RESUMO
Chalcones are an important class of natural compounds that exhibit numerous biological activities. In this paper, we report the synthesis and characterization of new fluorinated chalcone (FCH). The molecular geometry was determined by means of single crystal X-ray diffraction, and density functional theory (DFT) at B3LYP, M06-2X functionals and MP2 method, with the 6-311++G(d,p) basis set, was applied to optimize the ground state geometry and to study the molecular conformational stability. The molecular electrostatic potential (MEP) was also investigated at the same level of theory in order to identify and quantify the possible reactive sites. The FCH crystallizes in the centrossymmetric space group [Formula: see text] with two independent conformers (α and ß) in the asymmetric unit cell. The α conformer is arranged in planar layer whereas the ß creates a layer of non-classical dimer along c axis, that differ from α in about 11° in the orientation of phenyl groups. The stabilization of the ß conformer is achieved by C-H···π arrangement. The small energy difference between the conformers (0.086 kcal mol-1) and the absence of activation energy indicates that the conversion between them can takes place at room temperature and the ß isomer is stable only in solid state. The FCH most electrophilic site occurs on the oxygen atom from the carboxyl group with absolute MEP value of about -52 kcal mol-1 whereas the MEP value calculated for F site is about -23 kcal mol-1.
RESUMO
Several variable selection algorithms in multivariate calibration can be accelerated using Graphics Processing Units (GPU). Among these algorithms, the Firefly Algorithm (FA) is a recent proposed metaheuristic that may be used for variable selection. This paper presents a GPU-based FA (FA-MLR) with multiobjective formulation for variable selection in multivariate calibration problems and compares it with some traditional sequential algorithms in the literature. The advantage of the proposed implementation is demonstrated in an example involving a relatively large number of variables. The results showed that the FA-MLR, in comparison with the traditional algorithms is a more suitable choice and a relevant contribution for the variable selection problem. Additionally, the results also demonstrated that the FA-MLR performed in a GPU can be five times faster than its sequential implementation.