This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble lear...This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.展开更多
The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational per...The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.展开更多
Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,re...Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.展开更多
As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigat...As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.展开更多
Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptibl...Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.展开更多
In this paper,we consider the Fisher informations among three classical type β-ensembles when β>0 scales with n satisfying lim βn=∞.We offer the exact order of-the corresponding two Fisher informations,which in...In this paper,we consider the Fisher informations among three classical type β-ensembles when β>0 scales with n satisfying lim βn=∞.We offer the exact order of-the corresponding two Fisher informations,which indicates that theβ-Laguerre ensembles do not satisfy the logarithmic Sobolev inequality.We also give some limit theorems on the extremals of β-Jacobi ensembles for β>0 fixed.展开更多
Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradi...Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.展开更多
Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensem...Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.展开更多
The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this ...The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).展开更多
While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensificati...While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensification(RI),whose nonlinear characteristics impose great difficulties for numerical models.The ensemble sensitivity analysis(ESA)method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts,especially with RI.Six RI processes from five typhoons(Chaba,Haima,Meranti,Sarika,and Songda)in 2016,are applied with ESA,which also gives a composite initial condition that favors subsequent RI.Results from individual cases have generally similar patterns of ESA,but with different magnitudes,when various cumulus parameterization schemes are applied.To draw the initial conditions with statistical significance,sample-mean azimuthal components of ESA are obtained.Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels,intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels,a prominent warm core at around 300 hPa,and increased humidity at low levels.As the forecast lead time increases,the patterns of ESA are retained,while the sensitivity magnitudes decay.Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes,appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.展开更多
Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irr...Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.展开更多
Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyz...Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.展开更多
Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on ...Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.展开更多
Background:Diabetes is one of the fastest rising chronic illness worldwide,and early detection is very crucial for reducing complications.Traditional machine learning models often struggle with imbalanced data and mod...Background:Diabetes is one of the fastest rising chronic illness worldwide,and early detection is very crucial for reducing complications.Traditional machine learning models often struggle with imbalanced data and moderate accuracy.To overcome these limitations,we propose a SMOTE-based ensemble boosting strategy(SMOTEBEnDi)for more accurate diabetes classification.Methods:The framework uses the Pima Indians diabetes dataset(PIDD)consisting of eight clinical features.Preprocessing steps included normalization,feature relevance analysis,and handling of missing values.The class imbalance was corrected using the synthetic minority oversampling technique(SMOTE),and multiple classifiers such as K-nearest neighbor(KNN),decision tree(DT),random forest(RF),and support vector machine(SVM)were ensembled in a boosting architecture.Hyperparameter tuning with k-fold cross validation was applied to ensure robust performance.Results:Experimental analysis showed that the proposed SMOTEBEnDi model achieved 99.5%accuracy,99.39%sensitivity,and 99.59%specificity,outperforming baseline classifiers and demonstrating near-perfect detection.The improvements in performance metrics like area under curve(AUC),precision,and specificity confirm the effectiveness of addressing class imbalance.Conclusion:The study proves that combining SMOTE with ensemble boosting greatly enhances early diabetes detection.This reduces diagnostic errors,supports clinicians in timely intervention,and can serve as a strong base for computer-aided diagnostic tools.Future work should extend this framework for real-time prediction systems,integrate with IoT health devices,and adapt it across diverse clinical datasets to improve generalization and trust in real healthcare settings.展开更多
To address uncertainties in satellite orbit error prediction,this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system(BDS).Building o...To address uncertainties in satellite orbit error prediction,this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system(BDS).Building on ephemeris data and perturbation corrections,two new models are proposed:attention-enhanced BPNN(AEBP)and Transformer-ResNet-BiLSTM(TR-BiLSTM).These models effectively capture both local and global dependencies in satellite orbit data.To further enhance prediction accuracy and stability,the outputs of these two models were integrated using the gradient boosting decision tree(GBDT)ensemble learning method,which was optimized through a grid search.The main contribution of this approach is the synergistic combination of deep learning models and GBDT,which significantly improves both the accuracy and robustness of satellite orbit predictions.This model was validated using broadcast ephemeris data from the BDS-3 MEO and inclined geosynchronous orbit(IGSO)satellites.The results show that the proposed method achieves an error correction rate of 65.4%.This ensemble learning-based approach offers a highly effective solution for high-precision and stable satellite orbit predictions.展开更多
The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting i...The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting is the foundation of conventional pain assessment methods,which may be unreliable.Deep learning is a promising alternative to resolve this limitation through automated pain classification.This paper proposes an ensemble deep-learning framework for pain assessment.The framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)signals.We integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)models.We then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s robustness.To improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature selection.This enables us to reduce the features’dimensionality without sacrificing the classification’s accuracy.With improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning classifiers.In our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment performance.However,due to differences in validation protocols,comparisons with previous studies are still limited.Combining deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification performance.The evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.展开更多
Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Compl...Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.展开更多
This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hour...This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.展开更多
Efficient water dissociation catalysts are important for reducing the activation energy barrier of water molecules in the field of energy conversio n.Herein,symmetry-bro ken Rh ensemble induced by mandated charge was ...Efficient water dissociation catalysts are important for reducing the activation energy barrier of water molecules in the field of energy conversio n.Herein,symmetry-bro ken Rh ensemble induced by mandated charge was established to boost the catalytic activity toward water dissociation.As an experimental verification,the turnover frequency of 1.0-RTO_(V4)in hydrogen generation from ammonia borane hydrolysis reaches up to 2838 min-1(24828 min^(-1)depend on Rh dispersion),exceeding the benchmark set up by state-of-the-art catalysts.The transfer of mandated charge from O_(V)to Rh near O_(V)breaks the local symmetry of Rh nanoparticle and forms Rh^(γ-)(electron-aggregation Rh)-Rh interfacial atomic ensemble.This symmetry-broken Rh ensemble is the reason for the high activity of the catalyst.This work provides an effective electronic regulation strategy based on symmetry-broken atomic ensemble induced by mandated charge,designed to stimulate the limiting activity of metal catalyst in the field of next generation energy chemistry.展开更多
基金the University of Transport Technology under the project entitled“Application of Machine Learning Algorithms in Landslide Susceptibility Mapping in Mountainous Areas”with grant number DTTD2022-16.
文摘This study was aimed to prepare landslide susceptibility maps for the Pithoragarh district in Uttarakhand,India,using advanced ensemble models that combined Radial Basis Function Networks(RBFN)with three ensemble learning techniques:DAGGING(DG),MULTIBOOST(MB),and ADABOOST(AB).This combination resulted in three distinct ensemble models:DG-RBFN,MB-RBFN,and AB-RBFN.Additionally,a traditional weighted method,Information Value(IV),and a benchmark machine learning(ML)model,Multilayer Perceptron Neural Network(MLP),were employed for comparison and validation.The models were developed using ten landslide conditioning factors,which included slope,aspect,elevation,curvature,land cover,geomorphology,overburden depth,lithology,distance to rivers and distance to roads.These factors were instrumental in predicting the output variable,which was the probability of landslide occurrence.Statistical analysis of the models’performance indicated that the DG-RBFN model,with an Area Under ROC Curve(AUC)of 0.931,outperformed the other models.The AB-RBFN model achieved an AUC of 0.929,the MB-RBFN model had an AUC of 0.913,and the MLP model recorded an AUC of 0.926.These results suggest that the advanced ensemble ML model DG-RBFN was more accurate than traditional statistical model,single MLP model,and other ensemble models in preparing trustworthy landslide susceptibility maps,thereby enhancing land use planning and decision-making.
基金National Natural Science Foundation of China (52075420)Fundamental Research Funds for the Central Universities (xzy022023049)National Key Research and Development Program of China (2023YFB3408600)。
文摘The burgeoning market for lithium-ion batteries has stimulated a growing need for more reliable battery performance monitoring. Accurate state-of-health(SOH) estimation is critical for ensuring battery operational performance. Despite numerous data-driven methods reported in existing research for battery SOH estimation, these methods often exhibit inconsistent performance across different application scenarios. To address this issue and overcome the performance limitations of individual data-driven models,integrating multiple models for SOH estimation has received considerable attention. Ensemble learning(EL) typically leverages the strengths of multiple base models to achieve more robust and accurate outputs. However, the lack of a clear review of current research hinders the further development of ensemble methods in SOH estimation. Therefore, this paper comprehensively reviews multi-model ensemble learning methods for battery SOH estimation. First, existing ensemble methods are systematically categorized into 6 classes based on their combination strategies. Different realizations and underlying connections are meticulously analyzed for each category of EL methods, highlighting distinctions, innovations, and typical applications. Subsequently, these ensemble methods are comprehensively compared in terms of base models, combination strategies, and publication trends. Evaluations across 6 dimensions underscore the outstanding performance of stacking-based ensemble methods. Following this, these ensemble methods are further inspected from the perspectives of weighted ensemble and diversity, aiming to inspire potential approaches for enhancing ensemble performance. Moreover, addressing challenges such as base model selection, measuring model robustness and uncertainty, and interpretability of ensemble models in practical applications is emphasized. Finally, future research prospects are outlined, specifically noting that deep learning ensemble is poised to advance ensemble methods for battery SOH estimation. The convergence of advanced machine learning with ensemble learning is anticipated to yield valuable avenues for research. Accelerated research in ensemble learning holds promising prospects for achieving more accurate and reliable battery SOH estimation under real-world conditions.
基金supported by the Basic Science Research Program through the National Research Foundation of Korea(NRF)funded by the Ministry of Education(NRF-2022R1I1A3063493).
文摘Smart manufacturing and Industry 4.0 are transforming traditional manufacturing processes by utilizing innovative technologies such as the artificial intelligence(AI)and internet of things(IoT)to enhance efficiency,reduce costs,and ensure product quality.In light of the recent advancement of Industry 4.0,identifying defects has become important for ensuring the quality of products during the manufacturing process.In this research,we present an ensemble methodology for accurately classifying hot rolled steel surface defects by combining the strengths of four pre-trained convolutional neural network(CNN)architectures:VGG16,VGG19,Xception,and Mobile-Net V2,compensating for their individual weaknesses.We evaluated our methodology on the Xsteel surface defect dataset(XSDD),which comprises seven different classes.The ensemble methodology integrated the predictions of individual models through two methods:model averaging and weighted averaging.Our evaluation showed that the model averaging ensemble achieved an accuracy of 98.89%,a recall of 98.92%,a precision of 99.05%,and an F1-score of 98.97%,while the weighted averaging ensemble reached an accuracy of 99.72%,a recall of 99.74%,a precision of 99.67%,and an F1-score of 99.70%.The proposed weighted averaging ensemble model outperformed the model averaging method and the individual models in detecting defects in terms of accuracy,recall,precision,and F1-score.Comparative analysis with recent studies also showed the superior performance of our methodology.
基金supported by the National Natural Science Foundation of China(22379021 and 22479021)。
文摘As batteries become increasingly essential for energy storage technologies,battery prognosis,and diagnosis remain central to ensure reliable operation and effective management,as well as to aid the in-depth investigation of degradation mechanisms.However,dynamic operating conditions,cell-to-cell inconsistencies,and limited availability of labeled data have posed significant challenges to accurate and robust prognosis and diagnosis.Herein,we introduce a time-series-decomposition-based ensembled lightweight learning model(TELL-Me),which employs a synergistic dual-module framework to facilitate accurate and reliable forecasting.The feature module formulates features with physical implications and sheds light on battery aging mechanisms,while the gradient module monitors capacity degradation rates and captures aging trend.TELL-Me achieves high accuracy in end-of-life prediction using minimal historical data from a single battery without requiring offline training dataset,and demonstrates impressive generality and robustness across various operating conditions and battery types.Additionally,by correlating feature contributions with degradation mechanisms across different datasets,TELL-Me is endowed with the diagnostic ability that not only enhances prediction reliability but also provides critical insights into the design and optimization of next-generation batteries.
文摘Small-drone technology has opened a range of new applications for aerial transportation. These drones leverage the Internet of Things (IoT) to offer cross-location services for navigation. However, they are susceptible to security and privacy threats due to hardware and architectural issues. Although small drones hold promise for expansion in both civil and defense sectors, they have safety, security, and privacy threats. Addressing these challenges is crucial to maintaining the security and uninterrupted operations of these drones. In this regard, this study investigates security, and preservation concerning both the drones and Internet of Drones (IoD), emphasizing the significance of creating drone networks that are secure and can robustly withstand interceptions and intrusions. The proposed framework incorporates a weighted voting ensemble model comprising three convolutional neural network (CNN) models to enhance intrusion detection within the network. The employed CNNs are customized 1D models optimized to obtain better performance. The output from these CNNs is voted using a weighted criterion using a 0.4, 0.3, and 0.3 ratio for three CNNs, respectively. Experiments involve using multiple benchmark datasets, achieving an impressive accuracy of up to 99.89% on drone data. The proposed model shows promising results concerning precision, recall, and F1 as indicated by their obtained values of 99.92%, 99.98%, and 99.97%, respectively. Furthermore, cross-validation and performance comparison with existing works is also carried out. Findings indicate that the proposed approach offers a prospective solution for detecting security threats for aerial systems and satellite systems with high accuracy.
基金supported by the NSFC(12171038)and 985 Projects。
文摘In this paper,we consider the Fisher informations among three classical type β-ensembles when β>0 scales with n satisfying lim βn=∞.We offer the exact order of-the corresponding two Fisher informations,which indicates that theβ-Laguerre ensembles do not satisfy the logarithmic Sobolev inequality.We also give some limit theorems on the extremals of β-Jacobi ensembles for β>0 fixed.
基金the Young Investigator Group“Artificial Intelligence for Probabilistic Weather Forecasting”funded by the Vector Stiftungfunding from the Federal Ministry of Education and Research(BMBF)and the Baden-Württemberg Ministry of Science as part of the Excellence Strategy of the German Federal and State Governments。
文摘Weather forecasts from numerical weather prediction models play a central role in solar energy forecasting,where a cascade of physics-based models is used in a model chain approach to convert forecasts of solar irradiance to solar power production.Ensemble simulations from such weather models aim to quantify uncertainty in the future development of the weather,and can be used to propagate this uncertainty through the model chain to generate probabilistic solar energy predictions.However,ensemble prediction systems are known to exhibit systematic errors,and thus require post-processing to obtain accurate and reliable probabilistic forecasts.The overarching aim of our study is to systematically evaluate different strategies to apply post-processing in model chain approaches with a specific focus on solar energy:not applying any post-processing at all;post-processing only the irradiance predictions before the conversion;post-processing only the solar power predictions obtained from the model chain;or applying post-processing in both steps.In a case study based on a benchmark dataset for the Jacumba solar plant in the U.S.,we develop statistical and machine learning methods for postprocessing ensemble predictions of global horizontal irradiance(GHI)and solar power generation.Further,we propose a neural-network-based model for direct solar power forecasting that bypasses the model chain.Our results indicate that postprocessing substantially improves the solar power generation forecasts,in particular when post-processing is applied to the power predictions.The machine learning methods for post-processing slightly outperform the statistical methods,and the direct forecasting approach performs comparably to the post-processing strategies.
基金funded by Taif University,Saudi Arabia,project No.(TU-DSPP-2024-263).
文摘Deep learning algorithms have been rapidly incorporated into many different applications due to the increase in computational power and the availability of massive amounts of data.Recently,both deep learning and ensemble learning have been used to recognize underlying structures and patterns from high-level features to make predictions/decisions.With the growth in popularity of deep learning and ensemble learning algorithms,they have received significant attention from both scientists and the industrial community due to their superior ability to learn features from big data.Ensemble deep learning has exhibited significant performance in enhancing learning generalization through the use of multiple deep learning algorithms.Although ensemble deep learning has large quantities of training parameters,which results in time and space overheads,it performs much better than traditional ensemble learning.Ensemble deep learning has been successfully used in several areas,such as bioinformatics,finance,and health care.In this paper,we review and investigate recent ensemble deep learning algorithms and techniques in health care domains,medical imaging,health care data analytics,genomics,diagnosis,disease prevention,and drug discovery.We cover several widely used deep learning algorithms along with their architectures,including deep neural networks(DNNs),convolutional neural networks(CNNs),recurrent neural networks(RNNs),and generative adversarial networks(GANs).Common healthcare tasks,such as medical imaging,electronic health records,and genomics,are also demonstrated.Furthermore,in this review,the challenges inherent in reducing the burden on the healthcare system are discussed and explored.Finally,future directions and opportunities for enhancing healthcare model performance are discussed.
基金support from the National Natural Science Foundation of China(22108052).
文摘The biomass and coal co-pyrolysis (BCP) technology combines the advantages of both resources, achieving efficient resource complementarity, reducing reliance on coal, and minimizing pollutant emissions. However, this process still encounters numerous challenges in attaining optimal economic and environmental performance. Therefore, an ensemble learning (EL) framework is proposed for the BCP process in this study to optimize the synergistic benefits while minimizing negative environmental impacts. Six different ensemble learning models are developed to investigate the impact of input features, such as biomass characteristics, coal characteristics, and pyrolysis conditions on the product profit and CO_(2) emissions of the BCP processes. The Optuna method is further employed to automatically optimize the hyperparameters of BCP process models for enhancing their predictive accuracy and robustness. The results indicate that the categorical boosting (CAB) model of the BCP process has demonstrated exceptional performance in accurately predicting its product profit and CO_(2) emission (R2>0.92) after undergoing five-fold cross-validation. To enhance the interpretability of this preferred model, the Shapley additive explanations and partial dependence plot analyses are conducted to evaluate the impact and importance of biomass characteristics, coal characteristics, and pyrolysis conditions on the product profitability and CO_(2) emissions of the BCP processes. Finally, the preferred model coupled with a reference vector guided evolutionary algorithm is carried to identify the optimal conditions for maximizing the product profit of BCP process products while minimizing CO_(2) emissions. It indicates the optimal BCP process can achieve high product profits (5290.85 CNY·t−1) and low CO_(2) emissions (7.45 kg·t^(−1)).
基金supported by the National Natural Science Foundation of China[grant numbers 42192553 and 41922036]the Fundamental Research Funds for the Central Universities–Cemac“GeoX”Interdisciplinary Program[grant number 020714380207]。
文摘While steady improvements have been achieved for the track forecasts of typhoons,there has been a lack of improvement for intensity forecasts.One challenge for intensity forecasts is to capture the rapid intensification(RI),whose nonlinear characteristics impose great difficulties for numerical models.The ensemble sensitivity analysis(ESA)method is used here to analyze the initial conditions that contribute to typhoon intensity forecasts,especially with RI.Six RI processes from five typhoons(Chaba,Haima,Meranti,Sarika,and Songda)in 2016,are applied with ESA,which also gives a composite initial condition that favors subsequent RI.Results from individual cases have generally similar patterns of ESA,but with different magnitudes,when various cumulus parameterization schemes are applied.To draw the initial conditions with statistical significance,sample-mean azimuthal components of ESA are obtained.Results of the composite sensitivity show that typhoons that experience RI in 24 h favor enhanced primary circulation from low to high levels,intensified secondary circulation with increased radial inflow at lower levels and increased radial outflow at upper levels,a prominent warm core at around 300 hPa,and increased humidity at low levels.As the forecast lead time increases,the patterns of ESA are retained,while the sensitivity magnitudes decay.Given the general and quantitative composite sensitivity along with associated uncertainties for different cumulus parameterization schemes,appropriate sampling of the composite sensitivity in numerical models could be beneficial to capturing the RI and improving the forecasting of typhoon intensity.
文摘Heart disease prediction is a critical issue in healthcare,where accurate early diagnosis can save lives and reduce healthcare costs.The problem is inherently complex due to the high dimensionality of medical data,irrelevant or redundant features,and the variability in risk factors such as age,lifestyle,andmedical history.These challenges often lead to inefficient and less accuratemodels.Traditional predictionmethodologies face limitations in effectively handling large feature sets and optimizing classification performance,which can result in overfitting poor generalization,and high computational cost.This work proposes a novel classification model for heart disease prediction that addresses these challenges by integrating feature selection through a Genetic Algorithm(GA)with an ensemble deep learning approach optimized using the Tunicate Swarm Algorithm(TSA).GA selects the most relevant features,reducing dimensionality and improvingmodel efficiency.Theselected features are then used to train an ensemble of deep learning models,where the TSA optimizes the weight of each model in the ensemble to enhance prediction accuracy.This hybrid approach addresses key challenges in the field,such as high dimensionality,redundant features,and classification performance,by introducing an efficient feature selection mechanism and optimizing the weighting of deep learning models in the ensemble.These enhancements result in a model that achieves superior accuracy,generalization,and efficiency compared to traditional methods.The proposed model demonstrated notable advancements in both prediction accuracy and computational efficiency over traditionalmodels.Specifically,it achieved an accuracy of 97.5%,a sensitivity of 97.2%,and a specificity of 97.8%.Additionally,with a 60-40 data split and 5-fold cross-validation,the model showed a significant reduction in training time(90 s),memory consumption(950 MB),and CPU usage(80%),highlighting its effectiveness in processing large,complex medical datasets for heart disease prediction.
基金supported by the National Key Research and Development Program[grant number 2022YFC3004203]the S&T Development Fund of CAMS(Chinese Academy of Meteorological Sciences)[grant numbers 2023KJ040 and 2024KJ013].
文摘Subseasonal forecasting of extreme events is crucial for early warning systems.However,the forecast skills for extreme events are limited.Taking the extreme cold events in January 2018 as a specific example,and analyzing the 34 extreme cold events in East Asia from 1998 to 2020,the authors evaluated the forecast skills of the ECMWF model ensemble members on subseasonal time scales.The results show that while the ensemble mean has limited skills for forecasting extreme cold events at the 3-week lead time,some individual members demonstrate high forecast skills.For most extreme cold events,there are>10%of members among the total ensembles that can well predict the rapid temperature transitions at the 14-day lead time.This highlights the untapped potential of the ECMWF model to forecast extreme cold events on subseasonal time scales.High-skill ensemble members rely on accurate predictions of atmospheric circulation patterns(500-hPa geopotential height,mean sea level pressure)and key weather systems,including the Ural Blocking and Siberian High,that influence extreme cold events.
基金the National Natural Science Foundation of China(No.62271466)the Natural Science Foundation of Beijing(No.4202025)+1 种基金the Tianjin IoT Technology Enterprise Key Laboratory Research Project(No.VTJ-OT20230209-2)the Guizhou Provincial Sci-Tech Project(No.ZK[2022]-012)。
文摘Aimed at the issues of high feature dimensionality,excessive data redundancy,and low recognition accuracy of using single classifiers on ground-glass lung nodule recognition,a recognition method was proposed based on CatBoost feature selection and Stacking ensemble learning.First,the method uses a feature selection algorithm to filter important features and remove features with less impact,achieving the effect of data dimensionality reduction.Second,random forests classifier,decision trees,K-nearest neighbor classifier,and light gradient boosting machine were used as base classifiers,and support vector machine was used as meta classifier to fuse and construct the ensemble learning model.This measure increases the accuracy of the classification model while maintaining the diversity of the base classifiers.The experimental results show that the recognition accuracy of the proposed method reaches 94.375%.Compared to the random forest algorithm with the best performance among single classifiers,the accuracy of the proposed method is increased by 1.875%.Compared to the recent deep learning methods(ResNet+GBM+Attention and MVCSNet)on ground-glass pulmonary nodule recognition,the proposed method’s performance is also better or comparative.Experiments show that the proposed model can effectively select features and make recognition on ground-glass pulmonary nodules.
文摘Background:Diabetes is one of the fastest rising chronic illness worldwide,and early detection is very crucial for reducing complications.Traditional machine learning models often struggle with imbalanced data and moderate accuracy.To overcome these limitations,we propose a SMOTE-based ensemble boosting strategy(SMOTEBEnDi)for more accurate diabetes classification.Methods:The framework uses the Pima Indians diabetes dataset(PIDD)consisting of eight clinical features.Preprocessing steps included normalization,feature relevance analysis,and handling of missing values.The class imbalance was corrected using the synthetic minority oversampling technique(SMOTE),and multiple classifiers such as K-nearest neighbor(KNN),decision tree(DT),random forest(RF),and support vector machine(SVM)were ensembled in a boosting architecture.Hyperparameter tuning with k-fold cross validation was applied to ensure robust performance.Results:Experimental analysis showed that the proposed SMOTEBEnDi model achieved 99.5%accuracy,99.39%sensitivity,and 99.59%specificity,outperforming baseline classifiers and demonstrating near-perfect detection.The improvements in performance metrics like area under curve(AUC),precision,and specificity confirm the effectiveness of addressing class imbalance.Conclusion:The study proves that combining SMOTE with ensemble boosting greatly enhances early diabetes detection.This reduces diagnostic errors,supports clinicians in timely intervention,and can serve as a strong base for computer-aided diagnostic tools.Future work should extend this framework for real-time prediction systems,integrate with IoT health devices,and adapt it across diverse clinical datasets to improve generalization and trust in real healthcare settings.
基金funded by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDA28040300)Project for Guangxi Science and Technology Base,and Talents(Grant No.GK AD22035957)+1 种基金the Informatization Plan of the Chinese Academy of Sciences(Grant No.CAS-WX2021SF-0304)the West Light Foundation of the ChineseAcademy of Sciences(Grant No.XAB2021YN19).
文摘To address uncertainties in satellite orbit error prediction,this study proposes a novel ensemble learning-based orbit prediction method specifically designed for the BeiDou navigation satellite system(BDS).Building on ephemeris data and perturbation corrections,two new models are proposed:attention-enhanced BPNN(AEBP)and Transformer-ResNet-BiLSTM(TR-BiLSTM).These models effectively capture both local and global dependencies in satellite orbit data.To further enhance prediction accuracy and stability,the outputs of these two models were integrated using the gradient boosting decision tree(GBDT)ensemble learning method,which was optimized through a grid search.The main contribution of this approach is the synergistic combination of deep learning models and GBDT,which significantly improves both the accuracy and robustness of satellite orbit predictions.This model was validated using broadcast ephemeris data from the BDS-3 MEO and inclined geosynchronous orbit(IGSO)satellites.The results show that the proposed method achieves an error correction rate of 65.4%.This ensemble learning-based approach offers a highly effective solution for high-precision and stable satellite orbit predictions.
基金funded by the Deanship of Graduate Studies and Scientific Research at Jouf University under grant No.(DGSSR-2023-02-02341).
文摘The potential applications of multimodal physiological signals in healthcare,pain monitoring,and clinical decision support systems have garnered significant attention in biomedical research.Subjective self-reporting is the foundation of conventional pain assessment methods,which may be unreliable.Deep learning is a promising alternative to resolve this limitation through automated pain classification.This paper proposes an ensemble deep-learning framework for pain assessment.The framework makes use of features collected from electromyography(EMG),skin conductance level(SCL),and electrocardiography(ECG)signals.We integrate Convolutional Neural Networks(CNN),Long Short-Term Memory Networks(LSTM),Bidirectional Gated Recurrent Units(BiGRU),and Deep Neural Networks(DNN)models.We then aggregate their predictions using a weighted averaging ensemble technique to increase the classification’s robustness.To improve computing efficiency and remove redundant features,we use Particle Swarm Optimization(PSO)for feature selection.This enables us to reduce the features’dimensionality without sacrificing the classification’s accuracy.With improved accuracy,precision,recall,and F1-score across all pain levels,the experimental results show that the suggested ensemble model performs better than individual deep learning classifiers.In our experiments,the suggested model achieved over 98%accuracy,suggesting promising automated pain assessment performance.However,due to differences in validation protocols,comparisons with previous studies are still limited.Combining deep learning and feature selection techniques significantly improves model generalization,reducing overfitting and enhancing classification performance.The evaluation was conducted using the BioVid Heat Pain Dataset,confirming the model’s effectiveness in distinguishing between different pain intensity levels.
基金funded by the National Natural Science Foundation of China(Grant No.52222708)。
文摘Accurately evaluating the safety status of lithium-ion battery systems in electric vehicles is imperative due to the challenges in effectively predicting potential battery failure risks under stochastic profiles.Complex battery fault mechanisms and limited poor-quality data collection impede fault detection for battery systems under real-world conditions.This paper proposes a novel graph-guided fault detection method designed to recognize concealed anomalies in realistic data.Graphs guided by physical relationships are constructed for learning the dynamic evolution of physical quantities under normal conditions and their potential change characteristics in fault scenarios.An ensemble Graph Sample and Aggregate Network model are developed to tackle sample distribution imbalances and non-uniformity battery system specifications across vehicles.Failure risk probabilities for diverse battery charging and discharging segments are derived.An ablation study verifies the necessity of ensemble learning in addressing imbalanced datasets.Analysis of 102,095 segments across 86 vehicles with different battery material systems,battery capacities,and numbers of cells and temperature sensors confirms the robustness and generalization of the proposed method,yielding a recall of 98.37%.By introducing the graph,spatio-temporal global fault characteristics of battery systems are automatically extracted.The coupling relationship and evolution of physical quantities under both normal and faulty states are established,effectively uncovering fault information hidden in collected battery data without observable anomalies.The safety state of battery systems is reflected in terms of failure risk probability,providing reliable data support for battery system maintenance.
基金supported by the National Natural Science Foundation of China(Grant Nos.42030610 and 42205006)the Startup Foundation for Introducing Talent of NUIST(2023r121)。
文摘This study focuses on an extreme rainfall event in East China during the mei-yu season,in which the capital city(Nanjing)of Jiangsu Province experienced a maximum 14-h rainfall accumulation of 209.6 mm and a peak hourly rainfall of 118.8 mm.The performance of two sets of convection-permitting ensemble forecast systems(CEFSs),each with 30 members and a 3-km horizontal grid spacing,is evaluated.The CEFS_ICBCs,using multiple initial and boundary conditions(ICs and BCs),and the CEFS_ICBCs Phys,which incorporates both multi-physics schemes and ICs/BCs,are compared to the CMA-REPS(China Meteorological Administration-Regional Ensemble Prediction System)with a coarser 10-km grid spacing.The two CEFSs demonstrate more uniform rank histograms and lower Brier scores(with higher resolution),improving precipitation intensity predictions and providing more reliable probability forecasts,although they overestimate precipitation over Mt.Dabie.It is challenging for the CEFSs to capture the evolution of mesoscale rainstorms that are known to be related to the errors in predicting the southwesterly low-level winds.Sensitivity experiments reveal that the microphysics and radiation schemes introduce considerable uncertainty in predicting the intensity and location of heavy rainfall in and near Nanjing and Mt.Dabie.In particular,the Asymmetric Convection Model 2(ACM2)planetary boundary layer scheme combined with the Pleim-Xiu surface layer scheme tends to produce a biased northeastward extension of the boundary-layer jet,contributing to the northeastward bias of heavy precipitation around Nanjing in the CEFS_ICBCs.
基金supported by the National Natural Science Foundation of China(No.22279118,No.22309164)the China Postdoctoral Science Foundation(No.2023M733214)+1 种基金the Young Top Talent Program of Zhongyuan-Yingcai-Jihua(No.30602674)the Special Projects of Henan Province Key Research and Development and Promotion(Science and Technology Research)(No.232102241033)。
文摘Efficient water dissociation catalysts are important for reducing the activation energy barrier of water molecules in the field of energy conversio n.Herein,symmetry-bro ken Rh ensemble induced by mandated charge was established to boost the catalytic activity toward water dissociation.As an experimental verification,the turnover frequency of 1.0-RTO_(V4)in hydrogen generation from ammonia borane hydrolysis reaches up to 2838 min-1(24828 min^(-1)depend on Rh dispersion),exceeding the benchmark set up by state-of-the-art catalysts.The transfer of mandated charge from O_(V)to Rh near O_(V)breaks the local symmetry of Rh nanoparticle and forms Rh^(γ-)(electron-aggregation Rh)-Rh interfacial atomic ensemble.This symmetry-broken Rh ensemble is the reason for the high activity of the catalyst.This work provides an effective electronic regulation strategy based on symmetry-broken atomic ensemble induced by mandated charge,designed to stimulate the limiting activity of metal catalyst in the field of next generation energy chemistry.
