Abstract
FASTEN is an H2020 project under a bilateral call UE-Brazil. Embraer is a global aerospace company, with manufacturing and assembly lines in Europe, Brazil and USA. FASTEN aims to advance IoT and IoT enabled applications to support Industry 4.0 concepts, namely in the area of automation and additive manufacturing. The project results will be demonstrated through two pilots: one in Brazil, lead by a ThyssenKrupp use case, and the other in Europe, at Embraer facilities in Portugal. The project results for the Embraer use case will be presented, with emphasis on bilateral collaboration gains provided by exploiting common frameworks for development and open architecture, and future opportunities for exploitation discussed.

Abstract
This paper presents the Adaptive Decision Support for Electricity Markets Negotiations (AiD-EM) system. AiD-EM is a multi-agent system that provides decision support to market players by incorporating multiple sub-(agent-based) systems, directed to the decision support of specific problems. These sub-systems make use of different artificial intelligence methodologies, such as machine learning and evolutionary computing, to enable players adaptation in the planning phase and in actual negotiations in auction-based markets and bilateral negotiations. AiD-EM demonstration is enabled by its connection to MASCEM (Multi-Agent Simulator of Competitive Electricity Markets).

Abstract
In today’s era, most of the people are suffering with chronic diseases because of their lifestyle, food habits and reduction in physical activities. Diabetes is one of the most common chronic diseases which has affected to the people of all ages. Diabetes complication arises in human body due to increase of blood glucose (sugar) level than the normal level. Type-2 diabetes is considered as one of the most prevalent endocrine disorders. In this circumstance, we have tried to apply Machine learning algorithm to create the statistical prediction based model that people having diabetes can be aware of their prevalence. The aim of this paper is to detect the prevalence of diabetes relevant complications among patients with Type-2 diabetes mellitus. The processing and statistical analysis we used are Scikit-Learn, and Pandas for Python. We also have used unsupervised Machine Learning approaches known as Artificial Neural Network (ANN) and K-means Clustering for developing classification system based prediction model to judge Type-2 diabetes mellitus chronic diseases.

Abstract
Literature has highlighted the challenges of selling experience (vs. search) products online. In addition, the role of celebrity endorsers in purchase intention and attitudes towards brands has been emphasized by scholars. This article argues that celebrities provide cues on products' sensorial characteristics that have been so far disregarded by extant literature. By choosing perfume as a complex experience product, twenty-seven participants from three countries were interviewed in order to find how endorsers could assist e-shoppers to identify fragrant characteristics in the absence of the real scent. The results of the qualitative content analysis reveal that endorsers' personality traits and lifestyle could act as predictor of the type of scent. Scent categorization based on such traits are presented. This article provides valuable contributions to both researchers and practitioners interested in online sales of experience goods. Limitations and avenues for future search are also provided.

Abstract
The increase in productivity is a demand for modern industries that need to be competitive in the actual business scenario. To face these challenges, companies are increasingly using robotic systems for end-of-line production tasks, such as wrapping and palletizing, as a mean to enhance the production line efficiency and products traceability, allowing human operators to be moved to more added value operations. Despite this increasing use of robotic systems, these equipments still present some inconveniences regarding the programming procedure, as the time required for its execution does not meet the current industrial needs. To face this drawback, offline robot programming methods are gaining great visibility, as their flexibility and programming speed allows companies to face the need of successive changes in the production line set-up. However, even with a great number of robots and simulators that are available in market, the efforts to support several robot brands in one software did not reach the needs of engineers. Therefore, this paper proposes a translation library named AdaptPack Studio Translator, which is capable to export proprietary codes for the ABB, Fanuc, Kuka, and Yaskawa robot brands, after their offline programming has been performed in the Visual Components software. The results presented in this paper are evaluated in simulated and real scenarios.

Abstract
This work addresses the automatic segmentation of the joint capsule in ultrasound images of the metacarpophalangeal joint using an adapted version of the well known UNet model. These images are used in the diagnosis of rheumatic diseases, one of the main causes of impairment and pain in developed countries. The identification of the joint capsule gives important clues about the presence or Rheumatoid Arthritis. This structure can be used to extract metrics to help quantify the disease stage and progression. The solution proposed here has the potential to reduce the burden on the radiologists as well as the subjectivity of the diagnosis by providing quantitative measurements, such as the synovitis area. The proposed approach was compared with two other works present in the literature. Results show that our solution outperforms the two reference methods with 90% of the joint capsules identified with a DICE higher than 0.67.