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Intelligent Optimization of the Film-to-Fiber Ratio of a Degradable Braided Bicomponent Ureteral Stent

机译:可降解编织双组分输尿管支架膜与纤维比的智能优化

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摘要

A hierarchical support vector regression (SVR) model (HSVRM) was employed to correlate the compositions and mechanical properties of bicomponent stents composed of poly(lactic-co-glycolic acid) (PGLA) film and poly(glycolic acid) (PGA) fibers for urethral repair for the first time. PGLA film and PGA fibers could provide ureteral stents with good compressive and tensile properties, respectively. In bicomponent stents, high film content led to high stiffness, while high fiber content resulted in poor compressional properties. To simplify the procedures to optimize the ratio of PGLA film and PGA fiber in the stents, a hierarchical support vector regression model (HSVRM) and particle swarm optimization (PSO) algorithm were used to construct relationships between the film-to-fiber weight ratio and the measured compressional/tensile properties of the stents. The experimental data and simulated data fit well, proving that the HSVRM could closely reflect the relationship between the component ratio and performance properties of the ureteral stents.
机译:采用分级支持向量回归(SVR)模型(HSVRM)来关联由聚乳酸-乙醇酸(PGLA)薄膜和聚乙醇酸(PGA)纤维组成的双组分支架的组成和力学性能,以用于首次进行尿道修复。 PGLA薄膜和PGA纤维可以分别为输尿管支架提供良好的压缩和拉伸性能。在双组分支架中,高膜含量导致高刚度,而高纤维含量导致差的压缩性能。为了简化优化支架中PGLA膜和PGA纤维比例的程序,使用了层次支持向量回归模型(HSVRM)和粒子群优化(PSO)算法来构建膜与纤维重量比之间的关系。测得的支架的压缩/拉伸特性。实验数据和模拟数据吻合良好,证明HSVRM可以紧密反映输尿管支架的成分比与性能之间的关系。

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