Strong and Tough Self-Healing Elastomers via BTA-Mediated Microstructure and Metal-ligand Coordination.

Creating elastomers with high strength, toughness, and rapid self-healing remains a key challenge. These seemingly contradictory properties require innovative design strategies. Herein, a novel approach is proposed by simultaneously incorporating a unique triple hydrogen bond unit, benzene-1,3,5-tricarboxamide (BTA), and imidazole-Zn dynamic coordination into the elastomer.

A Reinforcement Learning Approach to Robust Scheduling of Permutation Flow Shop.

The permutation flow shop scheduling problem (PFSP) stands as a classic conundrum within the realm of combinatorial optimization, serving as a prevalent organizational structure in authentic production settings. Given that conventional scheduling approaches fall short of effectively addressing the intricate and ever-shifting production landscape of PFSP, this study proposes an end-to-end deep reinforcement learning methodology with the objective of minimizing the maximum completion time. To tackle PFSP, we initially model it as a Markov decision process, delineating pertinent states, actions, and reward functions.