The Development of a Real-Time Modular Architecture for the Control of UAV Teams

Biological barriers are essential for the maintenance of organ homeostasis and their dysfunction is responsible for many prevalent diseases. Advanced in vitro models of biological barriers have been developed through the combination of 3D cell culture techniques and organ-on-chip (OoC) technology. However, real-time monitoring of tissue function inside the OoC devices has been challenging, with most approaches relying on off-chip analysis and imaging techniques. In this study, we designed and fabricated a low-cost barrier-on-chip (BoC) device with integrated electrodes for the development and real-time monitoring of biological barriers. The integrated electrodes were used to measure transepithelial electrical resistance (TEER) during tissue culture, thereby quantitatively evaluating tissue barrier function. A finite element analysis was performed to study the sensitivity of the integrated electrodes and to compare them with conventional systems. As proof-of-concept, a full-thickness human skin model (FTSm) was grown on the developed BoC, and TEER was measured on-chip during the culture. After 14 days of culture, the barrier tissue was challenged with a benchmark irritant and its impact was evaluated on-chip through TEER measurements. The developed BoC with an integrated sensing capability represents a promising tool for real-time assessment of barrier function in the context of drug testing and disease modelling.

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Development of a national-scale real-time Twitter data mining pipeline for social geodata on the potential impacts of flooding on communities

10.31224/osf.io/5d3sr ◽

2019 ◽

Author(s):

Luke Barker ◽

CJA MacLeod

Keyword(s):

Data Mining ◽

At Risk ◽

Real Time ◽

Language Processing ◽

Environmental Data ◽

Data Sources ◽

Extreme Weather Events ◽

Modular Architecture ◽

National Scale ◽

Twitter Data

Social media, particularly Twitter, is increasingly used to improve resilience during extreme weather events/emergency management situations, including floods: by communicating potential risks and their impacts, and informing agencies and responders. In this paper, we developed a prototype national-scale Twitter data mining pipeline for improved stakeholder situational awareness during flooding events across Great Britain, by retrieving relevant social geodata, grounded in environmental data sources (flood warnings and river levels). With potential users we identified and addressed three research questions to develop this application, whose components constitute a modular architecture for real-time dashboards. First, polling national flood warning and river level Web data sources to obtain at-risk locations. Secondly, real-time retrieval of geotagged tweets, proximate to at-risk areas. Thirdly, filtering flood-relevant tweets with natural language processing and machine learning libraries, using word embeddings of tweets. We demonstrated the national-scale social geodata pipeline using over 420,000 georeferenced tweets obtained between 20 and 29th June 2016.

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