEO in a collision course with Earth. However, the existing nuclear subsurface penetrator technology limits the impact velocity to less than 300m/s because higher impact velocities destroy prematurely the detonation electronic equipment. Thus, an innovative space system architecture utilizing high-energy nuclear explosives must be developed for a worst-case intercept mission resulting in relative closing velocities as high as 5-30km/s. An advanced system concept is proposed for nuclear subsurface explosion missions. The concept blends a hypervelocity kinetic-energy impactor with nuclear subsurface explosion, and exploits a 2-body space vehicle consisting of a fore body and an aft body. These 2 spacecraft bodies may be connected by a deployable boom. The fore body provides proper kinetic impact crater conditions for an aft body carrying nuclear explosives to make a deeper penetration into an asteroid body. For such a complex mission architecture design study, non-traditional, multidisciplinary research efforts in the areas of hypervelocity impact dynamics, nuclear explosion modeling, high-temperature thermal shielding, shock-resistant electronic systems, and advanced space system technologies are required. Expanding upon the current research activities, the Iowa State Asteroid Deflection Research Center will develop an innovative, advanced space system architecture that provides the planetary defense capabilities needed to enable a future real space mission more efficient, affordable, and reliable.</p>
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Election Results, Special Election Runoff, August 11, 2015
data.fultoncountyga.gov | Last Updated 2024-01-23T18:41:10.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, August 11, 2015 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Metropolitan King County Council District No. 8
data.kingcounty.gov | Last Updated 2018-12-15T00:44:53.000Z(Initial posting at 8:15 p.m. on Election Day) Election results, November 2011 general election. To see when this was updated, click "About" on the far right of this page. See the schedule for when results are posted, at http://www.kingcounty.gov/elections/elections/201111/resultsschedule.aspx
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Elections: Applications, Line, September 2020 Election
sharefulton.fultoncountyga.gov | Last Updated 2023-01-30T16:56:12.000ZThis dataset contains information on absentee ballot applications for the September 29, 2020 Special Election.
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Health Care In An Integrated System (LGHC Indicator)
healthdata.gov | Last Updated 2023-07-25T18:43:43.000ZThis is a source dataset for a Let's Get Healthy California indicator at https://letsgethealthy.ca.gov/. Percentage of Californians who receive care in an integrated system, defined as a Health Maintenance Organization that is tracked by the Department of Managed Health Care. Managed care refers to health care coverage that organizes doctors, hospitals and other providers into groups in order to enhance the quality and cost effectiveness of medical treatment. Today, 58 California counties receive their health care through six main models of managed care: Two-Plan, County Organized Health Systems (COHS), Geographic Managed Care (GMC), Regional Model (RM), Imperial, and San Benito. County enrollment information is compiled by Department of Managed Health Care Licensed Full Service Health Plans. This enrollment information is not standardized and may be designated by the member’s place of employment or home resulting in reporting inaccuracies.
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Election Results, General Election, November 6, 2018
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:39.000ZThis data set consists of all Fulton County Election results from the General Election, November 6, 2018 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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RSBS SMO: Kitchen Appliances, New York State Residential Statewide Baseline Study: Single and Multifamily Occupant Telephone or Web Survey
data.ny.gov | Last Updated 2019-11-15T22:21:25.000ZHow does your organization use this dataset? What other NYSERDA or energy-related datasets would you like to see on Open NY? Let us know by emailing OpenNY@nyserda.ny.gov. The New York State Energy Research and Development Authority (NYSERDA), in collaboration with the New York State Department of Public Service (DPS), conducted a statewide residential baseline study (study) from 2011 to 2014 of the single-family and multifamily residential housing segments, including new construction, and a broad range of energy uses and efficiency measures. This dataset includes 2,982 single-family and 379 multifamily occupant survey completes for a total of 3,361 responses. The survey involved 2,285 Web, 1,041 telephone, and 35 mini-inspection surveys. The survey collected information on the following building characteristics: building shell, kitchen appliances, heating and cooling equipment, water heating equipment, clothes washing and drying equipment, lighting, pool and spa equipment, small household appliances, miscellaneous energy consuming equipment, as well as behaviors and characteristics of respondents.
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Election Results, Special Election Runoff, December 5, 2017
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:27.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, December 5, 2017 to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Election Results, Special Election Runoff, February 3, 2015
data.fultoncountyga.gov | Last Updated 2024-01-30T22:52:06.000ZThis data set consists of all Fulton County Election results from the Special Election Runoff, February 3, 2015, to present. Included with each record is the race, candidate, precinct, number of election day votes, number of absentee by mail votes, number of advance in person votes, number of provisional votes, total number of votes, name of election, and date of election. This data set is updated after each election.
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Combining Discrete Element Modeling, Finite Element Analysis, and Experimental Calibrations for Modeling of Granular Material Systems Project
nasa-test-0.demo.socrata.com | Last Updated 2015-07-20T05:43:27.000ZThe current state-of-the-art in DEM modeling has two major limitations which must be overcome to ensure that the technique can be useful to NASA engineers and the commercial sector: the computational intensive nature of the software, and the lack of an established methodology to determine the particle properties to best accurately model a given physical system. The proposed work will address both of these limitations. We will look at two approaches to overcome the particle count limitations of DEM: investigate the scaling up of particle size; and combine FEA and DEM to look at problems of densely packed solids. We will explore regimes where DEM and FEA are applicable and establish a coupling methodology that can be further developed during phase II. To address the lack of an established methodology to determine the particle properties to best accurately model a given physical system, we will investigate several small scale experiments that can be used to characterize DEM models. The proposed work will advance the state-of-the-art in DEM. At the end of phase I we will show the feasibility of developing modeling approaches to overcome the main limitations of DEM.