Abstract
Transthyretin Familial Amyloid Polyneuropathy (TTR-FAP) is a neurological genetic disease that propagates from one family generation to the next. The disease can have severe effects on the life of patients after the first symptoms (onset) appear. Accurate prediction of the age of onset for these patients can help the management of the impact. This is, however, a challenging problem since both familial and non-familial characteristics may or may not affect the age of onset. In this work, we assess the importance of sets of genealogical features used for Predicting the Age of Onset of TTR-FAP Patients. We study three sets of features engineered from clinical and genealogical data records obtained from Portuguese patients. These feature sets, referred to as Patient, First Level and Extended Level Features, represent sets of characteristics related to each patient's attributes and their familial relations. They were compiled by a Medical Research Center working with TTR-FAP patients. Our results show the importance of genealogical data when clinical records have no information related with the ancestor of the patient, namely its Gender and Age of Onset. This is suggested by the improvement of the estimated predictive error results after combining First and Extended Level with the Patients Features.

Abstract
Even though the joint optimization of sequential activities in supply chains is known to yield significant cost savings, the literature concerning optimization approaches that handle the real-life features of industrial problems is scant. The problem addressed in this work is inspired by industrial contexts where vendor-managed inventory policies are applied. In particular, our study is motivated by a meat producer whose supply chain comprises a single meat processing centre with several production lines and a fleet of vehicles that is used to deliver different products to meat stores spread across the country. A considerable set of characteristics, such as product family setups, perishable products, and delivery time windows, needs to be considered in order to obtain feasible integrated plans. However, the dimensions of the problem make it impossible to be solved exactly by current solution methods. We propose a novel three-phase methodology to tackle a large Production-Routing Problem (PRP) combining realistic features for the first time. In the first phase, we attempt to reduce the size of the original problem by simplifying some dimensions such as the number of products, locations and possible routes. In the second phase, an initial PRP solution is constructed through a problem decomposition comprising several inventory-routing problems and one lot-sizing problem. In the third phase, the initial solution is improved by different mixed-integer programming models which focus on small parts of the original problem and search for improvements in the production, inventory management and transportation costs. Our solution approach is tested both on simpler instances available in the literature and on real-world instances containing additional details, specifically developed for a European company's case study. By considering an integrated approach, we achieve global cost savings of 21.73% compared to the company's solution.

Abstract
This article presents Nonio, a modular, easy-to-use, design space exploration framework focused on exploring custom combinations of compiler flags and compiler sequences. We describe the framework and discuss its use with two of the most popular compiler toolchains, GCC and Clang+LLVM. Particularly, we discuss implementation details in the context of flag selection, when using GCC, and phase selection and ordering, when using Clang+LLVM. The framework software organization allows to easily add new components as plug-ins (e.g., an exploration algorithm, an objective metric, integration with another compiler toolchain). The software architecture provides well-defined interfaces, in order to enable seamless composition and interaction between different components. We present, as an example, a use case where we rely on Nonio to obtain custom compiler flags for reducing the execution time and the energy consumption of a C program, in relation to the best predetermined optimization settings provided by the compiler (e.g., -O3). (C) 2019 The Authors. Published by Elsevier B.V.

Abstract
Distributed energy resources (DERs) change the supply-demand balance of power systems. To better manage these resources, they are operated as micro-grids (MGs) in both grid-connected and standalone modes. In the presence of MGs, the decision making framework in power systems is changing from the centralized structure into the decentralized one. In such framework, modeling the operation problem of MGs with the participation in energy and reserve markets in the presence of uncertainties is considered as an important challenge. Most of the studies use probability distribution functions (PDFs) to model the uncertain parameters. In this paper, the operation problem of a grid-connected MG is modeled while the MG operator (MGO) faces with uncertainties of renewable energy sources (RESs) without considering their PDFs. For this purpose, an information gap decision theory (IGDT)-based approach is employed to model the uncertain behavior of RESs as well as to control the risk level of the MGO on the optimal scheduling of DERs. To investigate the effectiveness of the model, a modified 15-bus low voltage MG us used as a test system. The results show that optimal decisions of the risk-averse MGO are different from those of a risky one. © 2019 IEEE.

Abstract
Autonomous underwater vehicles are applied in diverse fields, namely in tasks that are risky for human beings to perform, as optical inspection for the purpose of structures quality control. Optical sensors are more appealing cost and they supply a larger quantity of data. Lasers can be used to reconstruct structures in three dimensions, along with cameras, which create a faithful representation of the environment. However, in this context a visual approach was used and the paper presents a method that can put together the three-dimensional information that has been harvested over time, combining also RGB information for surface reconstruction. The map construction follows the motion estimated by a odometry method previously selected from the literature. Experiments conducted using real scenario show that the proposed solution is able to provide a reliable map for objects and even the seafloor.

Abstract
Light interaction with biological materials depends on the material’s optical properties. From those properties, the absorption and scattering coefficients are the most important, since they quantify how much of a light beam is attenuated when traveling inside a tissue. The scattering coefficient is known to be significantly higher than the absorption coefficient in biological materials, meaning that most of the light is scattered, turning optical methods in clinical practice limited. Such difference between the scattering and absorption coefficients is mainly due to a refractive index mismatch between tissue components and fluids. We explain this concept in the present chapter before introducing the technique that efficiently minimizes this effect in the following chapters. © 2019, The Author(s), under exclusive license to Springer Nature Switzerland AG.