Pulse shape optimization for electron-positron production in rotating fields

We optimize the pulse shape and polarization of time-dependent electric fields to maximize the production of electron-positron pairs via strong field quantum electrodynamics processes. The pulse is parametrized in Fourier space by a B-spline polynomial basis, which results in a relatively low-dimensional parameter space while still allowing for a large number of electric field modes. The optimization is performed by using a parallel implementation of the differential evolution, one of the most efficient metaheuristic algorithms. The computational performance of the numerical method and the results on pair production are compared with a local multistart optimization algorithm. These techniques allow us to determine the pulse shape and field polarization that maximize the number of produced pairs in computationally accessible regimes.Comment: 16 pages, 10 figure

Pushing the limits of surface-enhanced raman spectroscopy (SERS) with deep learning : identification of multiple species with closely related molecular structures

Raman spectroscopy is a non-destructive and label-free molecular identification technique capable of producing highly specific spectra with various bands correlated to molecular structure. Moreover, the enhanced detection sensitivity offered by Surface-Enhanced Raman spectroscopy (SERS) allows analyzing mixtures of related chemical species in a relatively short measurement time. Combining SERS with deep learning algorithms allows in some cases to increase detection and classification capabilities even further. The present study evaluates the potential of applying deep learning algorithms to SERS spectroscopy to differentiate and classify different species of bile acids, a large family of molecules with low Raman cross sections and molecular structures that often differ by a single hydroxyl group. Moreover, the study of these molecules is of interest for the medical community since they have distinct pathological roles and are currently viewed as potential markers of gut microbiome imbalances. A Convolutional Neural Network (CNN) model was developed and used to classify SERS spectra from five bile acid species. The model succeeded in identifying the five analytes despite very similar molecular structures and was found to be reliable even at low analyte concentrations

Metabolic constituents of grapevine and grape-derived products

The numerous uses of the grapevine fruit, especially for wine and beverages, have made it one of the most important plants worldwide. The phytochemistry of grapevine is rich in a wide range of compounds. Many of them are renowned for their numerous medicinal uses. The production of grapevine metabolites is highly conditioned by many factors like environment or pathogen attack. Some grapevine phytoalexins have gained a great deal of attention due to their antimicrobial activities, being also involved in the induction of resistance in grapevine against those pathogens. Meanwhile grapevine biotechnology is still evolving, thanks to the technological advance of modern science, and biotechnologists are making huge efforts to produce grapevine cultivars of desired characteristics. In this paper, important metabolites from grapevine and grape derived products like wine will be reviewed with their health promoting effects and their role against certain stress factors in grapevine physiology