Abstract
Surgical tool detection is attracting increasing attention from the medical image analysis community. The goal generally is not to precisely locate tools in images, but rather to indicate which tools are being used by the surgeon at each instant. The main motivation for annotating tool usage is to design efficient solutions for surgical workflow analysis, with potential applications in report generation, surgical training and even real-time decision support. Most existing tool annotation algorithms focus on laparoscopic surgeries. However, with 19 million interventions per year, the most common surgical procedure in the world is cataract surgery. The CATARACTS challenge was organized in 2017 to evaluate tool annotation algorithms in the specific context of cataract surgery. It relies on more than nine hours of videos, from 50 cataract surgeries, in which the presence of 21 surgical tools was manually annotated by two experts. With 14 participating teams, this challenge can be considered a success. As might be expected, the submitted solutions are based on deep learning. This paper thoroughly evaluates these solutions: in particular, the quality of their annotations are compared to that of human interpretations. Next, lessons learnt from the differential analysis of these solutions are discussed. We expect that they will guide the design of efficient surgery monitoring tools in the near future.

Abstract
Actual integration of high-tech devices brings opportunities for better monitoring, management and control of low voltage networks. In this new paradigm, efficient tools should cope with the great amount of dispersed and considerably distinct data to support smarter decisions in almost real time. Besides the use of tools to enable an optimal network reconfiguration and integration of dispersed and renewable generation, the impact evaluation of integrating storage systems, accurate load forecasting methods must be found even when applied to individual consumers (characterized by the high presence of noise in time series). As this effort becomes providential in the smart grids context, this article compares three different approaches: one based on Kernel Density Estimation, an alternative based on Artificial Neural Networks and a method using Support Vector Machines. The first two methods revealed unequivocal benefits when compared to a Naive method consisting of a simple reproduction of the last available day.

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
In a more and more competitive and industrialized market, it is essential that companies realize that the way forward must go through the optimization of their production processes, reducing the costs and increasing product quality. Nowadays it's necessary to adopt innovative management models that can provide increased productivity at minimal costs, such as the Lean thinking. The metalworking industry is integrated into one of the most competitive existing markets in Portugal. Given this, it's fundamental to reduce the waste in all sectors of the production process, using the good Lean principles and practices, such as the Single Minute Exchange of Die, also known as SMED methodology. This paper presents a project of implementing the SMED methodology in the cold profiling process, in a population of five different profiling machines. The results of the SMED implementation show an average OEE improvement of 10,8%.