A smooth evolutionary structural optimization procedure applied to plane stress problem

Topological optimization problems based on stress criteria are solved using two techniques in this paper. The first technique is the conventional Evolutionary Structural Optimization (ESO), which is known as hard kill, because the material is discretely removed; that is, the elements under low stress that are being inefficiently utilized have their constitutive matrix has suddenly reduced. The second technique, proposed in a previous paper, is a variant of the ESO procedure and is called Smooth ESO (SESO), which is based on the philosophy that if an element is not really necessary for the structure, its contribution to the structural stiffness will gradually diminish until it no longer influences the structure; its removal is thus performed smoothly. This procedure is known as "soft-kill"; that is, not all of the elements removed from the structure using the ESO criterion are discarded. Thus, the elements returned to the structure must provide a good conditioning system that will be resolved in the next iteration, and they are considered important to the optimization process. To evaluate elasticity problems numerically, finite element analysis is applied, but instead of using conventional quadrilateral finite elements, a plane-stress triangular finite element was implemented with high-order modes for solving complex geometric problems. A number of typical examples demonstrate that the proposed approach is effective for solving problems of bi-dimensional elasticity. (C) 2014 Elsevier Ltd. All rights reserved.

The influence of self-weight of elastic 2D structures in topology optimization via numerical technique Smooth Evolutionary Structural Optimization (SESO)

This paper deals with topology optimization in plane elastic-linear problems considering the influence of the self weight in efforts in structural elements. For this purpose it is used a numerical technique called SESO (Smooth ESO), which is based on the procedure for progressive decrease of the inefficient stiffness element contribution at lower stresses until he has no more influence. The SESO is applied with the finite element method and is utilized a triangular finite element and high order. This paper extends the technique SESO for application its self weight where the program, in computing the volume and specific weight, automatically generates a concentrated equivalent force to each node of the element. The evaluation is finalized with the definition of a model of strut-and-tie resulting in regions of stress concentration. Examples are presented with optimum topology structures obtaining optimal settings. (C) 2012 CIMNE (Universitat Politecnica de Catalunya). Published by Elsevier Espana, S.L.U. All rights reserved.

Comparative Chromosome Mapping of U2 snRNA and 5S rRNA Genes in Gymnotus Species (Gymnotiformes, Gymnotidae): Evolutionary Dynamics and Sex Chromosome Linkage in G. pantanal

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Building the sugarcane genome for biotechnology and identifying evolutionary trends

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Evolutionary History of the PER3 Variable Number of Tandem Repeats (VNTR): Idiosyncratic Aspect of Primate Molecular Circadian Clock

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

An evolutionary approach to improve connectivity prediction in mobile wireless sensor networks

Connectivity is the basic factor for the proper operation of any wireless network. In a mobile wireless sensor network it is a challenge for applications and protocols to deal with connectivity problems, as links might get up and down frequently. In these scenarios, having knowledge of the node remaining connectivity time could both improve the performance of the protocols (e.g. handoff mechanisms) and save possible scarce nodes resources (CPU, bandwidth, and energy) by preventing unfruitful transmissions. The current paper provides a solution called Genetic Machine Learning Algorithm (GMLA) to forecast the remainder connectivity time in mobile environments. It consists in combining Classifier Systems with a Markov chain model of the RF link quality. The main advantage of using an evolutionary approach is that the Markov model parameters can be discovered on-the-fly, making it possible to cope with unknown environments and mobility patterns. Simulation results show that the proposal is a very suitable solution, as it overcomes the performance obtained by similar approaches.

Evolutionary trends in Iridaceae: new cytogenetic findings from the New World

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Structure, organization, and expression of the alpha prolamin multigenic family bring new insights into the evolutionary relationships among grasses

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Genomic signatures of evolutionary transitions from solitary to group living

The evolution of eusociality is one of the major transitions in evolution, but the underlying genomic changes are unknown. We compared the genomes of 10 bee species that vary in social complexity, representing multiple independent transitions in social evolution, and report three major findings. First, many important genes show evidence of neutral evolution as a consequence of relaxed selection with increasing social complexity. Second, there is no single road map to eusociality; independent evolutionary transitions in sociality have independent genetic underpinnings. Third, though clearly independent in detail, these transitions do have similar general features, including an increase in constrained protein evolution accompanied by increases in the potential for gene regulation and decreases in diversity and abundance of transposable elements. Eusociality may arise through different mechanisms each time, but would likely always involve an increase in the complexity of gene networks.

Biology Teachers'Conceptions of the Diversity of Life and the Historical Development of Evolutionary Concepts

Fundação de Amparo à Pesquisa do Estado de São Paulo (FAPESP)

Perception of brazilian citizens about Darwinism and the (Non) Influence of God in the evolutionary process

The current context is unique in relation to the teaching of evolution in Brazil and the population's perception of evolution. On the one hand, it is said often about Darwinism in various media, especially due to the relatively recent commemoration of the two hundred years of the birth of Charles Darwin and one hundred and fifty years of the launch of the book The Origin of Species. On the other hand, it is clear, in recent years, a timid movement, more worryingly, in favor of equitable approach of creationist and evolutionist theories in the classroom. This article is a part of a research whose goal is to raise the design that Brazilian respondents have about the Darwinian view (which disregards the divine influence in the evolution of the species). The instrument used for data collection is a questionnaire, type Likert scale, which consists of a series of statements in which respondents must express their degree of agreement or disagreement with each statement. In this study, we present the results of the statement. "The thought of Darwin, which does not consider God as a participant in the process of evolution, is...". Analysis correlated with data on religion and education of the respondents are also held. The results point to a tendency of respondents not to accept the Darwinian view that disregards God's interference in the evolutionary process. The data also show that respondents' choices are influenced by religion and education. The frequency of responses that tend to accept the Darwinian view (which disregards the divine participation in the evolution of the species) is higher among respondents with higher levels of education. Adherents to religions "evangelical" tend to deny this view more often than followers of other religions. Given the potential risks of inserting creationist approaches in school education, it is necessary a discussion of the possible impacts of this rejection of Darwin's thinking (which does not consider God as a participant in the evolutionary process), indicated here, in the teaching of evolution. This work was supported by FAPEMIG.

Evolutionary optimization applied for fine-tuning parameter estimation in optical flow-based environments

Optical flow methods are accurate algorithms for estimating the displacement and velocity fields of objects in a wide variety of applications, being their performance dependent on the configuration of a set of parameters. Since there is a lack of research that aims to automatically tune such parameters, in this work we have proposed an evolutionary-based framework for such task, thus introducing three techniques for such purpose: Particle Swarm Optimization, Harmony Search and Social-Spider Optimization. The proposed framework has been compared against with the well-known Large Displacement Optical Flow approach, obtaining the best results in three out eight image sequences provided by a public dataset. Additionally, the proposed framework can be used with any other optimization technique.

Geoffroy's side-necked turtle [Phrynops geoffroanus (Schweigger, 1812), Testudines: Chelidae] as a model for evolutionary ecotoxicology: relationship between environmental contamination, conditions and genetic variability

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A self-learning simulated annealing algorithm for global optimizations of electromagnetic devices

A self-learning simulated annealing algorithm is developed by combining the characteristics of simulated annealing and domain elimination methods. The algorithm is validated by using a standard mathematical function and by optimizing the end region of a practical power transformer. The numerical results show that the CPU time required by the proposed method is about one third of that using conventional simulated annealing algorithm.