Abstract
A fully operational Multi-Terminal DC (MTDC) grid will play a key role for the creation of AC systems interconnection and to integrate offshore wind farms. Disturbances (at both AC and DC side) may culminate in the sudden disconnection of onshore HVDC-VSC (High Voltage Direct Current - Voltage Source Converter). To continue operating the DC grid under these conditions, the development of control functionalities is required. A communication-free advanced control scheme is proposed to be used as a supplementary local control acting at VSC level and aiming on providing fast active power accommodation in the DC grid, culminating on the mitigation of the resulting DC overvoltage. The implementation of the proposed control mechanisms exploits a set of coordinated local control rules at the converter stations and at wind turbines (WT) level. The performance of the proposed strategies is discussed and assessed through numerical simulation in the paper.

Abstract
Euclidean distance and cosine similarity are frequently used measures to implement the k-means clustering algorithm. The cosine similarity is widely used because of it's independence from document length, allowing the identification of patterns, more specifically, two documents can be seen as identical if they share the same words but have different frequencies. However, during each clustering iteration new centroids are still computed following Euclidean distance. Based on a consideration of these two measures we propose the k-Communities clustering algorithm (k-C) which changes the computing of new centroids when using cosine similarity. It begins by selecting the seeds considering a network of tags where a community detection algorithm has been implemented. Each seed is the document which has the greater degree inside its community. The experimental results found through implementing external evaluation measures show that the k-C algorithm is more effective than both the k-means and k-means++. Besides, we implemented all the external evaluation measures, using both a manual and an automatic "Ground Truth", and the results show a great correlation which is a strong indicator that it is possible to perform tests with this kind of measures even if the dataset structure is unknown.

Abstract
Currently, performance analysis on complex manufacturing systems is performed in an ad hoc way since the main objective is to verify if the strategy designed has been helping companies achieve their targets following a reactive approach. However, more and more companies are performing in competitive markets, forcing them to become more proactive than reactive. This way, a simple approach is no longer suitable for this type of companies, and a stronger and effective interaction between the strategic and operational layers is key. Therefore, this research proposes a framework composed of both qualitative and quantitative methods that allow decision-makers to better understand their production system. Moreover, using key leading indicators as reference, the idea is to provide companies with the ability to anticipate future performance behaviors based not only on the knowledge acquired, but also on a mathematical tool that will synthesize this knowledge and infer future performance behaviors. This paper explores a critical issue for contemporary industrial organizations and sustainability issues concerning energy consumption. In this scope, an important research was performed aiming at modeling and understanding the normal behavior of electricity consumption, as well as the factors affecting energy consumption in the painting line of an automotive plant. © Springer International Publishing Switzerland 2013.

Abstract
This paper proposes a new model to predict company failure in the construction industry. The model includes three major innovative aspects. The use of strategic variables reflecting the key specificities of construction companies, which are critical to explain company failure. The use of data mining techniques, i.e. support vector machine to predict company failure. The use of two different sampling methods (random undersampling and random oversampling with replacement) to balance class distributions. The model proposed was empirically tested using all Portuguese contractors that operated in 2009. It is concluded that support vector machine, with random oversampling and including strategic variables, is a very robust tool to predict company failure in the context of the construction industry. In particular, this model outperforms the results obtained with logistic regression.

Abstract
Music is widely perceived as expressive of emotion. However, there is no consensus on which factors in music contribute to the expression of emotions, making it difficult to find robust objective predictors for music emotion recognition (MER). Currently, MER systems use supervised learning to map non time-varying feature vectors into regions of an emotion space guided by human annotations. In this work, we argue that time is neglected in MER even though musical experience is intrinsically temporal. We advance that the temporal variation of music features rather than feature values should be used as predictors in MER because the temporal evolution of musical sounds lies at the core of the cognitive processes that regulate the emotional response to music. We criticize the traditional machine learning approach to MER, then we review recent proposals to exploit the temporal variation of music features to predict time-varying ratings of emotions over the course of the music. Finally, we discuss the representation of musical time as the flow of musical information rather than clock time. Musical time is experienced through auditory memory, so music emotion recognition should exploit cognitive properties of music listening such as repetitions and expectations.

Abstract
In the development of new regenerative medicine therapies for the treatment of central nervous system and spinal cord injuries, the identification of factors that inhibit or promote cell outgrowth in neurite outgrowth assays is fundamental, and the neurotrophic activity is commonly assessed based on the neurite/cell outgrowth. Neurites are projections from the cell body or the initial neurosphere and typically present low-contrast to background in phase contrast images. The extent of neurites is usually measured in a manual way and fluorescence images are the most used, generally requiring imunofluorescent staining. We present a novel automatic approach for the quantification of cell outgrowth from neurospheres, based on phase contrast and fluorescence images acquired from samples merely processed for DNA staining. Our approach detects the neurite/cell outgrowth, and its measures are in high agreement with the ones obtained manually. Furthermore, the image analysis time was reduced in more than 95% allowing the increase of the amount of data to be analyzed. © 2013 Springer-Verlag.