Affine Arithmetic: Concepts and Applications

Affine arithmetic is a model for self-validated numerical computation that keeps track of first-order correlations between computed and input quantities. We explain the main concepts in affine arithmetic and how it handles the dependency problem in standard interval arithmetic. We also describe some of its applications.     

Linear calibration procedure for the phase-to-height relationship in phase measurement profilometry

Calibration of the relationship between height and phase is of uttermost importance to perform accurate 3D measurements in phase measurement profilometry. This work reports a different approach to this problem by first looking at the analytical expression for this relationship and determining the regime spanned by the fringe analysis method. The conclusions thus ascertained, amply justify confronting the analytical expression with a simple normalization procedure of the experimental data, with a remarkable matching between both results. In light of this, a linear calibration procedure with just one plane is proposed and verified experimentally.     

Analysis and finite element simulations of a second‐order fluid model in a bounded domain

This article is concerned with the equations governing the steady motion of a viscoelastic incompressible second‐order fluid in a bounded domain. A new proof of existence and uniqueness of strong solutions is given. In addition, using appropriate finite element methods to approximate a coupled equivalent problem, sharp error estimates are obtained using a fixed point argument. The method is applied to the two‐dimensional lid‐driven cavity problem, at low Reynolds number and in a certain range of values of the viscoelastic parameters, to analyze the combined effects of inertia and viscoelasticity on the flow. © 2007 Wiley Periodicals, Inc. Numer Methods Partial Differential Eq, 2007     

Structure and magnetism of VnBz(n+1) sandwich clusters

Results on structural, energetic, electronic, and magnetic properties of linear sandwich VnBzn+1 clusters obtained using high-accuracy density functional computations are presented and analyzed. Energetically close-lying configurations and states of different spin-multiplicities are identified. The computed characteristics are in good agreement with the available experimental data. The computations predict that the most stable forms of the clusters in the size range n >/= 4 are chiral. This feature, combined with the magnetism of these systems, makes them of potential importance as building blocks of nanosystems with coupled optical and magnetic functionalities.     

Electron transfer in proteins: nonorthogonal projections onto donor-acceptor subspace of the Hilbert space

We develop nonorthogonal projectors, called L?wdin projectors, to construct an effective donor-acceptor system composed of localized donor (D) and acceptor (A) states of a long-distance electron transfer problem. When these states have a nonvanishing overlap with the bridge states these projectors are non-Hermitian and there are various possible effective two-level systems that can be built. We show how these can be constructed directly from the Schr?dinger or Dyson equation projected onto the D-A subspace of the Hilbert space and explore these equations to determine the connection between Hamiltonian and Green function partitioning. We illustrate the use of these effective two-level systems in estimating the electron transfer rate in the context of a simple electron transfer model.     

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.     

Sensing the ortho Positions in C6Cl6 and C6H4Cl2 from Cl2− Formation upon Molecular Reduction

The geometrical effect of chlorine atom positions in polyatomic molecules after capturing a low-energy electron is shown to be a prevalent mechanism yielding Cl₂⁻. In this work, we investigated hexachlorobenzene reduction in electron transfer experiments to determine the role of chlorine atom positions around the aromatic ring, and compared our results with those using ortho-, meta- and para-dichlorobenzene molecules. This was achieved by combining gas-phase experiments to determine the reaction threshold by means of mass spectrometry together with quantum chemical calculations. We also observed that Cl₂⁻ formation can only occur in 1,2-C₆H₄Cl₂, where the two closest C–Cl bonds are cleaved while the chlorine atoms are brought together within the ring framework due to excess energy dissipation. These results show that a strong coupling between electronic and C–Cl bending motion is responsible for a positional isomeric effect, where molecular recognition is a determining factor in chlorine anion formation.     

A New Supramolecular Tetraruthenated Cobalt (II) Porphyrazine Displaying Outstanding Electrocatalytical Performance in Oxygen Evolution Reaction

A new supramolecular electrocatalyst for Oxygen Evolution Reaction (OER) was synthesized from a central multibridging cobalt tetrapyridylporphyrazine (CoTPyPz) species by attaching four [Ru(bpy)₂Cl]⁺ groups. Both CoTPyPz and the tetraruthenated cobalt porphyrazine species, TRuCoTPyPz, form very homogenous molecular films just by dropcasting their methanol solutions onto GCE electrodes. Such films exhibited low overpotentials for O₂ evolution, e.g., 560 e 340 mV, respectively, displaying high stability, typically exceeding 15 h. The kinetic parameters obtained from the Tafel plots showed that the peripheral complexes are very important for the electrocatalytic activity. Hyperspectral Raman images taken along the electrochemical process demonstrated that the cobalt center is the primary active catalyst site, but its performance is enhanced by the ruthenium complexes, which act as electron-donating groups, in the supramolecular system.     

Development of Neuropeptide Y and Cell-Penetrating Peptide MAP Adsorbed onto Lipid Nanoparticle Surface

Functionalization of nanoparticles surfaces have been widely used to improve diagnostic and therapeutic biological outcome. Several methods can be applied to modify nanoparticle surface; however, in this article we focus toward a simple and less time-consuming method. We applied an adsorption method on already formulated nanostructured lipid carriers (NLC) to functionalize these nanoparticles with three distinct peptides sequences. We selected a cell-penetrating peptide (CPP), a lysine modified model amphipathic peptide (Lys(N₃)-MAP), CPP/drug complex, and the neuropeptide Y. The aim of this work is to evaluate the effect of several parameters such as peptide concentration, different types of NLC, different types of peptides, and incubation medium on the physicochemical proprieties of NLC and determine if adsorption occurs. The preliminary results from zeta potential analysis indicate some evidence that this method was successful in adsorbing three types of peptides onto NLC. Several non-covalent interactions appear to be involved in peptide adsorption with the possibility of three adsorption peptide hypothesis that may occur with NLC in solution. Moreover, and for the first time, in silico docking analysis demonstrated strong interaction between CPP MAP and NPY Y1 receptor with high score values when compared to standard antagonist and NPY.