Abstract
The small amount of public available medical images hinders the use of deep learning techniques for mammogram automatic diagnosis. Deep learning methods require large annotated training sets to be effective, however medical datasets are costly to obtain and suffer from large variability. In this work, a lightweight deep learning pipeline to detect, segment and classify anomalies in mammogram images is presented. First, data augmentation using the ground-truth annotation is performed and used by a cascade segmentation and classification methods. Results are obtained using the INbreast public database in the context of lesion detection and BI-RADS classification. Moreover, a pre-trained Convolutional Neural Network using ResNet50 is modified to generate the lesion regions proposals followed by a false positive reduction and contour refinement stages while a pre-trained VGG16 network is fine-tuned to classify mammograms. The detection and segmentation stage results show that the cascade configuration achieves a DICE of 0.83 without massive training while the multi-class classification exhibits an MAE of 0.58 with data augmentation.

Abstract
This paper addresses a distribution problem involving a set of different products that need to be distributed among a set of geographically disperse retailers and transported from the single warehouse to the aforementioned retailers. The disfribution and transportation are made in order to satisfy retailers' demand while satisfying storage limits at both the warehouse and the retailers, transportation limits between the warehouse and the retailers, and other operational constraints. This problem is combinatorial in nature as it involves the assignment of a discrete finite set of objects, while satisfying a given set of conditions. Hence, we propose a genetic algorithm that is capable of finding good quality solutions. The genetic algorithm proposed is used to a real case study involving the disfribution of eight products among 108 retailers from a single warehouse. The results obtained improve on those of company's current practice by achieving a cost reduction of about 13%.

Abstract
Challenges in the coordination between the transmission system operator (TSO) and the distribution system operator (DSO) have risen continuously with the integration of distributed energy resources (DER). These technologies have the possibility to provide reactive power support for system operators. Considering the Portuguese reactive power policy as an example of the regulatory framework, this paper proposes a methodology for proactive reactive power management of the DSO using the renewable energy sources (RES) considering forecast uncertainty available in the distribution system. The proposed method applies a stochastic sequential alternative current (AC)-optimal power flow (SOPF) that returns trustworthy solutions for the DSO and optimizes the use of reactive power between the DSO and DER. The method is validated using a 37-bus distribution network considering real data. Results proved that the method improves the reactive power management by taking advantage of the full capabilities of the DER and by reducing the injection of reactive power by the TSO in the distribution network and, therefore, reducing losses.

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.