Rice(Oryza sativa)is a staple food for more than half of the world's population and a critical crop for global agriculture.Understanding the regulatory mechanisms that control gene expression in the rice genome is...Rice(Oryza sativa)is a staple food for more than half of the world's population and a critical crop for global agriculture.Understanding the regulatory mechanisms that control gene expression in the rice genome is fundamental for advancing agricultural productivity and food security.In mechanism,cis-regulatory elements(including promoters,enhancers,silencers,and insulators)are key DNA sequences whose activities determine the spatial and temporal expression patterns of nearby genes(Yocca and Edger,2022;Schmitz et al.,2022).展开更多
Two metal-organic frameworks(MOFs),trans-[Co(L)(μ_(2)-H_(2)O)(H_(2)O)2]·2H_(2)O(1)and cis-[Mn(L)(Bipy)](2)(H_(2)L=2,2'-dimethyl-4,4'-biphenyldicarboxylic acid,Bipy=4,4'-bipyridine),have been synthesi...Two metal-organic frameworks(MOFs),trans-[Co(L)(μ_(2)-H_(2)O)(H_(2)O)2]·2H_(2)O(1)and cis-[Mn(L)(Bipy)](2)(H_(2)L=2,2'-dimethyl-4,4'-biphenyldicarboxylic acid,Bipy=4,4'-bipyridine),have been synthesized and character-ized by FTIR,thermogravimetric analysis(TGA),powder and single crystal X-ray diffraction.MOF 1 crystallizes in the triclinic system with a P1 space group and contains two crystallographically different Coions.Each trans-[CoO_(6)]octahedron is connected byμ_(2)-H_(2)O and L^(2-)ligand with a bis(unidentate)coordination mode to produce a 2D sql topological network.MOF 2 crystallizes in the monoclinic system with a C2/c space group.The Mncation adopts a cis-[MnO_(4)N_(2)]octahedron as a 6-connected node and is linked by L^(2-)ligand as a 4-connected node to gener-ate a binodal(4,6)-connected 3D fsc framework.The intermolecular interactions in 1 and 2 have been investigated by 3D Hirshfeld surface analyses and 2D fingerprint plots to reveal that the main interactions are H…H and O…H/H…O contacts in 1,and H…H and C…H/H…C contacts in 2.The TGA indicated that 1 and 2 were stable below 390 and 370℃,respectively.展开更多
Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a bal...Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.展开更多
基金supported by the National Natural Science Foundation of China(32070656)。
文摘Rice(Oryza sativa)is a staple food for more than half of the world's population and a critical crop for global agriculture.Understanding the regulatory mechanisms that control gene expression in the rice genome is fundamental for advancing agricultural productivity and food security.In mechanism,cis-regulatory elements(including promoters,enhancers,silencers,and insulators)are key DNA sequences whose activities determine the spatial and temporal expression patterns of nearby genes(Yocca and Edger,2022;Schmitz et al.,2022).
文摘Two metal-organic frameworks(MOFs),trans-[Co(L)(μ_(2)-H_(2)O)(H_(2)O)2]·2H_(2)O(1)and cis-[Mn(L)(Bipy)](2)(H_(2)L=2,2'-dimethyl-4,4'-biphenyldicarboxylic acid,Bipy=4,4'-bipyridine),have been synthesized and character-ized by FTIR,thermogravimetric analysis(TGA),powder and single crystal X-ray diffraction.MOF 1 crystallizes in the triclinic system with a P1 space group and contains two crystallographically different Coions.Each trans-[CoO_(6)]octahedron is connected byμ_(2)-H_(2)O and L^(2-)ligand with a bis(unidentate)coordination mode to produce a 2D sql topological network.MOF 2 crystallizes in the monoclinic system with a C2/c space group.The Mncation adopts a cis-[MnO_(4)N_(2)]octahedron as a 6-connected node and is linked by L^(2-)ligand as a 4-connected node to gener-ate a binodal(4,6)-connected 3D fsc framework.The intermolecular interactions in 1 and 2 have been investigated by 3D Hirshfeld surface analyses and 2D fingerprint plots to reveal that the main interactions are H…H and O…H/H…O contacts in 1,and H…H and C…H/H…C contacts in 2.The TGA indicated that 1 and 2 were stable below 390 and 370℃,respectively.
基金supported in part by the National Natural Science Foundation of China(U23A20340,62176238,62476254,62106230)the Key Research and Development Projects of the Ministry of Science and Technology of China(2022YFD2001200)+3 种基金the Natural Science Foundation Project of Henan Province(242300420277)the Key Research and Development Program of Henan(251111113900)the Frontier Exploration Projects of Longmen Laboratory(LMQYTSKT031)Chongqing University of Posts and Telecommunications Key Laboratory of Big Data Open Fund Project(BDIC-2023-B-005).
文摘Solving constrained multi-objective optimization problems(CMOPs)is a challenging task due to the presence of multiple conflicting objectives and intricate constraints.In order to better address CMOPs and achieve a balance between objectives and constraints,existing constrained multi-objective evolutionary algorithms(CMOEAs)predominantly focus on devising various strategies by leveraging the relationships between objectives and constraints,and the designed strategies usually are effective for the problems with simple constraints.However,these methods most ignore the relationship between decision variables and constraints.In fact,the essence of optimization is to find appropriate decision variables to meet various complex constraints.Therefore,it is hoped that the problem can be analyzed from the perspective of decision variables,so as to obtain more excellent results.Based on the above motivation,this paper proposes a decision variables classification approach,according to the relationship between decision variables and constraints,variables are divided into constraint-related(CR)variables and constraintindependent(CI)variables.Consequently,by optimizing these two types of variables independently,the population can sustain a favorable balance between feasibility and diversity.Furthermore,specific offspring generation strategies are proposed for the two categories of decision variables in order to achieve rapid convergence while maintaining population diversity.Experimental results on 31 test problems as well as 20 real-world problems demonstrate that the proposed algorithm is competitive compared to some state-of-the-art constrained multi-objective optimization algorithms.
