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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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Hospitals
data.memphistn.gov | Last Updated 2019-10-25T20:04:39.000ZThe data describes the hospital locations in Memphis (and Collierville) with 24 hour Emergency Departments. Each hospital location listed is a facility the Memphis Fire Department's Emergency Medical Services (EMS) Bureau provides transportation services to when patients require immediate medical attention beyond the EMS scope of practice.
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Child and Adult Care Food Program Participation
healthdata.gov | Last Updated 2024-01-30T04:00:40.000ZThe Child and Adult Care Food Program (CACFP) is a nutrition education and meal reimbursement program helping providers serve nutritious and safely prepared meals and snacks to children and adults in day care settings. This dataset includes the names and locations of participating day care sites and whether or not the site is Breastfeeding Friendly Certified with CACFP, participating in the Eat Well Play Hard in Child Care Settings (EWPHCCS) project, or participating in the Eat Well Play Hard in Day Care Homes (EWPHDCH) project. This dataset excludes Child and Adult Care Food Program participation provided at homeless shelters and legally-exempt day care home providers. Not all counties in NYS are serviced by the grantees implementing the project EWPHCSS. The EWPHDCH project is currently limited to the areas served by the contracted agencies. The Child and Adult Care Food Program dataset is related to the Child Care Related Programs dataset on the www.Open.ny.gov website, however this dataset includes additional nutrition information. The Office of Children and Family Services (OCFS) is currently working to update the Child Care Related Programs dataset on a more frequent schedule with the OPEN NY team. OCFS and DOH will then be able to synchronize in the near future. Temporarily, DOH has omitted addresses for regulated child care providers that provide home care since this information is available on www.Open.ny.gov by using this link: https://data.ny.gov/Human-Services/Child-Care-Regulated-Programs/cb42-qumz. For more information, please visit http://www.health.ny.gov/prevention/nutrition/cacfp/. The "About" tab contains additional details concerning this dataset.
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Nano Dust Analyzer Project
data.nasa.gov | Last Updated 2020-01-29T04:54:41.000Z<p> We propose to develop a new highly sensitive instrument to confirm the existence of the so-called nano-dust particles, characterize their impact parameters, and measure their chemical composition. Simultaneous theoretical studies will be used to derive the expected&nbsp; mass and velocity ranges of these putative particles to formulate science and measurement requirements for the future deployment of&nbsp; the proposed Nano-Dust Analyzer (NDA)&nbsp;</p> <p> Early dust instruments onboard Pioneer 8 and 9 and Helios spacecraft detected a flow of submicron sized dust particles coming from the direction of the Sun. These particles originate in the inner solar system from mutual collisions among meteoroids and move on&nbsp; hyperbolic orbits that leave the Solar System under the prevailing radiation pressure force. Later dust instruments with higher&nbsp; sensitivity had to avoid looking toward the Sun because of interference from the solar wind and UV radiation and thus contributed&nbsp; little to the characterization of the dust stream. The one exception is the Ulysses dust detector that observed escaping dust particles&nbsp; high above the solar poles, which confirm the suspicion that charged nanometer sized dust grains are carried to high heliographic&nbsp; latitudes by electromagnetic interactions with the Interplanetary Magnetic Field (IMF). Recently, the STEREO WAVES instruments&nbsp; recorded a large number of intense electric field signals, which were interpreted as impacts from nanometer sized particles striking the&nbsp; spacecraft with velocities of about the solar wind speed. This high flux and strong spatial and/or temporal variations of nanometer&nbsp; sized dust grains at low latitude appears to be uncorrelated with the solar wind properties. This is a mystery as it would require that&nbsp; the total collisional meteoroid debris inside 1 AU is cast in nanometer sized fragments. The observed fluxes of inner-source pickup ions&nbsp; also point to the existence of a much enhanced dust population in the nanometer size range.&nbsp;</p> <p> This new heliospherical phenomenon of nano-dust streams may have consequences throughout the planetary system, but as of yet no dust instrument exists that could be used to shed light on their properties. &nbsp;We propose to develop a dust analyzer capable to detect and&nbsp; analyze these mysterious dust particles coming from the solar direction and to embark upon complementary theoretical studies to&nbsp; understand their characteristics. The instrument is based on the Cassini Dust Analyzer (CDA) that has analyzed the composition of&nbsp; nanometer sized dust particles emanating from the Jovian and Saturnian systems but could not be pointed towards the Sun. By&nbsp; applying technologies implemented in solar wind instruments and coronagraphs a highly sensitive dust analyzer will be developed and&nbsp; tested in the laboratory. The dust analyzer shall be able to characterize impact properties (impact charge and energy distribution of&nbsp; ions from which mass and speed of the impacting grains may be derived) and chemical composition of individual nanometer sized&nbsp; particles while exposed to solar wind and UV radiation. The measurements will enable us to identify the source of the dust by&nbsp; comparing their elemental composition with that of larger micrometeoroid particles of cometary and asteroid origin and will reveal&nbsp; interaction of nano-dust with the interplanetary medium by investigating the relation of the dust flux with solar wind and IMF&nbsp; properties.&nbsp;</p> <p> Complementary theoretically studies will be performed to understand the characteristics of nano-dust particles at 1 AU to answer the&nbsp; following questions:&nbsp; - What is the speed range at which nanometer sized particles impact
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Fire / EMS Heat Map FY12 to Present
data.cityofgainesville.org | Last Updated 2023-09-28T14:19:17.000ZFire / EMS response data for FY2012 up to the most current month available.
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Child and Adult Care Food Programs (CACFP) – Child Care At-Risk Centers – Meal Reimbursement – Program Year 2017-2018
data.texas.gov | Last Updated 2021-06-18T18:16:46.000Z<b>About the Agency</b><br> The Texas Department of Agriculture administers 12 U.S. Department of Agriculture nutrition programs in Texas including the National School Lunch and School Breakfast Programs and the Child and Adult Care Food Program (CACFP). TDA’s Food and Nutrition division provides technical assistance and training resources to partners operating the programs and oversees the USDA reimbursements they receive to cover part of the cost associated with serving food in their facilities. By working to ensure these partners serve nutritious meals and snacks, the division adheres to its mission — <i>Feeding the Hungry and Promoting Healthy Lifestyles.</i><p> <i><b>For more information on these programs, please visit our <a href=http://www.SquareMeals.org target="_blank">website</a>.</b></i><br> <b>A detailed list of TDA Food and Nutrition datasets and data fields available on the Texas Open Data Portal can be downloaded as a PDF <a href=https://www.squaremeals.org/Portals/8/files/PIR/TDA%20Food%20and%20Nutrition%20Data%20Fields%20and%20Definitions.pdf target="_blank">here</a>.</b></i><p> <b>About the Dataset</b><br> This data set contains claims information for <b>meal reimbursement for sites participating as At-Risk Child Care Centers in CACFP for program year 2017-2018.</b> The CACFP program year begins October 1 and ends September 30. <p> <b>This dataset only represents claims submitted by CACFP sites operating as At-Risk Child Care Centers.</b> Sites can participate in multiple CACFP sub-programs. For reimbursement data on CACFP participants operating as Day Care Homes, Adult Day Care Centers, Child Care Centers, Head Start centers, emergency shelters, or centers providing care for students outside school hours, please refer to the corresponding “Child and Adult Care Food Programs (CACFP) – Meal Reimbursement” dataset for that sub-program available on the State of Texas Open Data Portal. <p> An overview of <b>all CACFP data available</b> on the Texas Open Data Portal can be found at our <b><a href=https://data.texas.gov/stories/s/iekx-7mdi target="_blank">TDA Data Overview - Child and Adult Care Food Programs</a></b> page.<p> An overview of <b>all TDA Food and Nutrition data available</b> on the Texas Open Data Portal can be found at our <b><a href=https://data.texas.gov/stories/s/TDA-Data-Overview-Food-and-Nutrition-Programs-Open/nk79-w2cs/ target="_blank">TDA Data Overview - Food and Nutrition Open Data</a></b> page. <p> <b>About Dataset Updates</b><br> TDA aims to post new program year data by December 15 of the active program year. <b>Participants have 60 days to file monthly reimbursement claims.</b> Dataset updates will occur monthly until 90 days after the close of the program year. After 90 days from the close of the program year, the dataset will be updated at six months and one year from the close of program year before becoming archived. Archived datasets will remain published but will not be updated. Any data posted during the active program year is subject to change.
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State Park Trails
data.ny.gov | Last Updated 2024-04-24T19:08:18.000ZThe New York State Office of Parks, Recreation and Historic Preservation (OPRHP) oversees more than 250 state parks, historic sites, recreational trails, golf courses, boat launches and more, encompassing nearly 350,000 acres, that are visited by 74 million people annually. These facilities contribute to the economic vitality and quality of life of local communities and directly support New York’s tourism industry. Parks also provide a place for families and children to be active and exercise, promoting healthy lifestyles. The agency is responsible for the operation and stewardship of the state park system as well as advancing a statewide parks, historic preservation, and open space mission. This dataset is a shapefile of the mapped trails in NYS Parks.
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BHO MH Engagement in Care: 2010-2014
data.ny.gov | Last Updated 2019-06-10T18:02:12.000ZThe Behavioral Health Organization (BHO) initiative oversees the transition to managed care for Medicaid recipients who receive mental health (MH) and substance use disorder (SUD) services in New York State. The metrics emphasize improving rates of timely follow-up treatment post discharge, timely filling of appropriate medication prescriptions post discharge, and reducing rates of readmission.The BHO MH Engagement in Care dataset is designed to assess the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving two or more outpatient mental health visits within thirty days of discharge and the degree to which individuals discharged from mental health inpatient treatment engage in outpatient treatment post discharge where "engagement" is defined as receiving four or more outpatient mental health visits within 60 days of discharge. The year 2015 saw the conclusion of the first phase of the Behavioral Health Organization initiative (BHO). A new Behavioral Health Managed Care Transition phase II is underway. The data contained in the BHO metrics span 2010 to 2014, using the 2010 calendar year for a baseline. Earlier in the program (2011‐2012) the metrics were calculated quarterly and on a year‐to‐date basis, later in (2013‐2014), New York State Office of Mental Health opted for semi‐annual and year‐to‐date aggregations.
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Vital Signs: Time in Congestion - Corridor (Updated October 2018)
data.bayareametro.gov | Last Updated 2018-10-24T00:31:33.000ZVITAL SIGNS INDICATOR Time Spent in Congestion (T7) FULL MEASURE NAME Time Spent in Congestion LAST UPDATED October 2018 DATA SOURCE MTC/Iteris Congestion Analysis No link available CA Department of Finance Forms E-8 and E-5 http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-8/ http://www.dof.ca.gov/Forecasting/Demographics/Estimates/E-5/ CA Employment Division Department: Labor Market Information http://www.labormarketinfo.edd.ca.gov/ CONTACT INFORMATION vitalsigns.info@bayareametro.gov METHODOLOGY NOTES (across all datasets for this indicator) Time spent in congestion measures the hours drivers are in congestion on freeway facilities based on traffic data. In recent years, data for the Bay Area comes from INRIX, a company that collects real-time traffic information from a variety of sources including mobile phone data and other GPS locator devices. The data provides traffic speed on the region’s highways. Using historical INRIX data (and similar internal datasets for some of the earlier years), MTC calculates an annual time series for vehicle hours spent in congestion in the Bay Area. Time spent in congestion is defined as the average daily hours spent in congestion on Tuesdays, Wednesdays and Thursdays during peak traffic months on freeway facilities. This indicator focuses on weekdays given that traffic congestion is generally greater on these days; this indicator does not capture traffic congestion on local streets due to data unavailability. This congestion indicator emphasizes recurring delay (as opposed to also including non-recurring delay), capturing the extent of delay caused by routine traffic volumes (rather than congestion caused by unusual circumstances). Recurring delay is identified by setting a threshold of consistent delay greater than 15 minutes on a specific freeway segment from vehicle speeds less than 35 mph. This definition is consistent with longstanding practices by MTC, Caltrans and the U.S. Department of Transportation as speeds less than 35 mph result in significantly less efficient traffic operations. 35 mph is the threshold at which vehicle throughput is greatest; speeds that are either greater than or less than 35 mph result in reduced vehicle throughput. This methodology focuses on the extra travel time experienced based on a differential between the congested speed and 35 mph, rather than the posted speed limit. To provide a mathematical example of how the indicator is calculated on a segment basis, when it comes to time spent in congestion, 1,000 vehicles traveling on a congested segment for a 1/4 hour (15 minutes) each, [1,000 vehicles x ¼ hour congestion per vehicle= 250 hours congestion], is equivalent to 100 vehicles traveling on a congested segment for 2.5 hours each, [100 vehicles x 2.5 hour congestion per vehicle = 250 hours congestion]. In this way, the measure captures the impacts of both slow speeds and heavy traffic volumes. MTC calculates two measures of delay – congested delay, or delay that occurs when speeds are below 35 miles per hour, and total delay, or delay that occurs when speeds are below the posted speed limit. To illustrate, if 1,000 vehicles are traveling at 30 miles per hour on a one mile long segment, this would represent 4.76 vehicle hours of congested delay [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 35 miles per hour) = 33.33 vehicle hours – 28.57 vehicle hours = 4.76 vehicle hours]. Considering that the posted speed limit on the segment is 60 miles per hour, total delay would be calculated as 16.67 vehicle hours [(1,000 vehicles x 1 mile / 30 miles per hour) - (1,000 vehicles x 1 mile / 60 miles per hour) = 33.33 vehicle hours – 16.67 vehicle hours = 16.67 vehicle hours]. Data sources listed above were used to calculate per-capita and per-worker statistics. Top congested corridors are ranked by total vehicle hours of delay, meaning that the highlighted corridors reflect a combination of slow speeds and heavy t
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U.S. State and Territorial Stay-At-Home Orders: March 15, 2020 – August 15, 2021 by County by Day
healthdata.gov | Last Updated 2023-07-26T01:25:18.000ZState and territorial executive orders, administrative orders, resolutions, and proclamations are collected from government websites and cataloged and coded using Microsoft Excel by one coder with one or more additional coders conducting quality assurance. Data were collected to determine when individuals in states and territories were subject to executive orders, administrative orders, resolutions, and proclamations for COVID-19 that require or recommend people stay in their homes. Data consists exclusively of state and territorial orders, many of which apply to specific counties within their respective state or territory; therefore, data is broken down to the county level. These data are derived from the publicly available state and territorial executive orders, administrative orders, resolutions, and proclamations (“orders”) for COVID-19 that expressly require or recommend individuals stay at home found by the CDC, COVID-19 Community Intervention and At-Risk Task Force, Monitoring and Evaluation Team & CDC, Center for State, Tribal, Local, and Territorial Support, Public Health Law Program from March 15, 2020 through August 15, 2021. These data will be updated as new orders are collected. Any orders not available through publicly accessible websites are not included in these data. Only official copies of the documents or, where official copies were unavailable, official press releases from government websites describing requirements were coded; news media reports on restrictions were excluded. Recommendations not included in an order are not included in these data. These data do not include mandatory business closures, curfews, or limitations on public or private gatherings. These data do not necessarily represent an official position of the Centers for Disease Control and Prevention.