West Parana State University

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Toledo, Brazil
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Da Silva S.,West Parana State University
International Journal of Non-Linear Mechanics | Year: 2011

The common metrics used in linear finite element (FE) model updating using vibration test data are generally functions of relationships based on unidimensional convolution, for example, distances involving natural frequencies, frequency response or impulse response functions, modal shapes, etc. When a structure has local elements or geometry, like joints, bolts, gaps, backlash, etc., these approaches can fail once it could to induce non-linear behavior. Thus, the methods for FE model updating, when considering the existence of localized non-linear parameters, have been receiving much attention in the last years. In this sense, the present paper proposes the use of a strategy through objective functions based on multiples convolutions described by the first order and second order discrete-time Volterra kernels. These kernels are effective metrics for a model updating into large FE model with local non-linearity. In order to improve the non-linear coefficient identification, an orthogonal basis involving Kautz filter is used to expand the kernels, called by Wiener kernel. To exemplify in full details the steps of the updating procedure, an FE model of a three-dimensional portal frame with commons non-linearities is simulated with different excitation forces and used to identify the non-linear parameters. These results allow us to characterize the practical applicability and the drawbacks of the proposed method with suggestions and remarks for further use in industrial structures. © 2010 Elsevier Ltd. All rights reserved.


Da Silva S.,West Parana State University
Mechanical Systems and Signal Processing | Year: 2011

The main goal of this short communication is to use orthogonal Kautz filters with multiple poles for non-parametric identification of the impulse response functions (IRFs) of mechanical structures. The IRF is estimated by using the covariance method based on the sum of convolution of the input signal processed by Kautz filters. The inclusion of different poles in the Kautz filter allows the use of a wide range of frequency and can improve the non-parametric identification process in the time domain by reducing the number of necessary terms. Some examples are provided to illustrate the simplicity and efficiency of the proposed approach. © 2010 Elsevier Ltd.


De Melo E.B.,West Parana State University
European Journal of Medicinal Chemistry | Year: 2010

Two multivariate studies, a PCA-SAR and a PLS-QSAR, of 3-aryl-4- hydroxyquinolin-2(1H)-one derivatives described as type I fatty acid synthase (FAS) inhibitors, are presented in this work. The variable selection was performed with the Fisher's weight and Ordered Predictors Selection (OPS) algorithm, respectively. In the PCA, a separation between active and inactive compounds was obtained by six descriptors (topological and geometrical). The PLS model presented five descriptors and two Latent Variables. Leave-N-out cross validation and y-randomization test showed that the model presented robustness and no chance correlation, respectively, and the descriptors indicated that the FAS inhibition depends on electronic distribution of the investigated compounds. The model obtained in this study may provide a guidance for proposition of new FAS inhibitors. © 2010 Elsevier Masson SAS. All rights reserved.


Among the methods of variable selection for Quantitative Structure-Property Relationship (QSPR) studies, one of the currently available alternatives is the Ordered Predictors Selection (OPS). Using this algorithm and descriptors obtained using only Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client, a QSPR study with a data set of 28 alkyl (1-phenylsulfonyl) cycloalkane-carboxylates and six different endpoints of environmental importance were developed and compared with other works. The comparison with models previously published was performed only with the internal validation, and four of the six new models proved to be superior. However, the six new models also presented high quality for external predictions, were robust and showed no chance correlation. The predicted endpoints of the six models were within the applicability domain. Thus, it can be concluded that the OPS algorithm was able to generate QSA(P)R models with high statistical quality for predicting of physicochemical and toxicological endpoints, thus showing its potential for development of predictive models of environmental interest. © 2012 Elsevier B.V.


Melo E.B.D.,West Parana State University
Physical Chemistry Chemical Physics | Year: 2015

The Ferreira-Kiralj hydrophobicity parameter Wc is a number fraction of hydrophobic carbon atoms and can be regarded as a constitutional descriptor since its calculation depends only on the number of polar and nonpolar carbons in a compound. Hydrophobicity is important to the toxicity of ionic liquids (ILs), which are salts by nature. Herein, a descriptor for this property was calculated using a simple adaptation of the type of polar carbon atoms included (WcAdap) to explore the possibility of its use in quantitative structure-activity relationship (QSAR) studies of ILs. The resulting model was tested using a database of ILs with toxicity against the Leukemia rat cell line IPC-81. Two other models were constructed using Crippen P and Mannhold P descriptors, which are both available in the free program PaDEL. The use of WcAdap led to a better and more indicative model. Thus, WcAdap may be a suitable molecular descriptor for the hydrophobicity of ILs in QSAR studies. © the Owner Societies 2015.


Valle M.E.,University of Londrina | Vicente D.M.G.,West Parana State University
Journal of Mathematical Imaging and Vision | Year: 2012

Sparsely connected autoassociative lattice memories (SCALMs) are very general models defined on complete lattices, a mathematical structure which is obtained by imposing some ordering on a set. They are computationally cheaper and mathematically simpler than "traditional" models and other memories such as the original autoassociative morphological memories (AMMs) of Ritter and Sussner because they only compute maximums and minimums. This paper provides theoretical results on SCALMs defined on a general complete lattice as well as an application of these memories for the storage and recall of color images. Precisely, we characterize the recall phase of SCALMs in terms of their fixed points. Then, we show that any endomorphic lattice polynomial-a concept that generalizes the notion of lattice polynomial of Birkhoff-on the fundamental memory set represents a fixed point of the SCALMs. Also, we discuss the relationship between SCALMs and the original AMMs. Finally, we provide some experimental results on the performance of SCALMs, defined on different color lattices, for the reconstruction of color images corrupted by either Gaussian or impulsive noise. © Springer Science+Business Media, LLC 2011.


dos Reis R.R.,West Parana State University | Sampaio S.C.,West Parana State University | De Melo E.B.,West Parana State University
Water Research | Year: 2013

Collecting data on the effects of pesticides on the environment is a slow and costly process. Therefore, significant efforts have been focused on the development of models that predict physical, chemical or biological properties of environmental interest. The soil sorption coefficient normalized to the organic carbon content (Koc) is a key parameter that is used in environmental risk assessments. Thus, several logKoc prediction models that use the hydrophobic parameter logP as a descriptor have been reported in the literature. Often, algorithms are used to calculate the value of logP due to the lack of experimental values for this property. Despite the availability of various algorithms, previous studies fail to describe the procedure used to select the appropriate algorithm. In this study, models that correlate logKoc with logP were developed for a heterogeneous group of nonionic pesticides using different freeware algorithms. The statistical qualities and predictive power of all of the models were evaluated. Thus, this study was conducted to assess the effect of the logP algorithm choice on logKoc modeling. The results clearly demonstrate that the lack of a selection criterion may result in inappropriate prediction models. Seven algorithms were tested, of which only two (ALOGPS and KOWWIN) produced good results. A sensible choice may result in simple models with statistical qualities and predictive power values that are comparable to those of more complex models. Therefore, the selection of the appropriate logP algorithm for modeling logKoc cannot be arbitrary but must be based on the chemical structure of compounds and the characteristics of the available algorithms. © 2013 Elsevier Ltd.


De Melo E.B.,West Parana State University | Ferreira M.M.C.,University of Campinas
Journal of Chemical Information and Modeling | Year: 2012

Despite highly active antiretroviral therapy (HAART) implementation, there is a continuous need to search for new anti-HIV agents. HIV-1 integrase (HIV-1 IN) is a recently validated biological target for AIDS therapy. In this work, a four-dimensional quantitative structure-activity relationship (4D-QSAR) study using the new methodology named LQTA-QSAR approach with a training set of 85 HIV-1 IN strand transfer inhibitors (INSTI), containing the β-diketo acid (DKA) substructure, was carried out. The GROMACS molecular dynamic package was used to obtain a conformational ensemble profile (CEP) and LQTA-QSAR was employed to calculate Coulomb and Lennard-Jones potentials and to generate the field descriptors. The partial least-squares (PLS) regression model using 14 field descriptors and 8 latent variables (LV) yielded satisfactory statistics (R 2= 0.897, SEC = 0.270, and F = 72.827), good performance in internal (QLOO 2 = 0.842 and SEV = 0.314) and external prediction (Rpred 2 = 0.839, SEP = 0.384, ARE pred = 4.942%, k = 0.981, k′ = 1.016, and |R0 2 - R0 ′2 = 0.0257). The QSAR model was shown to be robust (leave-N-out cross validation; average QLNO 2 = 0.834) and was not built by chance (y-randomization test; R 2 intercept = 0.109; Q 2 intercept = -0.398). Fair chemical interpretation of the model could be traced, including descriptors related to interaction with the metallic cofactors and the hydrophobic loop. The model obtained has a good potential for aid in the design of new INSTI, and it is a successful example of application of LQTA-QSAR as an useful tool to be used in computer-aided drug design (CADD). © 2012 American Chemical Society.


A quantitative structure-property relationship (QSPR) study for predicting the logarithm of bioconcentration factors (Log. BCF) of polychlorinated biphenyls (PCBs) is presented in this work. For this, the descriptors were obtained using only the Simplified Molecular Input Line Entry System (SMILES) strings in the free web server Parameter Client. The model was built using the Partial Least Squares (PLS) regression method. The best model presented five descriptors (one E-state index and four topological descriptors) and a high quality for fit, internal, and external predictions. The leave-N-out (LNO) cross validation and the y-randomization test showed the model is robust and has no shown chance correlation. With a second test set, the model was compared to other models and presented a root mean square error (RMSE) very close to the best model. The mechanistic interpretation was corroborated by other works in the literature and by the descriptors' theory. Thus, the results meet the five Organization for Economic Co-operation and Development (OECD) principles for validation of QSA(P)R models, and it is expected the model can effectively predict the BCF values in fishes of the PCB congeners without highly reliable experimental BCF. © 2011 Elsevier Inc.


Dos Reis R.R.,West Parana State University | Sampaio S.C.,West Parana State University | de Melo E.B.,West Parana State University
Water Research | Year: 2014

The collection of data to study the damage caused by pesticides to the environment and its ecosystems is slowly acquired and costly. Large incentives have been established to encourage research projects aimed at building mathematical models for predicting physical, chemical or biological properties of environmental interest. The organic carbon normalized soil sorption coefficient (Koc) is an important physicochemical property used in environmental risk assessments for compounds released into the environment. Many models for predicting logKoc that have used the parameters logP or logS as descriptors have been published in recent decades. The strong correlation between these properties (logP and logS) prevents them from being used together in multiple linear regressions. Because the sorption of a chemical compound in soil depends on both its water solubility and its water/organic matter partitioning, we assume that models capable of combining these two properties can generate more realistic results. Therefore, the objective of this study was to propose an alternative approach for modeling logKoc, using a simple descriptor of solubility, here designated as the logarithm of solubility corrected by octanol/water partitioning (logSP). Thus, different models were built with this descriptor and with the conventional descriptors logP and logS, alone or associated with other explanatory variables representing easy-to-interpret physicochemical properties. The obtained models were validated according to current recommendations in the literature, and they were compared with other previously published models. The results showed that the use of logSp instead of conventional descriptors led to simple models with greater statistical quality and predictive power than other more complex models found in the literature. Therefore, logSP can be a good alternative to consider for the modeling of logKoc and other properties that relate to both solubility and water/organic matter partitioning. © 2014 Elsevier Ltd.

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