UTCI field measurements in an urban park in Florence (Italy)

The aim of this study is to evaluate human thermal comfort in different green area settings in the city of Florence by using the Universal Thermal Climate Index (UTCI). Field measurements of air temperature, solar radiation, relative humidity, wind speed and black globe thermometer were collected during hot summer days in various parts of Cascine Park, the biggest urban park in Florence (Italy). UTCI was evaluated over different surfaces (asphalt, gravel and grass) completely exposed to the sun or shaded by a large lime tree (Tilia × europaea). The results showed strong differences in UTCI values depending on the exposure to tree shade, while no significant difference was found among ground-cover materials when all surfaces were equally exposed to solar radiation. Future studies are needed to investigate the microclimatic effects of different tree species on UTCI

Two Planets, One Species: Does a Mission to Mars Alter the Balance in Favour of Human Enhancement?

In this chapter we examine the implications of a crewed mission to Mars, possible colonisation of the planet, and the wider implications this may have on genetic enhancement in both a terrestrial and space context. We consider the usage of both somatic and germ-line genetic engineering, and its potential impact on the evolution of Homo sapiens. We acknowledge that a mission to Mars may require the usage of such technologies if it is to be successful. Our investigation suggests that the use of such technologies might ultimately be linked with the transformation of our own species. We also consider projected timescales for the development of these genetic enhancements and the ethical questions raised by the possibility of speciation. Cooperation among spacefaring nations in this context and the development of norms for the use of such technologies is desirable

Operator awareness in human–robot collaboration through wearable vibrotactile feedback

In industrial scenarios, requiring human–robot collaboration, the understanding between the human operator and his/her robot coworker is paramount. On the one side, the robot has to detect human intentions, and on the other side, the human needs to be aware of what is happening during the collaborative task. In this letter, we address the first issue by predicting human behavior through a new recursive Bayesian classifier, exploiting head, and hand tracking data. Human awareness is tackled by endowing the human with a vibrotactile ring that sends acknowledgments to the user during critical phases of the collaborative task. The proposed solution has been assessed in a human–robot collaboration scenario, and we found that adding haptic feedback is particularly helpful to improve the performance when the human–robot cooperation task is performed by nonskilled subjects. We believe that predicting operator's intention and equipping him/her with wearable interface, able to give information about the prediction reliability, are essential features to improve performance in a human–robot collaboration in industrial environments