Abstract
Swallowing is a dynamic, complex and synergistic process, composed of three phases with a refined neuromotor control. A malfunction of this process, denominated dysphasia, can occur in any age like a result of congenital, structural, functional and/or medical problems. The quantitative analysis of this process is crucial to understand the temporal relations between the mechanisms of the oropharyngeal deglutition. Designing a software to support the qualitative and quantitative analysis of the swallowing process through dynamic images obtained by videofluoroscopy is the main motivation and objective of this work. First, a survey of requirements for such a software was made, consisting in a research protocol for assessing dysphagia by videofluoroscopy. Secondly, best practices in human-computer interaction were used to design a conceptual model for the proposed software. Two protocols were selected for the assessment of dysphagia by videofluoroscopy: the Protocol of Boston and the Protocol used in the Hospital Privado da Trofa. These protocols allowed the identification of several events that are evaluated in the swallowing process and that can be recorded, measured and quantified during ingestion of the bolus. The second phase resulted in a conceptual model for an interactive system embodying the evaluation protocol selected and contemplates the integration of automatic algorithms for qualitative and quantitative evaluation of the parameters of swallowing. The proposed software model has a high potential to be a useful tool for assessing parameters of swallowing.

Abstract
This study examines the association between different types of dependency on resources and/or pressures from the international community and the reporting practices on the fight against corruption of companies in an emerging country setting, that of Turkey. More specifically, we focus on the influence of multinationality, cross-listing, and membership of the United Nations Global Compact on this type of reporting. We use ordinal regression analysis to explore the association between the three factors mentioned above and anti-corruption reporting for a sample of Turkish firms on the Borsa Istanbul 100 index, while controlling for some other factors likely to influence anti-corruption reporting. Findings show a low level of reporting. They also suggest that companies with their shares cross-listed and companies which are members of the Uited Nations Global Compact do present higher levels of anticorruption reporting than their counterparts.

Abstract
The effective monitoring and maintenance of power lines are becoming increasingly important due to a global growing dependence on electricity. The costs and risks associated with the traditional foot patrol and helicopter-based inspections can be reduced by using UAVs with the appropriate sensors. However, this implies developing algorithms to make the power line inspection process reliable and autonomous. In order to overcome the limitations of visual methods in the presence of poor light and noisy backgrounds, we propose to address the problem of power line detection and modeling based on LiDAR. The PL2DM, Power Line LiDAR-based Detection and Modeling, is a novel approach to detect power lines. Its basis is a scan-by-scan adaptive neighbor minimalist comparison for all the points in a point cloud. The power line final model is obtained by matching and grouping several line segments, using their collinearity properties. Horizontally, the power lines are modeled as a straight line, and vertically as a catenary curve. Using a real dataset, the algorithm showed promising results both in terms of outputs and processing time, adding real-time object-based perception capabilities for other layers of processing.

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
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.