(Liquid + liquid) equilibrium for the ternary systems {heptane + toluene + 1-allyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide} and {heptane + toluene + 1-methyl-3-propylimidazolium bis(trifluoromethylsulfonyl)imide} ionic liquids

The (liquid + liquid) equilibrium (LLE) data for two systems containing heptane, toluene, and 1-methyl-3-propylimidazolium bis(trifluoromethylsulfonyl)imide ([mpim][Tf₂N]) or 1-allyl-3-methylimidazolium bis(trifluoromethylsulfonyl)imide ([amim][Tf₂N]) ionic liquids (ILs) were determined at T = 313.2 K and atmospheric pressure. The effect of a double bond in an alkyl side chain in the imidazolium cation was evaluated in terms of selectivity and extractive capacity. The results show a decrease of the amount of toluene and heptane dissolved in the IL with the allyl group. Thus, the distribution ratios of toluene and heptane of [mpim][Tf₂N] IL are higher than those of [amim][Tf₂N] IL. On the other hand, the separation factor of the [amim][Tf₂N] IL increases comparing to [mpim][Tf₂N] IL. The NRTL model was used to correlate satisfactorily the experimental LLE data for the two studied ternary systems.     

Application of lag-k autocorrelation coefficient and the TGA signals approach to detecting and quantifying adulterations of extra virgin olive oil with inferior edible oils

The combination of lag-k autocorrelation coefficients (LCCs) and thermogravimetric analyzer (TGA) equipment is defined here as a tool to detect and quantify adulterations of extra virgin olive oil (EVOO) with refined olive (ROO), refined olive pomace (ROPO), sunflower (SO) or corn (CO) oils, when the adulterating agents concentration are less than 14%. The LCC is calculated from TGA scans of adulterated EVOO samples. Then, the standardized skewness of this coefficient has been applied to classify pure and adulterated samples of EVOO. In addition, this chaotic parameter has also been used to quantify the concentration of adulterant agents, by using successful linear correlation of LCCs and ROO, ROPO, SO or CO in 462 EVOO adulterated samples. In the case of detection, more than 82% of adulterated samples have been correctly classified. In the case of quantification of adulterant concentration, by an external validation process, the LCC/TGA approach estimates the adulterant agents concentration with a mean correlation coefficient (estimated versus real adulterant agent concentration) greater than 0.90 and a mean square error less than 4.9%.     

A new high-performance liquid chromatographic method with evaporative light scattering detector for the analysis of phospholipids. Application to Iberian pig subcutaneous fat

A new method for the analysis of phospholipids by normal-phase HPLC is described using a silica column. Addition of ammonia and triethylamine to a gradient based on chloroform/methanol/water promoted a good and rapid separation of phospholipid classes (20 min run). The use of an evaporative light scattering detector permitted an accurate analysis of a mixture of phospholipids. Calibration curves were linear within different range for each phospholipid class. The LOD and LOQ obtained were below 0.03 and 0.05 mg kg⁻¹ for all cases, respectively. Besides, a new method for the separation of phospholipids from total lipids before HPLC analysis by a solid-phase extraction (SPE) with Si cartridges has been developed. This methodology gave a good recovery ranging from 97 to 117%. The method was validated with a standard mixture of phospholipids. This method has been applied to characterize the phospholipid fraction of subcutaneous fat from Iberian pig. Cardiolipin, phosphatidylethanolamine, phosphatidylinositol, phosphatidylserine, phosphatidylcholine, and sphingomyelin have been described for first time in these samples. The fatty acid composition of the different phospholipid classes and their HPLC electrospray ionization mass spectrometry have been used for characterizing the molecular species present in each one.     

Brownian Paths Homogeneously Distributed in Space: Percolation Phase Transition and Uniqueness of the Unbounded Cluster

We consider a continuum percolation model on \(\mathbb {R}^d\), \(d\ge 1\). For \(t,\lambda \in (0,\infty )\) and \(d\in \{1,2,3\}\), the occupied set is given by the union of independent Brownian paths running up to time t whose initial points form a Poisson point process with intensity \(\lambda >0\). When \(d\ge 4\), the Brownian paths are replaced by Wiener sausages with radius \(r>0\). We establish that, for \(d=1\) and all choices of t, no percolation occurs, whereas for \(d\ge 2\), there is a non-trivial percolation transition in t, provided \(\lambda \) and r are chosen properly. The last statement means that \(\lambda \) has to be chosen to be strictly smaller than the critical percolation parameter for the occupied set at time zero (which is infinite when \(d\in \{2,3\}\), but finite and dependent on r when \(d\ge 4\)). We further show that for all \(d\ge 2\), the unbounded cluster in the supercritical phase is unique. Along the way a finite box criterion for non-percolation in the Boolean model is extended to radius distributions with an exponential tail. This may be of independent interest. The present paper settles the basic properties of the model and should be viewed as a springboard for finer results.     

Structure of CdS/SiO2 Nanocomposites: Influence of the Precursor and Cd Concentration

The influence of using TMOS or TEOS in the formation of CdS quantum dots in a silica matrix have been studied by X-ray absorption spectroscopy (XAS). The amount of Cd-S bonds have been monitored as a function of the nominal Cd concentration. The relative amount of CdS crystals depends on the precursor. The use of TEOS is not recommended because it gives a poor yield, especially for high Cd concentration. A discussion of the influence of CdS concentration in matrices from TMOS is carried out from structural models created from their pore volume distribution. The mean pore size becomes smaller and the size distribution more uniform when CdS concentration increases but the nanocrystals of low CdS nominal content present a more efficient quantum confinement.     

Some Basics on Privacy Techniques,Anonymization and their Big Data Challenges

With the progress in the information and communication fields, new opportunities and technologies for statistical analysis, knowledge discovery, data mining, and many other research areas have emerged, together with new challenges for privacy and data protection. Nowadays several personal records are kept in computerized databases. Personal data is collected and kept in census databases, medical databases, employee databases, among others. There has always been an asymmetry between the benefits of computerized databases and the rights of individual data subjects. Some data protection principles can be derived from the legal framework. In this survey, we present some basic cryptographic and non-cryptographic techniques that may be used for enhancing privacy, we focus mainly on anonymization in databases and networks, discuss some differences and interactions among the well-known models of k-anonymity and differential privacy and finally present some challenges to privacy that come from big data analytics.     

Estimating critical values for testing the i.i.d. in standardized residuals from GARCH models in finite samples

Taking into account that the BDS test—which is used as a misspecification test applied to standardized residuals from the GARCH(1,1) model—is characterized by size distortion and departure from normality in finite samples, this paper obtains the critical values for the finite sample distribution of the BDS test. We focus on bootstrap simulation to avoid the sampling uncertainty of parameter estimation and make use of estimated response surface regressions (RSR) derived from the experimental results. We consider an extensive grid of models to obtain critical values with the results of the bootstrap experiments. The RSR used to estimate them is an artificial neural network (ANN) model, instead of the traditional linear regression models. Specifically, we estimate critical values by using a bootstrap aggregated neural network (BANN) and by employing functions of the sample size and parameters used in the experiment as the embedding dimension and proximity parameters in the BDS statistic, GARCH parameters and even the q-quantiles of the BDS distributions. The main results confirm that the sample size and BDS parameters play a role in size distortion. Finally, an empirical application to three price indexes is performed, to highlight the differences between decisions made using the asymptotic or our predicted critical values for the BDS test in finite samples.