164 resultados para Redes alimentarias alternativas


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Neural Networks are a set of mathematical methods and computer programs designed to simulate the information process and the knowledge acquisition of the human brain. In last years its application in chemistry is increasing significantly, due the special characteristics for model complex systems. The basic principles of two types of neural networks, the multi-layer perceptrons and radial basis functions, are introduced, as well as, a pruning approach to architecture optimization. Two analytical applications based on near infrared spectroscopy are presented, the first one for determination of nitrogen content in wheat leaves using multi-layer perceptrons networks and second one for determination of BRIX in sugar cane juices using radial basis functions networks.

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Quantum chemistry describes the hydrogen atom as one of the few systems that permits an exact solution of the Schrödinger equation. Students tend to consider that little can be learned from the hydrogen atom and forget that it can be used as a standard to test numerical procedures used to calculate properties of multielectronic systems. In this paper, four different numerical procedures are described in order to solve the Schrödinger equation for the hydrogen atom. The basic motivation is to identify new insights and methods that can be obtained from the application of powerful numerical techniques in a well-known system.

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We introduce a global optimization method based on the cooperation between an Artificial Neural Net (ANN) and Genetic Algorithm (GA). We have used ANN to select the initial population for the GA. We have tested the new method to predict the ground-state geometry of silicon clusters. We have described the clusters as a piling of plane structures. We have trained three ANN architectures and compared their results with those of pure GA. ANN strongly reduces the total computational time. For Si10, it gained a factor of 5 in search speed. This method can be easily extended to other optimization problems.

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The influence of natural aging furthered by atmospheric corrosion of parts of electric transformers and materials, as well as of concrete poles and cross arms containing corrosion inhibitors was evaluated in Manaus. Results for painted materials, it could showed that loss of specular gloss was more intensive in aliphatic polyurethane points than in acrylic polyurethane ones. No corrosion was observed for metal and concrete samples until 400 days of natural aging. Corrosion in steel reinforcement was noticed in some poles, arising from manufacturing faults, such as low cement content, water/cement ratio, thin concrete cover thickness, etc. The performance of corrosion inhibitors was assessed by many techniques after natural and accelerated aging in a 3.5% saline aqueous solution. The results show the need for better chemical component selection and its concentration in the concrete mixture.

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Although several chemical elements were not known by end of the 18th century, Mendeleyev came up with an astonishing achievement: the periodic table of elements. He was not only able to predict the existence of (then) new elements but also to provide accurate estimates of their chemical and physical properties. This is certainly a relevant example of the human intelligence. Here, we intend to shed some light on the following question: Can an artificial intelligence system yield a classification of the elements that resembles, in some sense, the periodic table? To achieve our goal, we have fed a self-organized map (SOM) with information available at Mendeleyev's time. Our results show that similar elements tend to form individual clusters. Thus, SOM generates clusters of halogens, alkaline metals and transition metals that show a similarity with the periodic table of elements.

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The multilayer perceptron network was used to classify the gasoline. The main parameters used in the classification were established by the Ordinance nº 309 of the Agência Nacional do Petróleo, but without informing the network the legal limits of these parameters. The network used had 10 neurons in a single hidden layer, learning rate of 0.04 and 250 training epochs. The application of artificial neural network served classify 100% of the commercialized gas in the region of Londrina-PR and to identify the tampered gasoline even those suspected of tampering.

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The restricted availability of water sources suitable for consumption and high costs for obtaining potable water has caused an increase of the conscience concerning the use. Thus, there is a high demand for "environmentally safe methods" which are according to the principles of Green Chemistry. Moreover, these methods should be able to provide reliable results for the analysis of water quality for various pollutants, such as phenol. In this work, greener alternatives for sample preparation for phenol determination in aqueous matrices are presented, which include: liquid phase microextraction, solid phase microextraction, flow analysis, cloud point extraction and aqueous two-phase systems.

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Multivariate models were developed using Artificial Neural Network (ANN) and Least Square - Support Vector Machines (LS-SVM) for estimating lignin siringyl/guaiacyl ratio and the contents of cellulose, hemicelluloses and lignin in eucalyptus wood by pyrolysis associated to gaseous chromatography and mass spectrometry (Py-GC/MS). The results obtained by two calibration methods were in agreement with those of reference methods. However a comparison indicated that the LS-SVM model presented better predictive capacity for the cellulose and lignin contents, while the ANN model presented was more adequate for estimating the hemicelluloses content and lignin siringyl/guaiacyl ratio.

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This work propose a recursive neural network to solve inverse equilibrium problem. The acidity constants of 7-epiclusianone in ethanol-water binary mixtures were determined from multiwavelength spectrophotmetric data. A linear relationship between acidity constants and the %w/v of ethanol in the solvent mixture was observed. The proposed method efficiency is compared with the Simplex method, commonly used in nonlinear optimization techniques. The neural network method is simple, numerically stable and has a broad range of applicability.

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In this paper we built three co-authorship networks displaying the acquaintances between countries, universities and authors that have published papers in Quimica Nova from 1995 to 2008. Our research was conducted applying a bibliometric approach to 1782 papers and over 4200 authors. Centrality measures were used and the most significant actors of each network were pointed out. The results using the centrality metrics and the network structures indicated that Quimica Nova resembles a typical scientific community.

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This review considers some of the difficulties encountered with the analysis of basic solutes using reversed-phase chromatography, such as detrimental interaction with stationary phase silanol groups. Methods of overcoming these problems in reversed-phase separations, by judicious selection of the stationary phase and mobile phase conditions, are discussed. Developments to improve the chemical and thermal stability of stationary phases are also reviewed. It is shown that substantial progress has been made in the manufacturing of stationary phases, enabling their use over a wide variety of experimental conditions. In addition, general measures to significantly extend their lifespan are discussed.

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Metal-organic frameworks (MOFs) form a new class of materials with well-defined yet tunable properties. These are crystalline, highly porous and exhibit strong metal-ligand interactions. Importantly, their physical and chemical properties, including pore size, pore structure, acidity, and magnetic and optical characteristics, can be tailored by choosing the appropriate ligands and metal precursors. Here we review the key aspects of synthesis and characterization of MOFs, focusing on lanthanide-based and vanadium-based materials. We also outline some of their applications in catalysis and materials science.

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The objective of this work is to demonstrate the efficient utilization of the Principal Components Analysis (PCA) as a method to pre-process the original multivariate data, that is rewrite in a new matrix with principal components sorted by it's accumulated variance. The Artificial Neural Network (ANN) with backpropagation algorithm is trained, using this pre-processed data set derived from the PCA method, representing 90.02% of accumulated variance of the original data, as input. The training goal is modeling Dissolved Oxygen using information of other physical and chemical parameters. The water samples used in the experiments are gathered from the Paraíba do Sul River in São Paulo State, Brazil. The smallest Mean Square Errors (MSE) is used to compare the results of the different architectures and choose the best. The utilization of this method allowed the reduction of more than 20% of the input data, which contributed directly for the shorting time and computational effort in the ANN training.

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Os currículos médicos estão preocupados em formar médicos detentores de traços humanísticos, que passam, assim, a ser objetivos educacionais. Deve-se desenvolver, então, a sua pedagogia. Como objetivos, podem ser tratados pedagogicamente com base nos corpos conceituais teóricos que informam o planejamento de currículos, como a taxonomia de objetivos educacionais clássica, que compreende os domínios cognitivo, afetivo e psicomotor. Esta taxonomia dá conta de muitos dos objetivos relacionados aos traços humanísticos, organizando-os e facilitando a tarefa de planejamento educacional nessa área, mas deixa marginalizados alguns aspectos do conhecimento humano cruciais para as Humanidades, como os objetivos que se referem a autoconhecimento, amadurecimento e à individuação, reconhecimento dos próprios sentimentos e habilidades de comunicação interpessoal. Devem-se, então, buscar outros sistemas conceituais como referenciais teóricos mais adequados às Humanidades. É possível que se possa usar as taxonomias de maneira aditiva, procurando objetivos humanísticos em cada uma das categorias e subcategorias das diversas taxonomias. Assim, aumenta-se a abrangência do corpo de objetivos educacionais de natureza humanística.

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Este trabalho focaliza a comunicação na relação médico-paciente, identificando pontos potencialmente geradores de dificuldades linguísticas para o médico. Os aspectos focalizados dizem respeito, primeiramente, ao emprego, pelas partes envolvidas numa situação de comunicação, de variedades linguísticas diferentes; e em segundo lugar, às estratégias discursivas empregadas. Defendemos que, para o médico, é fundamental ter certeza de que compreendeu o problema que lhe foi trazido, mas, para isso, terá de procurar confirmar com o paciente, em diferentes momentos da consulta, sua compreensão das informações que está recebendo e saber passar-lhe, de modo compreensível, seu julgamento da situação clínica e as ações necessárias. As situações que ilustram os problemas aqui referidos fazem parte da experiência profissional dos autores