Abstract
The present study aims (a) to translate and adapt the Igroup Presence Questionnaire (IPQ) to the Portuguese context (semantic equivalence/ conceptual and content validity) and (b) to examine its psychometric properties (reliability and factorial validity). The sample consisted of 478 subjects (285 males and 193 females). The fidelity of the factors varied between 0.53 and 0.83. The confirmatory factor analysis results produced a 14-item version of IPQ-PT, accepting covariance between residual errors of some items of the instrument, as the best structural representation of the data analyzed. The CFA was conducted based on a three-variable model. The fit indexes obtained were X-2/df = 2.647, GFI = .948, CFI = .941, RSMEA = .059, and AIC = 254. These values demonstrate that the proposed Portuguese translation of the IPQ maintains its original validity, demonstrating it to be a robust questionnaire to measure the sense of presence in virtual reality studies. It is therefore recommended for use in presence research when using Portuguese samples.

Abstract
Concerned with predicting equipment failures, predictive maintenance has a high impact both at a technical and at a financial level. Most modern equipments have logging systems that allow us to collect a diversity of data regarding their operation and health. Using data mining models for anomaly and novelty detection enables us to explore those datasets, building predictive systems that can detect and issue an alert when a failure starts evolving, avoiding the unknown development up to breakdown. In the present case, we use a failure detection system to predict train door breakdowns before they happen using data from their logging system. We use sensor data from pneumatic valves that control the open and close cycles of a door. Still, the failure of a cycle does not necessarily indicates a breakdown. A cycle might fail due to user interaction. The goal of this study is to detect structural failures in the automatic train door system, not when there is a cycle failure, but when there are sequences of cycle failures. We study three methods for such structural failure detection: outlier detection, anomaly detection and novelty detection, using different windowing strategies. We propose a two-stage approach, where the output of a point-anomaly algorithm is post-processed by a low-pass filter to obtain a subsequence-anomaly detection. The main result of the two-level architecture is a strong impact in the false alarm rate.

Abstract
Resorting to creativity technique and their use to help innovation in the area of information systems had a growing interest. In fact, the global competitiveness and the organizations ability to make effective use of information technology and to focus on innovation and creativity are recognized as being important. So, the perspective of using creativity techniques seems to be promising. In this research work we argue that is possible in all IS areas to take advantages of the use of creative processes. We give a pragmatic reasoning and examples for the introduction of creative processes in all the main IS areas.

Abstract
It is widely recognized that skippers can have a significant role in their vessel performance levels. However, in many studies that seek to address differences in performance of vessels, the contribution of the skipper is often not quantified, or the influencing factors are not explained. This study examines the effect of social factors, such as family heritage, education and professional expertise, on skipper skill and the economic performance of the Portuguese artisanal dredge fleet. This is done using a stochastic production frontier model and data on the weekly activity of 54 vessels operating during 2013 and 2014. The results suggest that in this fishery age and education levels of skippers are important determinants of efficiency. Experience as a skipper was no determinant of performance, possibly due to the simplicity of the technical equipment onboard. From a managerial perspective, this indicates that local authorities should consider initiatives to enhance the education levels and professional training of skippers, if they aim to improve the effectiveness of artisanal fleets.

Abstract
In this paper the nonparametric identification of state-space linear parameter-varying models with dynamic mapping between the scheduling signal and the model matrices is considered. Indeed, we are particularly interested on the problem of estimating a model using data generated from an LPV system with static dependence, which is however represented on a different state-basis from the one considered by the estimator.

Abstract
A distributed system is often built on top of an overlay network. Overlay networks enable network topology transparency while, at the same time, can be designed to provide efficient data dissemination, load balancing, and even fault tolerance. They are constructed by defining logical links between nodes creating a node graph. In practice, this is materialized by a Peer Sampling Service (PSS) that provides references to other nodes to communicate with. Depending on the configuration of the PSS, the characteristics of the overlay can be adjusted to cope with application requirements and performance concerns. Unfortunately, overlay efficiency comes at the expense of dependability. To overcome this, one often deploys an application overlay focused on efficiency, along with a safety-net overlay to ensure dependability. However, this approach results in significant resource waste since safety-net overlays are seldom used. In this paper, we focus on safety-net overlay networks and propose an adaptable mechanism to minimize resource usage while maintaining dependability guarantees. In detail, we consider a random overlay network, known to be highly dependable, and propose BUZZPSS, a new Peer Sampling Service that is able to autonomously fine-tune its resource consumption usage according to the observed system stability. When the system is stable and connectivity is not at risk, BUZZPSS autonomously changes its behavior to save resources. Alongside, it is also able to detect system instability and act accordingly to guarantee that the overlay remains operational. Through an experimental evaluation, we show that BUZZPSS is able to autonomously adapt to the system stability levels, consuming up to 6x less resources than a static approach.