Objective: The study aimed to examine the experience of disaster healthcare workers with simulation training using the Psychological First Aid (PFA) mobile app. Methods: This study was designed using qualitative research methodology with focus group interviews. The participants were 19 disaster healthcare workers from community mental health service centers who attended disaster simulation training in flood, fire, or leakage of hazardous chemicals. Before the simulation, participants were provided the PFA mobile app and allowed to practice the PFA techniques to apply them during the simulation. Data were collected through focus group interviews and qualitatively analyzed using the content analysis method. Results: The findings were divided into 6 categories: experience in realistic disaster situations, satisfaction with education methods using a mobile app, effectiveness of the PFA app in disaster relief, confidence in disaster relief by integrating experience and knowledge of the PFA app, self-reflection as a disaster healthcare worker, and identifying limitations and making developmental suggestions. Conclusions: Based on the participants’ developmental proposals in this study, the disaster simulation training, incorporating improvements in the disaster simulation training and the PFA app features, will serve as a new framework for disaster support education and systematic mental health services to survivors by disaster healthcare workers.

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A smart city is a future city that enables citizens to enjoy Information and Communication Technology (ICT) based smart services with any device, anytime, anywhere. It heavily utilizes Internet of Things. It includes many video cameras to provide various kinds of services for smart cities. Video cameras continuously feed big video data to the smart city system, and smart cities need to process the big video data as fast as it can. This is a very challenging task because big computational power is required to shorten processing time. This paper introduces UTOPIA Smart Video Surveillance, which analyzes the big video images using MapReduce, for smart cities. We implemented the smart video surveillance in our middleware platform. This paper explains its mechanism, implementation, and operation and presents performance evaluation results to confirm that the system worked well and is scalable, efficient, reliable, and flexible.

In the paper, we describe the design and implementation of middleware for NCW, which provides automatic military operation services based on the inferred battlefield contexts using ubiquitous computing. Middleware for NCW is composed of two layers which support automatic military operations by providing a decision support system using context-awareness service in ubiquitous computing for various military applications. For the traditional Platform Centric Warfare model, we have developed middleware consists of two layers for Network Centric Warfare based on ubiquitous computing: Context-aware Reasoning Layer and Ubiquitous Main Layer. Context-aware Reasoning Layer provides the intelligent context by processing data obtained through military sensor network. Executing specific military operations, Ubiquitous Main Layer generates proper military operations and controls military devices which makes applications or services used everywhere such as display, Army PC, or MSP (Military Smart Phone) with the choice of a number of input devices by provides the automatic computing environment.