In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predi...In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.展开更多
Declaration of Competing Interest statements were not included in the published version of the following articles that appeared in previous issues of Journal of Automation and Intelligence.The appropriate Declaration ...Declaration of Competing Interest statements were not included in the published version of the following articles that appeared in previous issues of Journal of Automation and Intelligence.The appropriate Declaration of Competing Interest statements,provided by the Authors,are included below.1.“A survey on computationally efficient neural architecture search”[Journal of Automation and Intelligence,1(2022)100002].10.1016/j.jai.2022.100002。展开更多
The best tissue-engineered spinal cord grafts not only match the structural characteristics of the spinal cord but also allow the seed cells to grow and function in situ.Platelet-derived growth factor（PDGF） has been...The best tissue-engineered spinal cord grafts not only match the structural characteristics of the spinal cord but also allow the seed cells to grow and function in situ.Platelet-derived growth factor（PDGF） has been shown to promote the migration of bone marrow stromal cells;however,cytokines need to be released at a steady rate to maintain a stable concentration in vivo.Therefore,new methods are needed to maintain an optimal concentration of cytokines over an extended period of time to effectively promote seed cell localization,proliferation and differentiation.In the present study,a partition-type tubular scaffold matching the anatomical features of the thoracic 8–10 spinal cord of the rat was fabricated using chitosan and then subsequently loaded with chitosan-encapsulated PDGF-BB microspheres（PDGF-MSs）.The PDGF-MS-containing scaffold was then examined in vitro for sustained-release capacity,biocompatibility,and its effect on neural progenitor cells differentiated in vitro from multilineage-differentiating stress-enduring cells（MUSE-NPCs）.We found that pre-freezing for 2 hours at-20°C significantly increased the yield of partition-type tubular scaffolds,and 30 μL of 25% glutaraldehyde ensured optimal crosslinking of PDGF-MSs.The resulting PDGF-MSs cumulatively released 52% of the PDGF-BB at 4 weeks in vitro without burst release.The PDGF-MS-containing tubular scaffold showed suitable biocompatibility towards MUSE-NPCs and could promote the directional migration and growth of these cells.These findings indicate that the combination of a partition-type tubular scaffold,PDGF-MSs and MUSENPCs may be a promising model for the fabrication of tissue-engineered spinal cord grafts.展开更多
基金Supported by National Natural Science Foundation of China(Grant No.51805447)Natural Science Foundation of Jiangsu Higher Education of China(Grant No.22KJB460010)+2 种基金Jiangsu Provincial Innovation and Promotion Project of Forestry Science and Technology of China(Grant No.LYKJ[2023]06)Yangzhou Science and Technology Plan(City School Cooperation Project)of China(Grant No.YZ2022193)Cyan Blue Project of Yangzhou University of China。
文摘In the realm of engineering practice,various factors such as limited availability of measurement data and complex working conditions pose significant challenges to obtaining accurate load spectra.Thus,accurately predicting the fatigue life of structures becomes notably arduous.This paper proposed an approach to predict the fatigue life of structure based on the optimized load spectra,which is accurately estimated by an efficient hinging hyperplane neural network(EHH-NN)model.The construction of the EHH-NN model includes initial network generation and parameter optimization.Through the combination of working conditions design,multi-body dynamics analysis and structural static mechanics analysis,the simulated load spectra of the structure are obtained.The simulated load spectra are taken as the input variables for the optimized EHH-NN model,while the measurement load spectra are used as the output variables.The prediction results of case structure indicate that the optimized EHH-NN model can achieve the high-accuracy load spectra,in comparison with support vector machine(SVM),random forest(RF)model and back propagation(BP)neural network.The error rate between the prediction values and the measurement values of the optimized EHH-NN model is 4.61%.In the Cauchy-Lorentz distribution,the absolute error data of 92%with EHH-NN model appear in the intermediate range of±1.65%.Also,the fatigue life analysis is performed for the case structure,based on the accurately predicted load spectra.The fatigue life of the case structure is calculated based on the comparison between the measured and predicted load spectra,with an accuracy of 93.56%.This research proposes the optimized EHH-NN model can more accurately reflect the measurement load spectra,enabling precise calculation of fatigue life.Additionally,the optimized EHH-NN model provides reliability assessment for industrial engineering equipment.
文摘Declaration of Competing Interest statements were not included in the published version of the following articles that appeared in previous issues of Journal of Automation and Intelligence.The appropriate Declaration of Competing Interest statements,provided by the Authors,are included below.1.“A survey on computationally efficient neural architecture search”[Journal of Automation and Intelligence,1(2022)100002].10.1016/j.jai.2022.100002。
基金supported by the Natural Science Foundation of China,No.81501610,81350030the Priority Academic Program Development of Jiangsu Higher Education Institutes of China
文摘The best tissue-engineered spinal cord grafts not only match the structural characteristics of the spinal cord but also allow the seed cells to grow and function in situ.Platelet-derived growth factor（PDGF） has been shown to promote the migration of bone marrow stromal cells;however,cytokines need to be released at a steady rate to maintain a stable concentration in vivo.Therefore,new methods are needed to maintain an optimal concentration of cytokines over an extended period of time to effectively promote seed cell localization,proliferation and differentiation.In the present study,a partition-type tubular scaffold matching the anatomical features of the thoracic 8–10 spinal cord of the rat was fabricated using chitosan and then subsequently loaded with chitosan-encapsulated PDGF-BB microspheres（PDGF-MSs）.The PDGF-MS-containing scaffold was then examined in vitro for sustained-release capacity,biocompatibility,and its effect on neural progenitor cells differentiated in vitro from multilineage-differentiating stress-enduring cells（MUSE-NPCs）.We found that pre-freezing for 2 hours at-20°C significantly increased the yield of partition-type tubular scaffolds,and 30 μL of 25% glutaraldehyde ensured optimal crosslinking of PDGF-MSs.The resulting PDGF-MSs cumulatively released 52% of the PDGF-BB at 4 weeks in vitro without burst release.The PDGF-MS-containing tubular scaffold showed suitable biocompatibility towards MUSE-NPCs and could promote the directional migration and growth of these cells.These findings indicate that the combination of a partition-type tubular scaffold,PDGF-MSs and MUSENPCs may be a promising model for the fabrication of tissue-engineered spinal cord grafts.
