Abstract
The Aswan High Dam, Egypt, was built in the 1960s and is one of the biggest dams in the world. It stopped the seasonal flood of Nile river allowing the urban expansion of cities/villages and the full year cultivation, producing 10x10(9) kWh of power annually. The dam is located in an area where several earthquakes (M-L<6) occurred from 1981 to 2007. In this paper, we want to identify any potential damage that could be caused to the dam, and assess its overall structural stability using Multi-Temporal InSAR (MT-InSAR). To reach this goal, we process Envisat data from descending orbits acquired between 2003 and 2010. Our initial estimates show relatively small rates (maximum around -3 mm/yr in the satellite Line-Of-Sight) of subsidence, whose implications must be further investigated. In addition, we perform a preliminary stress-strain analysis of the dam using FEL and FEM methods to assess if the detected movements correspond to the expected vertical behavior for such mega-structure.

Abstract
In this paper, we studied 2017' indexes related to people life conditions: happiness, prosperity, sustainable mobility, quality of life. Some quality of life sub-indexes, regarding mobility, traffic commute time and the pollution, were also considered. A two variable representation was displayed through a simple regression, in order to measure the strength level of the relations between the variables and their influence towards the adoption of sustainable mobility practices. It was possible to conclude that the strongest correlations found were between the happiness and the prosperity indexes; the traffic commute time and the quality of life indexes, by cities; and the pollution and the quality of life indexes, by cities, which aim to explain sustainable mobility behaviors and its impact in the improvement of the population well-being and satisfaction. The information compiled in this study might be relevant to a large group of stakeholders and decision-makers, focused on the definition of policies to increase the citizens' quality of life. This study might be, also useful for increasing the degree of awareness and involvement of the population through the adoption of mobility solutions that will be critical to creating a sustainable way of life in the future.

Abstract
This paper presents recent research conducted at the JRC Smart Grid Interoperability Lab and analyses key parameters that should be taken into consideration for the development of interoperable and sustainable electricity systems. Increasing energy efficiency aims at reducing the overall energy consumption and consequently lower the stress on the environment by using less energy. The first research activity illustrated is on the use of Advanced Metering Infrastructure as a gateway to improve Demand Response/Demand Side Management. The second one focuses on the use of photovoltaic in a low voltage distribution network and studies the effect of penetration in voltage unbalances. The last one addresses the power quality performance of electric vehicle chargers under low temperature conditions and provides hints for improvements. The paper underlines several factors that could affect the efficiency of systems towards making improvements that increase the stability of the relevant operations.

Abstract
In the current competitive and globalized manufacturing scenario Distributed Manufacturing Environments are increasing, and it turns mandatory to explore improved operational approaches. For enhanced simultaneous balancing and scheduling jobs in a Distributed Manufacturing Environment (DME) a mathematical model of Ranked Sequence Positional Weight (RSPW) is proposed. The model capabilities are analysed through a test problem and the results have demonstrated that the proposed RSPW heuristics mathematical model do perform better than other competitive approaches. (C) 2017 The Authors. Published by Elsevier B.V.

Abstract
This paper presents a Support Vector Machine (SVM) based approach for energy consumption forecasting. The proposed approach includes the combination of both the historic log of past consumption data and the history of contextual information. By combining variables that influence the electrical energy consumption, such as the temperature, luminosity, seasonality, with the log of consumption data, it is possible for the proposed method by find patterns and correlations between the different sources of data and therefore improves the forecasting performance. A case study based on real data from a pilot microgrid located at the GECAD campus in the Polytechnic of Porto is presented. Data from the pilot buildings are used, and the results are compared to those achieved by several states of the art forecasting approaches. Results show that the proposed method can reach lower forecasting errors than the other considered methods.

Abstract
This paper introduces a novel strategy for the task of simultaneously locating two key anatomical landmarks in retinal images of the eye fundus, namely the optic disc and the fovea. For that, instead of attempting to classify each pixel as belonging to the background, the optic disc, or the fovea center, which would lead to a highly class-imbalanced setting, the problem is reformulated as a pixelwise regression task. The regressed quantity consists of the distance from the closest landmark of interest. A Fully-Convolutional Deep Neural Network is optimized to predict this distance for each image location, implicitly casting the problem into a per-pixel Multi-Task Learning approach by which a globally consistent distribution of distances across the entire image can be learned. Once trained, the two minimal distances predicted by the model are selected as the locations of the optic disc and the fovea. The joint learning of every pixel position relative to the optic disc and the fovea favors an automatic understanding of the overall anatomical distribution. This results in an effective technique that can detect both locations simultaneously, as opposed to previous methods that handle both tasks separately. Comprehensive experimental results on a large public dataset validate the proposed approach.