A practical method for the clean preparation of multisubstituted pyrroles via an iodine-catalyzed multicomponent reaction under metal- and solvent-free conditions was developed. In the gram-scale synthesis, the pyrroles can be easily collected through simple extraction.

A novel metal catalyst-free and environmentally friendly method for the regioselective iodination of imidazo[1,2α]pyridines at their C3 position is disclosed, which has a wide substrate scope and could be sustainable. This reaction proceeds through ultrasound acceleration in the presence of a green alcohol solvent. Compared with a conventional heating system, the reaction efficiency and the rate are significantly improved and the iodine atom economy is maximized using ultrasound techniques.

The global demand for electric power has been greatly increasing because of industrial development and the change in people’s daily life. A lot of overhead transmission lines have been installed to provide reliable power across long distancess. Therefore, research on overhead transmission lines inspection is very important for preventing sudden wide-area outages. In this paper, we propose an Overhead Transmission Line Classifier (OTL-Classifier) based on deep learning techniques to classify images returned by future unmanned maintenance drones or robots. In the proposed model, a binary classifier based on Inception architecture is incorporated with an auxiliary marker algorithm based on ResNet and Faster-RCNN(Faster Regions with Convolutional Neural Networks features). The binary classifier defines images with foreign objects such as balloons and kites as abnormal class, regardless the type, size, and number of the foreign objects in a single image. The auxiliary marker algorithm marks foreign objects in abnormal images, in order to provide additional help for quick location of hidden foreign objects. Our OTL-Classifier model achieves a recall rate of 95% and an error rate of 10.7% in the normal mode, and a recall rate of 100% and an error rate of 35.9% in the Warning–Review mode.