Abstract
The development of relationships by private non-profit organizations (NPOs) with companies is fundamental to their sustainability. However, relational marketing and the definition of fundraising and communication strategies tend to be under-explored, resulting in punctual relationships with donors. This paper proposes to explore ways of developing relationships between NPOs and companies. After a systematization of the main contributions present in the literature, a qualitative and exploratory study was presented, in which semi-structured interviews were conducted with 8 NPO managers with different fields of activity and 16 managers of small, medium and large companies, all with headquarters in the district of Aveiro. The results show that what drives companies to support NPOs are essentially non-material benefits, such as the emotional impact on employees and other stakeholders. There are clear differences between small and large companies, the latter being more prone to formal relations. It was verified, however, that even the occasional supports can be recurrent and repeated, lacking the capacity to protocol the relation. Overall, the data offer interesting clues to NPO staff, namely resulting from a comparison of the different perspectives of the participants.

Abstract
The integration of renewable generation and electric vehicles (EVs) into smart grids poses an additional challenge to the stochastic energy resource management problem due to the uncertainty related to weather forecast and EVs user-behavior. Moreover, when electricity markets are considered, market price variations cannot be disregarded. In this paper, a two-stage stochastic programming approach to schedule the day-ahead operation of energy resources in smart grids under uncertainty is presented. A realistic case study is performed using a large-scale scenario with nearly 4 million variables with the goal to minimize expected operation cost of energy aggregators. Three scenarios are analyzed to understand the effect of market transactions and external suppliers on the aggregator model. The results suggest that the market transactions can reduce expected cost, while the external supplier offers risk-free price. In addition, the performance metric shows the superiority of the stochastic approach over an equivalent deterministic model. © 2018 Power Systems Computation Conference.

Abstract
This paper presents a platform for real and simulated autonomous sailing competitions, which can also be used as a research tool to test and assess navigation algorithms. The platform provides back-end services – competition server, boat modelling and data storage – and supports external browsers and software agents as front-end clients. The back-end adopts the Multi-Agent System (MAS) paradigm for the internal modelling of sailing boats and offers a Web Service Application Programming Interface (API) for the external software agents and a Web application for Web browsers. As a whole, the platform offers tracking (real competitions) and simulation (simulated competitions) modes. The testing and assessment of navigation algorithms and boat models correspond to private simulated competitions. In simulation mode, the back-end internal boat agent implements a simplified physical model, including the weight, sail area, angle of the sail and rudder, velocity and direction of the wind and position and velocity of the hull, whereas the front-end external boat agent implements the navigation algorithm on the team side, ensuring the privacy of strategic knowledge. The Web application allows the configuration and launching of competitions, the registration of teams and researchers, the uploading of boat physical features for simulation as well as the live or playback viewing of real and simulated competitions. The simulation mode is illustrated with the help of a case study. The proposed platform, which is open, scalable, modular and distributed, was designed for the research community to prepare, run and gather data from real and simulated autonomous sailing competitions.

Abstract
IEEE 1687 standard (IJTAG), as an extension to the IEEE 1149.1, facilitates efficient access to embedded instruments by supporting reconfigurable scan networks. Specifically, IJTAG allows each IP to be wrapped by a test data register (TDR) whose access is controlled by a segment insertion bit (SIB) or a scan-mux control bit (SCB). Because the TDRs and the SIB/SCB network are typically not public, but critical for accessing embedded instruments, they might be used for illegitimate purposes, such as dumping credential data and reverse engineering IP design. Machine learning has been proposed to detect such attacks, but the large number of instruments and parallel execution enabled by the IJTAG produce high-dimensional data, which poses a challenge to on-chip detection. In this paper, we propose to reduce the high-dimensional but sparse data using a low-density parity-check (LDPC) matrix. Experiments using a modified version of the OpenSPARC T2 to include IJTAG functionality demonstrate that the use of feature reduction eliminates 91% of the features, leading to 43% reduction in circuit size without affecting detection accuracy. Also, the on-chip detector adds moderate overhead (similar to 8%) to the IJTAG.

Abstract
Many real-world robotic scenarios require performing task planning to decide courses of actions to be executed by (possibly heterogeneous) robots. A classical centralized planning approach that considers in the same search space all combinations of robots and goals could lead to inefficient solutions that do not scale well. Multi-Agent Planning (MAP) provides a good framework to solve this kind of tasks efficiently. Some MAP techniques have proposed to previously assign goals to agents (robots) so that the planning effort decreases. However, these techniques do not scale when the number of agents and goals grow, as in most real world scenarios with big maps or goals that cannot be reached by subsets of robots. In this paper we propose to help the computation of which goals should be assigned to each agent by using Actuation Maps (AMs). Given a map, AMs can determine the regions each agent can actuate on. They help on alleviating the effort of MAP techniques knowing which goals can be tackled by each agent, as well as cheaply estimating the cost of using each agent to achieve every goal. Experiments show that when information extracted from AMs is provided to the Multi Agent planner, goal assignment is significantly faster, speeding-up the planning process considerably. Experiments also show that this approach greatly outperforms classical centralized planning.

Abstract
The Braçal –Malhada mining region, 30km NE of Aveiro included several silver-lead mines that operated until mid 20th century. Mineralization consists on sulphides (galena, sphalerite, pyrite) in quartz veins occurring in the complex Porto-Tomar shear zone, Fig. 1. Veins develop in a 10km wide area, show different orientations, are found in granites, schists and even quartzite, and can pass through different rock types with no alteration. Exploration work in the area dates from the 1950´s and 70’s. This study compiles recent geological mapping, preliminary regional geochemical and geophysical surveys. It aims to obtain a general information on the area, delineate regions for further studies, that is, fieldwork with closer grids, complementary methods (such as electromagnetics, induced polarization), etc. Because of space limitations, only selected maps are given herein. Geophysics consisted on preliminary magnetic and gravimetric mapping. Residual, gradient, second derivative and Euler deconvolution magnetic and gravimetric maps were produced, interpreted against known geology, tectonics and interesting features for further exploration work are revealed. A stream sediments sampling geochemical campaign, overall 101 samples, was carried out and allowed identifying the main geochemical association of elements. Finally, geophysical and geochemical data are integrated to provide an enhanced view of the area.