第二阶段 - HazPrep 工人培训 - 社区风险概况-猫眼课题宝

第二阶段 - HazPrep 工人培训 - 社区风险概况

批准号：

9753672

负责人：

Henry Ryng

金额：

$19.99万

依托单位：

INXSOL, LLC

依托单位国家：

美国

项目类别：

财政年份：

2017

资助国家：

美国

项目状态：

已结题

起止时间：

2017-06-01 至 2021-05-31

来源：

https://reporter.nih.gov/project-details/9753672

关键词：

Accidents Achievement Address Algorithms Authorization documentation Awareness Big Data Businesses Case Study Code Coin Communities Community of Practice Containment Credentialing Data Databases Effectiveness Emergency Situation Emergency response Event Excision Exercise Feasibility Studies Feedback Feeds Fees Fire - disasters Formulation Fostering Frequencies Generations Hazardous Substances Hazardous Waste Health Individual Ingestion Injury Internet Knowledge Learning Legal patent Libraries Location Machine Learning Maintenance Metadata Methods Modeling National Institute of Environmental Health Sciences Occupations Pathway interactions Performance Phase Plants Privatization Process Production Recording of previous events Records Reporting Resources Risk S Phase Safety Site Source System Systems Integration Techniques Technology Testing Time Training Transportation Update Work base cloud based commercialization crowdsourcing data archive design e-commerce emergency service responder experience feeding first responder flexibility handheld mobile device hazard innovation learning strategy machine learning algorithm off-patent personalized learning portability programs prototype repaired skills social learning tool wasting web site

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PROJECT SUMMARY/ABSTRACT A worker's personal hazard profile (PHP) is a function of hazards present and his/her exposure level to those hazards. Workers with an elevated level of risk are those who can be engaged in activities related to - or working around - hazardous materials, waste generation, removal, containment, transportation, and emergency response. inXsol’s Phase I feasibility study verified appeal and effectiveness of a new form of crowdsourced social learning platform. Our approach using cloud technology creates a dynamically growing library of incidents/scenarios, highly personalized (occupation/task/geo) risk profile and generates learning activities to train on risk awareness and mitigation techniques. The Phase II proposal includes implementation of an innovative use of big data algorithms for community profiles and fusion with PHP allowing for targeted and personalized training completing the HazPrep prototype developed and exercised by our beta test team in Phase I.

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