Abstract
The digital transformation in the manufacturing world raised an additional interest in data collection and connectivity, preferably to the one performed in real time. Internet of Things (IoT) and Machine to Machine (M2M) technologies allow the collection of the huge amount of generated data in the shop floor, at the desired rates and without any human intervention. This paper describes the application of IoT technologies to create an automatic data collection solution for an industrial metal stamping unit, supporting its posterior processing, aiming to develop monitoring, prediction and optimization in an industrial intelligent and predictive maintenance system.

Abstract
BACKGROUND: The Medtronic Registry for Epilepsy (MORE; Medtronic Inc, Dublin, Ireland) is an open label observational study evaluating the long-term effectiveness, safety, and performance of deep brain stimulation (DBS) of the anterior nucleus of thalamus (ANT) for the treatment of refractory epilepsy. OBJECTIVE: To compare the difference in success rate of placing contacts at ANT-target region (ANT-TR) between transventricular (TV) and extraventricular (EV) lead trajectories in 73 ANT-DBS implants in 17 European centers participating in the MORE registry. METHODS: The success rate of placing contacts at ANT-TR was evaluated using a screening method combining both individual patient imaging information and stereotactic atlas information to identify contacts at ANT-TR. RESULTS: EV lead trajectory was used in 53% of the trajectories. Approximately, 90% of the TV lead trajectories had at least 1 contact at ANT-TR, vs only 71% of the EV lead trajectories. The success rate for placing at least 1 contact at ANT-TR bilaterally was 84% for TV implants and 58% for EV implants (P <.05; Fisher's exact). No intracranial bleedings were observed, but 1 cortical infarct was reported following EV lead trajectory. CONCLUSION: The results of this registry support the use of TV lead trajectories for ANT-DBS as they have a higher probability in placing contacts at ANT-TR, without appearing to compromise procedural safety. Follow-up data collection is continuing in the MORE registry. These data will provide outcomes associated with TV and EV trajectories.

Abstract
This paper aims to understand the importance of ICT (Information and Communication Technologies) in the tourism sector, focusing on the Island of Porto Santo. We are in an increasingly digital world and information and communication technologies represents a way to be up to date and present in the market. The use of social networks by companies, are used as means of communication and promotion, with the purpose of understanding the behavior of tourist consumers. In order to understand the importance of ICT and social networks in the tourism sector of the Island of Porto Santo, a questionnaire was prepared and distributed in several places on the Island of Porto Santo. The results are presented in this article. For a better understanding of ICT and social networks, a literature review on the topic was also carried out. © 2019 AISTI.

Abstract
This article arises from the proposal of a new approach regarding the inclusion of soft-digital skills training in higher education. The study carried out on several curricular units in different higher education courses in Portugal led us to reflect on a different educational model, which combines the development of soft skills in digital environments. Digitalization and the use of technologies since early ages in the educational process are raising interesting questions. This article intends to go deeper on the use of digital technologies, namely through the virtual environments imposed by higher education institutions as a form of study. The main question is how pedagogies and the use of technologies have a meeting point where it is possible to continue humanization in education through the utilization of virtual environments to support the teaching/learning process. The methodology used in this study has its support on questionnaires made to students of higher education in different areas of knowledge, such as medicine, nursing, engineering, management, arts and literature. The main conclusions of this study are the importance of creating and using digital platforms that not only support the study but also contemplate the use of a virtual reality where students can interact with others in the discussion and resolution of real life situations.

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.