Advancing Meta-Selective C-H Amination through Non-Covalent Interactions.

Regioselective C-H amination of simple arenes is highly desirable, but accessing meta-sites of ubiquitous arenes has proven challenging due to the lack of both electronic and spatial preference. This study demonstrates the successful use of various privileged nitrogen-containing functionalities found in pharmaceutical compounds to direct meta-C-H amination of arenes, overcoming the long-standing requirement for a redundant directing group. The remarkable advancements in functional group accommodation for precise regiochemical control were achieved through the discovery of an unprecedented organo-initiator and the strategic utilization of non-covalent interactions. This protocol has been successfully applied in the concise synthesis and late-stage derivatization of drug molecules, which would have been otherwise challenging to achieve.

A Decision-Making Strategy for Car Following Based on Naturalist Driving Data via Deep Reinforcement Learning.

To improve the satisfaction and acceptance of automatic driving, we propose a deep reinforcement learning (DRL)-based autonomous car-following (CF) decision-making strategy using naturalist driving data (NDD). This study examines the traits of CF behavior using 1341 pairs of CF events taken from the Next Generation Simulation (NGSIM) data. Furthermore, in order to improve the random exploration of the agent's action, the dynamic characteristics of the speed-acceleration distribution are established in accordance with NDD. The action's varying constraints are achieved via a normal distribution 3σ boundary point-to-fit curve. A multiobjective reward function is designed considering safety, efficiency, and comfort, according to the time headway (THW) probability density distribution. The introduction of a penalty reward in mechanical energy allows the agent to internalize negative experiences. Next, a model of agent-environment interaction for CF decision-making control is built using the deep deterministic policy gradient (DDPG) method, which can explore complicated environments. Finally, extensive simulation experiments validate the effectiveness and accuracy of our proposal, and the driving strategy is learned through real-world driving data, which is better than human data.