Abstract
In this paper the first steps for the derivation of a mathematical model to describe the mechanical behaviour of a cylindrical electromagnetic vibration energy harvester, designed to extract energy from human gait to power biomedical implantable devices, are provided. As it is usual, in the modelling of such devices, the proposed mechanical model is also based on the solution of Newton's second law, but here a nonlinear closed-form expression is used for the resulting magnetic force of the system, unlike what has been done in previous works where, traditionally, that expression is a linear or is a nonlinear approximation of the real one. The main feature of this mechanical model is that it depends on several parameters which are related to the main characteristics of this kind of devices, which constitutes a major advantage with respect to the usual models available in the literature since these characteristics can always be changed in order to optimize the device.

Abstract
Metaheuristics are very useful to achieve good solutions in reasonable execution times. Sometimes they even obtain optimal solutions. However, to achieve near-optimal solutions, the appropriate tuning of parameters is required. This paper presents a Racing based learning module proposal for an autonomous parameter tuning of Metaheuristics. After a literature review on Metaheuristics parameter tuning and Racing approaches, the learning module is presented. A computational study for the resolution of the Scheduling problem is also presented. Comparing the preliminary obtained results with previous published results allow to conclude about the effectiveness and efficiency of this proposal. © 2015 AISTI.

Abstract
In this paper we present a methodology to implement multiple traversal algorithms in a functional programming setting. The implementations we obtain s of highly modular and intermediate structure free programs, that rely on the concept of functional zippers to navigate on data structures. Even though our methodology is developed and presented under Haskell, a lazy functional language, we do not make essential use of laziness. This is an essential difference with respect to other attribute grammar embeddings. This also means that an approach similar to ours can be followed in a strict functional setting such as Ocaml, for example. In the paper, our technique is applied to a significant number of problems that are well-known to the functional programming community, demonstrating its practical interest.

Abstract
Hybrid logics, which add to the modal description of transition structures the ability to refer to specific states, offer a generic framework to approach the specification and design of reconfigurable systems, i.e., systems with reconfiguration mechanisms governing the dynamic evolution of their execution configurations in response to both external stimuli or internal performance measures. A formal representation of such systems is through transition structures whose states correspond to the different configurations they may adopt. Therefore, each node is endowed with, for example, an algebra, or a first-order structure, to precisely characterise the semantics of the services provided in the corresponding configuration. This paper characterises equivalence and refinement for these sorts of models in a way which is independent of (or parametric on) whatever logic (propositional, equational, fuzzy, etc) is found appropriate to describe the local configurations. A Hennessy-Milner like theorem is proved for hybridised logics.

Abstract
The high penetration of distributed energy resources (DER) in distribution networks and the competitive environment of electricity markets impose the use of new approaches in several domains. The network cost allocation, traditionally used in transmission networks, should be adapted and used in the distribution networks considering the specifications of the connected resources. The main goal is to develop a fairer methodology trying to distribute the distribution network use costs to all players which are using the network in each period. In this paper, a model considering different type of costs (fixed, losses, and congestion costs) is proposed comprising the use of a large set of DER, namely distributed generation (DG), demand response (DR) of direct load control type, energy storage systems (ESS), and electric vehicles with capability of discharging energy to the network, which is known as vehicle-to-grid (V2G). The proposed model includes three distinct phases of operation. The first phase of the model consists in an economic dispatch based on an AC optimal power flow (AC-OPF); in the second phase Kirschen's and Bialek's tracing algorithms are used and compared to evaluate the impact of each resource in the network. Finally, the MW-mile method is used in the third phase of the proposed model. A distribution network of 33 buses with large penetration of DER is used to illustrate the application of the proposed model.

Abstract
Bipolar Disorder (BD) is a chronic and disabling disease that usually appears around 20 to 30 years old. Patients who suffer with BD may struggle for years to achieve a correct diagnosis, and only 50% of them generally receive adequate treatment. In this work we apply a machine learning technique called Inductive Logic Programming (ILP) in order to model relapse and no-relapse patients in a first attempt in this area to improve diagnosis and optimize psychiatrists' time spent with patients. We use ILP because it is well suited for our multi-relational dataset and because a human can easily interpret the logical rules produced. Our classifiers can predict relapse cases with 92% Recall and no-relapse cases with 73% Recall. The rules and variable theories generated by ILP reproduce some findings from the scientific literature. The generated multi-relational models can be directly interpreted by clinicians and researchers, and also open space to research biological mechanisms and interventions. © 2015 IMIA and IOS Press.