Abstract
The optical Vernier effect magnifies the sensing capabilities of an interferometer, allowing for unprecedented sensitivities and resolutions to be achieved. Just like a caliper uses two different scales to achieve higher resolution measurements, the optical Vernier effect is based on the overlap in the responses of two interferometers with slightly detuned interference signals. Here, we present a novel approach in detail, which introduces optical harmonics to the Vernier effect through Fabry-Perot interferometers, where the two interferometers can have very different frequencies in the interferometric pattern. We demonstrate not only a considerable enhancement compared to current methods, but also better control of the sensitivity magnification factor, which scales up with the order of the harmonics, allowing us to surpass the limits of the conventional Vernier effect as used today. In addition, this novel concept opens also new ways of dimensioning the sensing structures, together with improved fabrication tolerances.

Abstract

Abstract
Research on tourist destination images is vast and embraces many destinations, approaches, and methods. More attention has been given to the perspective of final customers, instead of the ones who sell the tourist products to them. This chapter aims to understand how foreign tourist intermediaries, from the top outbound countries, perceive and sell Portugal as a tourist destination. It aims also to analyze the travel motivations they expect Portugal can satisfy and the information sources used to collect information to create tourist products regarding this destination. Foreign tourist intermediaries have a very positive image of Portugal as a destination, associating it to a set of cognitive attributes and psychological motivations.

Abstract
Electricity markets are complex environments with very dynamic characteristics. The large-scale penetration of renewable energy sources has brought an increased uncertainty to generation, which is consequently, reflected in electricity market prices. In this way, novel advanced forecasting methods that are able to predict electricity market prices taking into account the new variables that influence prices variation are required. This paper proposes a new model for day-ahead electricity market prices forecasting based on the application of an artificial neural network. The main novelty of this paper relates to the pre-processing phase, in which the relevant data referring to the different variables that have a direct influence on market prices such as generation, temperature, consumption, among others, is analysed. The association between these variables is performed using spearman correlation, from which results the identification of which data has a larger influence on the market prices variation. This pre-analysis is then used to adapt the training process of the artificial neural network, leading to improved forecasting results, by using the most relevant data in an appropriate way. © 2019 IEEE.

Abstract
This work proposes an integrated formulation for the joint production and transportation scheduling problem in flexible manufacturing environments. In this type of systems, parts (jobs) need to be moved around as the production operations required involve different machines. The transportation of the parts is typically done by a limited number of Automatic Guided Vehicles (AGVs). Therefore, machine scheduling and AGV scheduling are two interrelated problems that need to be addressed simultaneously. The joint production and transportation scheduling problem is formulated as a novel mixed integer linear programming model. The modeling approach proposed makes use of two sets of chained decisions, one for the machine and another for the AGVs, which are inter-connected through the completion time constraints both for machine operations and transportation tasks. The computational experiments on benchmark problem instances using a commercial software (Gurobi) show the efficiency of the modeling approach in finding optimal solutions.

Abstract
Three-dimensional imaging techniques have been endeavouring at reaching affordable ubiquity. Nevertheless, its use in clinical practice can be hampered by less than naturally looking surfaces that greatly impact its visual inspection. This work considers the task of surface reconstruction from point clouds of non-rigid scenes acquired through structured-light-based methods, wherein the reconstructed surface contains some level of imperfection to be inpainted before visualized by experts in a clinically oriented context. Appertain to the topic, the recovery of colour information for missing or damaged partial regions is considered. A local geometry-based interpolation method is proposed for the reconstruction of the bare human torso and compared against a reference differential equations based inpainting method. Widely used perceptual distance-based metrics, such as PSNR, SSIM and MS-SSIM, and the evaluation from a panel of experienced breast cancer surgeons is presented for the discussion on inpainting quality assessment.