Abstract
Due to the current demands from the market, technological improvements and action of competitors, manufacturing companies are pushed to compete in shorter product development cycles. This poses a great challenge, as the conventional product development cycle is shortened. Hence, companies are forced to introduce some improvement action plans and adopt certain manufacturing operational strategies to remain competitive and achieve a good market share. Due to the importance of this topic, in this paper we revise the current manufacturing improvement plans in the literature, to establish the basis of our future research work. The findings of this work point towards the need to develop a system that integrates the evaluation of the manufacturing improvement plans in the future overall performance of the manufacturing plant and aid in the process of decision-making.

Abstract
In this work the use of guided wave photo-polymerization for the fabrication of novel polymeric micro tips for optical trapping is demonstrated. It is shown that the selective excitation of linear polarized modes, during the fabrication process, has a direct impact on the shape of the resulting micro structures. Tips are fabricated with modes LP02 and LP21 and their shapes and output intensity distribution are compared. The application of the micro structures as optical tweezers is demonstrated with the manipulation of yeast cells.

Abstract
The increasing number of vehicles and mobile users has led to a huge increase in the development of Advanced Driver Assistance Systems (ADAS). In this paper we propose a multi-agent-based driving simulator which integrates a testbed that allows ADAS developers to compress testing time and carry out tests in a controlled environment while using a low-cost setup. We use the SUMO microscopic simulator and a serious-game-based driving simulator which has geodata provided from standard open sources. This simulator connects to an Android device and sends data such as the current GPS coordinates and transportation network data. One important feature of this application is that it allows ADAS validation without the need of field testing. Also important is the suitability of our architecture to serve as an appropriate means to conduct behaviour elicitation through peer-designed agents, as well as to collect performance measures related to drivers' interaction with ADAS solutions.

Abstract
Manufacturing companies are currently forced to reconsider their production processes by adopting more flexible, robust, and adaptive systems, aiming to improve their competitiveness. Multiagent systems (MASs) technology is suitable to address this challenge by providing an alternative way to design these complex systems based on the decentralization of the control functions over distributed entities. This paper describes the installation of a MAS solution in an industrial factory plant producing laundry washing machines. The installed solution focuses on the integration of quality and process control, and contributes to the maximization of the factory profitability facing changing conditions by applying self-adaptation procedures at the local and global levels. The preliminary results show improvements in the production efficiency and product quality, as well as a reduction of the scrap costs.

Abstract
This chapter describes outdoor transmission tests and field measurements in TV white spaces (TVWS) carried out in Europe. TVWS Measurements in Germany showed that the extended Hata model is appropriate to describe the path loss over distances up to a few kilometers. During the TVWS trial in Slovenia, we combine infrastructure sensing with geo-location database access to protect not only DVB-T, but also wireless microphone (WM) signals from TVWS devices interference. © 2015 John Wiley & Sons, Inc.

Abstract
Generalized additive models are well-known as a powerful and palatable predictive modelling technique. Scorecards, the discretized version of generalized additive models, are a long-established method in the industry, due to its balance between simplicity and performance. Scorecards are easy to apply and easy to understand. Moreover, in spite of their simplicity, scorecards can model nonlinear relationships between the inputs and the value to be predicted. In the scientific community, scorecards have been largely overlooked in favor of more recent models such as neural networks or support vector machines. In this paper, we address scorecard development, introducing a new formulation more suitable to support regularization. We tackle both the binary and the ordinal data classification problems. In both settings, the proposed methodology shows advantages when evaluated using real datasets.