Abstract
The aim of the present work was to evaluate the possibility of using zinc-air batteries in intraoral medical devices. We analyzed the electrical behavior of zinc-air batteries when submitted to different levels of temperature, humidity, and limited quantities of air. The experimental setup was divided in three different parts. Firstly, a set of batteries were tested within a climatic chamber and subjected to discharging tests similar to those recommended by the manufacturer. The climatic chamber allowed an accurate variation of humidity and temperature. Secondly, the batteries were placed in a small prototype of intraoral medical device and tested in the absence of air. Lastly, we used a robot arm to repeatedly immerse the prototype in artificial saliva. The results obtained demonstrated the viability of zinc-air batteries as a power solution for intraoral medical devices, as they tolerate high levels of humidity and are capable of working with limited quantities of air. In addition, this kind of battery presents a volume to electrical capacity ratio more than three times higher than lithium batteries, which may open important improvement for powered medical devices.

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The performance of multi-objective evolutionary algorithms (MOEA) is severely deteriorated when applied to many-objective problems. For Pareto dominance based techniques, available information about optimal solutions can be used to improve their performance. This is the case of corner solutions. This work considers the behaviour of three multi-objective algorithms (NSGA-II, SMPSO and GDE3) when corner solutions are inserted into the population at different evolutionary stages. Corner solutions are found using specific algorithms. Preliminary results are presented concerning the behaviour of the aforementioned algorithms in five benchmark problems (DTLZ1-5).

Abstract
The particle swarm optimization (PSO), one of the most successful natural inspired algorithms, is revisited in the context of a proposal for a new teaching experiment. The problem considered is the open-loop step identification procedure, which is studied as an optimization problem. The PSO canonical algorithm main issues addressed within the proposed open-loop step identification experience are: the swarm random initialization methodology, the population size variation, and the inertia weight selection. The teaching experience learning outcomes are stated, simulation results presented, and feedback results from students analyzed. (c) 2011 Wiley Periodicals, Inc. Comput Appl Eng Educ 22:227-237, 2014; View this article online at ; DOI