Abstract

Abstract
Automated negotiation plays a crucial role in the decision support for bilateral energy transactions. In fact, an adequate analysis of past actions of opposing negotiators can improve the decision-making process of market players, allowing them to choose the most appropriate parties to negotiate with in order to increase their outcomes. This paper proposes a new model to estimate the expected prices that can be achieved in bilateral contracts under a specific context, enabling adequate risk management in the negotiation process. The proposed approach is based on an adaptation of the Q-Learning reinforcement learning algorithm to choose the best scenario (set of forecast contract prices) from a set of possible scenarios that are determined using several forecasting and estimation methods. The learning process assesses the probability of occurrence of each scenario, by comparing each expected scenario with the real scenario. The final chosen scenario is the one that presents the higher expected utility value. Besides, the learning method can determine which is the best scenario for each context, since the behaviour of players can change according to the negotiation environment. Consequently, these conditions influence the final contract price of negotiations. This approach allows the supported player to be prepared for the negotiation scenario that is the most probable to represent a reliable approximation of the actual negotiation environment.

Abstract
This paper describes a Decision Support System (DSS) that aims to plan and maintain the weekly self-promotion space for an over the air TV station. The self-promotion plan requires the assignment of several self-promotion advertisements to a given set of available time slots over a pre-specified planning period. The DSS consists of a data base, a statistic module, an optimization module, and a user interface. The input data is provided by the TV station and by an external audiometry company, which collects daily audience information. The statistical module provides estimates based on the data received from the audiometry company. The optimization module uses a genetic algorithm that can find good solutions quickly. The interface reports the solution and corresponding metrics and can also be used by the decision makers to manually change solutions and input data. Here, we report mainly on the optimization module, which uses a genetic algorithm (GA) to obtain solutions of good quality for realistic sized problem instances in a reasonable amount of time. The GA solution quality is assessed using the optimal solutions obtained by using a branch-and-bound based algorithm to solve instances of small size, for which optimality gaps below 1% are obtained.

Abstract
Over the years robotics has made great progress. Nowadays, robots begin to be part of the life of any person, designated social robotic. Humanoid robots are fascinating and have several advantages, such as they can work in places where there is a risk of contamination, risk of health, danger of life, places that are difficult to access. They also are able to access different types of terrain and to climb stairs. NAO robot is currently the humanoid platform with high sensory capacity that it has lower costs in the market. This robot is similar to human in order to have a more real and natural with society. Using the capabilities of the robot and adding other sensors, we can have a more powerful machine in our society. NAO robot is presented in this paper as a domestic robot. © CLAWAR Association.

Abstract
FASTEN is an H2020 project under a bilateral call UE-Brazil. Embraer is a global aerospace company, with manufacturing and assembly lines in Europe, Brazil and USA. FASTEN aims to advance IoT and IoT enabled applications to support Industry 4.0 concepts, namely in the area of automation and additive manufacturing. The project results will be demonstrated through two pilots: one in Brazil, lead by a ThyssenKrupp use case, and the other in Europe, at Embraer facilities in Portugal. The project results for the Embraer use case will be presented, with emphasis on bilateral collaboration gains provided by exploiting common frameworks for development and open architecture, and future opportunities for exploitation discussed.

Abstract
NU Ori is a massive spectroscopic and visual binary in the Orion Nebula Cluster, with four components: Aa, Ab, B, and C. The B0.5 primary (Aa) is one of the most massive B-type stars reported to host a magnetic field. We report the detection of a spectroscopic contribution from the C component in high-resolution ESPaDOnS spectra, which is also detected in a Very Large Telescope Interferometer data set. Radial velocity (RV) measurements of the inner binary (designated Aab) yield an orbital period of 14.3027(7) d. The orbit of the third component (designated C) was constrained using both RVs and interferometry. We find C to be on a mildly eccentric 476(1) d orbit. Thanks to spectral disentangling of mean line profiles obtained via least-squares deconvolution, we show that the Zeeman Stokes V signature is clearly associated with C, rather than Aa as previously assumed. The physical parameters of the stars were constrained using both orbital and evolutionary models, yielding M-Aa = 14.9 +/- 0.5 M-circle dot, M-Ab = 3.9 +/- 0.7 M-circle dot, and M-C = 7.8 +/- 0.7 M-circle dot. The rotational period obtained from longitudinal magnetic field (B-z) measurements is P-rot = 1.09468(7)d, consistent with previous results. Modelling of (B-z) indicates a surface dipole magnetic field strength of similar to 8 kG. NU Ori C has a magnetic field strength, rotational velocity, and luminosity similar to many other stars exhibiting magnetospheric Ha emission, and we find marginal evidence of emission at the expected level (similar to 1 per cent of the continuum).