Abstract
In our daily life, we often have a sense of being exhausted due to mental or physical work, together with a feeling of performance degradation in the accomplishment of simple tasks. This is in part due to the fact that the working capacity and the performance of an individual, either physical or mental, generally decrease as the day progresses, although factors like motivation also play a significant role. These negative effects are especially significant when carrying out long or demanding tasks, as often happens in an educational context. In order to avoid these effects, initiatives to promote a good management of the time and effort invested in each task are mandatory. Such initiatives, when effective, can have a wide range of positive effects, including on the performance, productivity, attention and even mental health. Seeking to find a viable and realistic approach to address this problem, this paper presents a non-invasive and non-intrusive way to measure mental workload, one of the aspects that affects mental fatigue the most. Specifically, we target scenarios of e-learning, in which the professor may not be present to assess the student's state. The aim is to create a tool that enables an actual management of fatigue in such environments and thus allows for the implementation of more efficient learning processes, adapted to the abilities and state of each student.

Abstract
Humans exhibit their personality and their behavior through their daily actions. Moreover, these actions also show how behaviors differ between different scenarios or contexts. However, Human behavior is a complex issue as it results from the interaction of various internal and external factors such as personality, culture, education, social roles and social context, life experiences, among many others. This implies that a specific user may show different behaviors for a similar circumstance if one or more of these factors change. In past work we have addressed the development of behavior-based user identification based on keystroke and mouse dynamics. However, user states such as stress or fatigue significantly change interaction patterns, risking the accuracy of the identification. In this paper we address the effects of these variables on keystroke and mouse dynamics. We also show how, despite these effects, user identification can be successfully carried out, especially if task-specific information is considered.

Abstract
In our busy daily life, we often have the feeling of being exhausted, accompanied with a sense of performance degradation and increase of discomfort in the execution of even simple tasks. This often takes place in the workplace and in a silent way, influencing our productivity, our performance the number of errors or the quality of our production. This paper details a chair to be used in workplace environments that is sensitive to the onset of fatigue. Based on built-in accelerometers it recognizes signs of discomfort, which may be related to mental fatigue, to point out moments when an individual should consider taking a pause or a rest. This chair complements a previously developed software for the assessment of mental fatigue from the analysis of the individual's interaction with the computer. © Springer International Publishing Switzerland 2015.

Abstract
Keeping students interested and motivated is perhaps one of the most difficult and traditional tasks assigned to teachers. With technology being engaged increasingly into learning activities, with its advantages and disadvantages, some new aspects need to be considered. Undoubtedly, technology acts as an enhancer for learning, opening new paths for teaching. However there are some drawbacks too. Keeping students in the right track, doing what they are expected to do, with commitment and motivation, becomes an enormous challenge when an amazing digital world full of all kind of temptations is at the distance of their personal smartphones or even in the computer they use to study. This excess of stimuli and the process of switching and choosing between them has as potential effects on attention, stress and mental fatigue. Stressed or fatigued students fail to deliver the required performance for the task they are engaged in. This paper presents a non-intrusive approach for monitoring student's performance in real time and measure the effect of these external variables on students. The long-term goal is to empower teachers with valuable information about the students' state, allowing them to better manage their students and teaching methodologies.

Abstract
In a time of economic austerity, more pressure is being put on the existing transport systems to be more sustainable and, at the same time, more equitable and socially inclusive. Regular public road transportation traditionally uses fixed routes and schedules, which can be extremely expensive in rural areas and certain periods of the day in urban areas due to low and unpredictable demand. Demand Responsive Transportation systems are a kind of hybrid transportation approach between a taxi and a bus that try to address these problems with routes and frequencies that may vary according to the actual observed demand. Demand Responsive Transportation seems to have potential to answer the sustainability and social inclusion challenges in a context of austerity. However, DRT projects may fail: it is not only important to solve the underlying model in an efficient way, but also to understand how different ways of operating the service affect customers and operators. To help design DRT services, we developed an innovative approach integrating simulation and optimization. Using this simulator, we compared a real night-time bus service in the city of Porto, Portugal, with a hypothetical flexible DRT service for the same scenario.

Abstract
Traditionally, travel demand modelling focused on long-term multiple socio-economic scenarios and land-use configurations to estimate the required transport supply. However, the limited number of transportation requests in demand-responsive flexible transport systems require a higher resolution zoning. This work analyses users short-term destination choice patterns, with a careful analysis of the available data coming from various different sources, such as GPS traces and social networks. We use a Multinomial Logit Model, with a social component for utility and characteristics, both derived from Social Network Analyses. The results from the model show meaningful relationships between distance and attractiveness for all the different alternatives, with the variable distance being the most significant.