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

Abstract
This article presents preliminary findings on the application of both Speculative Design and Game Design towards the conception of two prototypes of serious games with focus on anorexia. The first prototype focuses on psychoeducation of school-age youth, and the second aims to support research and sharing of knowledge about the disease, able to be used in focus groups and interviews. Anorexia is a complex and often fatal disease that has no cure, and by conceiving and playing these first prototypes we were able get a glimpse of the its context, making us more ready for this research’s next stages. © 2019, ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering.

Abstract

Abstract
Nowadays, with the widely usage of on-line stream video platforms, the number of media resources available and the volume of crowd-sourced feedback volunteered by viewers is increasing exponentially. In this scenario, the adoption of recommendation systems allows platforms to match viewers with resources. However, due to the sheer size of the data and the pace of the arriving data, there is the need to adopt stream mining algorithms to build and maintain models of the viewer preferences as well as to make timely personalised recommendations. In this paper, we propose the adoption of optimal individual hyper-parameters to build more accurate dynamic viewer models. First, we use a grid search algorithm to identify the optimal individual hyper-parameters (IHP) and, then, use these hyper-parameters to update incrementally the user model. This technique is based on an incremental learning algorithm designed for stream data. The results show that our approach outperforms previous approaches, reducing substantially the prediction errors and, thus, increasing the accuracy of the recommendations.

Abstract
The interaction of light with matter in near-to-resonant conditions opens a path for the exploration of nontrivial optical response that can play an important role in future photonics-driven technology. But as the attention shifts towards many-level atomic systems and involving multi-dimensional experimental scenarios, the complexity of the physical systems makes the analytical approach to the semiclassical model of the MaxwellBloch equations impossible without any strongly-limiting approximations. In this context, robust and highperformance computational tools are mandatory. In this work, we describe the development and implementation of a cross-platform Maxwell-Bloch numerical solver that is capable to exploit the different hardware available to tackle efficiently the problems under consideration. Moreover, it is demonstrated that this simulation tool can address a vast class of problems with considerable reduction of simulation time, featuring speedups up to 30 when running in massive parallel GPUs compared with the same codes running on a CPU, showing its potential towards addressing a large class of modern problems in photonics.