2 resultados para Peanut Hypersensitivity
em Universidad de Alicante
Resumo:
Nowadays, the intensive use of Technology Information (TI) provide solutions to problems of the high population density, energy conservation and cities management. This produces a newest concept of the city, Smart City. But the inclusion of TI in the city brings associated new problems, specifically the generation of electromagnetic fields from the available and new technological infrastructures installed in the city that did not exist before. This new scenario produces a negative effect on a particular group of the society, as are the group of persons with electromagnetic hypersensitivity pathology. In this work we propose a system that would allow you to detect and prevent the continuous exposure to such electromagnetic fields, without the need to include more devices or infrastructure which would only worsen these effects. Through the use of the architecture itself and Smart City services, it is possible to infer the necessary knowledge to know the situation of the EMF radiation and thus allow users to avoid the areas of greatest conflict. This knowledge, not only allows us to get EMF current map of the city, but also allows you to generate predictions and detect future risk situations.
Resumo:
Today, faced with the constant rise of the Smart cities around the world, there is an exponential increase of the use and deployment of information technologies in the cities. The intensive use of Information Technology (IT) in these ecosystems facilitates and improves the quality of life of citizens, but in these digital communities coexist individuals whose health is affected developing or increasing diseases such as electromagnetic hypersensitivity. In this paper we present a monitoring, detection and prevention system to help this group, through which it is reported the rates of electromagnetic radiation in certain areas, based on the information that the own Smart City gives us. This work provides a perfect platform for the generation of predictive models for detection of future states of risk for humans.