Background Particle-based drug delivery systems (DDSs) have a demonstrated value for drug discovery and development. However, some problems remain to be solved, such as limited stimuli, visual-monitoring. Aim To develop an intelligent multicolor DDSs with both near-infrared (NIR) controlled release and macroscopic color changes. Materials and Methods Microparticles comprising GO/pNIPAM/PEGDA composite hydrogel inverse opal scaffolds, with dextran and calcium alginate hydrogel were synthesized using SCCBs as the template. The morphology of microparticle was observed under scanning electron microscopy, and FITC-dextran-derived green fluorescence images were determined using a confocal laser scanning microscope. During the drug release, FITC-dextran-derived green fluorescence images were captured using fluorescent inverted microscope. The relationship between the power of NIR and the drug release rate was obtained using the change in optical density (OD) values. Finally, the amount of drug released could be estimated quantitatively used the structural color or the reflection peak position. Results A fixed concentration 8% (v/v) of PEGDA and 4mg/mL of GO was chosen as the optimal concentration based on the balance between appropriate volume shrinkage and structure color. The FITC-dextran was uniformly encapsulated in the particles by using 0.2 wt% sodium alginate. The microcarriers shrank because of the photothermal response and the intrinsic fluorescence intensity of FITC-dextran in the microparticles gradually decreased at the same time, indicating drug release. With an increasing duration of NIR irradiation, the microparticles gradually shrank, the reflection peak shifted toward blue and the structural color changed from red to orange, yellow, green, cyan, and blue successively. The drug release quantity can be predicted by the structural color of microparticles. Conclusion The multicolor microparticles have great potential in drug delivery systems because of its vivid reporting color, excellent photothermal effect, and the good stimuli responsivity.

In this paper, a novel chaotic cultural-based particle swarm optimization algorithm (CCPSO) is proposed for constrained optimization problems by employing cultural-based particle swarm optimization (CPSO) algorithm and the notion of chaotic local search strategy. In the CCPSO, the shortcoming of cultural-based particle swarm optimization (CPSO) that it is easy to trap into local minimum be overcome, the chaotic local search strategy is introduced in the influence functions of cultural algorithm. Simulation results based on well-known constrained engineering design problems demonstrate the effectiveness, efficiency and robustness on initial populations of the proposed method.

Beidou navigation system (BDS) has been developed as an integrated system. The third BDS, BSD-3, will be capable of providing not only global positioning and navigation but also data communication. When the volume of data transmitted through BDS-3 continues to increase, BDS-3 will encounter network traffic congestion, unbalanced resource usage, or security attacks as terrestrial networks. The network traffic monitoring is essential for automatic management and safety assurance of BDS-3. A dynamic traffic detection method including traffic prediction by Long Short-Term Memory (LSTM) and a dynamically adjusting polling strategy is proposed to unevenly sample the traffic of each link. A distributed traffic detection architecture is designed for collection of the detected traffic and its related temporal and spatial information with low delay. A time-varying graph (TVG) model is introduced to represent the dynamic topology, the time-varying link, and its traffic. The BDS-3 network is simulated by STK. The WIDE dataset is used to simulate the traffic between the satellite and ground station. Simulation results show that the dynamic traffic detection method can follow the variation of the traffic of each link with uneven sampling. The detected traffic can be transmitted to the ground station in near real time through the distributed traffic detection architecture. The traffic and its related information are stored by using Neo4j in terms of the TVG model. The nodes, edges, and traffic of BDS-3 can be quickly queried through Neo4j. The presented dynamic traffic detection and representation schemes will support BDS-3 to establish automatic management and security system and develop business.