M. P. Frías

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This paper presents the adaptation of CO

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RBFN for short-term forecasting of the extra virgin olive oil price in the Spanish market

RBFN, an evolutionary cooperative-competitive hybrid algorithm for the design of Radial Basis Function Networks, for short-term forecasting of the price of extra virgin olive oil. In the proposed cooperative-competitive environment, each individual represents a Radial Basis Function, and the entire population is responsible for the final solution. In order to calculate the application probability of the evolutive operators over a certain Radial Basis Function, a Fuzzy Rule Based System has been used. The olive oil time series have been analyzed using CO

Antimicrobial and antibiofilm activities of procyanidins extracted from laurel wood against a selection of foodborne microorganisms

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Spectral-Marginal-Based Estimation of Spatiotemporal Long-Range Dependence

RBFN. The results obtained have been compared with Auto-Regressive Integrated Moving Average (ARIMA) models and other data mining methods such as a fuzzy system developed with a GA-P algorithm, a multilayer perceptron trained with a conjugate gradient algorithm, and a radial basis function network trained using an LMS algorithm. The experimentation shows the high efficiency achieved by these methods, especially the data mining methods, which have slightly outperformed the ARIMA methodology.

CO 2 RBFN for Short and Medium Term Forecasting of the Extra-Virgin Olive Oil Price

Summary This study aimed to evaluate the antimicrobial and antibiofilm activities of two procyanidins isolated from an ethyl acetate extract of laurel wood against a selection of foodborne pathogens. The analysis of the extract by HPLC–DAD/ESI–MS allowed us to detect the presence of two procyanidins, which were selectively isolated and identified by chromatographic and spectroscopic means as cinnamtannin B-1 (1) and procyanidin B-2 (2). Procyanidins 1 and 2 exhibited two biological activities: inhibition of bacterial growth at high concentrations and prevention of biofilm formation at lower concentrations. Synergistic effect was also detected when both compounds were tested in combination against Listeria monocytogenes. Significant effects were also detected on disruption of preformed biofilm. The ability of procyanidins to inhibit microbial growth and biofilm formation and to synergistically work with each other may stimulate a market as natural food preservatives, and/or natural sanitisers for processing equipment where foodborne pathogens reside.

Wavelet-Based Semiparametric Estimation of Ocean Surface Temperature

Long-memory and strong spatial dependence are two features which can arise jointly or separately depending on the tail behavior of the temporal and spatial covariance functions of a given spatiotemporal process. Under certain conditions, such a behavior can be related to the variation of temporal and spatial frequencies in a neighborhood of the origin. In particular, a spatiotemporal process displaying long-memory and/or strong spatial dependence can be built, in terms of separable heavy-tail filters, from an input second-order process satisfying suitable regularity and moment conditions. A parameter estimation method based on the marginal spectral densities is implemented to approximate the long-memory and/or strong-spatial-dependence parameters.

Heterogeneous Spatial Dynamical Regression in a Hilbert-Valued Context

In this paper an adaptation of CO2RBFN, evolutionary COoperative- COmpetitive algorithm for Radial Basis Function Networks design, applied to the prediction of the extra-virgin olive oil price is presented. In this algorithm each individual represents a neuron or Radial Basis Function and the population, the whole network. Individuals compite for survival but must cooperate to built the definite solution. The forecasting of the extra-virgin olive oil price is addressed as a time series forecasting problem. In the experimentation medium-term predictions are obtained for first time with these data. Also short-term predictions with new data are calculated. The results of CO2RBFN have been compared with the traditional statistic forecasting Auto-Regressive Integrated Moving Average method and other data mining methods such as other neural networks models, a support vector machine method or a fuzzy system.

Wavelet-Based Estimation of Anisotropic Spatiotemporal Long-Range Dependence

Fractional-order pseudodifferential equations are considered to represent ocean climate variability when anomalous diffusion processes affect heat transfer in ocean surface. The driven process of these equations is assumed to be a regular spatiotemporal Gaussian random field representing normal conditions in the ocean. Linear regression in the log-wavelet domain is applied for the estimation of the parameters characterizing the pseudodifferential equation defining the anomalous diffusion process. The non-parametric framework is adopted in the estimation of the probability distribution of the driven spatiotemporal random field. Finally, ocean surface temperature values are approximated by plug-in least-square estimation from the computed parameter estimates, the estimated distribution of the driven process, and the integral version of the fractional-order pseudodifferential equation. The ability of the approach presented to process strong spatial-correlated ocean surface temperature curve data is illustrated with a real-data example, where sample information from weather stations in Hawaii ocean is analyzed.

A study on the medium-term forecasting using exogenous variable selection of the extra-virgin olive oil with soft computing methods

This article introduces a Hilbert-valued spatially dynamic regression model. The spatially heterogeneous functional trend is modeled by functional multiple regression, with varying regression operators. The spatial autoregressive Hilbertian model of order one (SARH(1) model, see [37]) is considered to represent the spatial correlation and dynamics displayed by the functional error term. The RKHS theory is applied in the construction of suitable bases for projection and regularization of the associated estimation problems. The performance of the proposed Hilbert-valued modeling and estimation methodology is illustrated with a real-data example, related to financing decisions from firm panel data.