![]() The biomass-derived HPC shows a three-dimensionally interconnected morphology which can offer a continuous pathway for ionic transport. The bio-oil pyrolyzed from the rubber sawdust, followed by the polymerization reaction to form resole phenolic resin, can be used as a carbon source to prepare HPC. In this article, hierarchical porous carbon (HPC) with high surface area of 1604.9 m2/g is prepared by the pyrolysis of rubberwood sawdust using CaCO3 as a hard template. Besides, the Ni(OH)2/CuO composites also reveal remarkable biocompatibility and strong photocatalytic activity in the degradation of antibiotics such as ciprofloxacin (CIP) and tetracycline (TC) and inactivation of Escherichia coli (E. The results reveal that the Ni(OH)2/CuO (1:1) heterostructures show the best photocatalytic efficiency, which is 2.18 and 6.13 times higher than that of pure Ni(OH)2 and CuO, respectively. The photocatalytic capability of the heterostructured composites with different Ni(OH)2/CuO molar ratios is evaluated by the photodegradation of methylene blue under visible light illumination. The construction of the heterojunction also improves the photogenerated carrier transport and inhibits the electron-hole separation due to the enhanced absorbance and the well alignment of the energy band at the Ni(OH)2/CuO interface. ![]() The results indicate that the Ni(OH)2/CuO heterostructured composite exhibits a strong absorption in the UV and Vis regions. In this study, the Ni(OH)2/CuO heterostructured photocatalysts have been prepared via microwave (MW) hydrothermal method. This attempt indicated that ML application may benefit in the early identification of SAP. Conclusion: The ML models could effectively predict SAP in sICH populations, and our novel ensemble model demonstrated reliable robust performance outcomes despite the populational and algorithmic differences. The ESVM method combining the other six methods had moderate but robust abilities in both cross-validation and external validation and achieved an AUC of 0.843 (95% CI: 0.784-0.902) in external validation. The internal and cross-validations revealed the superior and robust performance of the GNB model with the highest AUC value (0.861, 95% CI: 0.793-0.930), while the LR model had the highest AUC value (0.867, 95% CI: 0.812-0.923) in external validation. Six independent variables for SAP were identified and selected for ML prediction model derivations and validations. Results: A total of 468 individuals with sICH were included in this work. The accuracy, sensitivity, specificity, and area under the curve (AUC) were adopted to evaluate the predictive value of each model with internal/cross-/external validations. Univariate and multivariate analyses were used for variable filtering, and logistic regression (LR), Gaussian naïve Bayes (GNB), random forest (RF), K-nearest neighbor (KNN), support vector machine (SVM), extreme gradient boosting (XGB), and ensemble soft voting model (ESVM) were adopted for ML model derivations. The primary outcome was SAP during hospitalization. Methods: The data of eligible supratentorial sICH individuals were extracted from the Risa-MIS-ICH database and split into training, internal validation, and external validation datasets. We aimed to derive and validate novel supervised machine learning (ML) models to predict SAP events in supratentorial sICH populations. None of the previously developed predictive scoring systems are widely accepted. Accurate prediction and early intervention of SAP are associated with prognosis. Background: Stroke-associated pneumonia (SAP) contributes to high mortality rates in spontaneous intracerebral hemorrhage (sICH) populations.
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