Abstract
In Portugal people aged 65 and above will be the populational group with the highest growth rate of the next decades. Healthy ageing constitutes a challenge as not only the number of years lived are essential, but also the quality of life becomes a priority. The aim of this study was to understand the relationship between health self-perception (HSP), food consumption and nutritional status among Portuguese older adults. Four hundred fifty-nine older adults (aged >= 65) were interviewed, almost half had a positive HSP. The most significant predictor of HSP was nutritional status, p = 0.005, but independence in shopping had the largest impact on how they compared to others, p < 0.001. Interventions need to focus on factors like nutritional status, independency on Activities of Daily Living, Satisfaction with Food-Related Life and utilize them to modify the negative HSP and attitudes towards ageing and health and maximize the positive aspects of old age.

Abstract
The 4th industrial revolution is marked by the use of Cyber-Physical Systems (CPSs) to achieve higher levels of flexibility and adaptation in production systems that need to cope with a demanding and ever-changing market, driven by mass customization and high quality products. In this context, data analysis is a key technology enabler in the development of intelligent machines and products. However, in addition to Cloud-based data analysis services, the realization of such CPS requires technologies and approaches capable to effectively support distributed and embedded data analysis capabilities. The advances in Edge Computing have promoted the data processing near or at the devices that produce data, which combined with Multi-Agent Systems, allow to develop solutions based on distributed and interacting autonomous entities in open and dynamic environments. In this sense, this paper presents a modular agent-based architecture to design and embed cyber-physical components with data analysis capabilities. The proposed approach defines a set of data processing modules that can be combined to build cyber-physical agents to be deployed at different computational layers. The proposed approach was applied in a smart inspection station for electric motors, where agents embedding data analysis algorithms were distributed among Edge, Fog and Cloud layers. The experimental results illustrated the benefits of distributing the data analysis by different computational layers. © 2019, Springer Nature Switzerland AG.

Abstract
The authors wish to make the following erratum to this paper [1]: Equations (1), (7), and (9) are incorrect and must be replaced by the following equations: [Formula presented] The authors apologize for this literal mistake, but emphasize that the content of the article is still correct, since all calculations were performed with the correct equations. The manuscript will be updated and the original will remain online on the article webpage, with a reference to this Erratum.

Abstract
This study presents an ac-dc-ac converter with eight controlled switches. The proposed topology is applied as a universal active power filter, compensating for disturbances in the grid voltage, voltage sags/swells, and harmonics generated by non-linear loads. The difference between the proposed and conventional solution is the addition of an uncontrolled leg (composed only of diodes) to increase the number of voltage levels, reducing harmonics and the average switching frequency. For performance comparisons, current total harmonic distortion (THD), voltage weighted THD, switching and conduction losses of the semiconductors, and average switching frequency were used as parameters. Control and pulse-width modulation techniques based on space vector approaches were developed to eliminate zero-crossing distortions caused by the use of the uncontrolled leg. Simulation and experimental results were presented for validation of the mathematical model.

Abstract
This paper addresses the problem of course (path) generation when a learner's available time is not enough to follow the complete course. We propose a method to recommend successful paths regarding a learner's available time and his/her knowledge background. Our recommender is an instance of long term goal recommender systems (LTRS). This method, after locating a target learner in a course graph, applies a depth-first search algorithm to find all paths for the learner given a time limitation. In addition, our method estimates learning time and score for all paths. It also indicates the probability of error for the estimated time and score for each path. Finally, our method recommends a path that satisfies the learner's time restriction while maximizing expected learning score. In order to evaluate our proposals for time and score estimation, we used the mean absolute error and average MAE. We have evaluated time and score estimation methods, including one proposed in the literature, on two E-learning datasets.

Abstract
This article presents MixMash, an interactive tool which streamlines the process of music mashup creation by assisting users in the process of finding compatible music from a large collection of audio tracks. It extends the harmonic mixing method by Bernardes, Davies and Guedes with novel degrees of harmonic, rhythmic, spectral, and timbral similarity metrics. Furthermore, it revises and improves some interface design limitations identified in the former model software implementation. A new user interface design based on cross-modal associations between musical content analysis and information visualisation is presented. In this graphic model, all tracks are represented as nodes where distances and edge connections display their harmonic compatibility as a result of a force-directed graph. Besides, a visual language is defined to enhance the tool's usability and foster creative endeavour in the search of meaningful music mashups.