Engineering topics
Huang, Bin
Publications and source records attributed to Huang, Bin.
Population-level incidence of human papillomavirus–positive oropharyngeal, cervical, and anal cancers, by smoking status
Abstract We estimated the population-level incidence of human papillomavirus (HPV)–positive oropharyngeal, cervical, and anal cancers by smoking status. We combined HPV DNA genotyping data from the Centers for Disease Control and Prevention’s Cancer Registry Sentinel Surveillance System with data from the Kentucky Cancer Registry and Behavioral Risk Factor Surveillance System across smoking status. During 2004-2005 and 2014-2015 in Kentucky, most cases of oropharyngeal (63.3%), anal (59.7%), and cervical (54.9%) cancer were among individuals who ever smoked. The population-level incidence rate was higher among individuals who ever smoked than among those who never smoked for HPV-positive oropharyngeal (7.8 vs 2.1; adjusted incidence rate ratio = 2.6), cervical (13.7 vs 6.8; adjusted incidence rate ratio = 2.0), and anal (3.9 vs 1.6; adjusted incidence rate ratio = 2.5) cancers. These findings indicate that smoking is associated with increased risk of HPV-positive oropharyngeal, cervical, and anal cancers, and the population-level burden of these cancers is higher among individuals who ever smoked.
Bi-Level Adaptive Storage Expansion Strategy for Microgrids Using Deep Reinforcement Learning
Battery energy storage (BES) is a versatile resource for the secure and economic operation of microgrids (MGs). Prevailing stochastic optimization-based approaches for BES expansion planning for MGs are computationally complicated. This work proposes a data-driven bi-level multi-period BES expansion planning framework to determine the siting, sizing, and timing of BES installations. The proposed planning framework unifies deep reinforcement learning (DRL) and linear programming, thereby decoupling the determinations for the integer and continuous decision variables in two time scales, respectively. In the upper level, a rainbow DRL agent with quantile regression is trained to provide dynamic planning policies to accommodate stochastic renewable energy resources (RESs), load, and battery price changes efficiently. Further, the lower level computes the optimal operation of MGs with frequency constraints to hedge the islanding contingency. The two levels communicate with one another by exchanging storage configuration and operating expenses in order to accomplish the shared goal of minimizing investment and operation costs. Comparative case studies on an MG are carried out to demonstrate the superiority of the proposed DRL-based solution to the mixed-integer linear programming counterpart on efficiency, scalability, and adaptability.
Applications of Physics-Informed Neural Networks in Power Systems - A Review
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Nonmetallic metal toward a pressure-induced bad-metal state in two-dimensional Cu3LiRu2O6
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