Abstract
In general, car manufacturers face trade-offs between safety, efficiency and environmental performance when choosing between mass, length, engine power, and fuel efficiency. Moreover, the information available to the consumers makes difficult to assess all these components at once, especially when aiming to compare vehicles across different categories and/or to compare vehicles in the same category but across different model years. The main objective of this research was to develop an integrated tool able to assess vehicle's performance simultaneously for safety and environmental domains, leading to the research output of a Safety, Fuel Efficiency and Green Emissions (SEG) indicator able to evaluate and rank vehicle's performance across those three domains. For this purpose, crash data was gathered in Porto (Portugal) for the period 2006-2010 (N = 1374). The crash database was analyzed and crash severity prediction models were developed using advanced logistic regression models. Following, the methodology for the SEG indicator was established combining the vehicle's safety and the environmental evaluation into an integrated analysis. The obtained results for the SEG indicator do not show any trade-off between vehicle's safety, fuel consumption and emissions. The best performance was achieved for newer gasoline passenger vehicles (<5year) with a smaller engine size (<1400 cm(3)). According to the SEG indicator, a vehicle with these characteristics can be recommended for a safety-conscious profile user, as well as for a user more interested in fuel economy and/or in green performance. On the other hand, for larger engine size vehicles (>2000 cm(3)) the combined score for safety user profile was in average more satisfactory than for vehicles in the smaller engine size group (<1400 cm(3)), which suggests that in general, larger vehicles may offer extra protection. The achieved results demonstrate that the developed SEG integrated methodology can be a helpful tool for consumers to evaluate their vehicle selection through different domains (safety, fuel efficiency and green emissions). Furthermore, SEG indicator allows the comparison of vehicles across different categories and vehicle model years. Hence, this research is intended to support the decision-making process for transportation policy, safety and sustainable mobility, providing insights not only to policy makers, but also for general public guidance.

Abstract
The rise of a new digital industrial paradigm known as industry 4.0, powered by several enabling technologies such as flexible and collaborative robots, autonomous vehicles, internet of things, cloud and manufacturing servitization, is seen as the key enabler for the fourth industrial revolution: the digital manufacturing. However, there are still several challenges related to the effective adoption of these technologies and to enterprise level interoperability so that an entire cyber physical production system can work seamlessly. Besides the set of challenges faced by industrialists, this research work presents innovative results towards a plug and play seamlessly integration of technologies, from the shop floor to the enterprise management layers, ensuring a total integrated architecture supporting the full product lifecycle management.

Abstract
In this paper, a number of possibilities are presented and discussed for the ownership of distributed storage devices (DSD) in a Smart Grid environment. The cases in which the distribution system operator (DSO) has either full control (grid owned storage) or no control whatsoever over the operation of the DSD (independently owned storage) will be differentiated. For each ownership possibility, the technical and regulatory implications are discussed, with analysis and validation of the results being performed on real MV distribution networks, both rural and urban. In order to evaluate each ownership possibility, a number of multi-period optimization models are presented, corresponding to different assumptions in regards to the operation of the DSD. The resulting daily operation strategies are subsequently used as a basis for carrying out distribution reinforcement planning.

Abstract
Time series forecasting is a challenging task, where the non-stationary characteristics of the data portrays a hard setting for predictive tasks. A common issue is the imbalanced distribution of the target variable, where some intervals are very important to the user but severely underrepresented. Standard regression tools focus on the average behaviour of the data. However, the objective is the opposite in many forecasting tasks involving time series: predicting rare values. A common solution to forecasting tasks with imbalanced data is the use of resampling strategies, which operate on the learning data by changing its distribution in favor of a given bias. The objective of this paper is to provide solutions capable of significantly improving the predictive accuracy of rare cases in forecasting tasks using imbalanced time series data. We extend the application of resampling strategies to the time series context and introduce the concept of temporal and relevance bias in the case selection process of such strategies, presenting new proposals. We evaluate the results of standard regression tools and the use of resampling strategies, with and without bias over 24 time series data sets from 6 different sources. Results show a significant increase in predictive accuracy of rare cases associated with the use of resampling strategies, and the use of biased strategies.

Abstract

Abstract
The integration of Information and Communication Technologies (ICT) in the tourism industry is an essential element for the success of any tourism enterprise. ICTs provide access to information of tourism products from anywhere and at any time. Tour companies may also reach out to target customers around the world through a series of emerging technologies. This paper aims to make a review of the main key factors of ICT in Tourism. Aspects such as the quality of the website, Digital Marketing, Social Networking, Multimedia, Mobile Technologies and Intelligent Environments are discussed.