The Five-Pillar Methodology: A Crucial Approach for a Successful Community Service Project

The five-pillar methodology has been applied in the community service project Gonçalinho in Brazil. This paper provides an extension and guidelines on how to apply our methodology as a foundation for a successful general community service project. Further advice regarding step-by-step approach will be provided. Behind the five-pillar methodology, a central point related to education as a foundation and lessons learned for life will be pointed out. Important aspects regarding challenges, strategies and future trends will be addressed. Finally, additional examples based on a real community service project will be highlighted

Energy dependence of non-local potentials

Recently a variety of studies have shown the importance of including non-locality in the description of reactions. The goal of this work is to revisit the phenomenological approach to determining non-local optical potentials from elastic scattering. We perform a $\chi^2$ analysis of neutron elastic scattering data off $^{40}$Ca, $^{90}$Zr and $^{208}$Pb at energies $E \approx 5-40$ MeV, assuming a Perey and Buck or Tian, Pang, and Ma non-local form for the optical potential. We introduce energy and asymmetry dependencies in the imaginary part of the potential and refit the data to obtain a global parameterization. Independently of the starting point in the minimization procedure, an energy dependence in the imaginary depth is required for a good description of the data across the included energy range. We present two parameterizations, both of which represent an improvement over the original potentials for the fitted nuclei as well as for other nuclei not included in our fit. Our results show that, even when including the standard Gaussian non-locality in optical potentials, a significant energy dependence is required to describe elastic-scattering data.Comment: 6 pages, 3 figures, accepted by Phys. Rev. C Rapid Communicatio

On Learning by Exchanging Advice

One of the main questions concerning learning in Multi-Agent Systems is: (How) can agents benefit from mutual interaction during the learning process?. This paper describes the study of an interactive advice-exchange mechanism as a possible way to improve agents' learning performance. The advice-exchange technique, discussed here, uses supervised learning (backpropagation), where reinforcement is not directly coming from the environment but is based on advice given by peers with better performance score (higher confidence), to enhance the performance of a heterogeneous group of Learning Agents (LAs). The LAs are facing similar problems, in an environment where only reinforcement information is available. Each LA applies a different, well known, learning technique: Random Walk (hill-climbing), Simulated Annealing, Evolutionary Algorithms and Q-Learning. The problem used for evaluation is a simplified traffic-control simulation. Initial results indicate that advice-exchange can improve learning speed, although bad advice and/or blind reliance can disturb the learning performance.Comment: 12 pages, 6 figures, 1 table, accepted in Second Symposium on Adaptive Agents and Multi-Agent Systems (AAMAS-II), 200