pdi gene from Medicago sativa L. ,encoding Protein Disulfide Isomerase（ mPDI ）, has been cloned and sequenced. According to the mRNA and amino acid sequence, the character of mPDI such as the physical and chemical p...pdi gene from Medicago sativa L. ,encoding Protein Disulfide Isomerase（ mPDI ）, has been cloned and sequenced. According to the mRNA and amino acid sequence, the character of mPDI such as the physical and chemical properties, hydrophilicity/hydrophobicity, signal peptide, secondary structure, coiled coil, transmembrane domains, O-glycogylation site, active site, subcellular localization, functional structural domains and three-dimensional structure were analyzed by a series of bioinformatics software. The results showed that mPDI was a hydrophobic and stable protein with 3 coiled coils, 30-glycogylation sites, 2 structural domains of thioredoxin, 2 active sites of thioredoxin, and located in rough endoplasmic reticulum. It has 512 amino acids, the theoretical pl is 4.98, and signal peptide located in 1-24AA. In the secondary structure, a-helix, random coil, extended chain is 26.37%, 53.32%, 20.31% respectively. The validation of modeling accords with the stereochemistry.展开更多
The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper coo...The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.展开更多
文摘pdi gene from Medicago sativa L. ,encoding Protein Disulfide Isomerase（ mPDI ）, has been cloned and sequenced. According to the mRNA and amino acid sequence, the character of mPDI such as the physical and chemical properties, hydrophilicity/hydrophobicity, signal peptide, secondary structure, coiled coil, transmembrane domains, O-glycogylation site, active site, subcellular localization, functional structural domains and three-dimensional structure were analyzed by a series of bioinformatics software. The results showed that mPDI was a hydrophobic and stable protein with 3 coiled coils, 30-glycogylation sites, 2 structural domains of thioredoxin, 2 active sites of thioredoxin, and located in rough endoplasmic reticulum. It has 512 amino acids, the theoretical pl is 4.98, and signal peptide located in 1-24AA. In the secondary structure, a-helix, random coil, extended chain is 26.37%, 53.32%, 20.31% respectively. The validation of modeling accords with the stereochemistry.
基金Project(61801495)supported by the National Natural Science Foundation of China
文摘The application of multiple UAVs in complicated tasks has been widely explored in recent years.Due to the advantages of flexibility,cheapness and consistence,the performance of heterogeneous multi-UAVs with proper cooperative task allocation is superior to over the single UAV.Accordingly,several constraints should be satisfied to realize the efficient cooperation,such as special time-window,variant equipment,specified execution sequence.Hence,a proper task allocation in UAVs is the crucial point for the final success.The task allocation problem of the heterogeneous UAVs can be formulated as a multi-objective optimization problem coupled with the UAV dynamics.To this end,a multi-layer encoding strategy and a constraint scheduling method are designed to handle the critical logical and physical constraints.In addition,four optimization objectives:completion time,target reward,UAV damage,and total range,are introduced to evaluate various allocation plans.Subsequently,to efficiently solve the multi-objective optimization problem,an improved multi-objective quantum-behaved particle swarm optimization(IMOQPSO)algorithm is proposed.During this algorithm,a modified solution evaluation method is designed to guide algorithmic evolution;both the convergence and distribution of particles are considered comprehensively;and boundary solutions which may produce some special allocation plans are preserved.Moreover,adaptive parameter control and mixed update mechanism are also introduced in this algorithm.Finally,both the proposed model and algorithm are verified by simulation experiments.
