Abstract
Ordinal arrangement of objects is a common property in biomedical images. Traditional methods to deal with semantic image segmentation in this setting are ad-hoc and application specific. In this paper, we propose ordinal-aware deep learning architectures for image segmentation that enforce pixelwise consistency by construction. We validated the proposed architectures on several real-life biomedical datasets and achieved competitive results in all cases. © 2018 IEEE.

Abstract
Service applications are increasingly being deployed in virtualized environments, such as virtual machines (VMs) as a means to provide elasticity and to allow fast recovery from failures. The recent trend is now to deploy applications in containers (e.g., Docker or RKT containers), which allow, among many other benefits, to further reduce recovery time, since containers are much more lightweight than VMs. Although several performance benchmarks exist for web services (e.g., TPC-App and SPEC SPECjEnterprise2010) or even virtualized environments (e.g., SPEC Cloud IaaS 2016, TPCx-V), understanding the behavior of containerized services in the presence of faults has been generally disregarded. This paper proposes an experimental approach for evaluating the performance of containerized services in presence of operator faults. The approach is based on the injection of a simple set of operator faults targeting the containers and middleware. Results show noticeable differences regarding the impact of operator faults in Docker and RKT, with the latter one allowing for faster recovery, despite showing the lowest throughput.

Abstract
Depression is a psychiatric disorder and major contributor to the burden of disease worldwide. The strength of evidence of the benefits of exercise as a therapeutic intervention for patients with depression has expanded in the last 30 years. In fact, the available evidence indicates exercise can not only help manage depressive symptoms, but also effect significant improvements in other health outcomes. Clinical guidelines including such recommendations have been issued by different agencies, namely the UK National Institute for Health and Clinical Excellence (NICE), the American Psychiatric Association (APA), and the Royal Australian and New Zealand College of Psychiatrists (RANZCP). With increasing recognition of the benefits of exercise and shortcomings of healthcare systems, other countries, such as Sweden and Canada, have included exercise in their national guidelines for treating depression. Unfortunately, progress in incorporating exercise guidelines into clinical practice has been slow, and Portugal and Brazil reflect this reality. In this update, we reemphasize the importance of bridging this gap and integrating exercise into clinical practice guidelines as an essential component of depression treatment.

Abstract
Long period fiber gratings (LPFGs) were coated with iron (Fe) and exposed to oxidation in air and in water having different concentrations of sodium chloride (NaCl) to detect the formation of iron oxides and hydroxides. The process was monitored in real time by measuring the characteristics of the LPFGs attenuation bands. Thin films of Fe were deposited on top of silica (SiO2) substrates, annealed in air, and exposed to water with NaCl. The composition of the oxide and hydroxide layers was analyzed by SEM/EDS and X-ray diffraction. It observed the formation of oxide phases, Fe3O4 (magnetite), and Fe2O3 (hematite) when annealing in air, and Fe-2(OH)(3) Cl (hibbingite) and FeO(OH) (lepidocrocite) when exposed to water with NaCl. Results shows that Fe-coated LPFGs can be used as sensors for real-time monitoring of corrosion in offshore and in coastal projects where metal structures made of iron alloys are in contact with sea or brackish water. In addition, LPFGs coated with hematite were characterized for sensing, leading to the conclusion that the sensitivity to the refractive index of the surrounding medium can be tuned by proper choice of hematite thickness.

Abstract
Solving the robot localization problem is one of the most necessary requirements for autonomous robots. Several methodologies can be used to determine its location as accurately as possible. What makes this difficult is the existence of uncertainty in the sensing of the robot. The uncertain information needs to be combined in an optimal way. This paper stresses a Kalman filter to combine information from the odometry and Ultra Wide Band Time of Flight distance modules, which lacks the orientation. The proposed system validated in a real developed platform performs the fusion task which outputs position and orientation of the robot. It is used to localize the robot and make a 3 DoF scanning of magnetic field in a room. Other examples can be pointed out with the same localization techniques in service and industrial autonomous robots. © Springer International Publishing AG 2018.

Abstract
This work addresses the lot-sizing and scheduling problem under multistage demand uncertainty. A flexible production system is considered, with the possibility to adjust the size and the schedule of lots in every time period based on a rolling-horizon planning scheme. Computationally intractable multistage stochastic programming models are often employed on this problem. An adaptation strategy to the multistage setting for two-stage programming and robust optimization models is proposed. We also present an approximate heuristic strategy to address the problem more efficiently, relying on multistage stochastic programming and adjustable robust optimization. In order to evaluate each strategy and model proposed, a Monte Carlo simulation experiment under a rolling-horizon scheme is performed. Results show that the strategies are promising in solving large-scale problems: the approximate strategy based on adjustable robust optimization has, on average, 6.72% better performance and is 7.9 times faster than the deterministic model.