Abstract
Feed in generation was introduced in Portugal in 1988 to induce investments in endogenous and renewable energy resources. The feed in mechanism was adapted along time and its application was very successful so that currently more than 40% of the installed capacity is under this regime. Typically feed in tariffs are larger than average market prices so that there is a recurrent debate on whether this regime is pressing or not the end-user tariffs. This paper reports the main results obtained by the first author in his MSc Thesis in assessing the total generation cost under the current legal provisions on one side and, on the other, eliminating feed in generation from the mix. The results obtained using public data for 2017 indicate that the generation cost with feed in generation is 2,70% larger than the value obtained if it was eliminated from the market clearing process. © 2020 IEEE.

Abstract
Current research shows discrepancies in the gait pattern of patients with peripheral artery disease (PAD). Some studies suggest a change in gait pattern after the manifestation of claudication pain while others found patients with PAD already show a pathological gait, even before the intermittent claudication arises, and no change once the pain manifests. This exploratory research examines what change in gait pattern can be detected once claudication pain arises with the help of an accelerometer embedded in a smartphone. This study aims to contribute to the development of a process to remotely collect accelerometer data in a mobile health application, which then can be used to analyze gait pattern in patients with PAD on a larger scale. The findings of this exploratory study show that processing and analyzing accelerometer data from smartphone for gait analysis is viable and establishes a methodology for collecting and analyzing PAD patients' data. The major limitation of this study is the small sample size that do not provide the necessary reliability of the findings, about gait pattern changes. Further gait data should be collected to help understanding the gait pattern of PAD patients and build an extended dataset to be analyzed at a larger scale.

Abstract
The fourth industrial revolution promotes Industrial Cyber Physical Systems (ICPS) as the key to achieve smart, efficient, flexible and self-organizing production plants. In a shop floor there are heterogeneous physical and logical assets that form the ICPS. But without proper communication and composition techniques the integration of these assets in ICPS is compromised. Component Based Software Engineering (CBSE) is a discipline of growing relevance for ICPS because integration and composition issues have been extensively researched in the software domain. Under the Reference Architecture for Industry 4.0 (RAMI 4.0), the Industry 4.0 Component Model inherits aspects of CBSE to specify how several industrial plant assets can form an ICPS. The technological aspects for physical assets digitalization and integration have been explored, but the I4.0 Component model lacks proposals and use cases for dealing with industrial software components. In this work we discuss the development of the Smart Component Model as a proposal for integration of software components in ICPS. Furthermore, we focus on how prediction and monitoring applications could be converted in I4.0 Components and integrated in ICPS. To sustain our proposals, we describe a real industrial case study where these developments are being applied. © 2020 IEEE.

Abstract
Elite swimmers and swimming pool employees are likely to be at greater health risk due to their regular and intense exposure to air stressors in the indoor swimming pool environment. Since data on the real long-term exposure is limited, a long-term monitoring and sampling plan (22 non-consecutive days, from March to July 2017) was carried out in an indoor Olympic-size pool with a chlorine-based disinfection method to characterize indoor environments to which people involved in elite swimming and maintenance staff may be exposed to. A comprehensive set of parameters related with comfort and environmental conditions (temperature, relative humidity (RH), carbon dioxide (CO2) and monoxide and ultrafine particles (UFP)) were monitored both indoors and outdoors in order to determine indoor-to-outdoor (I/O) ratios. Additionally, an analysis of volatile organic compounds (VOC) concentration and its dynamics was implemented in three 1-hr periods: early morning, evening elite swimmers training session and late evening. Samplings were simultaneously carried out in the air layer above the water surface and in the air surrounding the pool, selected to be representative of swimmers and coaches/employees' breathing zones, respectively. The results of this work showed that the indoor climate was very stable in terms of air temperature, RH and CO 2 . In terms of the other measured parameters, mean indoor UFP number concentrations (5158 pt/cm(3)) were about 50% of those measured outdoors whereas chloroform was the predominant substance detected in all samples collected indoors (13.0-369.3 mu g/m(3)), among a varied list of chemical compounds. An I/O non-trihalomethanes (THM) VOC concentration ratio of 2.7 was also found, suggesting that, beyond THM, other potentially hazardous VOC have also their source(s) indoors. THM and non-THM VOC concentration were found to increase consistently during the evening training session and exhibited a significant seasonal pattern. Compared to their coaches, elite swimmers seemed to be exposed via inhalation to significantly higher total THM levels, but to similar concentrations of non-THM VOC, during routine training activities. Regarding swimming employees, the exposure to THM and other VOC appeared to be significantly minimized during the early morning period. The air/water temperature ratio and RH were identified as important parameters that are likely to trigger the transfer processes of volatile substances from water to air and of their accumulation in the indoor environment of the swimming pool, respectively.

Abstract

Abstract
The deep inferior epigastric perforator (DIEP) is the most commonly used free flap in mastectomy reconstruction. Preoperative imaging techniques are routinely used to detect location, diameter and course of perforators, with direct intervention from the imaging team, who subsequently draw a chart that will help surgeons choosing the best vascular support for the reconstruction. In this work, the feasibility of using a computer software to support the preoperative planning of 40 patients proposed for breast reconstruction with a DIEP flap is evaluated for the first time. Blood vessel centreline extraction and local characterization algorithms are applied to identify perforators and compared with the manual mapping, aiming to reduce the time spent by the imaging team, as well as the inherent subjectivity to the task. Comparing with the measures taken during surgery, the software calibre estimates were worse for vessels smaller than 1.5 mm (P = 6e-4) but better for the remaining ones (P = 2e-3). Regarding vessel location, the vertical component of the software output was significantly different from the manual measure (P = 0.02), nonetheless that was irrelevant during surgery as errors in the order of 2-3 mm do not have impact in the dissection step. Our trials support that a reduction of the time spent is achievable using the automatic tool (about 2 h/case). The introduction of artificial intelligence in clinical practice intends to simplify the work of health professionals and to provide better outcomes to patients. This pilot study paves the way for a success story. (C) 2020 The Authors. Published by Elsevier Ltd.