{"title":"Development of computational model for description of magnetic drug targeting for cancer therapy: Modeling and validation","authors":"","doi":"10.1016/j.apt.2024.104577","DOIUrl":null,"url":null,"abstract":"<div><p>Computation of blood flow containing ferrofluid would be useful for analysis of drug carrier motion for cancer therapy. A thorough understanding nanoparticles behavior is challenging and needs to be addressed by developing sophisticated theoretical methods. A hybrid modeling for analysis of blood motion containing ferrofluid was implemented via mechanistic modeling combined with artificial intelligence. The system of analysis also considered external magnetic force for control of nanoparticles motion in the blood vessel. This research focuses on the analysis of velocity field based on a dataset consisting of variables x(m), y(m), and U(m/s). The objective is to develop accurate predictive models using Gaussian Process Regression (GPR), Kernel ridge regression (KRR), and Polynomial Regression (PR). The Dragonfly Algorithm (DA) was employed for hyper-parameter optimizing. The results demonstrate the performance of these models in relation to R<sup>2</sup> score, RMSE, and MAE. The GPR model achieves the highest score of 0.99603 in terms of R<sup>2</sup>, indicating excellent predictive accuracy. It also exhibits the lowest RMSE of 7.1443x10^-3 and MAE of 5.35436 x10^-3, suggesting minimal deviations between the expected and predicted velocity values. The PR model also has a significant performance with an R<sup>2</sup> test score of 0.99348, RMSE of 9.1376 x10^-3, and MAE of 7.22828 x10^-3. The aforementioned results underscore the effectiveness of these models in accurately forecasting velocity based on the provided input variables.</p></div>","PeriodicalId":7232,"journal":{"name":"Advanced Powder Technology","volume":null,"pages":null},"PeriodicalIF":4.2000,"publicationDate":"2024-07-30","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Advanced Powder Technology","FirstCategoryId":"5","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S092188312400253X","RegionNum":2,"RegionCategory":"工程技术","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q2","JCRName":"ENGINEERING, CHEMICAL","Score":null,"Total":0}
引用次数: 0
Abstract
Computation of blood flow containing ferrofluid would be useful for analysis of drug carrier motion for cancer therapy. A thorough understanding nanoparticles behavior is challenging and needs to be addressed by developing sophisticated theoretical methods. A hybrid modeling for analysis of blood motion containing ferrofluid was implemented via mechanistic modeling combined with artificial intelligence. The system of analysis also considered external magnetic force for control of nanoparticles motion in the blood vessel. This research focuses on the analysis of velocity field based on a dataset consisting of variables x(m), y(m), and U(m/s). The objective is to develop accurate predictive models using Gaussian Process Regression (GPR), Kernel ridge regression (KRR), and Polynomial Regression (PR). The Dragonfly Algorithm (DA) was employed for hyper-parameter optimizing. The results demonstrate the performance of these models in relation to R2 score, RMSE, and MAE. The GPR model achieves the highest score of 0.99603 in terms of R2, indicating excellent predictive accuracy. It also exhibits the lowest RMSE of 7.1443x10^-3 and MAE of 5.35436 x10^-3, suggesting minimal deviations between the expected and predicted velocity values. The PR model also has a significant performance with an R2 test score of 0.99348, RMSE of 9.1376 x10^-3, and MAE of 7.22828 x10^-3. The aforementioned results underscore the effectiveness of these models in accurately forecasting velocity based on the provided input variables.
期刊介绍:
The aim of Advanced Powder Technology is to meet the demand for an international journal that integrates all aspects of science and technology research on powder and particulate materials. The journal fulfills this purpose by publishing original research papers, rapid communications, reviews, and translated articles by prominent researchers worldwide.
The editorial work of Advanced Powder Technology, which was founded as the International Journal of the Society of Powder Technology, Japan, is now shared by distinguished board members, who operate in a unique framework designed to respond to the increasing global demand for articles on not only powder and particles, but also on various materials produced from them.
Advanced Powder Technology covers various areas, but a discussion of powder and particles is required in articles. Topics include: Production of powder and particulate materials in gases and liquids(nanoparticles, fine ceramics, pharmaceuticals, novel functional materials, etc.); Aerosol and colloidal processing; Powder and particle characterization; Dynamics and phenomena; Calculation and simulation (CFD, DEM, Monte Carlo method, population balance, etc.); Measurement and control of powder processes; Particle modification; Comminution; Powder handling and operations (storage, transport, granulation, separation, fluidization, etc.)