The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic op-timization.In this letter,the solution of unsteady adjoint equations is accelerated by dynamic mode decomposi-...The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic op-timization.In this letter,the solution of unsteady adjoint equations is accelerated by dynamic mode decomposi-tion(DMD).The pseudo-time marching of every real-time step is approximated as an infinite-dimensional linear dynamical system.Thereafter,DMD is utilized to analyze the adjoint vectors sampled from these pseudo-time marching.First-order zero frequency mode is selected to accelerate the pseudo-time marching of unsteady adjoint equations in every real-time step.Through flow past a stationary circular cylinder and an unsteady aerodynamic shape optimization example,the efficiency of solving unsteady adjoint equations is significantly improved.Re-sults show that one hundred adjoint vectors contains enough information about the pseudo-time dynamics,and the adjoint dominant mode can be precisely predicted only by five snapshots produced from the adjoint vectors,which indicates DMD analysis for pseudo-time marching of unsteady adjoint equations is efficient.展开更多
Dysregulation of histone deacetylases(HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has...Dysregulation of histone deacetylases(HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has led to the approval of five HDAC inhibitors(HDACis). However, currently traditional HDACis, although effective in approved indications, exhibit severe off-target toxicities and low sensitivities against solid tumors, which have urged the development of next-generation of HDACi. This review investigates the biological functions of HDACs, the roles of HDACs in oncogenesis, the structural features of different HDAC isoforms, isoform-selective inhibitors, combination therapies, multitarget agents and HDAC PROTACs. We hope these data could inspire readers with new ideas to develop novel HDACi with good isoform selectivity, efficient anticancer effect, attenuated adverse effect and reduced drug resistance.展开更多
Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is app...Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.展开更多
基金the Natural Science Foundation of Jiangsu Province(Grants No.BK20230202)Basic Science(Natural Science)Re-search Project of Colleges and Universities in Jiangsu Province(Grant No.22KJB130005)+3 种基金Changzhou Science and Technology Project(Grant No.CJ20220242)for financial supportJiaqing Kou would like to thank the support of the Alexander von Humboldt Foundation(Ref 3.5-CHN-1227287-HFST-P)Wenkai Yang would like to thank the support of the National Natural Science Foundation of China(Grant No.52205335)supported by Changzhou Sci&Tech Pro-gram(Grant No.CM20223013).
文摘The computational cost of unsteady adjoint equations remains high in adjoint-based unsteady aerodynamic op-timization.In this letter,the solution of unsteady adjoint equations is accelerated by dynamic mode decomposi-tion(DMD).The pseudo-time marching of every real-time step is approximated as an infinite-dimensional linear dynamical system.Thereafter,DMD is utilized to analyze the adjoint vectors sampled from these pseudo-time marching.First-order zero frequency mode is selected to accelerate the pseudo-time marching of unsteady adjoint equations in every real-time step.Through flow past a stationary circular cylinder and an unsteady aerodynamic shape optimization example,the efficiency of solving unsteady adjoint equations is significantly improved.Re-sults show that one hundred adjoint vectors contains enough information about the pseudo-time dynamics,and the adjoint dominant mode can be precisely predicted only by five snapshots produced from the adjoint vectors,which indicates DMD analysis for pseudo-time marching of unsteady adjoint equations is efficient.
基金supported by the National Natural Science Foundation of China (81874288, 82003590 and 92053105)the Natural Science Foundation of Shandong Province (ZR2020QH342, China)+1 种基金the Key Project of Natural Science Foundation of Anhui Province for College Scholar (2022AH051216, China)Scientific Research Project of Anhui Provincial Health Commission (AHWJ2022b005, China)。
文摘Dysregulation of histone deacetylases(HDACs) is closely related to tumor development and progression. As promising anticancer targets, HDACs have gained a great deal of research interests and two decades of effort has led to the approval of five HDAC inhibitors(HDACis). However, currently traditional HDACis, although effective in approved indications, exhibit severe off-target toxicities and low sensitivities against solid tumors, which have urged the development of next-generation of HDACi. This review investigates the biological functions of HDACs, the roles of HDACs in oncogenesis, the structural features of different HDAC isoforms, isoform-selective inhibitors, combination therapies, multitarget agents and HDAC PROTACs. We hope these data could inspire readers with new ideas to develop novel HDACi with good isoform selectivity, efficient anticancer effect, attenuated adverse effect and reduced drug resistance.
基金This work was supported by the National Numerical Wind tunnel Project(Grant NNW2018-ZT1B01)the National Natural Science Foundation of China(Grant 91852115).
文摘Adjoint method is widely used in aerodynamic design because only once solution of flow field is required for it to obtain the gradients of all design variables. However, the computational cost of adjoint vector is approximately equal to that of flow computation. In order to accelerate the solution of adjoint vector and improve the efficiency of adjoint-based optimization, machine learning for adjoint vector modeling is presented. Deep neural network (DNN) is employed to construct the mapping between the adjoint vector and the local flow variables. DNN can efficiently predict adjoint vector and its generalization is examined by a transonic drag reduction of NACA0012 airfoil. The results indicate that with negligible computational cost of the adjoint vector, the proposed DNN-based adjoint method can achieve the same optimization results as the traditional adjoint method.