Abstract
Context. Z CMa is a complex pre-main sequence binary with a current separation of 110 mas, known to consist of an FU Orionis star (SE component) and an embedded Herbig Be star (NW component). Although it represents a well-studied and characterized system, the origin of photometric variabilities, the component properties, and the physical configuration of the system remain mostly unknown. Aims. Immediately when the late-2008 outburst of Z CMa was announced to the community, we initiated a high angular resolution imaging campaign aimed at characterizing the outburst state of both components of the system in the near-infrared. Methods. We used the VLT/NACO and the Keck/NIRC2 near-infrared adaptive optics instrument to monitor the astrometric position and the near-infrared photometry of the Z CMa components during the outburst phase and one year after. The VLT/SINFONI and Keck/OSIRIS integral field spectroscrographs were in addition used to characterize for the first time the resolved spectral properties of the FU Orionis and the Herbig Be component during and after the outburst. Results. We confirm that the NW star dominates the system flux in the 1.1-3.8 mu m range and is responsible for the photometric outburst. We extract the first medium-resolution (R similar to 2000-4000) near-infrared (1.1-2.4 mu m) spectra of the individual components. The SE component has a spectrum typical of FU Orionis objects. The NW component spectrum is characteristic of embedded outbursting protostars and EX Or objects. It displays numerous emission lines whose intensity correlates with the system activity. In particular, we find a correlation between the Br gamma equivalent width and the system brightness. The bluing of the continuum of the NW component along with the absolute flux and color-variation of the system during the outburst suggests that the outburst was caused by a complex interplay between a variation of the extinction in the line of sight of the NW component on one hand, and the emission of shocked regions close to the NW component on the other. We confirm the recently reported wiggling of the SE component jet from [Fe II] line emission. We find a point-like structure associated with a peak emission at 2.098 mu m coincidental with the clump or arm seen in broadband polarization di ff erential imaging as well as additional di ff use emission along a PA = 214 degrees. The origin of these two structures is unclear and deserves further investigation.

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 project management area uses several techniques and tools to identify, select, monitor and manage the projects portfolio of the organizations. However, these techniques still present problems and difficulties which, sometimes, inhibit their use. Project Portfolio Management (PPM) is considered a problem of considerable difficulty and complexity, which requires its constant monitoring by the organization, according to the strategic guidelines chosen. Thus, this paper presents an innovative approach for selecting the portfolio of projects by using DEA, and the development of a set of generic indicators, to support the decision makers in considering multiple projects. Each decision maker can use all or some of the indicators proposed, thus each one's knowledge, sensitivity and intuition are taken into account, namely for indicators such as risk perception, level of innovation, market clock speed, project's complexity.

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
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