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Puebla de Zaragoza, Mexico

de la Calleja E.M.,Federal University of Rio Grande do Sul | de la Calleja E.M.,Brazilian National Council for Scientific and Technological Development | Cervantes F.,CINVESTAV | de la Calleja J.,Polytechnic University of Puebla
Annals of Physics | Year: 2016

In this study, we determined the degree of order for 22 Jackson Pollock paintings using the Hausdorff-Besicovitch fractal dimension. Based on the maximum value of each multi-fractal spectrum, the artworks were classified according to the year in which they were painted. It has been reported that Pollock's paintings are fractal and that this feature was more evident in his later works. However, our results show that the fractal dimension of these paintings ranges among values close to two. We characterize this behavior as a fractal-order transition. Based on the study of disorder-order transition in physical systems, we interpreted the fractal-order transition via the dark paint strokes in Pollock's paintings as structured lines that follow a power law measured by the fractal dimension. We determined self-similarity in specific paintings, thereby demonstrating an important dependence on the scale of observations. We also characterized the fractal spectrum for the painting entitled Teri's Find. We obtained similar spectra for Teri's Find and Number 5, thereby suggesting that the fractal dimension cannot be rejected completely as a quantitative parameter for authenticating these artworks. © 2016 Elsevier Inc. Source

Sedeno-Monge V.,UPAEP University | Arcega-Revilla R.,Instituto Mexicano del Seguro Social | Rojas-Morales E.,Polytechnic University of Puebla | Santos-Lopez G.,Instituto Mexicano del Seguro Social | And 6 more authors.
Journal of Neuroimmunology | Year: 2014

Multiple sclerosis (MS) is an autoimmune disease characterized by a triad of inflammation, demyelination and gliosis. Because the suppressors of cytokine signaling (Socs) regulate the immune response, we quantified SOCS1 and SOCS3 transcription in peripheral blood leukocytes of patients with MS. SOCS1 transcription decreased significantly in MS patients compared with neurologically healthy persons (0.08 ± 0.02 vs 1.02 ± 0.23; p= 0.0001); while SOCS3 transcription increased in MS patients compared with controls (2.76 ± 0.66 vs 1.03 ± 0.27; p= 0.0008). Our results showed an imbalance of SOCS1 and SOCS3 transcription in MS patients, and a moderated negative correlation between them (Spearman's r= - 0.57; p= 0.0003). © 2014 Elsevier B.V. Source

Hernandez J.A.,Autonomous University of the State of Morelos | Colorado D.,Autonomous University of the State of Morelos | Cortes-Aburto O.,Polytechnic University of Puebla | El Hamzaoui Y.,Autonomous University of the State of Morelos | And 2 more authors.
Applied Thermal Engineering | Year: 2013

In this paper, inverse neural network (ANNi) is applied to optimization of operating conditions or parameters in energy processes. The proposed method ANNi is a new tool which inverts the artificial neural network (ANN), and it uses a Nelder-Mead optimization method to find the optimum parameter value (or unknown parameter) for a given required condition in the process. In order to accomplish the target, first, it is necessary to build the artificial neural network (ANN) that simulates the output parameters for a polygeneration process. In general, this class of ANN model is constituted of a feedforward network with one hidden layer to simulate output layer, considering well-known input parameters of the process. Normally, a Levenberg-Marquardt learning algorithm, hyperbolic tangent sigmoid transfer-function, linear transfer-function and several neurons in the hidden layer (due to the complexity of the process) are considered in the constructed model. After that, ANN model is inverted. With a required output value and some input parameters it is possible to calculate the unknown input parameter using the Nelder-Mead algorithm. ANNi results on three different applications in energy processes showed that ANNi is in good agreement with target and calculated input data. Consequently, ANNi is applied to determine the optimal parameters, and it already has applications in different processes with a very short elapsed time (seconds). Therefore, this methodology can be useful for the controlling of engineering processes. © 2012 Published by Elsevier Ltd. Source

El Hamzaoui Y.,Mexico State University | Ali B.,National Autonomous University of Mexico | Alfredo Hernandez J.,Mexico State University | Aburto O.C.,Polytechnic University of Puebla | Oubram O.,National Autonomous University of Mexico
Chemical Product and Process Modeling | Year: 2012

The coefficient of performance (COP) for a water purification process integrated to an absorption heat transformer with energy recycling was optimized using the artificial intelligence. The objective of this paper is to develop an integrated approach using artificial neural network inverse (ANNi) coupling with optimization methods: genetic algorithms (GAs) and particle swarm algorithm (PSA). Therefore, ANNi was solved by these optimization methods to estimate the optimal input variables when a COP is required. The paper adopts two cases studies to accomplish the comparative study. The results illustrate that the GAs outperforms the PSA. Finally, the study shows that the GAs based on ANNi is a better optimization method for control on-line the performance of the system, and constitutes a very promising framework for finding a set of "good solutions". © 2012 De Gruyter. All rights reserved. Source

Barcenas E.,Polytechnic University of Puebla | Lavalle J.,Autonomous University of Puebla
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2013

The Semantic Web lays its foundations on the study of graph and tree logics. One of the most expressive graph logics is the fully enriched μ-calculus, which is a modal logic equipped with least and greatest fixed-points, nominals, inverse programs and graded modalities. Although it is well-known that the fully enriched μ-calculus is undecidable, it was recently shown that this logic is decidable when its models are finite trees. In the present work, we study the fully-enriched μ-calculus for trees extended with Presburger constraints. These constraints generalize graded modalities by restricting the number of children nodes with respect to Presburger arithmetic expressions. We show that the logic is decidable in EXPTIME. This is achieved by the introduction of a satisfiability algorithm based on a Fischer-Ladner model construction that is able to handle binary encodings of Presburger constraints. © Springer-Verlag 2013. Source

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