Determination of resonant frequencies of triangular and rectangular microstrip antennas, using artificial neural networks

This paper presents models that can be used in the design of microstrip antennas for mobile communications. The antennas can be triangular or rectangular. The presented models are compared with deterministic and empirical models based on artificial neural networks (ANN) presented in the literature. The models are based on Perceptron Multilayer (PML) and Radial Basis Function (RBF) ANN. RBF based models presented the best results. Also, the models can be embedded in CAD systems, in order to design microstrip antennas for mobile communications.

Poly(Lactide-co-Glycolide) Nanocapsules Containing Benzocaine: Influence of the Composition of the Oily Nucleus on Physico-Chemical Properties and Anesthetic Activity

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

A Prodrug Approach to Improve the Physico-Chemical Properties and Decrease the Genotoxicity of Nitro Compounds

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

Physico-chemical properties of MTA and a novel experimental cement

Aim: To evaluate the release of calcium ions, pH and conductivity of a new experimental dental cement (EC) and to compare them with those of mineral trioxide aggregate (MTA-Angelus). Methodology: Five samples of each cement were prepared using plastic tubes 1 mm in diameter and 10 mm long. Each sample was sealed in a test tube containing 10 mL deionized water which was analysed after 24, 48, 72, 96, 192, 240 and 360 h for pH, electrical conductivity and calcium release. The concentration of calcium ions was obtained through atomic absorption spectroscopy technique. The data were analysed statistically using the analysis of variance (ANOVA) and the Student's test (t-test). Results: The pH of the storage solutions was not affected by the material and the interaction of material with time (P > 0.05). However, the time of immersion was significant (P < 0.01) for both materials. For the electric conductivity and calcium release, the interaction of material with time was statistically significant (P < 0.01), indicating that EC and MTA-Angelus did not behave in a similar manner. Conclusions: The experimental cement released calcium and increased the pH of the storage solutions in a similar manner to MTA-Angelus. However, EC showed significantly higher calcium release than commercial MTA-Angelus after 24 h. © 2005 International Endodontic Journal.

Xylanase and β-Xylosidase from Penicillium janczewskii: Production, Physico-Chemical properties, and application of the crude extract to pulp biobleaching

Extracellular xylanase and β-xylosidase production by a Penicillium janczewskii strain were investigated in liquid cultures with xylan from oat spelts under different physical and chemical conditions. The selected conditions for optimized production of xylanase and β-xylosidase were 7 days, pH 6.5, at 30 °C and 8 days, pH 5.0, at 25 °C, respectively. The xylanase exhibited optimal activity in pH 5.0 at 50 °C and the β- xylosidase in pH 4.0 at 75 °C. The xylanase was more stable at pH 6.0 to 9.5, while the β-xylosidase remained stable at pH ranging from 1.6 to 5.5. The xylanase half-life (T50) at 40, 50, and 60 °C was 183, 15, and 3 min, respectively. β-xylosidase half-life was 144, 8, and 4 min at 50, 65, and 75 °C, respectively. When applied to the biobleaching of Eucalyptus kraft pulp, xylanase dosages of 2 and 4 U/g dried pulp reduced, respectively, kappa number by 3.0 and 3.3 units after 1 h treatment, demonstrating that the use of P. janczewskii xylanases in this process is quite promising. The pulp viscosity was not altered, confirming the absence of cellulolytic enzymes in the fungal extract.

Xylanase and beta-Xylosidase from Penicillium janczewskii: Production, Physico-chemical Properties, and Application of the Crude Extract to Pulp Biobleaching

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

Physico-chemical properties of starches isolated from potato cultivars grown in soils with different phosphorus availability

Conselho Nacional de Desenvolvimento Científico e Tecnológico (CNPq)

Prediction of occurrences of diverse chemical classes in the Asteraceae through artificial neural networks

The training and the application of a neural network system for the prediction of occurrences of secondary metabolites belonging to diverse chemical classes in the Asteraceae is described. From a database containing about 604 genera and 28,000 occurrences of secondary metabolites in the plant family, information was collected encompassing nine chemical classes and their respective occurrences for training of a multi-layer net using the back-propagation algorithm. The net supplied as output the presence or absence of the chemical classes as well as the number of compounds isolated from each taxon. The results provided by the net from the presence or absence of a chemical class showed a 89% hit rate; by excluding triterpenes from the analysis, only 5% of the genera studied exhibited errors greater than 10%. Copyright (C) 2004 John Wiley Sons, Ltd.

Comparative study between RBF and radial-PPS neural networks

The study of function approximation is motivated by the human limitation and inability to register and manipulate with exact precision the behavior variations of the physical nature of a phenomenon. These variations are referred to as signals or signal functions. Many real world problem can be formulated as function approximation problems and from the viewpoint of artificial neural networks these can be seen as the problem of searching for a mapping that establishes a relationship from an input space to an output space through a process of network learning. Several paradigms of artificial neural networks (ANN) exist. Here we will be investigated a comparative of the ANN study of RBF with radial Polynomial Power of Sigmoids (PPS) in function approximation problems. Radial PPS are functions generated by linear combination of powers of sigmoids functions. The main objective of this paper is to show the advantages of the use of the radial PPS functions in relationship traditional RBF, through adaptive training and ridge regression techniques.

Modeling and analysis of artificial neural networks applied in operations research

Artificial neural networks are dynamic systems consisting of highly interconnected and parallel nonlinear processing elements. Systems based on artificial neural networks have high computational rates due to the use of a massive number of these computational elements. Neural networks with feedback connections provide a computing model capable of solving a rich class of optimization problems. In this paper, a modified Hopfield network is developed for solving problems related to operations research. The internal parameters of the network are obtained using the valid-subspace technique. Simulated examples are presented as an illustration of the proposed approach. Copyright (C) 2000 IFAC.

Nitrogen content identification in crop plants using spectral reflectance and artificial neural networks

The accurate identification of the nitrogen content in crop plants is extremely important since it involves economic aspects and environmental impacts. Several experimental tests have been carried out to obtain characteristics and parameters associated with the health of plants and its growing. The nitrogen content identification involves a lot of nonlinear parametes and complexes mathematical models. This paper describes a novel approach for identification of nitrogen content thought spectral reflectance of plant leaves using artificial neural networks. The network acts as identifier of relationships among pH of soil, fertilizer treatment, spectral reflectance and nitrogen content in the plants. So, nitrogen content can be estimated and generalized from an input parameter set. This approach can be form the basis for development of an accurate real time nitrogen applicator.

Artificial neural networks and clustering techniques applied in the reconfiguration of distribution systems

One objective of the feeder reconfiguration problem in distribution systems is to minimize the power losses for a specific load. For this problem, mathematical modeling is a nonlinear mixed integer problem that is generally hard to solve. This paper proposes an algorithm based on artificial neural network theory. In this context, clustering techniques to determine the best training set for a single neural network with generalization ability are also presented. The proposed methodology was employed for solving two electrical systems and presented good results. Moreover, the methodology can be employed for large-scale systems in real-time environment.

Artificial neural networks for load flow and external equivalents studies

In this paper an artificial neural network (ANN) based methodology is proposed for (a) solving the basic load flow, (b) solving the load flow considering the reactive power limits of generation (PV) buses, (c) determining a good quality load flow starting point for ill-conditioned systems, and (d) computing static external equivalent circuits. An analysis of the input data required as well as the ANN architecture is presented. A multilayer perceptron trained with the Levenberg-Marquardt second order method is used. The proposed methodology was tested with the IEEE 30- and 57-bus, and an ill-conditioned 11-bus system. Normal operating conditions (base case) and several contingency situations including different load and generation scenarios have been considered. Simulation results show the excellent performance of the ANN for solving problems (a)-(d). (C) 2010 Elsevier B.V. All rights reserved.

A partial least squares and principal component regression study of quinone compounds with trypanocidal activity

A quantitative structure-activity relationship (QSAR) study of 19 quinone compounds with trypanocidal activity was performed by Partial Least Squares (PLS) and Principal Component Regression (PCR) methods with the use of leave-one-out crossvalidation procedure to build the regression models. The trypanocidal activity of the compounds is related to their first cathodic potential (Ep(c1)). The regression PLS and PCR models built in this study were also used to predict the Ep(c1) of six new quinone compounds. The PLS model was built with three principal components that described 96.50% of the total variance and present Q(2) = 0.83 and R-2 = 0.90. The results obtained with the PCR model were similar to those obtained with the PLS model. The PCR model was also built with three principal components that described 96.67% of the total variance with Q(2) = 0.83 and R-2 = 0.90. The most important descriptors for our PLS and PCR models were HOMO-1 (energy of the molecular orbital below HOMO), Q4 (atomic charge at position 4), MAXDN (maximal electrotopological negative difference), and HYF (hydrophilicity index).

Evaluation and identification of lightning models by artificial neural networks

This paper describes a novel approach for mapping lightning models using artificial neural networks. The networks acts as identifier of structural features of the lightning models so that output parameters can be estimated and generalized from an input parameter set. Simulation examples are presented to validate the proposed approach. More specifically, the neural networks are used to compute electrical field intensity and critical disruptive voltage taking into account several atmospheric and structural factors, such as pressure, temperature, humidity, distance between phases, height of bus bars, and wave forms. A comparative analysis with other approaches is also provided to illustrate this new methodology.