Abstract
The brain emotional learning (BEL) control paradigm has been gathering increased interest by the control systems design community. However, the lack of a consistent mathematical formulation and computer based tools are factors that have prevented its more widespread use. In this article both features are tackled by providing a coherent mathematical framework for both the continuous and discrete-time formulations and by presenting a SIMULINK (R) computational tool that can be easily used for fast prototyping BEL based control systems.

Abstract
This paper presents a General Morphological Analysis (GMA) meta-model aiming to help decision-makers wishing to integrate sustainability concerns into the company strategy. This is made by joining Operational Research (OR) analysts, decision-makers and stakeholders as participants in the problem structuring and formulation process. This is particularly relevant in societal issues, where public transport companies are particularly important. Indeed, public transport companies play a quite visible role in the dimensions of corporate social responsibility, namely because of four reasons: (i) they provide daily services crucial to mass customers' mobility; (ii) their investments are usually of high value and rather sensitive to technological development; (iii) they play a crucial role in the energy sector and (iv) are strongly dependent upon macro-policies. © 2017 The Authors. Published by Elsevier B.V.

Abstract
The design of efficient and high power density electrical machines needs an accurate characterization of magnetic phenomena. Core losses estimation is usually addressed by empirical models, where its lack of accuracy is well known. Hysteresis models are able to take an insight into the magnetic physical mechanisms. Compared to the empirical models, they contribute to a higher accuracy in modeling electromagnetic systems, including core losses estimation. At a macroscopic level, two models are often used: the Preisach and the Jiles-Atherton (J-A) models. This paper presents their basic formulation, as well the main limitations and scope of application. This is a first step to investigate the possible application of hysteresis models, in order to reach accurate core losses estimation in switched reluctance machines.

Abstract
In this paper we design a model based method to locate a leakage and estimate its size in a gas network, using a linearised version of an hyperbolic PDE. To do this, the problem is reduced to two identical ODEs, allowing in this way for a representation of the pressure as well as the mass flow in terms of its system of fundamental solutions. Then using the available measurements at the grid boundary points, the correspondent coefficients can be determined. Assuming pressure continuity, we check for consistency of the coefficients in order to find faulty pipelines. Thence, the location of the leakage can be found either graphically or using a numerical method for a specific pipe. Next, its size can also be estimated. © Springer International Publishing Switzerland 2017.

Abstract
Emerging markets contain the vast majority of the world's population. Despite the huge number of inhabitants, these markets still lack a proper finance infrastructure. One of the main difficulties felt by customers is the access to loans. This limitation arises from the fact that most customers usually lack a verifiable credit history. As such, traditional banks are unable to provide loans. This paper proposes credit scoring modeling based on non-traditional data, acquired from smartphones, for loan classification processes. We use Logistic Regression (LR) and Support Vector Machine (SVM) models which are the top performers in traditional banking. Then we compared the transformation of the training datasets creating boolean indicators against recoding using Weight of Evidence (WoE). Our models surpassed the performance of the manual loan application selection process, loans granted through the models criteria presented fewer overdues, also the approval criteria of the models increased the amount of granted loans substantially. Compared to the baseline, the loans approved by meeting the criteria of the SVM model presented -196.80% overdue rate. At the same time, the approval criteria of the SVM model generated 251.53% more loans. This paper shows that credit scoring can be useful in emerging markets. The non-traditional data can be used to build algorithms that can identify good borrowers as in traditional banking.

Abstract