Thermal and dilute-solution properties of zwitterionic copolymers

We report the synthesis, characterization, thermal and dilute-solution properties of zwitterionic copolymers of the sulfobetaine type, where the proportion of ionic groups is varied over a wide range. Two different structures are considered: poly (2-vinyl pyridine sulfopropyl betaine) and poly (4-vinyl pyridine sulfopropyl betaine). Synthesis is carried out to obtain random copolymers which are further characterized and their ionic content determined. At low proportions of ionic groups, the behavior is similar to that observed in ionomers, with substantial differences between the two structures, Increased interaction among the zwitterionic groups at higher charge densities is evidenced, which leads to the formation of clusters and agglomerates. An increase in the intrinsic viscosity with salt content in aqueous solutions is also observed. This behavior is analogous to that of homopolymers with 100% ionic content, but with particular features of interest.     

Some remarks on the homogenization of the dam problem

The homogenization of the dam problem is discussed in the general weak formulation by means of the technique of correctors. In the two special cases of horizontal layers and of the isotropic coefficients with separated variables the problem is solved. However, in general, the unsaturated region is not stable for the homogenization, as a counterexample by Alt shows.     

Determination of manganese in ores by activation analysis using a californium-252 neutron source

A²⁵²Cf neutron source has been used to analyse manganese in ores such as pyrolusite, rodonite (manganese silicate) and blends used in dry-batteries. Samples with about 150 mg and standards of manganese dioxide were irradiated for about 20 min and counted using a well-type NaI(Tl) scintillation counter and scaler, with or without pulse-height discriminator between the detector and the scaler. The interferences of nuclear reactions⁵⁶Fe(n,p)⁵⁶Mn and⁵⁹Co(n,α)⁵⁶Mn were studied, as well as problems in connection with neutron shadowing during irradiation, gamma-rays attenuation during counting and influence of granulometry of samples. Some of the samples were also analysed by wet-chemical method (sodium bismuthate) in order to compare results.     

Neural Networks for Financial Time Series Forecasting

Financial and economic time series forecasting has never been an easy task due to its sensibility to political, economic and social factors. For this reason, people who invest in financial markets and currency exchange are usually looking for robust models that can ensure them to maximize their profile and minimize their losses as much as possible. Fortunately, recently, various studies have speculated that a special type of Artificial Neural Networks (ANNs) called Recurrent Neural Networks (RNNs) could improve the predictive accuracy of the behavior of the financial data over time. This paper aims to forecast: (i) the closing price of eight stock market indexes; and (ii) the closing price of six currency exchange rates related to the USD, using the RNNs model and its variants: the Long Short-Term Memory (LSTM) and the Gated Recurrent Unit (GRU). The results show that the GRU gives the overall best results, especially for the univariate out-of-sample forecasting for the currency exchange rates and multivariate out-of-sample forecasting for the stock market indexes.