With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration predict...With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.展开更多
Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Metho...Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.展开更多
Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resou...Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.展开更多
The surface ageing of silicone rubber composite insulators,widely used in power systems,poses significant challenges.This study integrates Fourier transform infrared(FTIR)spectroscopy with machine learning to evaluate...The surface ageing of silicone rubber composite insulators,widely used in power systems,poses significant challenges.This study integrates Fourier transform infrared(FTIR)spectroscopy with machine learning to evaluate ageing states and explore underlying mechanisms under various environmental conditions.A dataset covering light,medium,and severe ageing was built through FTIR experiments,spectral feature extraction,and data augmentation.An ensemble learning model achieved a classification accuracy of 95.42%.SHapley Additive exPlanations(SHAP)analysis indicated that silicon-oxygen backbones,silylmethyl groups,and hydroxyl groups are key to the ageing process.The silicon-oxygen backbone is dominant in initial oxidation and cross-linking,whereas silylmethyl group reactions occur later.Hydroxyl group changes are complex and strongly environment-dependent during severe ageing.The model was also applied to naturally aged samples from Xizang and Inner Mongolia,showing strong classification performance and revealing clear regional differences.These findings are valuable for assessing surface ageing,analysing ageing mechanisms and developing grading standards for composite insulators.展开更多
Gravity Recovery and Climate Experiment(GRACE)satellite data monitors changes in terrestrial water storage,including groundwater,at a regional scale.However,the coarse spatial resolution limits its applicability to sm...Gravity Recovery and Climate Experiment(GRACE)satellite data monitors changes in terrestrial water storage,including groundwater,at a regional scale.However,the coarse spatial resolution limits its applicability to small watershed areas.This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution.The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021.The factors influencing Groundwater Storage Anomalies(GWSA)were explored using Permutation Importance(Pi)and other methods.The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy;the Root Mean Square Error(RMSE)can be reduced by up to 18.95%.Furthermore,Blender ensemble learning decreased the RMSE by 3.58%,achieving an R-Square(R3)value of 0.7924.Restricting the downscaling inversion to June-August data greatly enhanced the accuracy,as evidenced by a holdout dataset test with an R2 value of 0.8247.The overall GWSA variation from January to August exhibited'slow rise,slow fall,sharp fall,and sharp rise.Additionally,heavy rain exhibits a lag effect on the groundwater supply.Meteorological and topographical factors drive fluctuations in GwSA values and changes in spatial distribution.Human activities also have a significant impact.展开更多
A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCo...A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies.The identified compositions were synthesized and tested for their crystal structures and mechanical properties(hardness and Young’s modulus),resulting in single-phase face-centered cubic(FCC)structures.Additionally,the measured Young’s moduli were in good qualitative agreement with computational predictions.The SHapley Additive exPlanations(SHAP)analysis of the SEML model revealed a relationship between elemental concentration and USFE.Meanwhile,SHAP analysis of the CNN models uncovered correlations between the local clustering of MPEA elements and their mechanical properties.This computational workflow,along with the fundamental insights gained,can be readily expanded and applied to the design of MPEAs with different elemental compositions,as well as to materials beyond MPEAs.展开更多
Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency.Breeding crop varieties with a"smart"canopy,charac...Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency.Breeding crop varieties with a"smart"canopy,characterized by erect upper-layer leaves and flat lower-layer leaves,can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture.Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency.In this study,we developed a strategy to estimate the leaf optical properties based on leaf anatomical features.We developed a Directional Spectrum Detection Instrument(DSDI)system and associated Bidirectional Reflectance Distribution Function(BRDF)analysis software to precisely describe leaf light distribution.BRDF parameters were quantified with high accuracy(R^(2)>0.95)for adaxial and abaxial surfaces of maize,rice,cotton,and poplar leaves across canopy layers.Leaf phenotypic traits,surface roughness,pigments content,specific leaf weight and thickness were also assessed.Ensemble learning(EL)model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R^(2) between 0.83 and 0.99.Compared to existing BRDF measurement systems,the DSDI achieves broader angular coverage(-π/36 to 35π/36)via mechanical rotation design,and the ensemble learning model establishes the first direct predictive relationship between BRDF pa-rameters and leaf phenotypic traits.This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties,which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.展开更多
基金supported by General Scientific Research Funding of the Science and Technology Development Fund(FDCT)in Macao(No.0150/2022/A)the Faculty Research Grants of Macao University of Science and Technology(No.FRG-22-074-FIE).
文摘With the rapid development of economy,air pollution caused by industrial expansion has caused serious harm to human health and social development.Therefore,establishing an effective air pollution concentration prediction system is of great scientific and practical significance for accurate and reliable predictions.This paper proposes a combination of pointinterval prediction system for pollutant concentration prediction by leveraging neural network,meta-heuristic optimization algorithm,and fuzzy theory.Fuzzy information granulation technology is used in data preprocessing to transform numerical sequences into fuzzy particles for comprehensive feature extraction.The golden Jackal optimization algorithm is employed in the optimization stage to fine-tune model hyperparameters.In the prediction stage,an ensemble learning method combines training results frommultiplemodels to obtain final point predictions while also utilizing quantile regression and kernel density estimation methods for interval predictions on the test set.Experimental results demonstrate that the combined model achieves a high goodness of fit coefficient of determination(R^(2))at 99.3% and a maximum difference between prediction accuracy mean absolute percentage error(MAPE)and benchmark model at 12.6%.This suggests that the integrated learning system proposed in this paper can provide more accurate deterministic predictions as well as reliable uncertainty analysis compared to traditionalmodels,offering practical reference for air quality early warning.
基金This study received financial support from the Jilin Province Health and Technology Capacity Enhancement Project(Project Number:222Lc132).
文摘Objective: This study investigates the auxiliary role of resting-state electroencephalography (EEG) in the clinical diagnosis of attention-deficit hyperactivity disorder (ADHD) using machine learning techniques. Methods: Resting-state EEG recordings were obtained from 57 children, comprising 28 typically developing children and 29 children diagnosed with ADHD. The EEG signal data from both groups were analyzed. To ensure analytical accuracy, artifacts and noise in the EEG signals were removed using the EEGLAB toolbox within the MATLAB environment. Following preprocessing, a comparative analysis was conducted using various ensemble learning algorithms, including AdaBoost, GBM, LightGBM, RF, XGB, and CatBoost. Model performance was systematically evaluated and optimized, validating the superior efficacy of ensemble learning approaches in identifying ADHD. Conclusion: Applying machine learning techniques to extract features from resting-state EEG signals enabled the development of effective ensemble learning models. Differential entropy and energy features across multiple frequency bands proved particularly valuable for these models. This approach significantly enhances the detection rate of ADHD in children, demonstrating high diagnostic efficacy and sensitivity, and providing a promising tool for clinical application.
文摘Detection of cracks in concrete structures is critical for their safety and the sustainability of maintenance processes.Traditional inspection techniques are costly,time-consuming,and inefficient regarding human resources.Deep learning architectures have become more widespread in recent years by accelerating these processes and increasing their efficiency.Deep learning models(DLMs)stand out as an effective solution in crack detection due to their features such as end-to-end learning capability,model adaptation,and automatic learning processes.However,providing an optimal balance between model performance and computational efficiency of DLMs is a vital research topic.In this article,three different methods are proposed for detecting cracks in concrete structures.In the first method,a Separable Convolutional with Attention and Multi-layer Enhanced Fusion Network(SCAMEFNet)deep neural network,which has a deep architecture and can provide a balance between the depth of DLMs and model parameters,has been developed.This model was designed using a convolutional neural network,multi-head attention,and various fusion techniques.The second method proposes a modified vision transformer(ViT)model.A two-stage ensemble learning model,deep featurebased two-stage ensemble model(DFTSEM),is proposed in the third method.In this method,deep features and machine learning methods are used.The proposed approaches are evaluated using the Concrete Cracks Image Data set,which the authors collected and contains concrete cracks on building surfaces.The results show that the SCAMEFNet model achieved an accuracy rate of 98.83%,the ViT model 97.33%,and the DFTSEM model 99.00%.These findings show that the proposed techniques successfully detect surface cracks and deformations and can provide practical solutions to realworld problems.In addition,the developed methods can contribute as a tool for BIM platforms in smart cities for building health.
基金supported by Science and Technology Project of SGCC(Development of Composite Insulators with Long Service Life,Project Number:5500-202455305A-1-2-LZ).
文摘The surface ageing of silicone rubber composite insulators,widely used in power systems,poses significant challenges.This study integrates Fourier transform infrared(FTIR)spectroscopy with machine learning to evaluate ageing states and explore underlying mechanisms under various environmental conditions.A dataset covering light,medium,and severe ageing was built through FTIR experiments,spectral feature extraction,and data augmentation.An ensemble learning model achieved a classification accuracy of 95.42%.SHapley Additive exPlanations(SHAP)analysis indicated that silicon-oxygen backbones,silylmethyl groups,and hydroxyl groups are key to the ageing process.The silicon-oxygen backbone is dominant in initial oxidation and cross-linking,whereas silylmethyl group reactions occur later.Hydroxyl group changes are complex and strongly environment-dependent during severe ageing.The model was also applied to naturally aged samples from Xizang and Inner Mongolia,showing strong classification performance and revealing clear regional differences.These findings are valuable for assessing surface ageing,analysing ageing mechanisms and developing grading standards for composite insulators.
基金supported by National Natural Science Foundation of China:[grant no U1304402,41977284]Natural science and technology project of Department of Natural Resources of Henan Province:[grant no 2019-378-16].
文摘Gravity Recovery and Climate Experiment(GRACE)satellite data monitors changes in terrestrial water storage,including groundwater,at a regional scale.However,the coarse spatial resolution limits its applicability to small watershed areas.This study introduces a novel ensemble learning-based model using meteorological and topographical data to enhance spatial resolution.The effectiveness was evaluated using groundwater-level observation data from the Henan rainstorm-affected area in July 2021.The factors influencing Groundwater Storage Anomalies(GWSA)were explored using Permutation Importance(Pi)and other methods.The results demonstrate that feature engineering and Blender ensemble learning improve downscaling accuracy;the Root Mean Square Error(RMSE)can be reduced by up to 18.95%.Furthermore,Blender ensemble learning decreased the RMSE by 3.58%,achieving an R-Square(R3)value of 0.7924.Restricting the downscaling inversion to June-August data greatly enhanced the accuracy,as evidenced by a holdout dataset test with an R2 value of 0.8247.The overall GWSA variation from January to August exhibited'slow rise,slow fall,sharp fall,and sharp rise.Additionally,heavy rain exhibits a lag effect on the groundwater supply.Meteorological and topographical factors drive fluctuations in GwSA values and changes in spatial distribution.Human activities also have a significant impact.
基金supported by GlycoMIP,a National Science Foundation(NSF)Materials Innovation Platform(MIP)funded through Cooperative Agreement DMR-1933525.This work made use of the synthesis facilities of the NSF-funded MIP,Platform for the Accelerated Realization,Analysis,and Discovery of Interface Materials(PARADIM)under Cooperative Agreement DMR-2039380The authors would like to acknowledge Advanced Research Computing(ARC)at Virginia Tech for computational resources+1 种基金This research also used resources of the National Energy Research Scientific Computing Center(NERSC),a scientific computing facility for the Office of Science in the United States Department of Energy,operated under Contract No.DE-AC02-05CH11231F.W.would like to thank Dr.Dan Luo from Lehigh University for the discussion on convolutional neural networks(CNNs)and their application on sequential data similar to the one in this study.
文摘A computational workflow integrating a stacked ensemble machine learning(SEML)model and a convolutional neural network(CNN)model with evolutionary algorithms has been developed to identify new compositions of FeNiCrCoCu MPEAs with high bulk modulus and unstable stacking fault energies.The identified compositions were synthesized and tested for their crystal structures and mechanical properties(hardness and Young’s modulus),resulting in single-phase face-centered cubic(FCC)structures.Additionally,the measured Young’s moduli were in good qualitative agreement with computational predictions.The SHapley Additive exPlanations(SHAP)analysis of the SEML model revealed a relationship between elemental concentration and USFE.Meanwhile,SHAP analysis of the CNN models uncovered correlations between the local clustering of MPEA elements and their mechanical properties.This computational workflow,along with the fundamental insights gained,can be readily expanded and applied to the design of MPEAs with different elemental compositions,as well as to materials beyond MPEAs.
基金financial support of the National Natural Science Foundation of China(32270428,U22A20464)to Q.S.and X.Z.the Earmarked Fund for XJARS-Cotton(XJARS-03)to Y.Z.+2 种基金Open Project of the Key Laboratory of Oasis Eco-agriculture,Xinjiang Production and Construction Corps(202102)to Q.S.the Strategic Priority Research Program of the Chinese Academy of Sciences(XDB0630300)and CASNWO Joint grant(482.20.700)to Q.S.and X.Z.the Natural Science Foundation of Shanghai(23ZR1455900)and the Shanghai Rising-Star Program(21QC1401200)to Q.S.
文摘Light intensity and spectral distribution within plant canopies provides insights into the effects of optimizing canopy architecture on light use efficiency.Breeding crop varieties with a"smart"canopy,characterized by erect upper-layer leaves and flat lower-layer leaves,can be supported with a 3D canopy model which can simulate light distribution for a particular canopy architecture.Leaf optical properties are required parameters for such canopy photosynthesis model to accurately predict canopy microclimate and hence photosynthetic efficiency.In this study,we developed a strategy to estimate the leaf optical properties based on leaf anatomical features.We developed a Directional Spectrum Detection Instrument(DSDI)system and associated Bidirectional Reflectance Distribution Function(BRDF)analysis software to precisely describe leaf light distribution.BRDF parameters were quantified with high accuracy(R^(2)>0.95)for adaxial and abaxial surfaces of maize,rice,cotton,and poplar leaves across canopy layers.Leaf phenotypic traits,surface roughness,pigments content,specific leaf weight and thickness were also assessed.Ensemble learning(EL)model showed excellent predictive performance for leaf optical properties based on phenotypic traits with R^(2) between 0.83 and 0.99.Compared to existing BRDF measurement systems,the DSDI achieves broader angular coverage(-π/36 to 35π/36)via mechanical rotation design,and the ensemble learning model establishes the first direct predictive relationship between BRDF pa-rameters and leaf phenotypic traits.This work presents a new approach to quantify leaf optical properties and offers predictive models for leaf optical properties,which can support canopy light distribution prediction and hence support design leaf features for higher canopy photosynthesis efficiency.
