Vinylazides: versatile synthons and magical precursors for the construction of N-heterocycles.

Nowadays, application of vinylazides as precursors is a key method for the construction of N-heterocycles in organic synthesis. These versatile three-atom synthons can be converted into intermediates such as 2H-azirines, iminyl radicals, iminyl metal complexes, iminyl inions and nitrilium ions that subsequently afford a wide range of polyfunctional cyclic nitrogen-containing compounds. In this review, the reactions of vinylazides leading to these products (in the last decade) are categorized based on the types of the resulting N-heterocyclic rings and a brief and concise description of the reaction mechanisms is presented.

Synthesis and pharmacological properties of polysubstituted 2-amino-4H-pyran-3-carbonitrile derivatives.

2-Amino-3-cyano-4H-chromenes are structural core motifs that received increasing attention in the last years due to their interesting potential pharmacological properties. In this review, the synthetic methods for these compounds are classified based on the type of catalyst in the pertinent reactions. In addition, the wide range of pharmacological properties of these compounds is covered in a separate section.

An insight into the estimation of drilling fluid density at HPHT condition using PSO-, ICA-, and GA-LSSVM strategies.

The present study evaluates the drilling fluid density of oil fields at enhanced temperatures and pressures. The main objective of this work is to introduce a set of modeling and experimental techniques for forecasting the drilling fluid density via various intelligent models. Three models were assessed, including PSO-LSSVM, ICA-LSSVM, and GA-LSSVM. The PSO-LSSVM technique outperformed the other models in light of the smallest deviation factor, reflecting the responses of the largest accuracy. The experimental and modeled regression diagrams of the coefficient of determination (R2) were plotted. In the GA-LSSVM approach, R2 was calculated to be 0.998, 0.996 and 0.996 for the training, testing and validation datasets, respectively. R2 was obtained to be 0.999, 0.999 and 0.998 for the training, testing and validation datasets, respectively, in the ICA-LSSVM approach. Finally, it was found to be 0.999, 0.999 and 0.999 for the training, testing and validation datasets in the PSO-LSSVM method, respectively. In addition, a sensitivity analysis was performed to explore the impacts of several variables. It was observed that the initial density had the largest impact on the drilling fluid density, yielding a 0.98 relevancy factor.