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
Learning difficulties in computing courses is a situation perceived in diverse universities from different countries, cultures and backgrounds. These difficulties directly affect achievement rates and increase course evasion. We believe in the existence of a foundation of cognitive processes, that without it, even the most motivated student would have trouble to transform the received information into knowledge. This work has focused mainly on the research of candidate methods for cognitive processes assessment with a strong background theory. With this kind of information would be possible to devise cognitive interventions, in order to evolve students cognitive level, and consequently, raise their success rates. A systematic review was conducted and among the many researched methods we selected Lawson Classroom Test of Scientific Reasoning – LCTSR. Authorized by its author, we conducted the first translation of LCTSR to Brazilian Portuguese and administered to students of three undergraduate computing courses: Information Systems, Computer Science and Software Engineering. We also present results of its administration that we consider important to reinforce the above suggested strategy.

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.