Abstract
This paper is concerned with the study of combined sizing and energy management algorithms for electric vehicles (EVs) endowed with batteries and supercapacitors (SCs). The main goal is to find the number of cells of each source that minimizes the installation and running costs of the EV, taking into account the performance requirements specified for the vehicle and the technical constraints of the energy sources. To tackle this problem, two methodologies will be investigated. The first considers a filter-based approach to perform the power split among the sources; it will be shown that, under some practical assumptions, the resultant sizing problem can be posed as a linear programming problem and solved using efficient numerical techniques. The second methodology employs an optimal noncausal energy management, which, when integrated with the sizing problem, yields a nonlinear optimization problem. These two methodologies will be then applied to size the storage unit of a small EV. The results indicate that the filter-based approach, although simple and numerically efficient, generally requires an oversized storage unit. Furthermore, it was also concluded that, if the range requirements of the EV are not very high (below 50 km, in our case study), the use of SCs enables energy savings of up to 7.8%.

Abstract

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

Abstract
Autonomous underwater vehicles (AUVs) are increasingly being used to perform mine countermeasures (MCM) operations but its capabilities are limited by the efficiency of the planning process. Here we study the problem of multiobjective MCM mission planning with AUVs. The vehicle should cover the operating area while maximizing the probability of detecting the targets and minimizing the required energy and time to complete the mission. A multi-stage algorithm is proposed and evaluated. Our algorithm combines an evolutionary algorithm (EA) with a local search procedure, aiming at a more flexible and effective exploration and exploitation of the search space. An artificial neural network (ANN) model was also integrated in the evolutionary procedure to guide the search. The combination of different techniques creates another problem, related to the high amount of parameters that needs to be tuned. Thus, the effect of these parameters on the quality of the obtained Pareto Front was assessed. This allowed us to define an adaptive tuning procedure to control the parameters while the algorithm is executed. Our algorithm is compared against an implementation of a known EA as well as another mission planner and the results from the experiments show that the proposed strategy can efficiently identify a higher quality solution set. © 2014 World Scientific Publishing Company.

Abstract
The aim of this article has been to classify swimmers based on kinematics, hydrodynamics, and anthropometrics. Sixty-seven young swimmers made a maximal 25 m front-crawl to measure with a speedometer the swimming velocity (v), speed-fluctuation (dv) and dv normalized to v (dv/v). Another two 25 m bouts with and without carrying a perturbation device were made to estimate active drag coefficient (C-Da). Trunk transverse surface area (S) was measured with photogrammetric technique on land and in the hydrodynamic position. Cluster 1 was related to swimmers with a high speed fluctuation (ie, dv and dv/v), cluster 2 with anthropometrics (ie, S) and cluster 3 with a high hydrodynamic profile (ie, C-Da). The variable that seems to discriminate better the clusters was the dv/v (F = 53.680; P < .001), followed by the dv (F = 28.506; P < .001), C-Da (F = 21.025; P < .001), S (F = 6.297; P < .01) and v (F = 5.375; P = .01). Stepwise discriminant analysis extracted 2 functions: Function 1 was mainly defined by dv/v and S (74.3% of variance), whereas function 2 was mainly defined by C-Da (25.7% of variance). It can be concluded that kinematics, hydrodynamics and anthropometrics are determinant domains in which to classify and characterize young swimmers' profiles.

Abstract
Nowadays, education extends beyond the field of formal education delivered in universities and colleges, and is increasingly based on new technological developments. Long Life Learning (LLL) is a fundamental tool in today's world in order to remain competitive in the market. The ease of access to the Internet and computer technologies has turned into a new field for developing applications which can be distributed worldwide in an almost instantaneous manner, but must be appropriately designed in order to achieve their goals. Quality e-learning courseware requires several aspects to be covered in order to achieve its purposes. These include a thorough knowledge of the content as well as methods of delivery and learning theory. These requirements were taken into account in the development of a virtual-lab environment, named VEMA, developed by one of the authors and used in the Bachelor and Master's classes.