Geo-referencing livestock farms as tool for studying cystic echinococcosis epidemiology in cattle and water buffaloes from southern Italy.

Cystic echinococcosis (CE), caused by the larval stages of the tapeworm Echinococcus granulosus, is known to be one of the most important parasitic infection in livestock worldwide and one of the most widespread zoonoses known. In the present study, we used a geographical information system (GIS) to study the spatial structure of livestock (cattle, water buffaloes and sheep) populations to gain a better understanding of the role of sheep as reservoir for the transmission of CE to cattle and water buffaloes. To this end, a survey on CE in cattle and water buffaloes from the Campania region of southern Italy was conducted and the geo-referenced results linked to the regional farm geo-referenced data within a GIS. The results showed a noteworthy prevalence of CE in cattle and water buffalo farms (overall prevalence = 18.6%). The elaboration of the data with a GIS approach showed a close proximity of the bovine and/or water buffalo CE positive farms with the ovine farms present in the study area, thus giving important information on the significance of sheep and free-ranging canids in the transmission cycles of CE in relation to cattle and water buffaloes. The significantly higher prevalence found in cattle as compared to water buffalo farms (20.0% versus 12.4%) supports the key role of sheep in the CE transmission; indeed, within the 5 km radius buffer zones constructed around the cattle farms positive for CE, a higher number of (potentially infected) sheep farms were found compared to those found within the buffer zones around the water buffalo farms. Furthermore, the average distances between the sheep and cattle farms falling in the same buffer zones were significantly lower than those between the sheep and water buffalo farms. We emphasize that the use of GIS is a novel approach to further our understanding of the epidemiology and control of CE and we encourage other groups to make use of it

Socially assistive robots : a comprehensive approach to extending independent living

Demographic developments have challenged our research on how to assist elderly people by using robots. The KSERA (Knowledgeable SErvice Robots for Aging) project integrates smart home technology and a socially-assistive robot to extend independent living for elderly people, in particular those with COPD (Chronic Obstructive Pulmonary Disease). The social robot is the most visible component of the system playing the role of communication interface between the elderly, the smart home, and the external world. The robot’s behavior is determined in part by sensor information gathered through the smart home. To ensure user acceptance, we used user-centered design to implement the robot’s behavior. This paper describes the KSERA system, how it was developed based on user needs, treatment plans, and lab studies, and how we validated the approach through user studies and field trials. The key enabling technologies for successful socially-assistive robots include person- and self-localization abilities, person-aware navigation, speech recognition and generation, robot gestures, emulated emotions, eye contact and joint attention, and audio-video communication with family members and care givers

Socially assistive robots : a comprehensive approach to extending independent living

Demographic developments have challenged our research on how to assist elderly people by using robots. The KSERA (Knowledgeable SErvice Robots for Aging) project integrates smart home technology and a socially-assistive robot to extend independent living for elderly people, in particular those with COPD (Chronic Obstructive Pulmonary Disease). The social robot is the most visible component of the system playing the role of communication interface between the elderly, the smart home, and the external world. The robot’s behavior is determined in part by sensor information gathered through the smart home. To ensure user acceptance, we used user-centered design to implement the robot’s behavior. This paper describes the KSERA system, how it was developed based on user needs, treatment plans, and lab studies, and how we validated the approach through user studies and field trials. The key enabling technologies for successful socially-assistive robots include person- and self-localization abilities, person-aware navigation, speech recognition and generation, robot gestures, emulated emotions, eye contact and joint attention, and audio-video communication with family members and care givers