Abstract
The purpose of this paper is to present the main results of the ongoing analysis of applying dynamic network access tariffs in Portugal. For the 2015-2017 regulatory period, the Portuguese National Regulatory Authority, ERSE, required the three main Portuguese DSOs to submit, until the end of June 2016, plans for the implementation of network dynamic tariff schemes targeting Medium, High and Extra High Voltage customers, as well as the respective cost-benefit analysis. EDP Distribuição, the main Portuguese DSO, is preparing a report regarding the implementation of pilot projects on a sample of these segments of customers, which are due to be on the field during 2017. These pilots should help electrical energy stakeholders understand how the Electric System can benefit from the use of dynamic tariffs focused on networks, allowing for the quantification of benefits in a more accurate way. The level of demand response that results from price signals is a key issue that both the regulator and EDP Distribuição will quantify. Other important issue to assess in this study is the efficiency of cost recovery under a dynamic tariff scheme. In conclusion, this paper will present some results obtained from the cost-benefit analysis regarding the implementation of a Critical Peak Pricing scheme, as well as the key learnings supporting the introduction of dynamic schemes in the future, not only for EHV, HV and MV customers but also eventually extending it to LV ones.

Abstract

Abstract
This paper reports the design and development process of an artistic robot by a team of five engineering and design students from Belgian, Finland, Poland, Romania and Scotland. To contribute to this goal, the team designed and assembled GraphBot, a voice commanded drawing robot prototype, following the EPS@ISEP process. In addition, the team specified their target as young children and, in particular girls, and stated that their motivation was to introduce young generations to the world of science, technology, engineering and mathematics (STEM). In terms of outcomes, this project is expected to go beyond the boundaries of the traditional development of scientific and technical competences, by providing the students with a holistic learning experience, fostering also the development of personal and inter-personal skills within a multidisciplinary and multicultural teamwork set-up. © Springer International Publishing AG 2017.

Abstract
Coverage path planning (CPP) is a fundamental agricultural field task required for autonomous navigation systems. It is also important for resource management, increasingly demanding in terms of reducing costs and environmental polluting agents as well as increasing productivity. Additional problems arise when this task involves irregular agricultural terrains where the crop follows non-uniform configurations and extends over steep rocky slopes. For mountain vineyards, finding the optimal path to cover a restricted set of terraces, some of them with dead ends and with other constraints due to terrain morphology, is a great challenge. The problem involves other variables to be taken into account such as speed, direction and orientation of the vehicle, fuel consumption and tank capacities for chemical products. This article presents a decision graph-based approach, to solve a Rural Postman Coverage like problem using A* and Dijkstra algorithms simultaneously to find the optimal sequence of terraces that defines a selected partial coverage area of the vineyard. The decision structure is supported by a graph that contains all the information of the Digital Terrain Model (DTM) of the vineyard. In this first approach, optimality considers distance, cost and time requirements. The optimal solution was represented in a graphical user OpenGL application developed to support the path planning yprocess. Based on the results, it was possible to prove the applicability of this approach for any vineyards which extend like routes. Near optimal solutions based on other specific criteria could also be considered for future work.

Abstract
The CloudMdsQL polystore provides integrated access to multiple heterogeneous data stores, such as RDBMS, NoSQL or even HDFS through a big data analytics framework such as MapReduce or Spark. The CloudMdsQL language is a functional SQL-like query language with a flexible nested data model. A major capability is to exploit the full power of each of the underlying data stores by allowing native queries to be expressed as functions and involved in SQL statements. The CloudMdsQL polystore has been validated with a good number of different data stores: HDFS, key-value, document, graph, RDBMS and OLAP engine. In this paper, we introduce the benchmarking of the CloudMdsQL polystore and evaluate the performance benefits of important features enabled by the query language and engine.

Abstract
We measure the impact of the August 2011 bans on covered short-selling adopted by several European countries. Our results provide evidence that the impact on prices was shortlived: the positive price impact disappears after ten days. The short-selling restrictions did not contribute to reduce the volatility of the financial stocks subjected to the bans; on the contrary, our findings indicate that volatility actually increased by a greater extent for these stocks than for other financial stocks with similar characteristics. The bans also had a negative impact on liquidity. Moreover, stocks subjected to the bans exhibit a longer delay in the assimilation of negative market news during the banning span.