摘要：The increasing pollution in the atmospheric layer has meant world-wide temperature variations, causing the melting of icecaps and floods, among other environmental factors. This change in temperature has been mainly caused by the indiscriminate emission of CO2, especially due to the rising number of vehicles in circulation. Researchers have identified that, among other types of fuel, diesel has the highest level of CO2 emission. Hence the need for the development of biodiesel, produced from oleaginous plants, aimed at reducing the emission of this harmful gas into the atmosphere, besides using renewable resources. However, as in any automation process, it is necessary to have sensors, actuators, and controllers, which together perform the automation and control of the production process. Besides that, there are other process variables to be accounted for, such as temperature, flow, and level. Considering such concept, and within the academic context, the creation process of a mini biodiesel plant will be described.

摘要：Pervasive and ubiquitous environments must handle the detection and management of users, devices and services, while guaranteeing the privacy of both the users and the environment itself. Current techniques for handling privacy found in the literature treating the subject in various ways, while concentrating on the device management, communication protocols, user profiles and environmental access. This paper examines a control model for privacy in pervasive environments from the perspective of the environment. A prototype was devised and tested to validate the generic model of privacy which was also used to compare taxonomic concepts in the literature. Moreover, the prototype was devised and tested to validate the generic model of privacy for control and manage various users, devices and environments and so on. The prototype was based on Percontrol (a system for pervasive user management), which was only intended to identify users using Wi-Fi, and now it is capable of managing temperature, luminosity and other preferences, measured by a WSN (wireless sensor network) embedded to Percontrol, and the data treatment is done by an ANN (artificial neural network). Results confirmed the viability of device detection with Wi-Fi, Bluetooth and RFID (radio frequency identification) for an increases slight of the latency in registering new devices on the system.