首页|Zhejiang University of Technology Reports Findings in Tissue Engineering [Hybrid machine learning model based predictions for properties of poly(2-hydroxy ethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacteri al ...]
Zhejiang University of Technology Reports Findings in Tissue Engineering [Hybrid machine learning model based predictions for properties of poly(2-hydroxy ethyl methacrylate)-poly(vinyl alcohol) composite cryogels embedded with bacteri al ...]
扫码查看
点击上方二维码区域，可以放大扫码查看
原文链接
NETL
NSTL
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – New research on Biomedical Engineering - Tissue Engineering is the subject of a report. According to news reporting or iginating from Hangzhou, People’s Republic of China, by NewsRx correspondents, r esearch stated, “Supermacroporous composite cryogels with enhanced adjustable fu nctionality have received extensive interest in bioseparation, tissue engineerin g, and drug delivery. However, the variations in their components significantly impactfinal properties.” Our news editors obtained a quote from the research from the Zhejiang University of Technology, “This study presents a two-step hybrid machine learning approach for predicting the properties of innovative poly(2-hydroxyethyl methacrylate)-p oly(vinyl alcohol) composite cryogels embedded with bacterial cellulose (pHEMA-P VA-BC) based on their compositions. By considering the ratios of HEMA (1.0-22.0 wt%), PVA (0.2-4.0 wt%), poly(ethylene glycol) diacryl ate (1.0-4.5 wt%), BC (0.1-1.5 wt%), and water (68.0-9 6.0 wt%) as investigational variables, overlay sampling uniform des ign (OSUD) was employed to construct a high-quality dataset for model developmen t. The random forest (RF) model was used to classify the preparation conditions. Then four models of artificial neural network, RF, gradient boosted regression trees (GBRT), and XGBoost were developed to predict the basic properties of the composite cryogels. The results showed that the RF model achieved an accurate th ree-class classification of preparation conditions. Among the four models, the G BRT model exhibited the best predictive performance of the basic properties, wit h the mean absolute percentage error of 16.04 %, 0.85 % , and 2.44 % for permeability, effective porosity, and height of t heoretical plate (1.0 cm/min), respectively. Characterization results of the rep resentative pHEMA-PVA-BC composite cryogel showed an effective porosity of 81.01 %, a permeability of 1.20 x 10 m, and a range of height of theoret ical plate between 0.40-0.49 cm at flow velocities of 0.5-3.0 cm/min. These indi cate that the pHEMA-PVA-BC cryogel was an excellent material with supermacropore s, low flow resistance and high mass transfer efficiency. Furthermore, the model output demonstrates that the alteration of the proportions of PVA (0.2-3.5 wt% ) and BC (0.1-1.5 wt%) components in composite cryogels resulted in significant changes in the material basic properties.”
HangzhouPeople’s Republic of ChinaAs iaAcrylatesBioengineeringBiomedical EngineeringBiomedicineBiotechnolog yCyborgsEmerging TechnologiesHealth and MedicineMachine LearningMethac rylatesTissue Engineering
NETL
NSTL
