Geometric Singular Perturbation Theory for Systems with Symmetry

Journal of Dynamics and Differential Equations - In this paper we focus on a class of symmetric vector fields in the context of singularly perturbed fast-slow dynamical systems. Our main question...     

A review on machine learning algorithms for the ionic liquid chemical space

There are thousands of papers published every year investigating the properties and possible applications of ionic liquids. Industrial use of these exceptional fluids requires adequate understanding of their physical properties, in order to create the ionic liquid that will optimally suit the application. Computational property prediction arose from the urgent need to minimise the time and cost that would be required to experimentally test different combinations of ions. This review discusses the use of machine learning algorithms as property prediction tools for ionic liquids (either as standalone methods or in conjunction with molecular dynamics simulations), presents common problems of training datasets and proposes ways that could lead to more accurate and efficient models.

In this review article, the authors discuss the use of machine learning algorithms as tools for the prediction of physical and chemical properties of ionic liquids.     

Synthesis,characterization, and transport properties of Nafion-polypyrrole membrane for direct methanol fuel cell (DMFC) application

Journal of Solid State Electrochemistry - Due to their distinctive chemical, electronic, and environmental properties, polypyrrole is used as a blocking barrier for methanol leakage in direct...     

Identification of metabolic markers in patients with type 2 Diabetes by Ultrafast gas chromatography coupled to electronic nose. A pilot study

Metabolomics is a potential tool for the discovery of new biomarkers in the early diagnosis of diseases. An ultra-fast gas chromatography system equipped to an electronic nose detector (FGC eNose) was used to identify the metabolomic profile of Volatile Organic Compounds (VOCs) in type 2 diabetes (T2D) urine from Mexican population. A cross-sectional, comparative, and clinical study with translational approach was performed. We recruited twenty T2D patients and twenty-one healthy subjects. Urine samples were taken and analyzed by FGC eNose. Eighty-eight compounds were identified through Kovats's indexes. A natural variation of 30% between the metabolites, expressed by study groups, was observed in Principal Component 1 and 2 with a significant difference (p < 0.001). The model, performed through a Canonical Analysis of Principal coordinated (CAP), allowed a correct classification of 84.6% between healthy and T2D patients, with a 15.4% error. The metabolites 2-propenal, 2-propanol, butane- 2,3-dione and 2-methylpropanal, were increased in patients with T2D, and they were strongly correlated with discrimination between clinically healthy people and T2D patients. This study identified metabolites in urine through FGC eNose that can be used as biomarkers in the identification of T2D patients. However, more studies are needed for its implementation in clinical practice.     

GDME-based methodology for the determination of free formaldehyde in cosmetics and hygiene products containing formaldehyde releasers

Analytical and Bioanalytical Chemistry - Formaldehyde is often applied in the industrial production of different products, such as textiles, insulation materials, or cosmetics, due to its...     

The Problem of the Pointwise Fourier Inversion for Piecewise Smooth Functions of Several Variables

We study the pointwise convergence problem for the inverse Fourier transform of piecewise smooth functions, i.e., whether S_rD f (\bx) ? f (\bx)S_{\rho D} f (\bx) \to f (\bx) as r? ￥\rho \to \infty . r? ￥\rho \to \infty . Here for \bx,\bxi ? \Rn\bx,\bxi \in \Rn S_rDf(\bmx)=\dsf1(2p)^n/2\intli_rD [^(f)](\bxi) e^{\dst iá\bmx,\bxi?} d\bxi . S_{\rho D}f(\bm{x})=\dsf1{(2\pi)^{n/2}}\intli_{\rho D} \widehat{f}(\bxi) e^{\dst i\langle\bm{x},\bxi\rangle} d\bxi~. is the partial sum operator using a convex and open set DD containing the origin, and rD={ r\bxi:\bxi ? D }\rho D=\left\{ \rho \bxi:\bxi\in D \right\}.