Hybrid adaptive control of a dragonfly model

Dragonflies show unique and superior flight performances than most of other insect species and birds. They are equipped with two pairs of independently controlled wings granting an unmatchable flying performance and robustness. In this paper, it is presented an adaptive scheme controlling a nonlinear model inspired in a dragonfly-like robot. It is proposed a hybrid adaptive (HA) law for adjusting the parameters analyzing the tracking error. At the current stage of the project it is considered essential the development of computational simulation models based in the dynamics to test whether strategies or algorithms of control, parts of the system (such as different wing configurations, tail) as well as the complete system. The performance analysis proves the superiority of the HA law over the direct adaptive (DA) method in terms of faster and improved tracking and parameter convergence

Application of fractional algorithms in the control of a robotic bird

In this paper, it is studied the dynamics of the robotic bird in terms of time response and robustness. It is analyzed the wing angle of attack and the velocity of the bird, the tail influence, the gliding flight and the flapping flight. The results are positive for the construction of flying robots. The development of computational simulation based on the dynamic of the robotic bird should allow testing strategies and different algorithms of control such as integer and fractional controllers

Dynamical Stability and Predictability of Football Players: The Study of One Match

The game of football demands new computational approaches to measure individual and collective performance. Understanding the phenomena involved in the game may foster the identification of strengths and weaknesses, not only of each player, but also of the whole team. The development of assertive quantitative methodologies constitutes a key element in sports training. In football, the predictability and stability inherent in the motion of a given player may be seen as one of the most important concepts to fully characterise the variability of the whole team. This paper characterises the predictability and stability levels of players during an official football match. A Fractional Calculus (FC) approach to define a player’s trajectory. By applying FC, one can benefit from newly considered modeling perspectives, such as the fractional coefficient, to estimate a player’s predictability and stability. This paper also formulates the concept of attraction domain, related to the tactical region of each player, inspired by stability theory principles. To compare the variability inherent in the player’s process variables (e.g., distance covered) and to assess his predictability and stability, entropy measures are considered. Experimental results suggest that the most predictable player is the goalkeeper while, conversely, the most unpredictable players are the midfielders. We also conclude that, despite his predictability, the goalkeeper is the most unstable player, while lateral defenders are the most stable during the match

A fuzzified systematic adjustment of the robotic Darwinian PSO

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 adapting the behavior of robots based on a set of context-based evaluation metrics. Those metrics are then used as inputs of a fuzzy system so as to systematically adjust the RDPSO parameters (i.e., outputs of the fuzzy system), thus improving its convergence rate, susceptibility to obstacles and communication constraints. The adapted RDPSO is evaluated in groups of physical robots, being further explored using larger populations of simulated mobile robots within a larger scenario

Saccharomyces cerevisiae as a toxicological model to study synthetic cannabinoids and its pyrolysis products

Poster presented at the 7th European Academy of Forensic Science Conference. Prague, 6-11 September 2015"Synthetic cannabinoids are among the major psychoactive drugs widespread as safe and legal alternatives to cannabis. They are commercially available as herbal incense products intended for smoke. This has led most of developed countries to concentrate efforts in order to ban the so called “legal highs”. Despite of their increasing use, there is still a lack of information on both synthetic and natural ingredients, pharmacokinetic properties and toxic effects. In fact some of the substances seem to have stronger toxicological effects when compared to their legal counterpart. Toxicological assays are paramount to know how harmful these new substances are, helping increase public awareness since several hospitalization cases have been reported due to consumption. To tackle the new challenges posed by novel drugs worldwide, we developed an approach using Saccharomyces cerevisiae as a model to investigate the toxicity of pyrolysis products of synthetic cannabinoids. S. cerevisiae.

Simulation of a robotic bird

In this paper it is studied the dynamics of the robotic bird. The system performances are analyzed in terms of time response and robustness. It is study the relation between the angle of attack and the velocity of the bird, the tail influence, the gliding flight and the flapping flight. In this model, a bird flies by the wind beat motion or using its tail down. The results are positive for the construction of flying robots. The development of computational simulation based on the dynamic of the robotic bird that should allow testing strategies and algorithms of control.N/

EGFR/erB-1, HER2/erB-2, CK7, LP34, Ki67 and P53 expression in preneoplastic lesions of bronchial epithelium: an immunohistochemical and genetic study

A prognostic interpretation of preneoplastic lesions would have impact in bronchial carcinoma early diagnosis and through the study of Erb-B family receptors as they have an important role in lung carcinogenesis. The existence of drugs as tyrosine kinase inhibitors stressed the importance of studying gene alterations for selected chemoprevention schemes and characterization of carcinogenesis. Bronchial preneoplastic lesions were characterized by immunohistochemistry using the antibodies LP34 (high weigh molecular cytokeratin), CK7, chromogranin A, Ki67, p53, C-erbB-2 and EGFR. HER2 and EGFR gene copy number was also evaluated by fluorescent in situ hybridization in those lesions. The expected results defined the origin cell for basal cell hyperplasia and squamous metaplasia as adaptative lesions and dysplasia. By known experiences and published data, beyond the stem cell, the spectral evolution of bronchial preneoplastic lesions was demonstrated by characterizing basal cells (LP34) and their neoplastic potentiality. Dysplasias showed a higher expression of EGFR, Ki67 and p53 with a stepwise increase with the gravity of the respective grading. C-erbB-2 immunohistochemical overexpression was a rare event in preneoplastic lesions. Polysomy was the main mechanism for EGFR and HER2/neu higher gene copy number and together with increased proliferation index (Ki67) will account to preview bronchial carcinogenesis.info:eu-repo/semantics/publishedVersio

Reply to: Comments on “Particle Swarm Optimization with Fractional-Order Velocity”

We agree with Ling-Yun et al. [5] and Zhang and Duan comments [2] about the typing error in equation (9) of the manuscript [8]. The correct formula was initially proposed in [6, 7]. The formula adopted in our algorithms discussed in our papers [1, 3, 4, 8] is, in fact, the following: ..