Search schemes for random optimization algorithms that preserve the asymptotic distribution

Markovian algorithms for estimating the global maximum or minimum of real valued functions defined on some domain Omega subset of R-d are presented. Conditions on the search schemes that preserve the asymptotic distribution are derived. Global and local search schemes satisfying these conditions are analysed and shown to yield sharper confidence intervals when compared to the i.i.d. case.

Optimization of the sampling scheme for maps of physical and chemical properties estimated by kriging

The sampling scheme is essential in the investigation of the spatial variability of soil properties in Soil Science studies. The high costs of sampling schemes optimized with additional sampling points for each physical and chemical soil property, prevent their use in precision agriculture. The purpose of this study was to obtain an optimal sampling scheme for physical and chemical property sets and investigate its effect on the quality of soil sampling. Soil was sampled on a 42-ha area, with 206 geo-referenced points arranged in a regular grid spaced 50 m from each other, in a depth range of 0.00-0.20 m. In order to obtain an optimal sampling scheme for every physical and chemical property, a sample grid, a medium-scale variogram and the extended Spatial Simulated Annealing (SSA) method were used to minimize kriging variance. The optimization procedure was validated by constructing maps of relative improvement comparing the sample configuration before and after the process. A greater concentration of recommended points in specific areas (NW-SE direction) was observed, which also reflects a greater estimate variance at these locations. The addition of optimal samples, for specific regions, increased the accuracy up to 2 % for chemical and 1 % for physical properties. The use of a sample grid and medium-scale variogram, as previous information for the conception of additional sampling schemes, was very promising to determine the locations of these additional points for all physical and chemical soil properties, enhancing the accuracy of kriging estimates of the physical-chemical properties.

Growth optimization procedures for the phytopathogen Xylella fastidiosa

For the first time, growth curves are shown for the phytopathogen Xylella fastidiosa on traditional growth media such as PW (periwinkle wilt), BCYE (buffered charcoal yeast extract), and on new ones such as GYE (glutamate yeast extract) and PYE (phosphate yeast extract) that were developed in this work. The optimal growth conditions on solid and liquid media as well as their measurements are presented, by using total protein content and turbidity determinations. The results demonstrated that yeast extract provided sufficient nutrients for X. fastidiosa, since the cells grew well on PYE medium.

Optimization of fibrolytic enzyme production by Aspergillus japonicus C03 with potential application in ruminant feed and their effects on tropical forages hydrolysis

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

Three-Dimensional Semiautomatic Road Extraction From a High-Resolution Aerial Image by Dynamic-Programming Optimization in the Object Space

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

Study of the potentiometric response of the doped spinel Li1.05Al0.02Mn1.98O4 for the optimization of a selective lithium ion sensor

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Toward the Optimization of an e-Tongue System Using Information Visualization: A Case Study with Perylene Tetracarboxylic Derivative Films in the Sensing Units

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

Topliss method in the optimization of salicylic acid derivatives as potential antimycobacterial agents

The Topliss method was used to guide a synthetic path in support of drug discovery efforts toward the identification of potent antimycobacterial agents. Salicylic acid and its derivatives, p-chloro, p-methoxy, and m-chlorosalicylic acid, exemplify a series of synthetic compounds whose minimum inhibitory concentrations for a strain of Mycobacterium were determined and compared to those of the reference drug, p-aminosalicylic acid. Several physicochemical descriptors (including Hammett's sigma constant, ionization constant, dipole moment, Hansch constant, calculated partition coefficient, Sterimol-L and -B-4 and molecular volume) were considered to elucidate structure-activity relationships. Molecular electrostatic potential and molecular dipole moment maps were also calculated using the AM1 semi-empirical method. Among the new derivatives, m-chlorosalicylic acid showed the lowest minimum inhibitory concentration. The overall results suggest that both physicochemical properties and electronic features may influence the biological activity of this series of antimycobacterial agents and thus should be considered in designing new p-aminosalicylic acid analogs.

Cyclodextrin glycosyltransferase from Bacillus licheniformis: Optimization of production and its properties

Cyclodextrin glycosyltransferase (EC 2.4.1.19) is an enzyme that produces cyclodextrins from starch via an intramolecular transglycosylation reaction. An alkalophilic Bacillus strain, isolated from cassava peels, was identified as Bacillus licheniformis. CGTase production by this strain was better when potato starch was used as carbon source, followed by cassava starch and amylopectin. Glucose and amylose, on the other hand, acted as synthesis repressors. When the cultivation was supplemented with sodium ions and had the pH adjusted between 6.0 and 9.0, the microorganism maintained the growth and enzyme production capacity. This data is interesting because it contradicts the concept that alkalophilic microorganisms do not grow in this pH range. After ultrafiltration-centrifugation, one protein of 85.2 kDa with CGTase activity was isolated. This protein was identified in plates with starch and phenolphthalein. Determination of the optimum temperature showed higher activities at 25 degrees C and 55 degrees C, indicating the possible presence of more than one CGTase in the culture filtrate. Km and Vmax values were 1.77 mg/mL and 0.0263 U/mg protein, respectively, using potato starch as substrate.

Multivariate optimization and validation of an analytical methodology by RP-HPLC for the determination of losartan potassium in capsules

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Projections Onto Convex Sets through Particle Swarm Optimization and its application for remote sensing image restoration

Image restoration attempts to enhance images corrupted by noise and blurring effects. Iterative approaches can better control the restoration algorithm in order to find a compromise of restoring high details in smoothed regions without increasing the noise. Techniques based on Projections Onto Convex Sets (POCS) have been extensively used in the context of image restoration by projecting the solution onto hyperspaces until some convergence criteria be reached. It is expected that an enhanced image can be obtained at the final of an unknown number of projections. The number of convex sets and its combinations allow designing several image restoration algorithms based on POCS. Here, we address two convex sets: Row-Action Projections (RAP) and Limited Amplitude (LA). Although RAP and LA have already been used in image restoration domain, the former has a relaxation parameter (A) that strongly depends on the characteristics of the image that will be restored, i.e., wrong values of A can lead to poorly restoration results. In this paper, we proposed a hybrid Particle Swarm Optimization (PS0)-POCS image restoration algorithm, in which the A value is obtained by PSO to be further used to restore images by POCS approach. Results showed that the proposed PSO-based restoration algorithm outperformed the widely used Wiener and Richardson-Lucy image restoration algorithms. (C) 2010 Elsevier B.V. All rights reserved.

The Poisson-exponential regression model under different latent activation schemes

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

A novel approach based on recurrent neural networks applied to nonlinear systems optimization

This paper presents an efficient approach based on recurrent neural network for solving nonlinear optimization. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The main advantage of the developed network is that it treats optimization and constraint terms in different stages with no interference with each other. Moreover, the proposed approach does not require specification of penalty and weighting parameters for its initialization. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are provided to demonstrate the performance of the proposed neural network. (c) 2005 Elsevier B.V. All rights reserved.

Stability and convergence analysis of a neural model applied in nonlinear systems optimization

A neural model for solving nonlinear optimization problems is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are computed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points that represent an optimal feasible solution. The network is shown to be completely stable and globally convergent to the solutions of nonlinear optimization problems. A study of the modified Hopfield model is also developed to analyze its stability and convergence. Simulation results are presented to validate the developed methodology.

A novel approach for solving constrained nonlinear optimization problems using neurofuzzy systems

A neural network model for solving constrained nonlinear optimization problems with bounded variables is presented in this paper. More specifically, a modified Hopfield network is developed and its internal parameters are completed using the valid-subspace technique. These parameters guarantee the convergence of the network to the equilibrium points. The network is shown to be completely stable and globally convergent to the solutions of constrained nonlinear optimization problems. A fuzzy logic controller is incorporated in the network to minimize convergence time. Simulation results are presented to validate the proposed approach.