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RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.

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Title: RE-PLAN: An Extensible Software Architecture to Facilitate Disaster Response Planning.
Authors: O'Neill, Martin1, Mikler, Armin R.1, Indrakanti, Saratchandra1, Tiwari, Chetan1, Jimenez, Tamara1
Source: IEEE Transactions on Systems, Man & Cybernetics. Systems. Dec2014, Vol. 44 Issue 12, p1569-1583. 15p.
Subjects: Emergency management, Computer architecture, Hazard mitigation, Ad hoc computer networks, Feasibility studies, Computer software
Abstract: Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated. [ABSTRACT FROM AUTHOR]
Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.)
Database: Engineering Source
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  Data: <searchLink fieldCode="DE" term="%22Emergency+management%22">Emergency management</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+architecture%22">Computer architecture</searchLink><br /><searchLink fieldCode="DE" term="%22Hazard+mitigation%22">Hazard mitigation</searchLink><br /><searchLink fieldCode="DE" term="%22Ad+hoc+computer+networks%22">Ad hoc computer networks</searchLink><br /><searchLink fieldCode="DE" term="%22Feasibility+studies%22">Feasibility studies</searchLink><br /><searchLink fieldCode="DE" term="%22Computer+software%22">Computer software</searchLink>
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  Data: Computational tools are needed to make data-driven disaster mitigation planning accessible to planners and policymakers without the need for programming or geographic information systems expertise. To address this problem, we have created modules to facilitate quantitative analyses pertinent to a variety of different disaster scenarios. These modules, which comprise the REsponse PLan ANalyzer framework, may be used to create tools for specific disaster scenarios that allow planners to harness large amounts of disparate data and execute computational models through a point-and-click interface. Bio-E, a user-friendly tool built using this framework, was designed to develop and analyze the feasibility of ad hoc clinics for treating populations following a biological emergency event. In this paper, the design and implementation of the RE-PLAN framework are described, and the functionality of the modules used in the Bio-E biological emergency mitigation tools are demonstrated. [ABSTRACT FROM AUTHOR]
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  Data: <i>Copyright of IEEE Transactions on Systems, Man & Cybernetics. Systems is the property of IEEE and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.)
PLink https://search.ebscohost.com/login.aspx?direct=true&site=eds-live&db=egs&AN=100026790
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        Value: 10.1109/TSMC.2014.2332137
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        Text: English
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