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In the semiconductor manufacturing industry, the Dynamic Flexible Job Shop Scheduling Problem is regarded as one of the most complex and significant scheduling problems. Existing studies consider the dynamic arrival of jobs, however, the insertion of urgent jobs such as testing chips poses a challenge to the production model, and there is an urgent need for new scheduling methods to improve the dynamic response and self-adjustment of the shop floor. In this work, deep reinforcement learning is utilized to address the dynamic flexible job shop scheduling problem and facilitate near-real-time shop floor decision-making. We extracted eight state features, including machine utilization, operation completion rate, etc., to reflect real-time shop floor production data. After examining machine availability time, the machine's earliest available time is redefined and incorporated into the design of compound scheduling rules. Eight compound scheduling rules have been developed for job selection and machine allocation. By using the state features as inputs to the Double Deep Q-Network, it is possible to acquire the state action values (Q-values) of each compound scheduling rule, and the intelligent agent can learn a reasonable optimization strategy through training. Simulation studies show that the proposed Double Deep Q-Network algorithm outperforms other heuristics and well-known scheduling rules by generating excellent solutions quickly. In most scenarios, the Double Deep Q-Network algorithm outperforms the Deep Q-Network, Q-Learning, and State-Action-Reward-State-Action (SARSA) frameworks. Moreover, the intelligent agent has good generalization ability in terms of optimization for similar objectives. [ABSTRACT FROM AUTHOR]

Solar energy was widely used in lots of fields, such as the heat pump technology, seawater desalination, wastewater treatment and photoelectric detection, where efficient use of solar energy was always the goal pursued by mankind. In the work, we proposed a simple and efficient MXene/Ti-based metamaterial absorber and investigated the optical and thermal radiation properties by using the finite-difference time-domain (FDTD) method. The results showed that the average absorption of the MXene/Ti-based metamaterial absorber was higher than 97.6 % from 400 nm to 1600 nm, the maximum absorption reached 99.6 %, and the solar spectrum weighted absorption was close to 98.5 %, and the physical mechanism of the broadband absorption was also explored by theoretical analysis. Compared with the latest reported absorbers, the MXene/Ti-based metamaterial not only had the advantages of low energy loss, high efficiency, and good thermal stability, but also its broadband absorption showed excellent robustness to structural parameters. Moreover, a 75° wide-angle absorption was obtained for the MXene/Ti-based metamaterial, which was independent of polarization. Last but not least, the MXene/Ti-based metamaterial achieved an ultra-high thermal emissivity of 98.3 % at 1600 K, which matched the blackbody radiation curves at 1600 K. These studies had significant implications for catalysis, solar photovoltaics, photovoltaic conversion and other fields. [ABSTRACT FROM AUTHOR]