Abstract
Object tracking in a moving frame is becoming a common requirement in a lot of mobile robotic applications, such as search and rescue, monitoring and surveillance, and even in some scientific applications, such as robotic soccer. In all these applications, the robots must be capable of estimating the target position and, sometimes, velocity on their own. Depending on the application and on the current scene situation, the estimates must be more or less accurate, depending on the robot intention to interact with the target, whether to catch it, follow it, etc. The problem is that a robot moves along the working area, having some uncertainty in its pose estimation. This paper proposes an approach based on a Kalman Filter to estimate the object position and velocity, regardless the robot pose. As a testbed, a Middle-Size League soccer robot tracking a moving ball example will be used. This approach allows the agent to follow and interact with a moving object, even if its localization is not available.

Abstract
Proportional, integrative and derivative (PID) controllers are among the most used in industrial control applications. Classical PID controller design methodologies can be significantly improved by incorporating recent computational intelligence techniques. Two techniques based on particle swarm optimization (PSO) algorithms are proposed to design PI-PID controllers. Both control design methodologies are directed to optimize PI-PID controller gains using two degrees-of-freedom control configurations, subjected to frequency domain robustness constraints. The first technique proposes a single-objective PSO algorithm, to sequentially design a two degrees-of-freedom control structure, considering the optimization of load disturbance rejection followed by set-point tracking optimization. The second technique proposes a many-objective PSO algorithm, to design a two degrees-of-freedom control structure, considering simultaneously, the optimization of four different design criteria. In the many-objective case, the control engineer may select the most adequate solution among the resulting optimal Pareto set. Simulation results are presented showing the effectiveness of the proposed PI-PID design techniques, in comparison with both classic and optimization based methods.

Abstract
OBJECTIVE: To evaluate endothelial dysfunction and microvascular damage in secondary Raynaud Phenomenon (SRP) and Systemic sclerosis (SSc)-associated patients as possible predictors of ischemic fingertip digital ulcers (DU) in a 3-year clinical follow-up. METHODS: Flow-mediated dilatation (FMD), nailfold videocapillaroscopy (NVC), endothelin-1 (ET-1) and asymmetric dimethylarginine (ADMA) were analysed in a 3-year observational cohort study of 77 SRP patients with systemic sclerosis. The primary outcome was the occurrence of a new DU. RESULTS: Risk factors for DU at baseline were low FMD% (p < 0.001), NVC pattern (p < 0.001), high microangiopathy evolution score (MES) (p < 0.001), increased ET-1 (p < 0.001) and increased ADMA serum levels (p = 0.001). Median time to the occurrence of a new DU was 4.50 (1.25-16.25) months. The risk factors for the occurrence of at least one new DU episode in follow-up included a history of at least one DU before enrolment (p < 0.001), autoantibody anti-scleroderma-70 (p = 0.012), NVC late pattern (p < 0.001), high MES score (p < 0.001), low FMD% (p < 0.001) and increased ET-1 serum levels (p < 0.001). We used univariate Cox regression analysis to show that FMD > 9.41% (HR: 0.37 95% CI: 0.14-0.99) and ET-1 > 11.85 pmol/L (HR: 3.81 95% CI: 1.41-10.26) and NVC (HR: 2.29 95% CI: 0.97-5.38) were predictors of DU recurrence. In terms of first DU event in naive DU patients at baseline, late NVC pattern (HR: 12.66 95% CI: 2.06-77.89) and MES score (HR: 1.693 95% CI: 1.257-2.279) were independent predictors. CONCLUSION: This study identified endothelium dysfunction biomarkers (FMD and ET-1) and severe microvascular damage in NVC as strong predictors of new DU in SSc patients.

Abstract
We propose a new linear regression model for interval-valued variables. The model uses quantile functions to represent the intervals, thereby considering the distributions within them. In this paper we study the special case where the Uniform distribution is assumed in each observed interval, and we analyze the extension to the Symmetric Triangular distribution. The parameters of the model are obtained solving a constrained quadratic optimization problem that uses the Mallows distance between quantile functions. As in the classical case, a goodness-of-fit measure is deduced. Two applications on up-to-date fields are presented: one predicting duration of unemployment and the other allowing forecasting burned area by forest fires.

Abstract
The Douro Valley of Portugal is a well-known wine region producing Port wine since the end of the 18th century, with quality table wines becoming increasingly important over the last 20 years. Port wine production is the most important economic sector of the region and Vintage Port is the top quality Port wine type, produced only from the best vintages. The purpose of this research was to examine how the variability of annual weather influences the quality of Vintage Port. A weather and climate data set for the period 1980-2009 and a consensus ranking that combined a collection of vintage chart scores into a ranking were used to characterize both the weather and the vintage quality. In order to more precisely model the weather influences on the quality of the vintages it was necessary to partition the growing season into smaller growth intervals in which several heat and precipitation variables were evaluated. The heat-related variables were defined according to the phenology of grapevines, using a partition of the growing season based on accumulated temperature, rather than on calendar dates. Precipitation variables were calculated using broad periods corresponding to the dormant, vegetative and maturation stages of the grapevines. A logistic regression model was used as a tool to identify the weather variables that help to explain the relationships between yearly weather characteristics and vintage quality. The results show that several weather characteristics are strongly associated with better quality vintages: growing season mean temperatures above the region's average, warm winters, cool July through veraison and cool temperatures during ripening. In summary, although the weather is not solely responsible for determining a vintage quality, it plays an important role on it; therefore, its understanding can provide invaluable management insights to growers and producers.

Abstract
The collection and analysis of data are continuously growing due to the pervasiveness of computing devices. The analysis of such information is fostering businesses and contributing beneficially to the society in many different fields. However, this storage and flow of possibly sensitive data poses serious privacy concerns. Methods that allow the knowledge extraction from data, while preserving privacy, are known as privacy-preserving data mining (PPDM) techniques. This paper surveys the most relevant PPDM techniques from the literature and the metrics used to evaluate such techniques and presents typical applications of PPDM methods in relevant fields. Furthermore, the current challenges and open issues in PPDM are discussed. © 2017 IEEE.