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Work-Ready Electronics (WRE)
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Work-Ready Electronics (WRE), a project of the NSF Advanced Technological Education program, is designing and developing instructional modules that may be used independently or integrated with current electronics curricula in college programs. The modules address those topics, knowledge, and skills that have not been sufficiently covered by even the most popular electronics textbooks but are considered essential to technicians in today?s industry. On-line modules will provide instructor-led learning activities, self-paced study, guidance and specifications for lab experiments, performance assessments, and optional learning opportunities such as Internet research, collaborative projects, a...
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Evaluating Electric Vehicle...
This resource contains a report by the U.S. Department of Energy on electric car charging presenting customer behaviors and their impact on the grid. Under the...
Electric Vehicle Safety...
This resource contains electric vehicle 3D visualization modules created through collaboration between the CAAT and another ATE Center, the Center for Aviation...
Alternative Fuels First Responder...
These modules were originally presented to First Responders during a two-day workshop at the North Carolina Solar Center (now known as the North Carolina Clean...
CS Unplugged: Computer Science...
What if we could teach kids the basics of computer science and programming without sitting them down for hours in front of a computer? That was the idea that...
2015 CAAT Conference - Future Cars...
This resource contains the agenda and presentations from the 2015 Center for Advanced Automotive Technology (CAAT) Conference: Future Cars, Future Workforce....



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AMSER is funded by the National Science Foundation as part of the National Science Digital Library, and is being created by a team of project partners led by Internet Scout.
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Wind generators line the hilltops on a modern wind farm.
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Reverend Thomas Bayes (1702-1761) was the mathematician who first used probability inductively and established a mathematical basis for probability inference, a means of calculating, from the number of times an event has not occurred, the probability that it will occur in future trials.


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