Abstract
This study is focused on the evaluation of the impact of Saccharomyces cerevisiae metabolism in the profile of compounds with antioxidant capacity in a synthetic wine during fermentation. A bioanalytical pipeline, which allows for biological systems fingerprinting and sample classification by combining electrochemical features with biochemical background, is proposed. To achieve this objective, alcoholic fermentations of a minimal medium supplemented with phenolic acids were evaluated daily during 11 days, for electrochemical profile, phenolic acids, and the volatile fermentation fraction, using cyclic voltametry, high-performance liquid chromatography-diode array detection, and headspace/solid-phase microextraction/gas chromatography-mass spectrometry (target and nontarget approaches), respectively. It was found that acetic acid, 2-phenylethanol, and isoamyl acetate are compounds with a significative contribution for samples metabolic variability, and the electrochemical features demonstrated redox-potential changes throughout the alcoholic fermentations, showing at the end a similar pattern to normal wines. Moreover, S. cerevisiae had the capacity of producing chlorogenic acid in the supplemented medium fermentation from simple precursors present in the minimal medium.

Abstract
The Darwinian Particle Swarm Optimization (DPSO) is an evolutionary algorithm that extends the Particle Swarm Optimization using natural selection to enhance the ability to escape from sub-optimal solutions. An extension of the DPSO to multi-robot applications has been recently proposed and denoted as Robotic Darwinian PSO (RDPSO), benefiting from the dynamical partitioning of the whole population of robots, hence decreasing the amount of required information exchange among robots. This paper further extends the previously proposed algorithm using fractional calculus concepts to control the convergence rate, while considering the robot dynamical characteristics. Moreover, to improve the convergence analysis of the RDPSO, an adjustment of the fractional coefficient based on mobile robot constraints is presented and experimentally assessed with 2 real platforms. Afterwards, this novel fractional-order RDPSO is evaluated in 12 physical robots being further explored using a larger population of 100 simulated mobile robots within a larger scenario. Experimental results show that changing the fractional coefficient does not significantly improve the final solution but presents a significant influence in the convergence time because of its inherent memory property.

Abstract
In this work, a novel method for segmentation of Remote Sensing (RS) images based on the Darwinian Particle Swarm Optimization (DPSO) for determining the n-1 optimal n-level threshold on a given image is proposed. The efficiency of the proposed method is compared with the Particle Swarm Optimization (PSO) based segmentation method. Results show that DPSO-based image segmentation performs better than PSO-based method in a number of different measures.

Abstract
In most real multi-robot applications, e. g., search-and-rescue, cooperative robots have to fulfill their tasks while driving and communicating among themselves without the aid of a network infrastructure. However, initially deploying autonomously a wireless sensor robot network in a real environment has not taken the proper attention. This paper presents an autonomous and realistic initial deployment strategy, based on a hierarchical approach, in which exploring agents, denoted as scouts, are autonomously deployed through explicit cooperation with supporting agents, denoted as rangers. To evaluate the initial deployment strategy proposed, experimental results with a team of heterogeneous robots are conducted using modified low-cost platforms previously developed by the authors. Preliminary results show the effectiveness of the method and pave the way for a whole series of possible new approaches.

Abstract
This paper presents a collective foraging algorithm designed to simulate natural selection in a group of swarm robots. The Robotic Darwinian Particle Swarm Optimization (RDPSO) previously proposed is improved using fractional calculus theory and evaluated on real low-cost mobile robots performing a distributed foraging task. This work aims at evaluating this novel exploration strategy, by studying the performance of the algorithm within a population of up to 12 robots, under communication constraints. In order to simulate the maximum allowed communication distance, robots were provided with a list of their teammates' addresses. Experimental results show that only 4 robots are needed to accomplish the proposed mission and, independently on the number of robots, maximum communication distance and fractional coefficient, the optimal solution is achieved in approximately 90% of the experiments.

Abstract
This paper covers a wide knowledge of physical and dynamical models useful for building flying robots and a new generation of flying platform developed in the similarity of flying animals. The goal of this work is to develop a simulation environment and dynamic control using the high-level calculation tool MatLab and the modeling, simulation, and analysis of dynamic systems tool Simulink. Once created the dynamic models to study, this work involves the study and understanding of the dynamic stability criteria to be adopted and their potential use in the control of flying models.