Abstract
The Intensive Care Unit (ICU) of Hospitals deals with patients in life critical conditions. The Intensive Care Information System (ICS) can therefore provide extremely important information to support medical doctors' (MDs) decisions. For instance, it is critical to manage well information about the evolution of a large amount of infections over time, about the antibiotics administered to each patient, and the impact on his/her life condition. Good quality information and interaction in such an extreme environment is therefore critical for helping MDs target well medicines to patients. This paper describes the initial stages of a project aiming at improving a real ICS, in particular from the interaction point of view, taking into account the stringent usability requirements from the MDs. Through a validated low definition prototype of the infection module of ICS, the paper proposes innovative active ways of providing suggestions to MDs on what actions to take.

Abstract
Many of the physically injured use electric wheelchairs as an aid to locomotion. Usually, for commanding this type of wheelchair, it is required the use of one's hands and this poses a problem to those who, besides being unable to use their legs, are also unable to properly use their hands. The aim of the work described here, is to create a prototype of a wheelchair command interface that do not require hand usage. Facial expressions were chosen instead, to provide the necessary visual information for the interface to recognize user commands. The facial expressions are captured by a digital camera and interpreted by an application running on a laptop computer on the wheelchair. The software includes digital image processing algorithms for feature detection, such as colour segmentation and edge detection, followed by the application of a neural network that uses these features to detect the desired facial expressions. A simple simulator, built on top of the known (Ciber-Mouse) was used to validate the approach by simulating the control of the intelligent wheelchair in a hospital environment. The results obtained from the platform provide strong evidence that it is possible to comfortably drive an intelligent wheelchair using facial expressions.

Abstract
This paper presents an interaction model for pervasive computing environments supporting localized activities, i.e., activities strongly associated to a specific physical environment. We are particularly interested in activities performed by occasional visitors to public spaces. This interaction model is characterized by an activity-centered approach to pervasive computing and is defined in a conceptual model inspired by Activity Theory. ActivitySpot, a software infrastructure implementing this conceptual model, is also presented. User interaction in ActivitySpot is based on simple, everyday pervasive computing devices, which facilitates usage learning and allows for a wide user population. ActivitySpot has supported the deployment of several pervasive computing solutions for localized activities. Our conceptual model has been evaluated by user studies run at different public spaces and global results demonstrate the model's suitability to the targeted type of scenario.

Abstract
The development of safety-critical embedded applications in domains such as automotive or avionics is an exceedingly challenging intellectual task. This task can, however, be significantly simplified through the use of middleware that offers specialized fault-tolerant services. This middleware must provide a high assurance level that it operates correctly. In this paper, we present a formal verification of a protocol for one such service, a Group Membership Service, using model checking. Through this verification we discovered that although the protocol specification is correct, a previously proposed implementation is not.

Abstract
Long-term contractual decisions are the basis of an efficient risk management. However those types of decisions have to be supported with a robust price forecast methodology. This paper reports a different approach for long-term price forecast which tries to give answers to that need. Making use of regression models, the proposed methodology has as main objective to find the maximum and a minimum Market Clearing Price (MCP) for a specific programming period, and with a desired confidence level a. Due to the problem complexity, the meta-heuristic Particle Swarm Optimization (PSO) was used to find the best regression parameters and the results compared with the obtained by using a Genetic Algorithm (GA). To validate these models, results from realistic data are presented and discussed in detail.

Abstract
Generating manipulator trajectories considering multiple objectives and obstacle avoidance is a non-trivial optimization problem. In this paper a multi-objective genetic algorithm based technique is proposed to address this problem. Multiple criteria are optimized considering up to five simultaneous objectives. Simulation results are presented for robots with two and three degrees of freedom, considering two and five objectives optimization. A subsequent analysis of the spread and solutions distribution along the converged non-dominated Pareto front is carried out, in terms of the achieved diversity.