On the basis of density functional theory calculations with generalized gradient approximation, we have investigated in detail the cooperative effects of uniaxial strain and degenerate perturbation on manipulating bandgap in silicene. The uniaxial strain would split π bands into π a and π z bands making Dirac cone to move. Then, the hexagonal antidot would split π a (π z ) bands into π a1 and π a2 (π z1 and π z2 ) bands accounting for the bandgap opening in the superlattices with Dirac cone being folded to Γ point, which is a different mechanism as compared to the sublattice equivalence breaking. The energy interval between the split π a and π z bands could be tuned to switch bandgap on/off, suggesting a reversible switch between the high charge carrier velocity properties of massless Fermions attributed to linear energy dispersion relation around Dirac point and the high on/off properties associated with sizable bandgap. Even more, the gap width could be continuously tuned by manipulating strain, showing fascinating application potentials.
Using the density functional theory with generalized gradient approximation, we have studied in detail the cooperative effects of degenerate perturbation and uniaxial strain on bandgap opening in graphene. The uniaxial strain could split π bands into πa and πz bands with an energy interval Es to move the Dirac cone. The inversion symmetry preserved antidot would then further split the πa (πz) bands into πa1 (πz1) and πa2 (πz2) bands with an energy interval Ed, which accounts for the bandgap opening in a kind of superlattices with Dirac cone being folded to Γ point. However, such antidot would not affect the semimetal nature of the other superlattices, showing a novel mechanism for bandstructure engineering as compared to the sublattice-equivalence breaking. For a superlattice with bandgap of ~Ed opened at Γ point, the Es could be increased by strengthening strain to close the bandgap, suggesting a reversible switch between the high velocity properties of massless Fermions attributed to the linear dispersion relation around Dirac cone and the high on/off ratio properties associated with the sizable bandgap. Moreover, the gap width actually could be continuously tuned by controlling the strain, showing attractive application potentials.
Background Acute kidney injury (AKI) after coronary artery bypass grafting (CABG) surgery is associated with poor outcomes. The objective of this study was to apply a new machine learning (ML) method to establish prediction models of AKI after CABG. Methods Totally 2780 patients from two medical centers in East China who underwent primary isolated CABG were enrolled. Then the dataset was randomly divided for model training (80%) and model testing (20%). Four ML models based on LightGBM, Support vector machine (SVM), Softmax and random forest (RF) algorithms respectively were established on Python. A total of 2051 patients from two other medical centers were assigned to an external validation group to verify the performances of the ML prediction models. The models were evaluated using the area under the receiver operating characteristics curve (AUC), Hosmer-Lemeshow goodness-of-fit statistic, Bland-Altman plots, and decision curve analysis. The outcome of the LightGBM model was interpreted using SHapley Additive exPlanations (SHAP). Results The incidence of postoperative AKI in the modeling group was 13.4%. Similarly, the incidence of postoperative AKI of the two medical centers in the external validation group was 8.2% and 13.6% respectively. LightGBM performed the best in predicting, with an AUC of 0.8027 in internal validation group and 0.8798 and 0.7801 in the external validation group. The SHAP revealed the top 20 predictors of postoperative AKI ranked according to the importance, and the top three features on prediction were the serum creatinine in the first 24h after operation, the last preoperative Scr level, and body surface area. Conclusion This study provides a LightGBM predictive model that can make accurate predictions for AKI after CABG surgery. This ML model shows good predictive ability in both internal and external validation. It can help cardiac surgeons identify high-risk patients who may experience AKI after CABG surgery.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.