Structural change and productivity growth 20 years later: old problems, new opportunities. Summary

Includes bibliography

Social-spider optimization-based artificial neural networks training and its applications for Parkinson's disease identification

Evolutionary algorithms have been widely used for Artificial Neural Networks (ANN) training, being the idea to update the neurons' weights using social dynamics of living organisms in order to decrease the classification error. In this paper, we have introduced Social-Spider Optimization to improve the training phase of ANN with Multilayer perceptrons, and we validated the proposed approach in the context of Parkinson's Disease recognition. The experimental section has been carried out against with five other well-known meta-heuristics techniques, and it has shown SSO can be a suitable approach for ANN-MLP training step.

Building the sugarcane genome for biotechnology and identifying evolutionary trends

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

Production of β-(1,3)-glucanases by Trichoderma harzianum Rifai: Optimization and Application to Produce Gluco-oligosaccharides from Paramylon and Pustulan

β-(1→3)-Glucanases were produced by Trichoderma harzianum Rifai PAMB-86 cultivated on botryosphaeran in a bench-fermenter and optimised by the response surface method. Maximal enzyme titres occurred at 5 days, initial pH 5.5 and aeration of 1.5vvm. β-(1→3)-The β-glucanolytic enzyme complex produced by T. harzianum Rifai PAMB- 86 was fractionated by gel filtration into 2 fractions (F-I, F-II), and employed to produce gluco-oligosaccharides from algal paramylon ((1→3)-β-D-glucan) and lichen pustulan ((1→6)-β-D-glucan). Both enzymes attacked paramylon to the extent of ~15-20% in 30 min releasing glucose and laminaribiose as major end-products, and laminarioligosaccharides of degree of polymerization (DP) ≥3. Only F-I degraded pustulan resulting in ~2% degradation at 30 min, with glucose, gentiobiose and gentio-oligosaccharides of DP ≥4 as major products. The difference in the nature of the hydrolysis products can be explained by the substrate specificities of each enzyme fraction, and the structural differences of the β-D-glucans attacked.

The effect of power-ultrasound on the pretreatment of acidified aqueous solutions of banana flower-stalk: Structural, chemical and statistical analysis

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

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)

Flutter analysis including structural uncertainties

The common practice in industry is to perform flutter analyses considering the generalized stiffness and mass matrices obtained from finite element method (FEM) and aerodynamic generalized force matrices obtained from a panel method, as the doublet lattice method. These analyses are often reperformed if significant differences are found in structural frequencies and damping ratios determined from ground vibration tests compared to FEM. This unavoidable rework can result in a lengthy and costly process of analysis during the aircraft development. In this context, this paper presents an approach to perform flutter analysis including uncertainties in natural frequencies and damping ratios. The main goal is to assure the nominal system’s stability considering these modal parameters varying in a limited range. The aeroelastic system is written as an affine parameter model and the robust stability is verified solving a Lyapunov function through linear matrix inequalities and convex optimization

Efficient Forest Data Structure for Evolutionary Algorithms Applied to Network Design

The design of a network is a solution to several engineering and science problems. Several network design problems are known to be NP-hard, and population-based metaheuristics like evolutionary algorithms (EAs) have been largely investigated for such problems. Such optimization methods simultaneously generate a large number of potential solutions to investigate the search space in breadth and, consequently, to avoid local optima. Obtaining a potential solution usually involves the construction and maintenance of several spanning trees, or more generally, spanning forests. To efficiently explore the search space, special data structures have been developed to provide operations that manipulate a set of spanning trees (population). For a tree with n nodes, the most efficient data structures available in the literature require time O(n) to generate a new spanning tree that modifies an existing one and to store the new solution. We propose a new data structure, called node-depth-degree representation (NDDR), and we demonstrate that using this encoding, generating a new spanning forest requires average time O(root n). Experiments with an EA based on NDDR applied to large-scale instances of the degree-constrained minimum spanning tree problem have shown that the implementation adds small constants and lower order terms to the theoretical bound.

Efficient Placement of Fault Indicators in an Actual Distribution System Using Evolutionary Computing

This paper proposes an evolutionary computing strategy to solve the problem of fault indicator (FI) placement in primary distribution feeders. More specifically, a genetic algorithm (GA) is employed to search for an efficient configuration of FIs, located at the best positions on the main feeder of a real-life distribution system. Thus, the problem is modeled as one of optimization, aimed at improving the distribution reliability indices, while, at the same time, finding the least expensive solution. Based on actual data, the results confirm the efficiency of the GA approach to the FI placement problem.

Structural and kinetic studies of Schistosoma mansoni adenylate kinases

The human parasite Schistosoma mansoni is totally dependent on the purine salvage pathway in order to supply large quantities of purine precursors for its energy and DNA biosynthetic needs. Adenylate kinase (ADK) is responsible for the conversion of AMP (produced by the adenosine kinase reaction) into ADP, which is subsequently converted into ATP by nucleoside diphosphate kinase (NDPK). ADK and NDPK are the most active enzymes of the pathway, probably reflecting an evolutionary adaptation due to the intense use of the branch in which they participate. However, notwithstanding their importance very little information has been accumulated found regarding these enzymes. In this work two adenylate kinases from S. mansoni were cloned and heterologously expressed in Escherichia coil. The purified products were utilized in activity assays, and displayed kinetic parameters similar to the corresponding human orthologous proteins. The cytosolic S. mansoni ADK was crystallized and its structure solved allowing us to detect a difference in the nucleotide binding site when compared with the human ortholog. (C) 2012 Elsevier B.V. All rights reserved.

A survey of evolutionary algorithms for decision-tree induction

This paper presents a survey of evolutionary algorithms that are designed for decision-tree induction. In this context, most of the paper focuses on approaches that evolve decision trees as an alternate heuristics to the traditional top-down divide-and-conquer approach. Additionally, we present some alternative methods that make use of evolutionary algorithms to improve particular components of decision-tree classifiers. The paper's original contributions are the following. First, it provides an up-to-date overview that is fully focused on evolutionary algorithms and decision trees and does not concentrate on any specific evolutionary approach. Second, it provides a taxonomy, which addresses works that evolve decision trees and works that design decision-tree components by the use of evolutionary algorithms. Finally, a number of references are provided that describe applications of evolutionary algorithms for decision-tree induction in different domains. At the end of this paper, we address some important issues and open questions that can be the subject of future research.

Cash balance management: A comparison between genetic algorithms and particle swarm optimization

This work aimed to apply genetic algorithms (GA) and particle swarm optimization (PSO) in cash balance management using Miller-Orr model, which consists in a stochastic model that does not define a single ideal point for cash balance, but an oscillation range between a lower bound, an ideal balance and an upper bound. Thus, this paper proposes the application of GA and PSO to minimize the Total Cost of cash maintenance, obtaining the parameter of the lower bound of the Miller-Orr model, using for this the assumptions presented in literature. Computational experiments were applied in the development and validation of the models. The results indicated that both the GA and PSO are applicable in determining the cash level from the lower limit, with best results of PSO model, which had not yet been applied in this type of problem.

Self-Assembly of Poly(4-vynil-N-alkylpyridinium) Bromide onto Silica: Effect of Side-Chain Length on Structural Aspects at a Molecular Level

Self-assembly of poly(4-vynil-N-alkyl)pyridinium bromide with alkyl side chains of 2, 5, 7, 10, or 16 carbons from ethanolic solutions onto flat silica surfaces was studied by means of ellipsometry, atomic force microscopy (AFM), contact angle measurements, and sum-frequency generation (SFG) vibrational spectroscopy in the CH3 and CH2 stretch region. Ab initio quantum-chemical calculations on the N-alkylpyridinium side-group with restricted Hartree-Fock (RHF) method and 6-311G (d,p) basis set were C one to estimate the charge distribution along the pyridinium ring and the alkyl side-chain. SFG results showed that longer side chains promote the disorientation of the alkyl groups at the surface, corroborating with the contact angle values. AFM images revealed film homogeneity, regardless the alkyl side group. However, after 24 h contact with water, ringlike structures appeared on the film surfaces, when the polycation alkyl side chain had 7 or less carbons, and as the alkyl chain increased to 10 or 16 carbons, the films dewetted because the hydrophobic interactions prevailed over the electrostatic interactions between the pyridinium charged groups and the negatively charged SiO2 surface. Under acid conditions (HCl 0.1 mol.L-1), the film mean thickness values decreased up to 50% of original values when the alkyl side chains were ethyl or pentyl groups due to ion-pair disruption, but for longer groups they remained unchanged. Quantum-chemical optimization and Mulliken electron population showed that (i) from C2 to C15 the positive charge at the headgroup (HG) decreased 0.025, while the charge at combined HG + alpha-CH2 increased 0.037; and (ii) for C6 or longer, the alkyl side group presents a tilt in the geometry, moving away from the plane. Such effects summed up over the whole polymer chain give support to suggest that when the side chains are longer than 7 carbons, the hydrophobic interaction decreases film stability and increases acid resistance.

New Insights on Nontechnical Losses Characterization Through Evolutionary-Based Feature Selection

Although nontechnical losses automatic identification has been massively studied, the problem of selecting the most representative features in order to boost the identification accuracy and to characterize possible illegal consumers has not attracted much attention in this context. In this paper, we focus on this problem by reviewing three evolutionary-based techniques for feature selection, and we also introduce one of them in this context. The results demonstrated that selecting the most representative features can improve a lot of the classification accuracy of possible frauds in datasets composed by industrial and commercial profiles.

Optimization of anti-Trypanosoma cruzi oxadiazoles leads to identification of compounds with efficacy in infected mice

We recently showed that oxadiazoles have anti-Trypanosoma cruzi activity at micromolar concentrations. These compounds are easy to synthesize and show a number of clear and interpretable structure-activity relationships (SAR), features that make them attractive to pursue potency enhancement. We present here the structural design, synthesis, and anti-T. cruzi evaluation of new oxadiazoles denoted 5a-h and 6a-h. The design of these compounds was based on a previous model of computational docking of oxadiazoles on the T. cruzi protease cruzain. We tested the ability of these compounds to inhibit catalytic activity of cruzain, but we found no correlation between the enzyme inhibition and the antiparasitic activity of the compounds. However, we found reliable SAR data when we tested these compounds against the whole parasite. While none of these oxadiazoles showed toxicity for mammalian cells, oxadiazoles 6c (fluorine), 6d (chlorine), and 6e (bromine) reduced epimastigote proliferation and were cidal for trypomastigotes of T. cruzi Y strain. Oxadiazoles 6c and 6d have IC50 of 9.5 +/- 2.8 and 3.5 +/- 1.8 mu M for trypomastigotes, while Benznidazole, which is the currently used drug for Chagas disease treatment, showed an IC50 of 11.3 +/- 2.8 mu M. Compounds 6c and 6d impair trypomastigote development and invasion in macrophages, and also induce ultrastructural alterations in trypomastigotes. Finally, compound 6d given orally at 50 mg/kg substantially reduces the parasitemia in T. cruzi-infected BALB/c mice. Our drug design resulted in potency enhancement of oxadiazoles as anti-Chagas disease agents, and culminated with the identification of oxadiazole 6d, a trypanosomicidal compound in an animal model of infection. (C) 2012 Elsevier Ltd. All rights reserved.