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Jian He,Xulin Hu,Luyao Xing,Dongliang Chen,Lijun Peng,Gaofeng Liang,Chengdong Xiong,Xiangchun Zhang,Lifang Zhang 한국공업화학회 2021 Journal of Industrial and Engineering Chemistry Vol.99 No.-
Tissue-engineered scaffolds acted as active natural extracellular matrices that allowed for cellattachment, migration, proliferation, and differentiation. To enhance the drug loading, degradationcontrol, and mechanical features of tissue-engineered scaffolds used in bone regeneration applications,we synthesized novel poly(trimethylene carbonate) (PTMC)/modified HA nanoparticles (KHA NPs,modified by silane coupling agent)/vancomycin hydrochloride (VH) porous microspheres scaffold. Thefabricated PTMC/KHA/VH scaffold has unique surface corrosion degradation, excellent surface properties,and high cytocompatibility. The novel PTMC/KHA/VH scaffold was characterized by 42% porosity,100 mmpore size, and 208.2 MPa compressive modulus. After the treatment of a rat femur defect for 4, 8, and 12weeks, the surface corrosion biodegraded of the PTMC/KHA/VH scaffold remarkably decreasedinflammation response and promoted bone regeneration, suggesting its use as a bioactive structurein bone tissue engineering.
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ACA: Automatic search strategy for radioactive source
Huo Jianwen,Hu Xulin,Wang Junling,Hu Li 한국원자력학회 2023 Nuclear Engineering and Technology Vol.55 No.8
Nowadays, mobile robots have been used to search for uncontrolled radioactive source in indoor environments to avoid radiation exposure for technicians. However, in the indoor environments, especially in the presence of obstacles, how to make the robots with limited sensing capabilities automatically search for the radioactive source remains a major challenge. Also, the source search efficiency of robots needs to be further improved to meet practical scenarios such as limited exploration time. This paper proposes an automatic source search strategy, abbreviated as ACA: the location of source is estimated by a convolutional neural network (CNN), and the path is planned by the A-star algorithm. First, the search area is represented as an occupancy grid map. Then, the radiation dose distribution of the radioactive source in the occupancy grid map is obtained by Monte Carlo (MC) method simulation, and multiple sets of radiation data are collected through the eight neighborhood self-avoiding random walk (ENSAW) algorithm as the radiation data set. Further, the radiation data set is fed into the designed CNN architecture to train the network model in advance. When the searcher enters the search area where the radioactive source exists, the location of source is estimated by the network model and the search path is planned by the A-star algorithm, and this process is iterated continuously until the searcher reaches the location of radioactive source. The experimental results show that the average number of radiometric measurements and the average number of moving steps of the ACA algorithm are only 2.1% and 33.2% of those of the gradient search (GS) algorithm in the indoor environment without obstacles. In the indoor environment shielded by concrete walls, the GS algorithm fails to search for the source, while the ACA algorithm successfully searches for the source with fewer moving steps and sparse radiometric data
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