Examinando por Autor "Cuesta-Herrera, L."

Mostrando 1 - 3 de 3
  • Publicación
    Acceso abierto
    Analysis of the atmospheric macro-physical using spatial methods
    (2020-12-01) Andrades-Grassi, J. E.; Cuesta-Herrera, L.; Torres-Mantilla, H. A.; López-Hernández, J. Y.
    The central western area of Venezuela has an unequal distribution of precipitation. Due to its agricultural importance, is necessary to plan water accounting and this requires a evaluation of spatial and temporal variability of precipitation and an estimate of local geophysical effect from the relief. In this research we use an iterative computationally lattice approach to perform a confirmatory analysis of the variability and the spatial correlation structure in monthly precipitation stations. Spatial correlograms and pooled empirical semivariogram were applied to evaluate the most appropriate spatial weighting matrix to estimate the Moran’s I. The altitude effect over monthly rainfall was estimated through spatial regression algorithm which determine the predominant spatial process in each slice. A homogeneous spatial stochastic process with positive spatial autocorrelation is evidenced. There is a trend towards a higher frequency of spatial error and spatial auto-regressive processes between the months of June and August whilst there are not dominant process between October and December. This response is caused by the dynamics of the intertropical convergence zone, which generates a seasonal effect on precipitation. These estimations allows decision-making in modeling and will lead to an improvement for analysis and forecasting in areas strongly affected by climate change and water stress.
  • Publicación
    Acceso abierto
    Cell lysis analysis for respiratory viruses through simulation modeling
    (2022-01-02) Cuesta-Herrera, L.; Pastenes, L.; Cordova-Lepe, F.; Arencibia, A.D.; Torres-Mantilla, Hugo A.
    An ordinary system of differential equations leading to a simulation model is propose as methodological approach to analysis the incidence of infectious-contagious diseases, in this case using SARS-CoV-2 virus as pathogenic model. The dynamics of the model are drive by the interaction between susceptible cells contemplating respiratory epithelial cells and viral infection mediated by two types of lysis response. To perform the simulations, values of some variables and parameters were selected from referenced sources, considering that previous reports suggested that the viral load in the lower respiratory tract might reach its peak in the second week after the beginning of disease symptoms. The scenarios described in the simulations evidence the performance of the cell lysis response from susceptible cells that have been infected. The recommend model shows that an excess response from both the original virus and the mutated virus leads to an increase in the approximate time to control viral infection within the organism.
  • Publicación
    Acceso abierto
    Simulation of inference test performance for minimum inhibitory concentration censored data
    (2022-02-05) Torres-Mantilla, Hugo A.; Cuesta-Herrera, L.; Andrades-Grassi, J.E.; Bianchi, G.
    The estimation of the minimum inhibitory concentration is usually performed by a method of serial dilutions by a factor of 2, introducing the overestimation of antimicrobial efficacy, quantified by a simulation model that shows that the variability of the bias is higher for the standard deviation, being dependent on the metric distance to the values of the concentrations used. We use a methodological approach through modeling and simulation for the measurement error of physical variables with censored information, proposing a new inference method based on the calculation of the exact probability for the set of possible samples from nmeasurements that allows quantifying the p-value in one or two independent sample tests for the comparison of censored data means. Tests based on exact probability methods offer a reasonable solution for small sample sizes, with statistical power varying according to the hypothesis evaluated, providing insight into the limitations of censored data analysis and providing a tool for decision making in the diagnosis of antimicrobial efficacy.