Abstract
Ambient Intelligence has always been associated with the promise of exciting new applications, aware of the users' needs and state, and proactive towards their goals. However, the acquisition of the necessary information for supporting such high-level learning and decision-making processes is not always straightforward. In this chapter we describe a multi-faceted smart environment for the acquisition of relevant contextual information about its users. This information, acquired transparently through the technological devices in the environment, supports the building of high-level knowledge about the users, including a quantification of aspects such as performance, attention, mental fatigue and stress. The environment described is particularly suited for milieus such as workplaces and classrooms, in which this kind of information may be very important for the effective management of human resources, with advantages for organizations and individuals alike. © 2017 The authors and IOS Press.

Abstract

Abstract
Attention is a very important cognitive and behavioral process, by means of which an individual is able to focus on a single aspect of information, while ignoring others. In a time in which we are drawn in notifications, beeps, vibrations and blinking messages, the ability to focus becomes increasingly important. This is true in many different domains, from the workplace to the classroom. In this paper we present a non-intrusive distributed system for monitoring attention in teams of people. It is especially suited for teams working at the computer. The presented system is able to provide real-time information about each individual as well as information about the team. It can be very useful for team managers to identify potentially distracting events or individuals, as well as to detect the onset of mental fatigue or boredom, which significantly influence attention. in the overall, this tool may prove very useful for team managers to implement better human resources management strategies.

Abstract
In the current world, performance is one of the most important issues concerning work and competition. Performance is strongly connected with learning and when it comes to acquiring new knowledge, attention is one the most important mechanisms as the level of the learner's attention affects learning results. When students are doing learning activities using new technologies, it is extremely important that the teacher has some feedback from the students' work in order to detect potential learning problems at an early stage. The goal of this research is to propose a system that measures the level of attentiveness in real scenarios, and detects patterns of behavior associated to different attention levels among different students. This system measures attention and uses this information for training a decision support system that shows the level of attention of a group of students in real time.

Abstract
There is currently a significant interest in consumer electronics in applications and devices that monitor and improve the user's well-being. This is one of the key aspects in the development of ambient intelligence systems. Nonetheless, existing approaches are generally based on physiological sensors, which are intrusive and cannot be realistically used, especially in ambient intelligence in which the transparency, pervasiveness and sensitivity are paramount. We put forward a new approach to the problem in which user behavioral cues are used as an input to assess inner state. This innovative approach has been validated by research in the last years and has characteristics that may enable the development of true unobtrusive, pervasive and sensitive ambient intelligent systems. © 2017 by SCITEPRESS - Science and Technology Publications, Lda.

Abstract
Resource efficiency assessment methods, along with eco-efficiency assessment methods are needed for various industrial sectors to support sustainable development, decision-making and evaluate efficiency performance. The combination of eco-efficiency with efficiency assessment allows to identify major inefficiencies and provides means to foster sustainability, through the efficient and effective material and energy use. Despite the available information for decision making, this proves to be a difficult task in the manufacturing industry, therefore, there is a real need to develop and use optimization techniques to enhance resource efficiency. In this context, and due to the lack of simple and integrated tools to assess and optimize resource efficiency, crossing the different environmental and economic aspects, arises the need to develop optimisations models, enabling support and optimize sustainable decision making process and identification of potential improvements. The optimisation method should provide robust knowledge to support decisionmaking, allow comparability of the results and consider a cost-saving approach to help set priorities. Moreover, the optimisation techniques should centre the process through design/configuration of the production system, without considering time, in order not to limit the physical agents.