To decrease critical micelle concentration (CMC), improve stability, and keep high drug-loading capacity, three pH-sensitive mixed micelles applied for anticancer drug controlled delivery were prepared by the mixture of polymers poly (N,N-diethylaminoethyl methacrylate)-b-poly(poly(ethylene glycol) methyl ether methacrylate) (PDEAEMA-PPEGMA) and polycaprolactone-b-poly (poly(ethylene glycol) methyl ether methacrylate) (PCL-PPEGMA), which were synthesized and confirmed by 1H NMR and gel permeation chromatographic (GPC). The critical micelle concentration (CMC) values of the prepared mixed micelles were low, and the micellar sizes and zeta potentials of the blank mixed micelles demonstrated good pH-responsive behavior. Combined experimental techniques with dissipative particle dynamics (DPD) simulation, the particle sizes, zeta potentials, drug loading content (LC), encapsulation efficiency (EE), aggregation morphologies, and doxorubicin (DOX) distribution of the mixed micelles were investigated, and the high DOX-loading capacity of the mixed micelles was found. Both in vitro DOX release profiles and DPD simulations of the DOX dynamics release process exhibited less leakage and good stability in neutral conditions and accelerated drug release behavior with a little initial burst in slightly acidic conditions. Cytotoxicity tests showed that the polymer PDEAEMA-PPEGMA and the blank mixed micelles had good biocompatibility, and DOX-loaded mixed micelles revealed certain cytotoxicity. These results suggest that the drug-loaded mixed micelles that consisted of the two polymers PDEAEMA-PPEGMA and PCL-PPEGMA can be new types of pH-responsive well-controlled release anticancer drug delivery mixed micelles.
In the process of multi-axis contour tracking control, the traditional time-invariant method could lead to a significant error in contour tracking due to the existence of two different motion conditions, namely single-axis independent motion and multi-axis coupled motion. In order to tackle this issue, a timevarying weighting matrix has been developed considering the trajectory and time-varying random disturbance. In this paper, a time-varying control method for multi-axis motion based on norm optimal cross-coupling iterative learning is proposed. Compared to the time-invariant control method, the simulation and experiment results demonstrate that the proposed method can effectively reduce the contour error improving the multi-axis control precision. INDEX TERMS Time-varying weighted matrix, Iterative learning, Norm optimal, cross coupling, Multiaxis motion control.
A nonlinear correlative time series prediction method is presented in this paper.It is based on the mutual information of time series and orthogonal polynomial basis neural network. Inputs of network are selected by mutual information, and orthogonal polynomial basis is used as active function.The network is trained by an error iterative learning algorithm.This proposed method for nonlinear time series is tested using two well known time series prediction problems:Gas furnace data time series and Mackey-Glass time series.
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