The nanoparticles of polylactide (PLA) and poly(lactide-co-glycolide) (PLGA) were prepared by the bi-nary organic solvent diffusion method. The yield, particle size and size distribution of these nanoparticles wereeva...The nanoparticles of polylactide (PLA) and poly(lactide-co-glycolide) (PLGA) were prepared by the bi-nary organic solvent diffusion method. The yield, particle size and size distribution of these nanoparticles wereevaluated. The yield of nanoparticles prepared by this method is over 90%, and the average size of the nanoparticlesis between 130-180 nm. In order to clarify the effect of the organic solvent used in the system on nanoparticle yieldand size, the cloud points of PLA and PLGA were examined by cloud point titration. The results indicate that theyields of nanoparticles increase with the increase of ethanol in the acetone solution and attain the maximum at thecloud point of ethanol, while the size of nanoparticles decreases with the increase of ethanol in the acetone solutionand attains the minimum at the cloud point of ethanol. The optimal composition ratio of binary organic solvents coin-cides to that near the cloud point and the optimal condition of binary organic solvents can be predicted.展开更多
Adding a moving baffle to the drum is a new way to enhance the motion and mixing of particles in rotating drums.To obtain its influence on binary particles,horizontal rotating drums provided with a moving baffle were ...Adding a moving baffle to the drum is a new way to enhance the motion and mixing of particles in rotating drums.To obtain its influence on binary particles,horizontal rotating drums provided with a moving baffle were investigated by discrete element method(DEM).AtΩ=15 r/min,increasing the length of moving baffle can increase the fluctuation amplitude of average particle velocity.AtΩ=60 r/min,the influence of the moving baffle on the average velocity fluctuation tends to be more random.At both rotational speeds,the moving baffle causes the average particle velocity to fluctuate more sharply.The moving baffle can enhance particle mixing.AtΩ=15 r/min,the moving baffle with length ofδ=1/3 can best enhance particle mixing.However,atΩ=60 r/min,only the moving baffle with a specific length(δ=1/4)can enhance mixing.This basic research has a positive reference value for the application of the moving baffle in industry.展开更多
Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanc...Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.展开更多
基金Project ( 2001AA218011) supported by the National High Technology Development "863" Program of China
文摘The nanoparticles of polylactide (PLA) and poly(lactide-co-glycolide) (PLGA) were prepared by the bi-nary organic solvent diffusion method. The yield, particle size and size distribution of these nanoparticles wereevaluated. The yield of nanoparticles prepared by this method is over 90%, and the average size of the nanoparticlesis between 130-180 nm. In order to clarify the effect of the organic solvent used in the system on nanoparticle yieldand size, the cloud points of PLA and PLGA were examined by cloud point titration. The results indicate that theyields of nanoparticles increase with the increase of ethanol in the acetone solution and attain the maximum at thecloud point of ethanol, while the size of nanoparticles decreases with the increase of ethanol in the acetone solutionand attains the minimum at the cloud point of ethanol. The optimal composition ratio of binary organic solvents coin-cides to that near the cloud point and the optimal condition of binary organic solvents can be predicted.
基金Project(51676032)supported by the National Natural Science Foundation of ChinaProject(IRT_17R19)supported by the Program for Changjiang Scholars and Innovative Research Team in University,China
文摘Adding a moving baffle to the drum is a new way to enhance the motion and mixing of particles in rotating drums.To obtain its influence on binary particles,horizontal rotating drums provided with a moving baffle were investigated by discrete element method(DEM).AtΩ=15 r/min,increasing the length of moving baffle can increase the fluctuation amplitude of average particle velocity.AtΩ=60 r/min,the influence of the moving baffle on the average velocity fluctuation tends to be more random.At both rotational speeds,the moving baffle causes the average particle velocity to fluctuate more sharply.The moving baffle can enhance particle mixing.AtΩ=15 r/min,the moving baffle with length ofδ=1/3 can best enhance particle mixing.However,atΩ=60 r/min,only the moving baffle with a specific length(δ=1/4)can enhance mixing.This basic research has a positive reference value for the application of the moving baffle in industry.
基金Project(2012CB725403)supported by the National Basic Research Program of ChinaProjects(71210001,51338008)supported by the National Natural Science Foundation of ChinaProject supported by World Capital Cities Smooth Traffic Collaborative Innovation Center and Singapore National Research Foundation Under Its Campus for Research Excellence and Technology Enterprise(CREATE)Programme
文摘Short-term traffic flow prediction is one of the essential issues in intelligent transportation systems(ITS). A new two-stage traffic flow prediction method named AKNN-AVL method is presented, which combines an advanced k-nearest neighbor(AKNN)method and balanced binary tree(AVL) data structure to improve the prediction accuracy. The AKNN method uses pattern recognition two times in the searching process, which considers the previous sequences of traffic flow to forecast the future traffic state. Clustering method and balanced binary tree technique are introduced to build case database to reduce the searching time. To illustrate the effects of these developments, the accuracies performance of AKNN-AVL method, k-nearest neighbor(KNN) method and the auto-regressive and moving average(ARMA) method are compared. These methods are calibrated and evaluated by the real-time data from a freeway traffic detector near North 3rd Ring Road in Beijing under both normal and incident traffic conditions.The comparisons show that the AKNN-AVL method with the optimal neighbor and pattern size outperforms both KNN method and ARMA method under both normal and incident traffic conditions. In addition, the combinations of clustering method and balanced binary tree technique to the prediction method can increase the searching speed and respond rapidly to case database fluctuations.
