Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve th...For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps: fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.展开更多
Under special conditions on data set and underlying distribution, the limit of finite sample breakdown point of Tukey's halfspace median (1) has been obtained in the literature. In this paper, we establish the resu...Under special conditions on data set and underlying distribution, the limit of finite sample breakdown point of Tukey's halfspace median (1) has been obtained in the literature. In this paper, we establish the result under weaker assumptions imposed on underlying distribution (weak smoothness) and on data set (not necessary in general position). The refined representation of Tukey's sample depth regions for data set not necessary in general position is also obtained, as a by-product of our derivation.展开更多
Background Mycophenolic acid (MPA) as an anti-proliferative immune-suppressive agent is used in the majority of immunosuppressive regimens in solid organ transplantation. This study aimed to investigate the pharmaco...Background Mycophenolic acid (MPA) as an anti-proliferative immune-suppressive agent is used in the majority of immunosuppressive regimens in solid organ transplantation. This study aimed to investigate the pharmacokinetic (PK) characteristics of enteric-coated mycophenolate sodium (EC-MPS) and area under the curve (AUC) from 0 to 12 hours with limited sampling strategies (LSSs) in Chinese renal transplant recipients. Methods This study was conducted in 10 Chinese renal transplant patients receiving living donor and treated with EC-MPS, cyclosporine, and corticosteroids. MPA concentrations were measured by enzyme multiplied immunoassay technique (EMIT). Whole 12-hour PK profiles were obtained on Day 4 after operation. LSSs with jackknife technique, multiple stepwise regression analysis, and Bland-Altman analysis were developed to estimate MPAAUC. Results The mean maximum plasma concentration, the mean time for it to reach peak (Tmax), and the mean MPA AUC were (11.38±2.49) mg/L, (4.85±3.32) hours, and (63.19±13.54) mg.h.L1, respectively. Among the 10 profiles, MPA AUC of four patients was significantly higher than that of the other six patients, and the corresponding Tmax was significantly longer than that of the other six patients. No patient exhibited a second peak caused by enterohepatic recirculation. The best models were as follows: 27.46+0.94C3+3.24C8+2.81C10 (f2=0.972), which was used to predict AUC of fast metabolizer with a mean prediction error (MPE) of -0.21% and a mean absolute prediction error (MAE) of 2.59%; 36.65+3.08Ce+5.30C10-4.04C12 (r2=0.992), which was used to predict AUC of slow metabolizer with a MPE of 0.58% and a MAE of 1.95%. Conclusions The PKs of EC-MPS had a high variability among Chinese renal transplant recipients. The preliminary PK data indicated the existence of slow and fast metabolizer. These findings may be associated with the enterohepatic rec.irculation.展开更多
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
基金supported by the National Natural Science Foundation of China(61304218)
文摘For high reliability and long life systems, system pass/fail data are often rare. Integrating lower-level data, such as data drawn from the subsystem or component pass/fail testing,the Bayesian analysis can improve the precision of the system reliability assessment. If the multi-level pass/fail data are overlapping,one challenging problem for the Bayesian analysis is to develop a likelihood function. Since the computation burden of the existing methods makes them infeasible for multi-component systems, this paper proposes an improved Bayesian approach for the system reliability assessment in light of overlapping data. This approach includes three steps: fristly searching for feasible paths based on the binary decision diagram, then screening feasible points based on space partition and constraint decomposition, and finally simplifying the likelihood function. An example of a satellite rolling control system demonstrates the feasibility and the efficiency of the proposed approach.
基金Supported by NSF of China(Grant Nos.11601197,11461029 and 61563018)Ministry of Education Humanity Social Science Research Project of China(Grant No.15JYC910002)+2 种基金China Postdoctoral Science Foundation Funded Project(Grant Nos.2016M600511 and 2017T100475)NSF of Jiangxi Province(Grant Nos.20171ACB21030,20161BAB201024 and 20161ACB20009)the Key Science Fund Project of Jiangxi Provincial Education Department(Grant Nos.GJJ150439,KJLD13033 and KJLD14034)
文摘Under special conditions on data set and underlying distribution, the limit of finite sample breakdown point of Tukey's halfspace median (1) has been obtained in the literature. In this paper, we establish the result under weaker assumptions imposed on underlying distribution (weak smoothness) and on data set (not necessary in general position). The refined representation of Tukey's sample depth regions for data set not necessary in general position is also obtained, as a by-product of our derivation.
文摘Background Mycophenolic acid (MPA) as an anti-proliferative immune-suppressive agent is used in the majority of immunosuppressive regimens in solid organ transplantation. This study aimed to investigate the pharmacokinetic (PK) characteristics of enteric-coated mycophenolate sodium (EC-MPS) and area under the curve (AUC) from 0 to 12 hours with limited sampling strategies (LSSs) in Chinese renal transplant recipients. Methods This study was conducted in 10 Chinese renal transplant patients receiving living donor and treated with EC-MPS, cyclosporine, and corticosteroids. MPA concentrations were measured by enzyme multiplied immunoassay technique (EMIT). Whole 12-hour PK profiles were obtained on Day 4 after operation. LSSs with jackknife technique, multiple stepwise regression analysis, and Bland-Altman analysis were developed to estimate MPAAUC. Results The mean maximum plasma concentration, the mean time for it to reach peak (Tmax), and the mean MPA AUC were (11.38±2.49) mg/L, (4.85±3.32) hours, and (63.19±13.54) mg.h.L1, respectively. Among the 10 profiles, MPA AUC of four patients was significantly higher than that of the other six patients, and the corresponding Tmax was significantly longer than that of the other six patients. No patient exhibited a second peak caused by enterohepatic recirculation. The best models were as follows: 27.46+0.94C3+3.24C8+2.81C10 (f2=0.972), which was used to predict AUC of fast metabolizer with a mean prediction error (MPE) of -0.21% and a mean absolute prediction error (MAE) of 2.59%; 36.65+3.08Ce+5.30C10-4.04C12 (r2=0.992), which was used to predict AUC of slow metabolizer with a MPE of 0.58% and a MAE of 1.95%. Conclusions The PKs of EC-MPS had a high variability among Chinese renal transplant recipients. The preliminary PK data indicated the existence of slow and fast metabolizer. These findings may be associated with the enterohepatic rec.irculation.