A series of nanoparticles with phase-separated structures by 1,1-diphenylethene controlled one-step soap-free emulsion copolymerization and their application in drug release

A facile one-step approach to synthesize various phase-separated porous, raspberry-like, flower-like, core–shell and anomalous nanoparticles and nanocapsules via 1,1-diphenylethene (DPE) controlled soap-free emulsion copolymerization of styrene (S) with glycidyl methacrylate (GMA), or acrylic acid (AA) is reported. By regulating the mass ratio of S/GMA, transparent polymer solution, porous and anomalous P(S-GMA) particles could be produced. The P(S-GMA) particles turn from flower-like to raspberry-like and then to anomalous structures with smooth surface as the increase of divinylbenzene (DVB) crosslinker. Transparent polymer solution, nanocapsules and core–shell P(S-AA) particles could be obtained by altering the mole ratio of S/AA; anomalous and raspberry-like P(S-AA) particles are produced by adding DVB. The unpolymerized S resulted from the low monomer conversion in the presence of DPE aggregates to form nano-sized droplets, and migrates towards the external surfaces of the GMA-enriched P(S-GMA) particles and the internal bulk of the AA-enriched P(S-AA) particles. The nano-sized droplets function as in situ porogen, porous P(S-GMA) particles and P(S-AA) nanocapsules are produced when the porogen is removed. This novel, facile, one-step method with excellent controllability and reproducibility will inspire new strategies for creating hierarchical phase-separated polymeric particles with various structures by simply altering the species and ratio of comonomers. The drug loading and release experiments on the porous particles and nanocapsules demonstrate that the release of doxorubicin hydrochloride is very slow in weakly basic environment and quick in weakly acidic environment, which enables the porous particles and nanocapsules with promising potential in drug delivery applications.

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60°镜面光泽测量值不确定度的评定

本文采用光泽度仪对漆膜的60°镜面光泽进行了测量,对产生测量不确定度的因素进行了分析,并对其A类和B类不确定度进行了分别评定,最终获得其测量结果的扩展不确定度。     

Physics-Informed Multistage Machine Learning Strategy for the Nanomachining-Induced Plastic Deformation Behavior

The evolution of the dislocation density induced by the nanomachining process dominates the plastic deformation behaviors of materials, thus affecting the mechanical properties significantly. However, a challenging topic related to how to establish an accurate model for predicting the dislocation density based on the limited simulations and experiments arises due to the complicated thermal–mechanical coupling mechanism during the machining process. Herein, a multistage method integrating machine learning, physics, and high-throughput atomic simulation is proposed to investigate the effect of cutting speed on the dislocation behavior in polycrystal copper. Compared with the traditional one-step machine learning method, the constraint of physical features effectively improves the accuracy and generalization ability of the model. The results indicate that the dislocation behaviors depend on the competition between the cutting force and temperature. In the low-cutting speed, the predominated role of the cutting temperature leads to a rapid decline of the dislocation density. In contrast, the dislocation density tends to be stable under a high-speed cutting process due to the dynamic balance between the effects of the cutting force and temperature. Notably, the proposed strategy provides a new and universal framework to design the machining parameters to obtain high-quality products.     

An approximate mathematical model for solidification of a flowing liquid in a microchannel

A mathematical model is developed to analyse the combined flow and solidification of a liquid in a small pipe or two-dimensional channel. In either case, the problem reduces to solving a single equation for the position of the solidification front. Results show that for a large range of flow rates the closure time is approximately constant, and the value depends primarily on the wall temperature and channel width. However, the solid shape at closure will be very different for low and high fluxes. As the flow rate increases the closure time starts to depend on the flow rate until the closure time increases dramatically, subsequently the pipe will never close.     

Study of delay-independent decentralized guaranteed cost control for large scale systems

This study introduces delay independent decentralized guaranteed cost control design method based on two controller structures for nonlinear uncertain interconnected large scale systems with time delays. First, a set of equivalent Takagi-Sugeno (T-S) fuzzy models are extended to represent the systems. Then a decentralized state-feedback guaranteed cost performance controller is proposed for the fuzzy systems. Based on delay independent Lyapunov functional approach, some sufficient conditions for the existence of the controller can be cast into the feasible problem of LMIs irrespective of the sizes of the time delays so that the system can be asymptotically stabilized for all considered uncertainties whose sizes are not larger than their bounds. Finally, the minimizing approach is proposed to search the suboptimal upper bound value of guaranteed cost function. Moreover, the corresponding conditions are extended into the generalized dynamic output-feedback close-loop system. Finally, the better control performances of the proposed methods are shown by the simulation examples.     

公钥加密算法Rabin-OAEP安全性证明

针对Rabin-OAEP算法在自适应选择密文攻击下是否安全,采用Adversary和Simulator进行的合理交互,论证“分解大整数N=pq是困难的（其中P,q为2个大素数）,则Rabin-OAEP算法在自适应选择密文攻击下是安全的”的逆否命题,结果证明了Rabin—OAEP算法的安全性。     

Multi-scale object-based measurement of arid plant community structure

The measurement of plant community structure provides an extensive understanding of its function, succession and ecological process. The detection of plant community boundary is rather a challenge despite in situ work. Recent advances in object-based image analysis (OBIA) and machine learning algorithms offer new opportunities to address this challenge. This study presents a multi-scale segmentation approach to accurately identify the boundaries of each vegetation and plant community for mapping plant community structure. Initially, a very high resolution (VHR) Worldview-2 image of a desert area is hierarchically segmented from scale parameter 2 to 500. Afterward, the peak values of the standard deviation of brightness and normalized difference vegetation index (NDVI) across the segmentation scales are detected to determine the optimal segmentation scales of homogeneous single plant and plant community boundaries. A multi-scale classification of vegetation characterization with features of multiple bands, NDVI, grey-level co-occurrence matrix (GLCM) entropy and shape index is performed to identify dryland vegetation types. Finally, the four vegetation structural features on the type, diversity, object size and shape are calculated within the plant community boundaries and composed to plant community structure categories. Comparing the results with the object fitting index (FI) of the reference data, the validation indicates that the optimal segmentations of tree, shrub and plant communities are consistent with the identified peak values.     

Evolutionary level set method for structural topology optimization

This paper proposes an evolutionary accelerated computational level set algorithm for structure topology optimization. It integrates the merits of evolutionary structure optimization (ESO) and level set method (LSM). Traditional LSM algorithm is largely dependent on the initial guess topology. The proposed method combines the merits of ESO techniques with those of LSM algorithm, while allowing new holes to be automatically generated in low strain energy within the nodal neighboring region during optimization. The validity and robustness of the new algorithm are supported by some widely used benchmark examples in topology optimization. Numerical computations show that optimization convergence is accelerated effectively.