7th Annual TIDES Technology Field Demonstration

Event Organisers: 

The Center for Technology and National Security Policy (CTNSP) at National Defense University (NDU)

Venue City: 

Washington

Venue: 

The Center for Technology and National Security Policy (CTNSP) at National Defense University (NDU)

Date: 

01/10/2013 to 04/10/2013

Language of event: 

English

Description: 

The 7th annual TIDES Fall Field Demonstration will take place in Washington, DC at the National Defense University (NDU) campus from Tuesday, October 1st to Friday, October 4th.

This event will be live all week, so please feel free to visit anytime during normal working hours to interact, see, and discuss the policies, practices, technologies, and organizations that affect Humanitarian Assistance/Disaster Relief, Stabilization and Reconstruction, Building Partnership Capacity, and Defense Support to Civil Authorities. All activities will be independent of the power grid, and communications will be live.

This event will feature active demonstrations in the following scenarios:

  • Foreign disaster preparedness and humanitarian assistance. This will include integrated concepts for "survivable and sustainable" approaches that can be used with partner nations' governments and private sectors.
  • Disaster response in the US and Defense Support of Civil Authorities (DSCA). This will involve DHS/FEMA/NORTHCOM/USCG links, ties to commercial supply chains, empowering citizens in austere environments, and enhancing system resilience.
  • Building Integrated Partnership Capacity in austere environments. The focus will be on cross-cutting solutions that can be sustained by local populations and on information flows designed to reduce the likelihood of violent outbreaks in stressed situations.
  • Post-war stabilization and reconstruction. Many individuals and organizations with recent operational experience will be onsite to discuss approaches and share ideas in areas like sustainablesocio-economic development, green-white info sharing and "sensemaking" from large amounts of data.
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