RESUMO
Herein, we describe a nickel-catalyzed asymmetric dearomative aryl-difluoroallylation reaction of indoles with α-trifluoromethyl alkenes as an electrophilic coupling partner. The reaction proceeds via a cascade sequence involving dearomative Heck cyclization and reductive allylic defluorination. A series of gem-difluoroallyl substituted indolines are obtained in moderate to good yields (36-77% yield) with excellent enantioselectivity (up to 99% ee). The reaction features broad functional group tolerance, scaled-up synthesis, and late-stage diversification.
RESUMO
Conventional neural networks tend to fall into local extremum on large datasets, while the research on the strength of rubber concrete using intelligent algorithms to optimize artificial neural networks is limited. Therefore, to improve the prediction accuracy of rubber concrete strength, an artificial neural network model with hybrid algorithm optimization was developed in this study. The main strategy is to mix the simulated annealing (SA) algorithm with the particle swarm optimization (PSO) algorithm, using the SA algorithm to compensate for the weak global search capability of the PSO algorithm at a later stage while changing the inertia factor of the PSO algorithm to an adaptive state. For this purpose, data were first collected from the published literature to create a database. Next, ANN and PSO-ANN models are also built for comparison while four evaluation metrics, MSE, RMSE, MAE, and R2, were used to assess the model performance. Finally, compared with empirical formulations and other neural network models, the result shows that the proposed optimized artificial neural network model successfully improves the accuracy of predicting the strength of rubber concrete. This provides a new option for predicting the strength of rubber concrete.
