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
AbstractDiscovery of cancer drivers has traditionally focused on the identification of protein-coding genes. Here we present a comprehensive analysis of putative cancer driver mutations in both protein-coding and non-coding genomic regions across >2,500 whole cancer genomes from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We developed a statistically rigorous strategy for combining significance levels from multiple driver discovery methods and demonstrate that the integrated results overcome limitations of individual methods. We combined this strategy with careful filtering and applied it to protein-coding genes, promoters, untranslated regions (UTRs), distal enhancers and non-coding RNAs. These analyses redefine the landscape of non-coding driver mutations in cancer genomes, confirming a few previously reported elements and raising doubts about others, while identifying novel candidate elements across 27 cancer types. Novel recurrent events were found in the promoters or 5’UTRs ofTP53, RFTN1, RNF34,andMTG2,in the 3’UTRs ofNFKBIZandTOB1,and in the non-coding RNARMRP.We provide evidence that the previously reported non-coding RNAsNEAT1andMALAT1may be subject to a localized mutational process. Perhaps the most striking finding is the relative paucity of point mutations driving cancer in non-coding genes and regulatory elements. Though we have limited power to discover infrequent non-coding drivers in individual cohorts, combined analysis of promoters of known cancer genes show little excess of mutations beyondTERT.

Abstract
Background: Positron Emission Tomography - Computed Tomography (PET/CT) imaging is the basis for the evaluation of response-to-treatment of several oncological diseases. In practice, such evaluation is manually performed by specialists, which is rather complex and time-consuming. Evaluation measures have been proposed, but with questionable reliability. The usage of before and after-treatment image descriptors of the lesions for treatment response evaluation is still a territory to be explored. Methods: In this project, Artificial Neural Network approaches were implemented to automatically assess treatment response of patients suffering from neuroendocrine tumors and Hodgkyn lymphoma, based on image features extracted from PET/CT. Results: The results show that the considered set of features allows for the achievement of very high classification performances, especially when data is properly balanced. Conclusions: After synthetic data generation and PCA-based dimensionality reduction to only two components, LVQNN assured classification accuracies of 100%, 100%, 96.3% and 100% regarding the 4 response- to-treatment classes.

Abstract

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.