Stable and metastable phases in Agl-containing silver borate glasses

This paper studies the thermal stability and the glass formation region the Agl-Ag₂O-B₂O₃ system. The miscibility range of the components has been investigated by direct visual observation at 1000°C, i.e. where the system is liquid over the entire compositional range. By combining X-ray and differential thermal analysis (DTA) measurements, the large region of glass formation of the system has been determined. The dependence of the glass transition temperature upon the glass composition is discussed. Series of glasses have been annealed over extended time intervals above the glass transition temperature in order to characterize the “equilibrium” phase diagram for the system. Analysis of the annealed materials shows that several crystalline compounds form upon annealing.     

Complexes of nickel and copper with aromatic dithiocarbamates

Summary Phenylglycine and phenylalanine react with CS₂ in a basic medium to yield the corresponding dithiocarbamates, which react with Ni²⁺ and Cu²⁺ salts to form complexes of the general formula [Ni(dtc aa)₂] and [Cu(dtc aa)], respectively (aa = amino acid). Both series of complexes are diamagnetic and were characterized by analytical and physico-chemical techniques. It is concluded that the nickel compounds are square planar, while for the copper complexes reduction to the Cu⁺ state has taken place during synthesis.     

Ruthenium-Catalyzed Enantioselective Hydrogenation of 1,3-O-Disubstituted 1,3-Dihydroxypropan-2-ones

Optically active 1,3-O-disubstituted glycerols were obtained by enantioselective homogeneous hydrogenation of the corresponding 1,3-O-disubstituted 1,3-dihydroxypropan-2-ones with different Ru^II-binap complexes. The highest enantioselectivities were obtained with dimeric chloro-ruthenium complexes on the substrates bearing bulky trityloxy groups.     

High‐performance liquid chromatographic analysis: applications to nutraceutical content and urinary disposition of oxyresveratrol in rats

A high‐performance liquid chromatographic (HPLC) method was developed for the analysis of the stilbene, oxyresveratrol. This method involves the use of a Luna® C₁₈ column with ultraviolet detection at 320 nm. The mobile phase consisted of acetonitrile, water and formic acid (30 : 70 : 0.04 v/v) with a flow rate of 0.6 mL/min. The calibration curves were linear over the range of 0.5–100.0 μg/mL. The mean extraction efficiency was between 98.9 and 109%. The precision of the assay was 0.069–18.4% (RSD%), and within 20% at the limit of quantitation (0.5 μg/mL). The bias of the assay was <15% and within 15% at the limit of quantitation. This assay was successfully applied to pre‐clinical pharmacokinetic samples from rat urine and to nutraceutical product analysis. Copyright © 2009 John Wiley & Sons, Ltd.     

Palladium‐Catalyzed ortho‐Selective C?H Deuteration of Arenes: Evidence for Superior Reactivity of Weakly Coordinated Palladacycles

We disclose a protocol for the palladium‐catalyzed ortho‐selective C H deuteration of arenes. Phenylacetic acids and benzoic acids are suitable substrates for this reaction. This reaction offers a catalytic route to ortho‐deuterated phenylacetic acids and benzoic acids and demonstrates the sharp difference in reactivity of palladacycle intermediates held together by weak and strong coordination.     

Proximal methods for the latent group lasso penalty

We consider a regularized least squares problem, with regularization by structured sparsity-inducing norms, which extend the usual ? ₁ and the group lasso penalty, by allowing the subsets to overlap. Such regularizations lead to nonsmooth problems that are difficult to optimize, and we propose in this paper a suitable version of an accelerated proximal method to solve them. We prove convergence of a nested procedure, obtained composing an accelerated proximal method with an inner algorithm for computing the proximity operator. By exploiting the geometrical properties of the penalty, we devise a new active set strategy, thanks to which the inner iteration is relatively fast, thus guaranteeing good computational performances of the overall algorithm. Our approach allows to deal with high dimensional problems without pre-processing for dimensionality reduction, leading to better computational and prediction performances with respect to the state-of-the art methods, as shown empirically both on toy and real data.     

Surface removal of a weathered organic glass

A tubular, stratified glass–epoxy material composite has been subjected to either natural or artificial aging. After aging, the degradation of this material is characterised by the ablation of the organic matrix. For the first time, we have measured the ablation using a three-dimensional surface microanalysis system equipped with a sensitive topographical probe. In a previous study involving the measurement of the ablation during artificial photo-aging, an innovative hypothesis concerning the creation of a thin photo-oxidation layer in close proximity to the irradiated surface of the organic matrix was proposed. During weathering, from time to time, rain leaches the thin photo-oxidation layer and causes a significant ablation via a complementary mechanism. Given that the material surface always presents hemi-cylindrical concavity imprints, we studied the surface changes, due to the ablation, by measuring the cross-section half-circle radii of the concavities after different photo-aging times. The surface erosion is characterized by growth of the circle radii, whose changes progressively lead towards surface flatness. A 2-D model of the concavities’ growth has been validated: the simulation results lead to calculated circle radii growth values near the measured ones. Comparison between photo-aged organic matrix removal and erosion of a leached mineral glass shows that the morphological removal of this isotropic organic matrix evolves in a manner analogous to that of an amorphous inorganic material.     

Consistent learning by composite proximal thresholding

We investigate the modeling and the numerical solution of machine learning problems with prediction functions which are linear combinations of elements of a possibly infinite dictionary of functions. We propose a novel flexible composite regularization model, which makes it possible to incorporate various priors on the coefficients of the prediction function, including sparsity and hard constraints. We show that the estimators obtained by minimizing the regularized empirical risk are consistent in a statistical sense, and we design an error-tolerant composite proximal thresholding algorithm for computing such estimators. New results on the asymptotic behavior of the proximal forward–backward splitting method are derived and exploited to establish the convergence properties of the proposed algorithm. In particular, our method features a o(1 / m) convergence rate in objective values.