Wentao Su, Chunhua Zhu, Anping Hua, Shanchen Li, and Junhua Zhao
International Journal of Smart and Nano Materials, Vol 0, Iss 0, Pp 1-13 (2021)
boron nitride sheet, crack-tip shape, fracture behavior, molecular dynamics, finite element, Materials of engineering and construction. Mechanics of materials, and TA401-492
Nanoscale defects, including cracks, circular holes, and the triangular-shaped defects, often occur in the growth of boron nitride nanosheets (BNNS). In this study, the fracture behavior of chiral BNNS with different crack-tip shapes and the interactions of nanoscale crack-defects are studied using molecular dynamics (MD) simulations and finite element (FE) analysis. Both MD and FE results indicate that the fracture strength of BNNS with two crack tips (t = 2) is significantly higher than that with one crack tip (t = 1), in which the difference in zigzag (ZZ) direction is more obvious than that in armchair (AC) direction, mainly due to the fact that the change of bond angles near the crack tips is more substantial in the ZZ direction than those in the AC direction. Our results show that the fracture strength of BNNS strongly depends on crack-tip shapes, chiral angles, the defect-to-crack tip spacing and deflection angles. Checking against the current MD simulations and FE analysis shows the present results are reasonable. This study should be of great importance for enhancing the fracture performance of BNNS by modulating their crack-tip shapes and the interactions of nanoscale crack-defects.
deep reinforcement learning, model-based reinforcement learning, hybrid model, heating, ventilation, and air-conditioning control, deep deterministic policy gradient, and General Works
Buildings account for a large proportion of the total energy consumption in many countries and almost half of the energy consumption is caused by the Heating, Ventilation, and air-conditioning (HVAC) systems. The model predictive control of HVAC is a complex task due to the dynamic property of the system and environment, such as temperature and electricity price. Deep reinforcement learning (DRL) is a model-free method that utilizes the “trial and error” mechanism to learn the optimal policy. However, the learning efficiency and learning cost are the main obstacles of the DRL method to practice. To overcome this problem, the hybrid-model-based DRL method is proposed for the HVAC control problem. Firstly, a specific MDPs is defined by considering the energy cost, temperature violation, and action violation. Then the hybrid-model-based DRL method is proposed, which utilizes both the knowledge-driven model and the data-driven model during the whole learning process. Finally, the protection mechanism and adjusting reward methods are used to further reduce the learning cost. The proposed method is tested in a simulation environment using the Australian Energy Market Operator (AEMO) electricity price data and New South Wales temperature data. Simulation results show that 1) the DRL method can reduce the energy cost while maintaining the temperature satisfactory compared to the short term MPC method; 2) the proposed method improves the learning efficiency and reduces the learning cost during the learning process compared to the model-free method.
State estimation, Low-frequency data, High-frequency data, Super resolution perception, Data completeness, Engineering (General). Civil engineering (General), and TA1-2040
The smart grid is an evolving critical infrastructure, which combines renewable energy and the most advanced information and communication technologies to provide more economic and secure power supply services. To cope with the intermittency of ever-increasing renewable energy and ensure the security of the smart grid, state estimation, which serves as a basic tool for understanding the true states of a smart grid, should be performed with high frequency. More complete system state data are needed to support high-frequency state estimation. The data completeness problem for smart grid state estimation is therefore studied in this paper. The problem of improving data completeness by recovering high-frequency data from low-frequency data is formulated as a super resolution perception (SRP) problem in this paper. A novel machine-learning-based SRP approach is thereafter proposed. The proposed method, namely the Super Resolution Perception Net for State Estimation (SRPNSE), consists of three steps: feature extraction, information completion, and data reconstruction. Case studies have demonstrated the effectiveness and value of the proposed SRPNSE approach in recovering high-frequency data from low-frequency data for the state estimation.
Jennifer A. McKinney, Guliang Wang, Anirban Mukherjee, Laura Christensen, Sai H. Sankara Subramanian, Junhua Zhao, and Karen M. Vasquez
Nature Communications, Vol 11, Iss 1, Pp 1-12 (2020)
Z-DNA-forming CG repeats are mutagenic in mammalian cells but the mechanism has remained unknown so far. Here, the authors show that the nucleotide excision repair complex Rad10-Rad1 (ERCC1-XPF) and the mismatch repair complex Msh2-Msh3 (MSH2-MSH3) are required for Z-DNA-induced genetic instability in yeast and human cells.
Junhua Zhao, Hejun Wu, Yongjian Zhao, and Weiwei Liu
2014 IEEE 20th International Conference on Embedded and Real-Time Computing Systems and Applications Embedded and Real-Time Computing Systems and Applications (RTCSA), 2014 IEEE 20th International Conference on. :1-9 Aug, 2014