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- API data.nasa.gov | Last Updated 2020-01-29T03:56:56.000Z
We have completed our grant reporting period. The major contributions of our research effort are outlined below: Specific Aim 1: Statistical Shoulder Injury Analysis. The first specific aim is to analyze data for correlations between anthropometry, space suit components, and shoulder injury. Four hypotheses were proposed to relate injury to 1) body morphologies, 2) space suit HUT components, 3) training variables, and 4) previous injury. Each hypothesis was confirmed, since for both models variables for each of the first three hypotheses were identified and record of previous injury was associated with the Neutral Buoyancy Laboratory (NBL) model. The major contributions of this work are to: 1) Add quantitative statistical analysis to the causal mechanisms of injury found in the literature. 2) Provide a framework for identifying relevant predictor variables related to injury given the small number of data points, large number of predictor variables, and the differences in their distributions. 3) Identify variables related to injury which can be addressed and resolved through operational changes to training, suit design and accommodation, and identification of higher risk subjects given previous medical history. 4) Propose future areas of study for which additional data may continue to be collected and analyzed, such as HUT sizing information as related to clearance anthropometry. These contributions address the current gap in our understanding of the causal mechanisms of injury. Although HUT style has been reported as a major cause based on anecdotal evidence (Williams and Johnson 2003, Strauss 2004), it has not been until recently that this causal mechanism has been quantitatively evaluated (Scheuring, McCullouch et al., 2012). This research corroborates these findings, but expands upon them to include additional relevant factors not previously explored. It also includes other shoulder incidents, which, although not defined as medical injuries, have had negative impact on crew comfort and health, as well as impacting an astronaut’s operational availability. This work also supports the conclusions reached by Williams and Johnson (2003) regarding the import of the training environment as a contributory factor, but this is the first quantitative assessment of the impacts of training frequency and recovery. Finally, it supports that suit fit is essential to achieve the optimal working environment (Benson and Rajulu 2009, Gast and Moore 2010) and allows future designs to pinpoint the most relevant anthropometric dimensions for suit fit accommodation. This work provides a quantitative analysis through data mining grounded in our historical understanding of the use of the EMU and NBL training environment. The remainder of this research allows a look forward into how additional data collection on human-space suit interaction can help prevent the occurrence of future injury and discomfort. Specific Aim 2: Experimental Evaluation of Human-space Suit Interaction. Development of a wearable pressure sensing garment. The novel Polipo low-pressure sensing system for extreme environments achieved here has many advantages. With the Polipo human-suit interaction can be measured for the first time through dynamic movement. It can accurately measure low-pressures against the body over underneath the soft-goods. The system of 12 sensors is transferrable between many different people, creating an independent stand-alone pressure-sensing system. Sensors can easily be changed to allow for improved designs or to accommodate different target pressures. The wiring was intentionally designed to achieve the best trade-off between flexibility, resistance, and stretch ability. The system achieves near shirt-sleeve mobility as sensors are moved to accommodate users. It can also be used in conjunction with a high-pressure sensing mat placed over the shoulder to measure loading between the person and HUT. The electronics architect
- API data.oregon.gov | Last Updated 2021-06-10T14:32:26.000Z
Updated 4/2/2018 - New businesses registered with the Secretary of State Corporation Division during the previous month. Data is updated on the first working day of each month.
- API data.usaid.gov | Last Updated 2021-02-26T19:47:17.000Z
The Water, Sanitation, and Hygiene for Health (W4H) Activity is a five-year (February 2015–September 2020) cooperative agreement implemented by Global Communities with the goal to accelerate sustainable improvement in water and sanitation access and improve hygiene behaviors in 15 target Metropolitan, Municipal, and District Assemblies (MMDAs). USAID/Ghana commissioned the USAID Water, Sanitation and Hygiene Partnerships and Learning for Sustainability (WASHPaLS) to undertake this performance evaluation at the start of the activity’s final year. The primary objectives of the evaluation were to: 1) inform both the implementing partner and USAID/Ghana if the approaches employed by Global Communities are successfully meeting the activity’s goal of expanding and ensuring sustainable access to water and sanitation services; 2) inform the need for any course corrections or reemphasis of priorities to the activity in its final year of implementation; and 3) assess the approach to and progress of implementation to inform future USAID/Ghana water, sanitation, and hygiene (WASH) programming. The evaluation’s emphasis on ensuring sustainability of the interventions is in line with the technical proposal, which notes that “W4H has been designed to foster long-term sustainable change in the way that communities and government interact to achieve gains in WASH.” The evaluation did not focus on review, validation, or verification of GC targets under the cooperative agreement nor on the internal organization (finance, management, and deployment of staff) of delivery.
- API data.cityofchicago.org | Last Updated 2019-11-26T21:53:28.000Z
The Annual Appropriation Ordinance is the final City operating budget as approved by the City Council. It reflects the City’s operating budget at the beginning of the fiscal year on January 1. This dataset details the budgeted expenditures in the Ordinance and identifies them by department, appropriation account, and funding type: Local, Community Development Block Grant Program (CDBG), and other Grants. “Local” funds refer to those line items that are balanced with locally generated revenue sources, including but not limited to the Corporate Fund, Water Fund, Midway and O’Hare Airport funds, Vehicle Tax Fund, Library Fund and General Obligation Bond funds. For more information about the budget process, visit the Budget Documents page: http://j.mp/lPotWf.
- API data.medicaid.gov | Last Updated 2018-07-23T15:15:31.000Z
This dataset reports summary state-by-state total expenditures by program for the Medicaid Program, Medicaid Administration and CHIP programs. These state expenditures are tracked through the automated Medicaid Budget and Expenditure System/State Children's Health Insurance Program Budget and Expenditure System (MBES/CBES). For more information, visit https://medicaid.gov/medicaid/finance/state-expenditure-reporting/expenditure-reports/index.html.
- API data.lacity.org | Last Updated 2021-06-09T19:51:07.000Z
Los Angeles International Airport - Passenger Statistic By Terminal
- API data.medicaid.gov | Last Updated 2021-04-21T18:25:43.000Z
Drug utilization data are reported by states for covered outpatient drugs that are paid for by state Medicaid agencies since the start of the Medicaid Drug Rebate Program. The data includes state, drug name, National Drug Code, number of prescriptions and dollars reimbursed. Data descriptions are available on Medicaid.gov: https://www.medicaid.gov/medicaid/prescription-drugs/state-drug-utilization-data/state-drug-utilization-data-faq/index.html
- API data.honolulu.gov | Last Updated 2012-07-24T19:20:13.000Z
State Lessee Land on the island of Oahu
- API opendata.utah.gov | Last Updated 2020-06-19T21:42:12.000Z
- API data.edd.ca.gov | Last Updated 2020-07-07T22:48:07.000Z
The Occupational Employment Statistics (OES) Survey collects data from a sample of establishments and calculates employment and wage estimates by occupation, industry, and geographic area. The semiannual survey covers all non-farm industries. Data are collected by the Employment Development Department in cooperation with the Bureau of Labor Statistics, US Department of Labor. The OES Program estimates employment and wages for over 800 occupations from an annual sample of approx. 34,000 California employers. It also produces employment and wage estimates for statewide, Metropolitan Statistical Areas (MSAs), and Balance of State areas. Estimates are a snapshot in time and should not be used as a time series.