In this study,C_(f)/SiC composites with excellent mechanical and thermal properties were prepared by combining binder jetting(BJ)additive manufacturing with liquid silicon infiltration(LSI)process.The introduction of ...In this study,C_(f)/SiC composites with excellent mechanical and thermal properties were prepared by combining binder jetting(BJ)additive manufacturing with liquid silicon infiltration(LSI)process.The introduction of C_(f)into the C_(f)/SiC mixed powder reduced its spreading ability,which reduced the density,strength,and precision of the C_(f)/SiC green parts.However,phenolic resin infiltration and pyrolysis(PRIP)treatment compensated for the decrease in the density of the green parts resulting from the introduction of C_(f).By optimizing the number of PRIP cycles to increase the pyrolytic carbon(PyC)content in the carbonized parts,the C_(f)in the green parts successfully prevented the reaction with molten Si in the LSI and played an important role in strengthening and toughening the composites.The flexural strength,fracture toughness,and thermal conductivity of the C_(f)/SiC composites reached the maximum values of 316±16 MPa,4.81±0.12 MPa·m^(1/2),and 140 W/m·K,respectively.This study presents future opportunities for the cost-effective and efficient industrial manufacturing of C_(f)/SiC complex structures.展开更多
Background:China is rapidly aging,increasing the burden on families,society,and public health services.The health of elderly individuals tends to deteriorate with age,and chronic conditions like frailty become more pr...Background:China is rapidly aging,increasing the burden on families,society,and public health services.The health of elderly individuals tends to deteriorate with age,and chronic conditions like frailty become more prevalent,driving up the use of healthcare services.Early screening and intervention for frailty are crucial in managing this demographic shift.While tools like the Fried Frailty Phenotype and Frailty Index assess frailty in communities,they are resource-intensive and only indicate frailty status without predicting risk or providing management recommendations.This study aims to develop a risk prediction model for frailty using real-world data,which can support the early detection of high-risk individuals in community settings.Objectives:To analyze the prevalence of frailty and its influencing factors in community-dwelling elderly,to construct a frailty risk prediction model and develop a nomogram,and to validate the model and assess its clinical utility.Methods:A cross-sectional survey of 420 elderly individuals in a Shanghai community health center was conducted(August 2022–March 2023).Data from various assessment tools were used to build a frailty prediction model through logistic regression,with validation conducted on 180 additional participants.The model’s predictive performance was evaluated using the ROC,AUC,calibration curves,and decision curve analysis(DCA).Results:The frailty prevalence was 7.4%.Independent risk factors included social support,malnutrition,fatigue,sarcopenia,reduced grip strength,and sleep duration.The prediction model achieved an AUC of 0.968 in the training set and 0.939 in the validation set,indicating high discrimination and calibration.DCA confirmed the model’s clinical utility.Conclusion:This study highlights a frailty prevalence rate of 7.4%among elderly individuals in Shanghai,with key risk factors identified.The validated frailty risk prediction model provides accurate and clinically effective frailty risk assessment,supporting targeted early interventions to prevent frailty in community settings.展开更多
基金supported by National Defense Basic Scientific Research Program of China(Grant No.JCKY2022213C008)Fundamental Research Funds for the Central Universities of China(Grant No.YCJJ20230353)。
文摘In this study,C_(f)/SiC composites with excellent mechanical and thermal properties were prepared by combining binder jetting(BJ)additive manufacturing with liquid silicon infiltration(LSI)process.The introduction of C_(f)into the C_(f)/SiC mixed powder reduced its spreading ability,which reduced the density,strength,and precision of the C_(f)/SiC green parts.However,phenolic resin infiltration and pyrolysis(PRIP)treatment compensated for the decrease in the density of the green parts resulting from the introduction of C_(f).By optimizing the number of PRIP cycles to increase the pyrolytic carbon(PyC)content in the carbonized parts,the C_(f)in the green parts successfully prevented the reaction with molten Si in the LSI and played an important role in strengthening and toughening the composites.The flexural strength,fracture toughness,and thermal conductivity of the C_(f)/SiC composites reached the maximum values of 316±16 MPa,4.81±0.12 MPa·m^(1/2),and 140 W/m·K,respectively.This study presents future opportunities for the cost-effective and efficient industrial manufacturing of C_(f)/SiC complex structures.
文摘Background:China is rapidly aging,increasing the burden on families,society,and public health services.The health of elderly individuals tends to deteriorate with age,and chronic conditions like frailty become more prevalent,driving up the use of healthcare services.Early screening and intervention for frailty are crucial in managing this demographic shift.While tools like the Fried Frailty Phenotype and Frailty Index assess frailty in communities,they are resource-intensive and only indicate frailty status without predicting risk or providing management recommendations.This study aims to develop a risk prediction model for frailty using real-world data,which can support the early detection of high-risk individuals in community settings.Objectives:To analyze the prevalence of frailty and its influencing factors in community-dwelling elderly,to construct a frailty risk prediction model and develop a nomogram,and to validate the model and assess its clinical utility.Methods:A cross-sectional survey of 420 elderly individuals in a Shanghai community health center was conducted(August 2022–March 2023).Data from various assessment tools were used to build a frailty prediction model through logistic regression,with validation conducted on 180 additional participants.The model’s predictive performance was evaluated using the ROC,AUC,calibration curves,and decision curve analysis(DCA).Results:The frailty prevalence was 7.4%.Independent risk factors included social support,malnutrition,fatigue,sarcopenia,reduced grip strength,and sleep duration.The prediction model achieved an AUC of 0.968 in the training set and 0.939 in the validation set,indicating high discrimination and calibration.DCA confirmed the model’s clinical utility.Conclusion:This study highlights a frailty prevalence rate of 7.4%among elderly individuals in Shanghai,with key risk factors identified.The validated frailty risk prediction model provides accurate and clinically effective frailty risk assessment,supporting targeted early interventions to prevent frailty in community settings.
