Abstract
Design-for-X (DfX) approaches continues to prove their importance to support design management in increased complexity products and towards sustainable development. Permanent increasing of market competitiveness lead the companies to narrow budgets and increase the application of Lean practices among their departments. Lean Design for eXcellence (LeanDfX) methodology was developed to cross Lean Thinking and Design-for-X project support, assessing multiple domains such as optimization, manufacturing, assembly, maintenance, eco-design, modularity or adaptability. This approach brings a systematic applicability for design engineers and technical managers, assessing the effectiveness and efficiency of a given product design. A LeanDIX index metric, ranging between 0-100%, and original scorecard were created for consistent decision support for comparison of different design concepts or design versions of products, integrating different 'X' domains. In this work, the LeanDIX methodology results are presented for a real industrial case study related to a robot gripper design of a palletizing production cell. (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
Given an heterogeneous social network, can we forecast its future? Can we predict who will start using a given hashtag on twitter? Can we leverage side information, such as who retweets or follows whom, to improve our membership forecasts? We present TENSORCAST, a novel method that forecasts time-evolving networks more accurately than current state-of-the-art methods by incorporating multiple data sources in coupled tensors. TENSORCAST is (a) scalable, being linearithmic on the number of connections; (b) effective, achieving over 20% improved precision on top-1000 forecasts of community members; (c) general, being applicable to data sources with different structure. We run our method on multiple real-world networks, including DBLP, epidemiology data, power grid data, and a Twitter temporal network with over 310 million nonzeros, where we predict the evolution of the activity of the use of political hashtags.

Abstract
The high flexibility of the wireless mesh networks (WMNs) physical infrastructure can be exploited to provide communication with different technologies and support for a variety of different services and scenarios. Context information may trigger the need to build different logical networks on top of physical networks, where users can be grouped according to similarity of their context, and can be assigned to the logical networks matching their context. When building logical networks, network virtualization can be a very useful technique allowing a flexible utilization of a physical network infrastructure. Moreover, dynamic resource management using multiple channels and interfaces, directional antennas and power control, is able to provide a higher degree of flexibility in terms of resource allocation among the available virtual networks, to enable isolated and non-interfering communications while maximizing the network efficiency. In this paper we propose a resource management approach that uses transmit power control algorithm with both omnidirectional and directional antennas, to determine the resources of each virtual network while minimizing interference between virtual networks, considering the support of multiple services and users. Each virtual network can be extended to include the nodes of the WMN required by new users. The results of the proposed approach show that the support of multiple virtual networks for multiple services highly improves the network performance when compared to the support of the services in only one virtual network, with no interference minimization nor dynamic resource control.

Abstract
This paper provides an overview of the development of a solar dehydrator, a project undertaken by a team of six Erasmus students from different countries during the European Project Semester at the Instituto Superior de Engenharia do Porto in the spring of 2019. The main objective of the European Project Semester is to develop teamwork, communication and problem-solving skills through team work and project-based learning. The purpose of the project was to design a sustainable solution to dehydrate and preserve food, build and test the corresponding proof-of-concept prototype, while respecting requirements such as the budget, the use of reusable materials and components or European Union directives. To achieve this goal, the team considered the technological, ethical and deontological, economic and environmental perspectives in the design of the Dryfoo prototype. This paper describes, after a short introduction, the performed research, the development and the testing of the proof-of-concept prototype, as well as the personal outcomes of this learning experience.

Abstract
Among conventional generation technologies in Spain, Combined Cycle Gas Turbines (CCGT) is the one that has experienced the largest development over the first decade of the 21st century. However, despite its promising future, multiple factors (such as the renewable generation increase, demand decline, adverse regulatory policies, etc.) have compromised their competitive position, reducing their capacity factor and undermining their financial viability. Because of those issues, electricity companies are giving up on new CCGTs investments, or even considering closing or mothballing some of their recently built plants. However, many still claim for the necessity of maintaining flexible backup technologies to cope with the variability of renewable energies, as a transition technology until energy storage or other future technologies emerge. This paper makes a profitability analysis of CCGTs in the Spanish electric power sector under different scenarios of RES penetration, carbon plants decommissioning, CO2 emission costs and EV penetration.

Abstract
Deep learning models have as of late risen as popular function approximators for single-agent reinforcement learning challenges, by accurately estimating the value function of complex environments and being able to generalize to new unseen states. For multi-agent fields, agents must cope with the non-stationarity of the environment, due to the presence of other agents, and can take advantage of information sharing techniques for improved coordination. We propose an neural-based actor-critic algorithm, which learns communication protocols between agents and implicitly shares information during the learning phase. Large numbers of agents communicate with a self-learned protocol during distributed execution, and reliably learn complex strategies and protocols for partially observable multi-agent environments. © Springer Nature Switzerland AG 2019.