Abstract
Abstract: In this work, different weight contents of as-grown carbon nanofibers (CNFs), produced by chemical vapor deposition, were melt-extruded with polypropylene (PP) and their morphologic, structure and dielectric properties examined. The morphologic analysis reveals that the CNFs are randomly distributed in the form of agglomerates within the PP matrix, whereas the structural results depicted by Raman analysis suggest that the degree of disorder of the as-received CNFs was not affected in the PP/CNF composites. The AC conductivity of PP/CNF composites at room temperature evidenced an insulator–conductor transition in the vicinity of 2 wt.%, corresponding to a remarkable rise of the dielectric permittivity up to ~ 12 at 400 Hz, with respect to the neat PP (~ 2.5). Accordingly, the AC conductivity and dielectric permittivity of PP/CNF 2 wt.% composites were evaluated by using power laws and discussed in the framework of the intercluster polarization model. Finally, the complex impedance and Nyquist plots of the PP/CNF composites are analyzed by using equivalent circuit models, consisting of a constant phase element (CPE). The analysis gathered in here aims at contributing to the better understanding of the enhanced dielectric properties of low-conducting polymer composites filled with carbon nanofibers. Graphic abstract: [Figure not available: see fulltext.]. © 2021, The Author(s), under exclusive licence to EDP Sciences, SIF and Springer-Verlag GmbH Germany, part of Springer Nature.

Abstract
The advanced and widespread use of microfluidic devices, which are usually fabricated in polydimethylsiloxane (PDMS), requires the integration of many sensors, always compatible with microfluidic fabrication processes. Moreover, current limitations of the existing optical and electrochemical oxygen sensors regarding long-term stability due to sensor degradation, biofouling, fabrication processes and cost have led to the development of new approaches. Thus, this manuscript reports the development, fabrication and characterization of a low-cost and highly sensitive dissolved oxygen optical sensor based on a membrane of PDMS doped with platinum octaethylporphyrin (PtOEP) film, fabricated using standard microfluidic materials and processes. The excellent mechanical and chemical properties (high permeability to oxygen, anti-biofouling characteristics) of PDMS result in membranes with superior sensitivity compared with other matrix materials. The wide use of PtOEP in sensing applications, due to its advantage of being easily synthesized using microtechnologies, its strong phosphorescence at room temperature with a quantum yield close to 50%, its excellent Strokes Shift as well as its relatively long lifetime (75 mu s), provide the suitable conditions for the development of a miniaturized luminescence optical oxygen sensor allowing long-term applications. The influence of the PDMS film thickness (0.1-2.5 mm) and the PtOEP concentration (363, 545, 727 ppm) in luminescent properties are presented. This enables to achieve low detection levels in a gas media range from 0.5% up to 20%, and in liquid media from 0.5 mg/L up to 3.3 mg/L at 1 atm, 25 degrees C. As a result, we propose a simple and cost-effective system based on a LED membrane photodiode system to detect low oxygen concentrations for in situ applications.

Abstract
Optical networks-on-chip (ONoCs) are a promising solution for high-performance multicore integration with better latency and bandwidth than traditional electrical NoCs. Wavelength-routed ONoCs (WRONoCs) offer yet additional performance guarantees. However, WRONoC design presents new EDA challenges which have not yet been fully addressed. So far, most topology analysis is abstract, i.e., overlooks layout concerns, while for layout the tools available perform place and route (P&R) but no topology optimization. Thus, a need arises for a novel optimization method combining both aspects of WRONoC design. In this article, such a method, PSION+, is laid out. This new procedure uses a linear programming model to optimize a WRONoC physical layout template to optimality. This template-based optimization scheme is a new idea in this area that seeks to minimize problem complexity while keeping design flexibility. A simple layout template format is introduced and explored. Finally, multiple model reduction techniques to reduce solver run-time are also presented and tested. When compared to the state-of-the-art design procedure, results show a decrease in maximum optical insertion loss of 41%.

Abstract
Measuring and forecasting changes in coastal and deep-water ecosystems and climates requires sustained long-term measurements from marine observation systems. One of the key considerations in analyzing data from marine observatories is quality assurance (QA). The data acquired by these infrastructures accumulates into Giga and Terabytes per year, necessitating an accurate automatic identification of false samples. A particular challenge in the QA of oceanographic datasets is the avoidance of disqualification of data samples that, while appearing as outliers, actually represent real short-term phenomena, that are of importance. In this paper, we present a novel cross-sensor QA approach that validates the disqualification decision of a data sample from an examined dataset by comparing it to samples from related datasets. This group of related datasets is chosen so as to reflect upon the same oceanographic phenomena that enable some prediction of the examined dataset. In our approach, a disqualification is validated if the detected anomaly is present only in the examined dataset, but not in its related datasets. Results for a surface water temperature dataset recorded by our Texas A&M-Haifa Eastern Mediterranean Marine Observatory (THEMO)-over a period of 7 months, show an improved trade-off between accurate and false disqualification rates when compared to two standard benchmark schemes.

Abstract
When the COVID-19 pandemic hits Portugal in early March 2020, medical doctors, engineers and researchers, with the encouragement of the Northern Region Health Administration, teamed up to develop and build, locally and in a short time, a ventilator that might eventually be used in extreme emergency situations in the hospitals of northern Portugal. This letter tells you the story of Pneuma, a low-cost emergency ventilator designed and built under harsh isolation constraints, that gave birth to derivative designs in Brazil and Morocco, has been industrialized with 200 units being produced, and is now looking forward to the certification as a medical device that will possibly support a go-tomarket launch. Open intellectual property (IP), multi disciplinarity teamwork, fast prototyping and product engineering have shortened to a few months an otherwise quite longer idea-to-product route, clearly demonstrating that when scientific and engineering knowledge hold hands great challenges can be successfully faced.

Abstract
This paper addresses Visual Odometry (VO) estimation in challenging underwater scenarios. Robot visual-based navigation faces several additional difficulties in the underwater context, which severely hinder both its robustness and the possibility for persistent autonomy in underwater mobile robots using visual perception capabilities. In this work, some of the most renown VO and Visual Simultaneous Localization and Mapping (v-SLAM) frameworks are tested on underwater complex environments, assessing the extent to which they are able to perform accurately and reliably on robotic operational mission scenarios. The fundamental issue of precision, reliability and robustness to multiple different operational scenarios, coupled with the rise in predominance of Deep Learning architectures in several Computer Vision application domains, has prompted a great a volume of recent research concerning Deep Learning architectures tailored for visual odometry estimation. In this work, the performance and accuracy of Deep Learning methods on the underwater context is also benchmarked and compared to classical methods. Additionally, an extension of current work is proposed, in the form of a visual-inertial sensor fusion network aimed at correcting visual odometry estimate drift. Anchored on a inertial supervision learning scheme, our network managed to improve upon trajectory estimates, producing both metrically better estimates as well as more visually consistent trajectory shape mimicking.