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Livestock industry is daily producing large amounts of multi-scale data (pathogen-, animal-, site-, system-, regional- level) from different sources such as diagnostic laboratories, trade and production records, management and environmental monitoring systems; however, all these data are still presented and used separately and are largely infra-utilized to timely (i.e., near real-time) inform livestock health decisions. Recent advances in the automation of data capture, standardization, multi-scale integration and sharing/communication (i.e. The Internet Of Things) as well as in the development of novel data mining analytical and visualization capabilities specifically adapted to the livestock industry are dramatically changing this paradigm. As a result, we expect vertical advances in the way we prevent and manage livestock diseases both locally and globally. Our team at the Center for Animal Disease Modeling and Surveillance (CADMS), in collaboration with researchers at Iowa State University and industry leaders at Boehringer Ingelheim and GlobalVetLINK have been working in an exceptional research-industry partnership to develop key data connections and novel Big Data capabilities within the Disease BioPortal (http://bioportal.ucdavis.edu/). This web-based platform includes automation of diagnostic interpretations and facilitates the combined analysis of health, production and trade data using novel space-time-genomic visualization and data mining tools. Access to confidential databases is individually granted with different levels of secure access, visualization and editing capabilities for participating producers, labs, veterinarians and other stakeholders. Each user can create and share customized dashboards and reports to inform risk-based, more cost-effective, decisions at site, system or regional level. Here we will provide practical examples of applications in the swine, poultry and aquaculture industries. We hope to contribute to the more coordinated and effective prevention and control of infectious diseases locally and globally.
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Situational Strategic Awareness Monitoring Surveillance System—Microcomputer and Microcomputer Clustering used for Intelligent, Economical, Scalable, and Deployable Approach for Safeguarding Materials
The principal objective of this research is to create a system that is quickly deployable, scalable, adaptable, and intelligent and provides cost-effective surveillance, both locally and globally. The intelligent surveillance system should be capable of rapid implementation to track (monitor) sensitive materials, i.e., radioactive or weapons stockpiles and person(s) within rooms, buildings, and/or areas in order to predict potential incidents proactively (versus reactively) through intelligence, locally and globally. The system will incorporate a combination of electronic systems that include commercial and modifiable off-the-shelf microcomputers to create a microcomputer cluster which acts as a mini supercomputer which leverages real-time data feed if a potential threat is present. Through programming, software, and intelligence (artificial intelligence, machine learning, and neural networks), the system should be capable of monitoring, tracking, and warning (communicating) the system observer operations (command and control) within a few minutes when sensitive materials are at potential risk for loss. The potential customer is government agencies looking to control sensitive materials and/or items in developing world markets intelligently, economically, and quickly.
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- Award ID(s):
- 1842577
- PAR ID:
- 10185299
- Date Published:
- Journal Name:
- The Journal of imaging science and technology
- Volume:
- 63
- Issue:
- 6
- ISSN:
- 1062-3701
- Page Range / eLocation ID:
- 060408-1–060408-10
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
-
Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.2352/J.ImagingSci.Technol.2019.63.6.060408
