Abstract
This work proposes a multi-criteria decision-making approach to select suppliers in the olive oil sector. Besides several performance criteria required to the supplier, olive oil characteristics such as colour, smell, and density, as well as organoleptic tests are used. Hence, the assessment and selection of suppliers assumes a major importance and needs to be done yearly. The process of finding a set of suppliers to choose from involves two sequential stages, namely identification and elimination. The identification stage consists of finding a set of potential suppliers. Then, in the elimination stage, suppliers that are not able to meet the thresholds associated with some technical indicators are disregarded. Thus, only a small set of very promising suppliers need to be assessed. The assessment was performed by resorting to the Macbeth approach, resulting in a ranking. The results obtained were validated through sensitivity and robustness analyses.

Abstract
Purpose The purpose of this paper is to review the existing research on industrial divestment in order to identify the reasons for it, the process whereby it is achieved, and the outcomes of industrial sell-offs and closures. The study reports the main findings that have gained acceptance in the literature, gaps in the research and potential directions for future research. Design/methodology/approach A three stage systematic literature review protocol was used to conduct this review. The results are organized according to an "Antecedents - Process - Outcomes" framework. Findings The traditional accounts of industrial divestment have been framed in terms of firms' weak performance and over-diversification as antecedents to divestment, leading to corporate governance issues. However, the list of antecedents of industrial divestment is more extensive. There is no consensus over the impact of some factors on divestments, as is the case of firm and unit size. The results are not conclusive as to whether firm performance improves after divesting. Originality/value This paper synthesizes knowledge of the main reasons as to why firms completely dispose of their assets, contributing to this under-researched field.

Abstract
In a market dominated by many mobile applications for all purposes, there is currently no visible application of cultural content or commercial content to support the tourist when traveling to Mirandela. Such applications currently available to the tourism industry are limited in content and cultural offerings, and commercial applications only communicate with their partners and have no cultural content.This work presents a framework of a mobile application implementation to support the tourist information needs when he is visiting Mirandela in which interest points are combined with commercial content.This solution allows the tourist to have only one application that integrates the contents that he wishes/subscribe, both cultural and commercial, creating a parametrizable universal profile that can be accessed by any of the entities in the system. In addition to the tourist benefiting from this solution that facilitates the exploration of interest points in Mirandela, the entities involved in the system will also benefit because they are able to increase the dissemination of their products and/or services and thereby increase their market potential and consequently their sales. Copyright © 2018 International Business Information Management Association (IBIMA).

Abstract
The present study puts forward a regression analytic model based on the random forest algorithm, developed to predict, at an early stage, the global academic performance of the undergraduates of a polytechnic higher education institution. The study targets the universe of an institution composed of 5 schools rather than following the usual procedure of delimiting the prediction to one single specific degree course. Hence, we intend to provide the institution with one single tool capable of including the heterogeneity of the universe of students as well as educational dynamics. A different approach to feature selection is proposed, which enables to completely exclude categories of predictive variables, making the model useful for scenarios in which not all categories of data considered are collected. The introduced model can be used at a central level by the decision-makers who are entitled to design actions to mitigate academic failure. © 2019, Springer Nature Switzerland AG.

Abstract
While the transmission system benefits from a high observability, the distribution system has a relatively low level of observability. This problem is already being addressed with the deployment of smart meters, in an effort to make the smart grid concept a reality. Nevertheless, as observability increases, so too does the volume of data, which makes the development of advanced software tools a very important subject. In this paper, the application of image analysis techniques to a low voltage grid is explored, by converting voltage data into an image format, using a cognitive network to evaluate and cluster grid operating modes. The proposed method is applied to a 33-bus low voltage grid to evaluate voltage profiles at each bus and the associated technical limits (voltage limits alarms).

Abstract