Direct, one-step synthesis of condensed heterocycles: a palladium-catalyzed coupling approach

[reaction: see text] A palladium-catalyzed one-step synthesis of fused aromatic heterocycles from bifunctional bromoenoates or bromoalkyl indoles and iodoarenes is reported. This method provides an efficient route to a wide variety of substituted polycyclic aromatic and heteroaromatic compounds from readily accessible starting materials.     

Silver-catalyzed facile decarboxylation of coumarin-3-carboxylic acids

A simple and highly efficient protocol with mild reaction conditions has been developed that allows the smooth protiodecarboxylation of diversely functionalized coumarin-3-carboxylic acids. In the presence of catalytic amounts of Ag₂CO₃ and acetic acid, even un-activated coumarin-3-carboxylic acids were converted in good to excellent yields and with great preparative ease to the corresponding coumarin derivatives.     

Guided waves characteristics of multi-walled carbon nanotubes

The theoretical analysis of propagation of guided waves in the multi-walled carbon nanotubes is presented within the framework of the classical electrodynamics. Electronic excitations of each wall of the system are modeled as an infinitesimally thin cylindrical layer of the π-electrons, whose dynamics are described by means of the fluid theory. General expressions of dispersion relations are obtained for the electromagnetic wave with the transverse magnetic and transverse electric modes, respectively, by solving Maxwell and fluid equations with appropriate boundary conditions.     

Deep generative models for vehicle speed trajectories

Generating realistic vehicle speed trajectories is a crucial component in evaluating vehicle fuel economy and in predictive control of self-driving cars. Traditional generative models rely on Markov chain methods and can produce accurate synthetic trajectories but are subject to the curse of dimensionality. They do not allow to include conditional input variables into the generation process. In this paper, we show how extensions to deep generative models allow accurate and scalable generation. Proposed architectures involve recurrent and feed-forward layers and are trained using adversarial techniques. Our models are shown to perform well on generating vehicle trajectories using a model trained on GPS data from Chicago metropolitan area.