Alejandro Urbina - Academia.edu (original) (raw)
Uploads
Papers by Alejandro Urbina
Journal of Parasitology, 2010
Background: Toxoplasma gondii (T. gondii) infection in pregnant women represents a risk for conge... more Background: Toxoplasma gondii (T. gondii) infection in pregnant women represents a risk for congenital disease. There is scarce information about the epidemiology of T. gondii infection in pregnant women in Mexico. Therefore, we sought to determine the prevalence of T. gondii infection and associated socio-demographic, clinical and behavioural characteristics in a population of pregnant women of Durango City, Mexico.
Journal of Thermal Analysis and Calorimetry, 2000
A new technique to thermally fractionate polymers using DSC has been recently developed in our la... more A new technique to thermally fractionate polymers using DSC has been recently developed in our laboratory. The applications of the novel successive self-nucleation and annealing (SSA) technique to characterize polyolefins with very dissimilar molecular structures are presented as well as the optimum conditions to thermally fractionate any suitable polymer sample with SSA. For ethylene/α-olefin copolymers, the SSA technique can give information on the distribution of short chain branching and lamellar thickness. In the case of functionalized polyolefins, detailed examinations of SSA results can help to establish possible insertion sites of grafted molecules. The application of the technique to characterize crosslinked polyethylene and crystallizable blocks within ABC triblock copolymers is also presented.
The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time serie... more The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analyzed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimization algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.
Journal of Parasitology, 2010
Background: Toxoplasma gondii (T. gondii) infection in pregnant women represents a risk for conge... more Background: Toxoplasma gondii (T. gondii) infection in pregnant women represents a risk for congenital disease. There is scarce information about the epidemiology of T. gondii infection in pregnant women in Mexico. Therefore, we sought to determine the prevalence of T. gondii infection and associated socio-demographic, clinical and behavioural characteristics in a population of pregnant women of Durango City, Mexico.
Journal of Thermal Analysis and Calorimetry, 2000
A new technique to thermally fractionate polymers using DSC has been recently developed in our la... more A new technique to thermally fractionate polymers using DSC has been recently developed in our laboratory. The applications of the novel successive self-nucleation and annealing (SSA) technique to characterize polyolefins with very dissimilar molecular structures are presented as well as the optimum conditions to thermally fractionate any suitable polymer sample with SSA. For ethylene/α-olefin copolymers, the SSA technique can give information on the distribution of short chain branching and lamellar thickness. In the case of functionalized polyolefins, detailed examinations of SSA results can help to establish possible insertion sites of grafted molecules. The application of the technique to characterize crosslinked polyethylene and crystallizable blocks within ABC triblock copolymers is also presented.
The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time serie... more The Polynomial Artificial Neural Network (PANN) has shown to be a powerful Network for time series forecasting. Moreover, the PANN has the advantage that it encodes the information about the nature of the time series in its architecture. However, the problem with this type of network is that the terms needed to be analyzed grow exponentially depending on the degree selected for the polynomial approximation. In this paper, a novel optimization algorithm that determines the architecture of the PANN through Genetic Programming is presented. Some examples of non linear time series are included and the results are compared with those obtained by PANN with Genetic Algorithm.