We have examined simultaneous ULF activity in the Pi2 and Pc3 bands at the near-equatorial magnetic stations in South America from SAMBA and MAGDAS arrays and low-orbiting CHAMP satellite during its passage over this meridional network. At the nighttime, both Pi2 and Pc3 waves in the upper ionosphere and on the ground are nearly of the same magnitude and in-phase. At the same time, the daytime Pc3 pulsations on the ground and in space are nearly out-of-phase. Comparison of observational results with the theoretical notions on the MHD wave interaction with the system ionosphere-atmosphere-ground suggests that nighttime low-latitude Pi2 and Pc3 wave signatures are produced by magnetospheric fast compressional mode. The daytime near-equatorial Pc3 waves still resist a quantative interpretation. These waves may be produced by a combination of two mechanisms: compressional mode leakage through the ionosphere, and by oscillatory ionospheric current spreading towards equatorial latitudes.

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A neural network-based group contribution method was developed in order to estimate the temperature-dependent surface tension of pure ionic liquids. A metaheuristic algorithm called gravitational search algorithm was employed in substitution of the traditional backpropagation learning algorithm to optimize the update weights of our neural network model. A total of 2307 experimental data points from 229 data sets of 162 different ionic liquid types, such as imidazolium, ammonium, phosphonium, pyridinium, pyrrolidinium, piperidinium, and sulfonium, were collected from the specialized literature. In this database, a wide temperature range from 263 to 533 K, and a wide surface tension range from 0.015 to 0.062 N·m–1, were covered. The input parameters contained the following properties: absolute temperature, the molecular weight of the ionic liquid, and 46 structural groups that composed the molecule. The accuracy of the proposed method was checked using the mean absolute percentage error (MAPE) and the correlation coefficient (R) between the calculated and experimental values. The results show that, for the training phase, our method presents a MAPE = 1.17% and R= 0.998, while for the prediction phase, the method shows a MAPE = 1.29% and R = 0.991. In addition, the relative contribution of each input parameter was calculated from the optimal weights of the network. Also, the effects of the temperature, molecular weight, and cation and anion types on the estimation of the surface tension were analyzed. Finally, the proposed method was compared with other methods available in the literature. All results demonstrated the high accuracy of our method to estimate the temperature-dependent surface tension for several ionic liquid types.

This article presents 24-h wind speed forecasting for the city of La Serena in Chile and a methodology to explore forecasting effects on the production of wind turbine power. To that end, we used meteorological data from a weather station located in the southern zone of the hyper-arid Atacama Desert. In this area, energy resources are economically and environmentally important, and wind speed forecasting plays a vital role in the management and marketing processes of wind potential via wind farms. To contribute to the development of this energy, we propose carrying out the short-term prediction of 12 and 24 h ahead (identified as Ws(t + 12) and Ws(t + 24), respectively) using an artificial neural network with backpropagation approach. Hourly time series of wind speed, temperature, and relative humidity (from 2003 to 2006) were considered to characterize the artificial neural network in the training phase, while we used data from the year 2007 to check the efficiency of our prediction. For artificial neural network Ws(t + 12) and Ws(t + 24) models, we obtained similar performance of wind speed prediction with root mean square error of around 0.7 m s −1 and with maximum and minimum residuals of +4 and-4 m s −1 , respectively. Based on the results, we gain a reliable tool to characterize wind speed properties in the range of 1 day within 20% of uncertainty. Moreover, this tool becomes useful to study the effects of our artificial neural network Ws(t + 12) and Ws(t + 24) models on the generation of wind energy from a wind power turbine parametrization.

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