Targeting the chromatin structural changes of antitumor immunity

Epigenomic imbalance drives abnormal transcriptional processes,promoting the onset and progression of cancer.Although defective gene regulation generally affects carcinogenesis and tumor suppression networks,tumor immunogenicity and immune cells involved in antitumor responses may also be affected by epigenomic changes,which may have significant implications for the development and application of epigenetic therapy,cancer immunotherapy,and their combinations.Herein,we focus on the impact of epigenetic regulation on tumor immune cell function and the role of key abnormal epigenetic processes,DNA methylation,histone post-translational modification,and chromatin structure in tumor immunogenicity,and introduce these epigenetic research methods.We emphasize the value of small-molecule inhibitors of epigenetic modulators in enhancing antitumor immune responses and discuss the challenges of developing treatment plans that combine epigenetic therapy and immuno-therapy through the complex interaction between cancer epigenetics and cancer immunology.

Parallel Integrated Model-Driven and Data-Driven Online Transient Stability Assessment Method for Power System

More and more uncertain factors in power systems and more and more complex operation modes of power systems put forward higher requirements for online transient stability assessment methods.The traditional modeldriven methods have clear physical mechanisms and reliable evaluation results but the calculation process is time-consuming,while the data-driven methods have the strong fitting ability and fast calculation speed but the evaluation results lack interpretation.Therefore,it is a future development trend of transient stability assessment methods to combine these two kinds of methods.In this paper,the rate of change of the kinetic energy method is used to calculate the transient stability in the model-driven stage,and the support vector machine and extreme learning machine with different internal principles are respectively used to predict the transient stability in the data-driven stage.In order to quantify the credibility level of the data-driven methods,the credibility index of the output results is proposed.Then the switching function controlling whether the rate of change of the kinetic energy method is activated or not is established based on this index.Thus,a newparallel integratedmodel-driven and datadriven online transient stability assessment method is proposed.The accuracy,efficiency,and adaptability of the proposed method are verified by numerical examples.

Circulating Current Suppression Method with Adaptive Virtual Impedance for Multi-bidirectional Power Converters Under Unbalanced Conditions

Multi-paralleled bidirectional power converters(BPCs)are commonly used to improve the power capacity and reliability in an AC/DC hybrid microgrid.However,circulating current through multi-BPCs has been one of the challenges and it can be aggravated in the presence of non-ideal operating conditions,such as unbalanced AC voltages,and the mismatch of hardware parameters.In order to suppress the circulating current,this paper proposes a distributed method based on adaptive virtual impedance,which also employs positive sequence power droop control and voltage deviation compensation control.The traditional positive sequence power droop control is adopted to only regulate the positive components of the BPCs output voltage.The negative sequence power term is fed to an adaptive virtual impedance generator to modify the damping characteristics of the BPCs.Also,an adaptive virtual impedance-based voltage deviation compensation method is proposed to recover the fluctuated output voltage of the BPCs due to droop action and the power fluctuations.The fully distributed regulation of adaptive virtual impedance enables the load power to be shared accurately among BPC modules and thus the circulating current can be effectively suppressed.The proposed control strategy does not require an additional communication system and the precise parameters of hardware equipment and line impedance.Furthermore,the effectiveness of the proposed method is verified by the experimental results.

Review on the Research and Practice of Deep Learning and Reinforcement Learning in Smart Grids

Smart grids are the developmental trend of power systems and they have attracted much attention all over the world.Due to their complexities,and the uncertainty of the smart grid and high volume of information being collected,artificial intelligence techniques represent some of the enabling technologies for its future development and success.Owing to the decreasing cost of computing power,the profusion of data,and better algorithms,AI has entered into its new develop-mental stage and AI 2.0 is developing rapidly.Deep learning(DL),reinforcement learning(RL)and their combination-deep reinforcement learning(DRL)are representative methods and relatively mature methods in the family of AI 2.0.This article introduces the concept and status quo of the above three methods,summarizes their potential for application in smart grids,and provides an overview of the research work on their application in smart grids.

Risk analysis of power systems for both real and reactive power

With the high penetration of renewable energy sources,the reliability of power systems becomes more vulnerable than ever because of the greater uncertainty and intermittence in power generation.Reactive power plays an important role in the power system reliability,because it is closely related to the system voltage stability and voltage collapse.However,reactive power-related reliability issues are seldom emphasized in conventional power reliability evaluations.This article investigates power system reliability of real and reactive power.Real and reactive power shortages and the associated voltage violations due to system failures are considered on reliability evaluation of power systems.A three-stage load-shedding technique for post contingencies is implemented to determine the contributions of real and reactive power on the system reliability and to find an optimal way to release network violation.The results provide the detailed information on power system planning and operation for system planners and operators from real and reactive power aspects.

钱林森（英文）

Linsen Qian was born in September 1937,in Taixing City,Jiangsu Province,China.He is a member of China Association for Promoting Democracy,member of Zhejiang Province Writers’Association.He is an extinguished expert in