The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))an...The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.展开更多
The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate...The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.展开更多
[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensem...[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.展开更多
Accurate prediction of slag compositions in converter is essential for optimizing steelmaking efficiency and improving resource utilization.A novel stacking deep network was established to enhance the accuracy of slag...Accurate prediction of slag compositions in converter is essential for optimizing steelmaking efficiency and improving resource utilization.A novel stacking deep network was established to enhance the accuracy of slag compositions prediction in converter.The established network was designed to incorporate deep learning as part of a collection of multiple base models,combined with tree-based models.This network introduced more complex decision boundaries to the axis-aligned geometry,thereby improving model diversity.This network differs from existing ensemble methods by using a first layer to integrate multiple base learners for diverse features capture,while the second layer’s skip connection mechanism reuses input features to address covariate shift and improve model expressiveness and stability.In addition,this network introduced an innovative stacking layer design,replacing the standard linear regression model with more sophisticated tree-based models and deep learning networks.This enhancement allowed the stacking layer not only to weight the outputs of the base models but also to learn deeper patterns and feature interactions.To improve the network’s generalization,K-fold bagging was employed to minimize the risk of overfitting.Feature importance analysis further underscores the established network’s ability to identify the key factors influencing slag compositions,offering valuable insights for optimizing the steelmaking process.Experimental results demonstrated that the established network significantly outperforms existing methods in predicting slag compositions in converter,with mean squared errors of 4.33,2.80,3.39,and 0.49 for CaO,SiO_(2),MgO and FeO,respectively,highlighting its robust generalization capability.The established network approach offers an effective solution for predicting slag compositions in converters,thereby overcoming the limitation of delayed slag detection.展开更多
文摘董志塬地区位于黄土高原中心地带,滑坡灾害频发,亟需明确滑坡易发性分区,以支持该区域滑坡隐患的科学防控。因此,本文以董志塬为研究区,选取高程、坡向和NDVI等12个影响因素作为评价因子,基于频率比(frequency ratio,FR)模型,结合随机森林(random forest,RF)与人工神经网络(artificial neural network,ANN)模型开展滑坡静态易发性评价,并分析各因子对评价精度的贡献。结果表明,FRRF和FR-ANN模型的曲线下面积(area under the curve,AUC)值分别为0.922和0.918,表明FR-RF模型在董志塬滑坡易发性评价中的精度更高。坡度、坡向和道路密度对滑坡易发性的贡献率分别为16.7%、15.3%和1.4%。为克服地形复杂和数据更新滞后的问题,本文将FR-RF模型的易发性结果与InSAR Stacking结果相结合,将静态滑坡易发性评价精度由6.9%提升到8.1%。动态易发性结果表明,董志塬滑坡高易发区主要分布于河流沿岸,占总面积的6.5%,该区域的滑坡数量占总滑坡数的23.6%,滑坡密度15.7个/km^(2)。低易发区主要位于远离河流的中部区域,占总面积的81.7%,滑坡数量占总滑坡数的57.8%,滑坡密度4.7个/km^(2)。本研究通过融合InSAR Stacking方法,解决了静态滑坡易发性评价数据更新滞后问题,减少了假阴性错误,为传统滑坡易发性评价赋予了时效性,可以实现董志塬滑坡易发性动态评价,为灾害防治提供了重要数据支持。
文摘The electrocatalytic reduction of nitric oxide for ammonia synthesis(NORR)is a key green energy conversion technology.Its efficiency relies on high-performance electrocatalysts to enhance both ammonia yield(Y_(NH3))and Faradaic efficiency(F_(NH3)).However,conventional experimental methods for screening high-activity NORR catalysts often entail high resource consumption and time costs.Machine learning combined with SHAP feature analysis was employed to establish a stacked ensemble model that integrates multiple algorithms,to allow for a systematic investigation of the key descriptors governing NORR performance based on an experimental dataset.Evaluation of eight model algorithms revealed that the Stacked-SVR model achieved an R^(2)of 0.9223 and an RMSE of 0.0608 for predicting on the test set,whereas the Stacked-RF model achieved an R^(2)of 0.9042 and an RMSE of 0.0900 for predicting.The stacked ensemble model integrates the strengths of individual algorithms and demonstrates strong NORR prediction performance while avoiding overfitting.SHAP feature analysis results revealed that the Cu content in the catalyst composition has the most significant impact on catalytic performance.Moreover,the combination of the wet chemical reduction synthesis,a carbon fiber(CF)conductive substrate,and HCl electrolyte is more favorable for enhancing catalytic activity.Additionally,moderately lowering the working potential,controlling the electrolyte volume at low to medium levels,reducing catalyst loading,and increasing electrolyte concentration were found to synergistically enhance both and.
基金the National Natural Science Foundation of China(Grant No.42272312)Ningbo Youth Science and Technology Innovation Talent Project(Grant No.2024QL057)the Zhejiang Provincial Xinmiao Talents Program(Grant No.2024R405B093).
文摘The soil-water retention curve(SWRC)plays a pivotal role in understanding water movement across numerous geological engineering applications.Despite significant advancements in theoretical modeling approaches,accurate prediction of SWRCs remains challenging due to the inherently sparse and incomplete nature of site-specific data.This study compiled a comprehensive dataset of SWRCs spanning a wide suction range from various published literature sources.Based on this dataset,multiple machine learning(ML)algorithms were employed to predict SWRCs.The performance of each algorithm was evaluated and ranked using four statistical indicators that quantify simulation accuracy.Feature importance analysis was subsequently conducted to reduce dimensionality by eliminating weakly correlated variables,thereby enhancing both model adaptability and computational efficiency.Following dimensionality reduction,a base learner pool was constructed and integrated through stacked generalization to create a multi-algorithm ensemble model.The proposed stacked model demonstrated robust performance in simulating SWRCs across diverse soil types,using only basic physical properties as inputs,achieving accuracy comparable to or marginally superior to the LightGBM model.The principal advantage of the stacked approach lies in its substantially improved accuracy within high suction ranges,effectively overcoming the limitations observed in LightGBM and enhancing the estimation under these conditions.This study provides valuable insights for researchers evaluating SWRCs through ML algorithms and demonstrates the potential of ensemble techniques in geotechnical prediction tasks.
文摘[Objectives]This study was conducted to achieve rapid and accurate detection of protein content in rice with a particle size of 1.0 mm.[Methods]A multi-model fusion strategy was proposed on the basis of Stacking ensemble learning.A base learner pool was constructed,containing Partial Least Squares(PLS),Support Vector Machine(SVM),Deep Extreme Learning Machine(DELM),Random Forest(RF),Gradient Boosting Decision Tree(GBDT),and Multilayer Perceptron(MLP).PLS,DELM,and Linear Regression(LR)were used as meta-learner candidates.Employing integer coding technology,systematic dynamic combinations of base learners and meta-learners were generated,resulting in a total of 40 non-repetitive fusion models.The optimal combination was selected through a comprehensive evaluation based on multiple assessment indicators.[Results]The combination"PLS-DELM-MLP-LR"(code 1367)achieved coefficients of determination of 0.9732 and 0.9780 on the validation set and independent test set,respectively,with relative root mean square errors of 2.35%and 2.36%,and residual predictive deviations of 6.1075 and 6.7479,respectively.[Conclusions]The Stacking fusion model significantly enhances the predictive accuracy and robustness of spectral quantitative analysis,providing an efficient and feasible solution for modeling complex agricultural product spectral data.
基金support from the Key Research and Development Project of School-Local Cooperation in Lvliang City(No.2023XDHZ05)Open Competition Scientific and Technological Research Projects of Heilongjiang Province(No.2022ZXJ03A02)+1 种基金China Baowu Low Carbon Metallurgy Innovation Foundation(No.BWLCF202318)Dr.Shi-Jian Dong’s guidance on this research.
文摘Accurate prediction of slag compositions in converter is essential for optimizing steelmaking efficiency and improving resource utilization.A novel stacking deep network was established to enhance the accuracy of slag compositions prediction in converter.The established network was designed to incorporate deep learning as part of a collection of multiple base models,combined with tree-based models.This network introduced more complex decision boundaries to the axis-aligned geometry,thereby improving model diversity.This network differs from existing ensemble methods by using a first layer to integrate multiple base learners for diverse features capture,while the second layer’s skip connection mechanism reuses input features to address covariate shift and improve model expressiveness and stability.In addition,this network introduced an innovative stacking layer design,replacing the standard linear regression model with more sophisticated tree-based models and deep learning networks.This enhancement allowed the stacking layer not only to weight the outputs of the base models but also to learn deeper patterns and feature interactions.To improve the network’s generalization,K-fold bagging was employed to minimize the risk of overfitting.Feature importance analysis further underscores the established network’s ability to identify the key factors influencing slag compositions,offering valuable insights for optimizing the steelmaking process.Experimental results demonstrated that the established network significantly outperforms existing methods in predicting slag compositions in converter,with mean squared errors of 4.33,2.80,3.39,and 0.49 for CaO,SiO_(2),MgO and FeO,respectively,highlighting its robust generalization capability.The established network approach offers an effective solution for predicting slag compositions in converters,thereby overcoming the limitation of delayed slag detection.
