The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significan...The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.展开更多
The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty.When an input variable is described by a specific interval rather than a certain prob...The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty.When an input variable is described by a specific interval rather than a certain probability distribution,the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods.Generally,the non-probabilistic importance analysis methods involve the Monte Carlo simulation(MCS)and the optimization-based methods,which both have high computational cost.In order to overcome this problem,this study proposes an interval important analytical method avoids the time-consuming optimization process.First,the original performance function is decomposed into a combination of a series of one-dimensional subsystems.Next,the interval of each variable is divided into several subintervals,and the response value of each one-dimensional subsystem at a specific input point is calculated.Then,the obtained responses are taken as specific values of the new input variable,and the interval importance is calculated by the approximated performance function.Compared with the traditional non-probabilistic importance analysis method,the proposed method significantly reduces the computational cost caused by the MCS and optimization process.In the proposed method,the number of function evaluations is equal to one plus the sum of the subintervals of all of the variables.The efficiency and accuracy of the proposed method are verified by five examples.The results show that the proposed method is not only efficient but also accurate.展开更多
Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because th...Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.展开更多
Major in engineering geology exploration of geology, topography, geomorphology, hydrology, meteorology, earthquake, and so on and so forth to examine, survey and the result is the basis of the engineering construction...Major in engineering geology exploration of geology, topography, geomorphology, hydrology, meteorology, earthquake, and so on and so forth to examine, survey and the result is the basis of the engineering construction, the designers must understand the engineering geological investigation of the construction site conditions to select the appropriate construction technology and materials, finally to ensure the quality of project construction in line with the acceptance criteria, hydrogeology will have a certain influence on the survey results in engineering geological survey.展开更多
For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP)based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the margi...For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP)based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marginal probability density function(PDF)of the epistemic variable and the conditional PDF of the aleatory one at the fixed epistemic variable,the epistemic and aleatory uncertainties are propagated to the response of the structure firstly in the presented method.And the computational model for calculating the importance measures of the epistemic variables is established.For solving the computational model,the high efficient SDP method is applied to estimating the first order high dimensional model representation(HDMR)to obtain the importance measures.Compared with the direct Monte Carlo method,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared with the existing approximation method,because it is suitable not only for the linear response functions,but also for nonlinear response functions.Several examples are used to demonstrate the advantages of the presented method.展开更多
Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of t...Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.展开更多
For the best dynamic performance of a co-cured composite damping instrument panel with light weight and high strength, a multilayer sandwich structure with polymethaerylimide （PMI） foam combined with embedded and co...For the best dynamic performance of a co-cured composite damping instrument panel with light weight and high strength, a multilayer sandwich structure with polymethaerylimide （PMI） foam combined with embedded and co-cured composite damping structure is proposed. The struetue can maintain the excellent mechanical properties of composite materials, and achieve the damping and light effect at the same time. Input variables which may affect the dynamic performance of the instrument panel were selected and variance based importance measure was analyzed through multi- finite element method （FEM） analysis. Using the results of the importance measure analysis, with other design requirements, the important design variable was optimized and an instrument panel with the best dynamic performance under the requirements of light weight and high strength was obtained. The structure of the instrument panel can provide reference for the design of precision, high speed, and dynamic composite component. The importance measure analysis of dynamic performance of the instrument panel can provide a reference for relative design.展开更多
BACKGROUND The peritumoral region possesses attributes that promote cancer growth and progression.However,the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.AIM To investig...BACKGROUND The peritumoral region possesses attributes that promote cancer growth and progression.However,the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.AIM To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer(LARC).METHODS This retrospective study included 409 patients with biopsy-confirmed LARC treated with neoadjuvant chemoradiotherapy and surgically.Patients were divided into training(n=273)and validation(n=136)sets.Based on intratumoral and peritumoral radiomic features extracted from pretreatment axial high-resolution small-field-of-view T2-weighted images,multivariate Cox models for progression-free survival(PFS)prediction were developed with or without clinicoradiological features and evaluated with Harrell’s concordance index(C-index),calibration curve,and decision curve analyses.Risk stratification,Kaplan-Meier analysis,and permutation feature importance analysis were performed.RESULTS The comprehensive integrated clinical-radiological-omics model(ModelICRO)integrating seven peritumoral,three intratumoral,and four clinicoradiological features achieved the highest C-indices(0.836 and 0.801 in the training and validation sets,respectively).This model showed robust calibration and better clinical net benefits,effectively distinguished high-risk from low-risk patients(PFS:97.2%vs 67.6%and 95.4%vs 64.8%in the training and validation sets,respectively;both P<0.001).Three most influential predictors in the comprehensive ModelICRO were,in order,a peritumoral,an intratumoral,and a clinicoradiological feature.Notably,the peritumoral model outperformed the intratumoral model(C-index:0.754 vs 0.670;P=0.015);peritumoral features significantly enhanced the performance of models based on clinicoradiological or intratumoral features or their combinations.CONCLUSION Peritumoral radiomics holds greater prognostic value than intratumoral radiomics for predicting PFS in LARC.The comprehensive model may serve as a reliable tool for better stratification and management postoperatively.展开更多
PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations...PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).展开更多
Converting CO_(2)with green hydrogen to methanol as a carbon-neutral liquid fuel is a promising route for the long-term storage and distribution of intermittent renewable energy.Nevertheless,attaining highly efficient...Converting CO_(2)with green hydrogen to methanol as a carbon-neutral liquid fuel is a promising route for the long-term storage and distribution of intermittent renewable energy.Nevertheless,attaining highly efficient methanol synthesis catalysts from the vast composition space remains a significant challenge.Here we present a machine learning framework for accelerating the development of high space-time yield(STY)methanol synthesis catalysts.A database of methanol synthesis catalysts has been compiled,consisting of catalyst composition,preparation parameters,structural characteristics,reaction conditions and their corresponding catalytic performance.A methodology for constructing catalyst features based on the intrinsic physicochemical properties of the catalyst components has been developed,which significantly reduced the data dimensionality and enhanced the efficiency of machine learning operations.Two high-precision machine learning prediction models for the activities and product selectivity of catalysts were trained and obtained.Using this machine learning framework,an efficient search was achieved within the catalyst composition space,leading to the successful identification of high STY multielement oxide methanol synthesis catalysts.Notably,the CuZnAlTi catalyst achieved high STYs of 0.49 and 0.65 g_(MeOH)/(g_(catalyst)h)for CO_(2)and CO hydrogenation to methanol at 250℃,respectively,and the STY was further increased to 2.63 g_(Me OH)/(g_(catalyst)h)in CO and CO_(2)co-hydrogenation.展开更多
Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output ...Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.展开更多
To gain a more comprehensive understanding and evaluate foam aluminum's performance,researchers have introduced various characterization indicators.However,the current understanding of the significance of these in...To gain a more comprehensive understanding and evaluate foam aluminum's performance,researchers have introduced various characterization indicators.However,the current understanding of the significance of these indicators in analyzing foam aluminum's performance is limited.This study employs the Generalized Regression Neural Network(GRNN)method to establish a model that links foam aluminum's microstructure characterization data with its mechanical properties.Through the GRNN model,researchers extracted four of the most crucial features and their corresponding weight values from the 13 pore characteristics of foam aluminum.Subsequently,a new characterization formula,called“Wang equivalent porosity”(WEP),was developed by using residual weights assigned to the feature weights,and four parameter coefficients were obtained.This formula aims to represent the relationship between foam aluminum's microstructural features and its mechanical performance.Furthermore,the researchers conducted model verification using compression data from 11 sets of foam aluminum.The validation results showed that among these 11 foam aluminum datasets,the Gibson-Ashby formula yielded anomalous results in two cases,whereas WEP exhibited exceptional stability without any anomalies.In comparison to the Gibson-Ashby formula,WEP demonstrated an 18.18%improvement in evaluation accuracy.展开更多
In order to understand the influence of brittleness and confining stress on rock cuttability,the indentation tests were carried out by a conical pick on the four types of rocks.Then,the experimental results were utili...In order to understand the influence of brittleness and confining stress on rock cuttability,the indentation tests were carried out by a conical pick on the four types of rocks.Then,the experimental results were utilized to take regression analysis.The eight sets of normalized regression models were established for reflecting the relationships of peak indentation force(PIF)and specific energy(SE)with brittleness index and uniaxial confining stress.The regression analyses present that these regression models have good prediction performance.The regressive results indicate that brittleness indices and uniaxial confining stress conditions have non-linear effects on the rock cuttability that is determined by PIF and SE.Finally,the multilayer perceptual neural network was used to measure the importance weights of brittleness index and uniaxial confining stress upon the influence for rock cuttability.The results indicate that the uniaxial confining stress is more significant than brittleness index for influencing the rock cuttability.展开更多
The accurate prediction of peak particle velocity(PPV)is essential for effectively managing blastinduced vibrations in mining operations.This study presents a novel PPV prediction method based on the social network se...The accurate prediction of peak particle velocity(PPV)is essential for effectively managing blastinduced vibrations in mining operations.This study presents a novel PPV prediction method based on the social network search and LightGBM(SNS-LightGBM)deep gradient cooperative learning framework.The SNS algorithm enhances LightGBM’s learning process by optimizing hyperparameters through global search capabilities and balancing model complexity to improve generalization.To assess its performance,five baseline machine learning models and a hybrid model combining SNS-LightGBM were developed for comparison.The predictive performance of these models was evaluated using metrics such as coefficient of determination(R^(2)),mean absolute error(MAE),mean absolute percentage error(MAPE),mean squared error(MSE),and root mean squared error(RMSE).The results indicate that the SNSLightGBM model substantially improves both the accuracy and stability of PPV predictions.The SNS-LightGBM model outperformed all other models,achieving an R^(2) of 0.975,MAE of 0.086,MAPE of 0.071,MSE of 0.019,and RMSE of 0.138.Additionally,a feature importance analysis revealed that distance and charge weight are the most significant factors influencing PPV,far surpassing other parameters.These findings offer valuable insights for improving the precision of blast vibration prediction and optimizing blasting designs.展开更多
The solid oxide electrolysis cell(SOEC)holds great promise to efficiently convert renewable energy into hydrogen.However,traditional modeling methods are limited to a specific or reported SOEC system.Therefore,four ma...The solid oxide electrolysis cell(SOEC)holds great promise to efficiently convert renewable energy into hydrogen.However,traditional modeling methods are limited to a specific or reported SOEC system.Therefore,four machine learning models are developed to predict the performance of SOEC processes of various types,operating parameters,and feed conditions.The impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot analyses.The preferredmodel is integratedwith a genetic algorithmto determine the optimal values of each input feature.Results show the improved extreme gradient enhanced regression(XGBoost)algorithm is the core of the machine learning model of the process since it has the highest R^(2)(>0.95)in the three outputs.The electrolytic cell descriptors have a greater impact on the system performance,contributing up to 54.5%.The effective area,voltage,and temperature are the three most influential factors in the SOEC system,contributing 21.6%,16.6%,and 13.0%to its performance.High temperature,high pressure,and low effective area are the most favorable conditions for H_(2)production rate.After conducting multi-objective optimization,the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm^(2)and 1.174 L/(h⋅cm^(2)).展开更多
China’s car market consists of two plates——domestic made (A) and imported (B) cars. The market has experienced a transition process from the past "B strong v.s. A weak" to today’s "A strong v.s. B w...China’s car market consists of two plates——domestic made (A) and imported (B) cars. The market has experienced a transition process from the past "B strong v.s. A weak" to today’s "A strong v.s. B weak".展开更多
Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universa...Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions.However,real-world medical data are dispersed across medical institutions,forming“data islands”due to data sharing limitations for security reasons.To this end,federated learning(FL)has been extensively employed in the medical field,which can effectively model across multiple institutions.Additionally,conventional supervised classification methods require fully labeled data classes,e.g.,binary classification requires labeling of positive and negative samples.Nevertheless,the process of labeling healthcare data is timeconsuming and labor-intensive,leading to the possibility of mislabeling negative samples.In this study,we validate an FL framework with a naive positive-unlabeled(PU)learning strategy.Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples.Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces.Additionally,our contribution extends to feature importance analysis,where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds.The study demonstrated an impressive accuracy of 84%,comparable to outcomes in supervised learning,thereby advancing the application of FL in abnormal heart sound detection.展开更多
Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucia...Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations.Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks,particularly in environmentally sensitive areas.This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu,India,leveraging the power of Artificial Neural Networks(ANNs)and integrating multi-dimensional geospatial datasets.Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness,reproducibility,and the ability to analyze the complex interplay of factors influencing landslide hazards quantitatively.The methodology involves rigorous pre-processing and integrating spatial data,including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility.These parameters encompass elevation,slope aspect,slope degree,distance to roads,land use patterns,geomorphology,lithology,drainage density,lineament density,and rainfall distribution.Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences.This process identifies the most relevant variables influencing landslide susceptibility,enhancing the model's predictive capabilities.The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors,enabling the development of a robust and accurate landslide susceptibility model.The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics,including accuracy,precision,and the Area under the Receiver Operating Characteristic(ROC)curve.Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods,demonstrating higher accuracy and reliability in predicting landslideprone areas.The resulting Landslide Susceptibility Map(LSM)categorises the study area into five distinct hazard zones,ranging from very high(664.1 km^(2)),high(598.9 km^(2)),moderate(639.7 km^(2)),low(478.9 km^(2))and to very low(170.9 km^(2)).Notably,the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences.The study's findings have far-reaching implications for disaster risk reduction efforts,landuse planning,and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.展开更多
In this study some soil phosphorous sorption parameters(PSPs)by using different machine learning models(Cubist(Cu),random forest(RF),support vector machines(SVM)and Gaussian process regression(GPR))were predicted.The ...In this study some soil phosphorous sorption parameters(PSPs)by using different machine learning models(Cubist(Cu),random forest(RF),support vector machines(SVM)and Gaussian process regression(GPR))were predicted.The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs.However,remote sensing data and its combination with soil properties were reliably used to predict PSPs(R^(2)=0.41 for MBC by RF model,R^(2)=0.49 for PBC by Cu model,R^(2)=0.37 for SPR by Cu model,and R^(2)=0.38 for SBC by RF model).The lowest RMSE values were obtained for MBC by RF model,PBC by SVM model,SPR by Cubist model and SBC by RF model.The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area.The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity(CEC)and clay content,and among the remote sensing indices B5/B7,Midindex,Coloration index,Saturation index,and OSAVI were the most imperative factors for predicting PSPs.Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.展开更多
文摘The authors regret that the original publication of this paper did not include Jawad Fayaz as a co-author.After further discussions and a thorough review of the research contributions,it was agreed that his significant contributions to the foundational aspects of the research warranted recognition,and he has now been added as a co-author.
文摘The importance analysis method represents a powerful tool for quantifying the impact of input uncertainty on the output uncertainty.When an input variable is described by a specific interval rather than a certain probability distribution,the interval importance measure of input interval variable can be calculated by the traditional non-probabilistic importance analysis methods.Generally,the non-probabilistic importance analysis methods involve the Monte Carlo simulation(MCS)and the optimization-based methods,which both have high computational cost.In order to overcome this problem,this study proposes an interval important analytical method avoids the time-consuming optimization process.First,the original performance function is decomposed into a combination of a series of one-dimensional subsystems.Next,the interval of each variable is divided into several subintervals,and the response value of each one-dimensional subsystem at a specific input point is calculated.Then,the obtained responses are taken as specific values of the new input variable,and the interval importance is calculated by the approximated performance function.Compared with the traditional non-probabilistic importance analysis method,the proposed method significantly reduces the computational cost caused by the MCS and optimization process.In the proposed method,the number of function evaluations is equal to one plus the sum of the subintervals of all of the variables.The efficiency and accuracy of the proposed method are verified by five examples.The results show that the proposed method is not only efficient but also accurate.
基金National Natural Science Foundation of China(No.61164009)the Science and Technology Research Project,Department of Education of Jiangxi Province,China(No.GJJ14420)Natural Science Foundation of Jiangxi Province,China(No.20132BAB206026)
文摘Importance analysis quantifies the critical degree of individual component. Compared with the traditional binary state system,importance analysis of the multi-state system is more aligned with the practice. Because the multi-valued decision diagram( MDD) can reflect the relationship between the components and the system state bilaterally, it was introduced into the reliability calculation of the multi-state system( MSS). The building method,simplified criteria,and path search and probability algorithm of MSS structure function MDD were given,and the reliability of the system was calculated. The computing methods of importance based on MDD and direct partial logic derivatives( DPLD) were presented. The diesel engine fuel supply system was taken as an example to illustrate the proposed method. The results show that not only the probability of the system in each state can be easily obtained,but also the influence degree of each component and its state on the system reliability can be obtained,which is conducive to the condition monitoring and structure optimization of the system.
文摘Major in engineering geology exploration of geology, topography, geomorphology, hydrology, meteorology, earthquake, and so on and so forth to examine, survey and the result is the basis of the engineering construction, the designers must understand the engineering geological investigation of the construction site conditions to select the appropriate construction technology and materials, finally to ensure the quality of project construction in line with the acceptance criteria, hydrogeology will have a certain influence on the survey results in engineering geological survey.
基金supported by the National Natural Science Foundation of China(Grant No.51175425)the Aviation Science Foundation(Grant No.2011ZA53015)the Doctorate Foundation of Northwestern Polytechnical University(Grant No.CX201205)
文摘For the structure system with epistemic and aleatory uncertainties,a new state dependent parameter(SDP)based method is presented for obtaining the importance measures of the epistemic uncertainties.By use of the marginal probability density function(PDF)of the epistemic variable and the conditional PDF of the aleatory one at the fixed epistemic variable,the epistemic and aleatory uncertainties are propagated to the response of the structure firstly in the presented method.And the computational model for calculating the importance measures of the epistemic variables is established.For solving the computational model,the high efficient SDP method is applied to estimating the first order high dimensional model representation(HDMR)to obtain the importance measures.Compared with the direct Monte Carlo method,the presented method can considerably improve computational efficiency with acceptable precision.The presented method has wider applicability compared with the existing approximation method,because it is suitable not only for the linear response functions,but also for nonlinear response functions.Several examples are used to demonstrate the advantages of the presented method.
基金supports of Special Foundation for State Major Basic Research Program of China(Grant No.2021YFD2101000).
文摘Corn to sugar process has long faced the risks of high energy consumption and thin profits.However,it’s hard to upgrade or optimize the process based on mechanism unit operation models due to the high complexity of the related processes.Big data technology provides a promising solution as its ability to turn huge amounts of data into insights for operational decisions.In this paper,a neural network-based production process modeling and variable importance analysis approach is proposed for corn to sugar processes,which contains data preprocessing,dimensionality reduction,multilayer perceptron/convolutional neural network/recurrent neural network based modeling and extended weights connection method.In the established model,dextrose equivalent value is selected as the output,and 654 sites from the DCS system are selected as the inputs.LASSO analysis is first applied to reduce the data dimension to 155,then the inputs are dimensionalized to 50 by means of genetic algorithm optimization.Ultimately,variable importance analysis is carried out by the extended weight connection method,and 20 of the most important sites are selected for each neural network.The results indicate that the multilayer perceptron and recurrent neural network models have a relative error of less than 0.1%,which have a better prediction result than other models,and the 20 most important sites selected have better explicable performance.The major contributions derived from this work are of significant aid in process simulation model with high accuracy and process optimization based on the selected most important sites to maintain high quality and stable production for corn to sugar processes.
文摘For the best dynamic performance of a co-cured composite damping instrument panel with light weight and high strength, a multilayer sandwich structure with polymethaerylimide （PMI） foam combined with embedded and co-cured composite damping structure is proposed. The struetue can maintain the excellent mechanical properties of composite materials, and achieve the damping and light effect at the same time. Input variables which may affect the dynamic performance of the instrument panel were selected and variance based importance measure was analyzed through multi- finite element method （FEM） analysis. Using the results of the importance measure analysis, with other design requirements, the important design variable was optimized and an instrument panel with the best dynamic performance under the requirements of light weight and high strength was obtained. The structure of the instrument panel can provide reference for the design of precision, high speed, and dynamic composite component. The importance measure analysis of dynamic performance of the instrument panel can provide a reference for relative design.
文摘BACKGROUND The peritumoral region possesses attributes that promote cancer growth and progression.However,the potential prognostic biomarkers in this region remain relatively underexplored in radiomics.AIM To investigate the prognostic value and importance of peritumoral radiomics in locally advanced rectal cancer(LARC).METHODS This retrospective study included 409 patients with biopsy-confirmed LARC treated with neoadjuvant chemoradiotherapy and surgically.Patients were divided into training(n=273)and validation(n=136)sets.Based on intratumoral and peritumoral radiomic features extracted from pretreatment axial high-resolution small-field-of-view T2-weighted images,multivariate Cox models for progression-free survival(PFS)prediction were developed with or without clinicoradiological features and evaluated with Harrell’s concordance index(C-index),calibration curve,and decision curve analyses.Risk stratification,Kaplan-Meier analysis,and permutation feature importance analysis were performed.RESULTS The comprehensive integrated clinical-radiological-omics model(ModelICRO)integrating seven peritumoral,three intratumoral,and four clinicoradiological features achieved the highest C-indices(0.836 and 0.801 in the training and validation sets,respectively).This model showed robust calibration and better clinical net benefits,effectively distinguished high-risk from low-risk patients(PFS:97.2%vs 67.6%and 95.4%vs 64.8%in the training and validation sets,respectively;both P<0.001).Three most influential predictors in the comprehensive ModelICRO were,in order,a peritumoral,an intratumoral,and a clinicoradiological feature.Notably,the peritumoral model outperformed the intratumoral model(C-index:0.754 vs 0.670;P=0.015);peritumoral features significantly enhanced the performance of models based on clinicoradiological or intratumoral features or their combinations.CONCLUSION Peritumoral radiomics holds greater prognostic value than intratumoral radiomics for predicting PFS in LARC.The comprehensive model may serve as a reliable tool for better stratification and management postoperatively.
基金supported by the Fine Particle Research Initiative in East Asia Considering National Differences Project through the National Research Foundation of Korea(NRF)funded by the Ministry of Science and ICT(No.NRF-2023M3G1A1090660)supported by a grant from the National Institute of Environmental Research(NIER),funded by the Ministry of Environment of the Republic of Korea(No.NIER-2023-04-02-056).
文摘PM_(1.0),particulate matter with an aerodynamic diameter smaller than 1.0μm,can adversely affect human health.However,fewer stations are capable of measuring PM_(1.0) concentrations than PM2.5 and PM10 concentrations in real time(i.e.,only 9 locations for PM_(1.0) vs.623 locations for PM2.5 or PM10)in South Korea,making it impossible to conduct a nationwide health risk analysis of PM_(1.0).Thus,this study aimed to develop a PM_(1.0) prediction model using a random forest algorithm based on PM_(1.0) data from the nine measurement stations and various environmental input factors.Cross validation,in which the model was trained in eight stations and tested in the remaining station,achieved an average R^(2) of 0.913.The high R^(2) value achieved undermutually exclusive training and test locations in the cross validation can be ascribed to the fact that all the locations had similar relationships between PM_(1.0) and the input factors,which were captured by our model.Moreover,results of feature importance analysis showed that PM2.5 and PM10 concentrations were the two most important input features in predicting PM_(1.0) concentration.Finally,the model was used to estimate the PM_(1.0) concentrations in 623 locations,where input factors such as PM2.5 and PM10 can be obtained.Based on the augmented profile,we identified Seoul and Ansan to be PM_(1.0) concentration hotspots.These regions are large cities or the center of anthropogenic and industrial activities.The proposed model and the augmented PM_(1.0) profiles can be used for large epidemiological studies to understand the health impacts of PM_(1.0).
基金supported by the Zhejiang Provincial Natural Science Foundation of China(LDT23E06012E06)National Key R&D Program of China(2023YFC3710800)+3 种基金the National EnergySaving and Low-Carbon Materials Production and Application Demonstration Platform Program(TC220H06N)Pioneer R&D Program of Zhejiang Province-China(2024SSYS0066,2023C03016)National Natural Science Foundation of China(42341208)Zhejiang Energy Group Research Fund(ZNKJ-2023-100)。
文摘Converting CO_(2)with green hydrogen to methanol as a carbon-neutral liquid fuel is a promising route for the long-term storage and distribution of intermittent renewable energy.Nevertheless,attaining highly efficient methanol synthesis catalysts from the vast composition space remains a significant challenge.Here we present a machine learning framework for accelerating the development of high space-time yield(STY)methanol synthesis catalysts.A database of methanol synthesis catalysts has been compiled,consisting of catalyst composition,preparation parameters,structural characteristics,reaction conditions and their corresponding catalytic performance.A methodology for constructing catalyst features based on the intrinsic physicochemical properties of the catalyst components has been developed,which significantly reduced the data dimensionality and enhanced the efficiency of machine learning operations.Two high-precision machine learning prediction models for the activities and product selectivity of catalysts were trained and obtained.Using this machine learning framework,an efficient search was achieved within the catalyst composition space,leading to the successful identification of high STY multielement oxide methanol synthesis catalysts.Notably,the CuZnAlTi catalyst achieved high STYs of 0.49 and 0.65 g_(MeOH)/(g_(catalyst)h)for CO_(2)and CO hydrogenation to methanol at 250℃,respectively,and the STY was further increased to 2.63 g_(Me OH)/(g_(catalyst)h)in CO and CO_(2)co-hydrogenation.
基金supported by the National Natural Science Foundation of China(No.NSFC51608446)the Fundamental Research Fund for Central Universities of China(No.3102016ZY015)
文摘Traditional Global Sensitivity Analysis（GSA） focuses on ranking inputs according to their contributions to the output uncertainty.However,information about how the specific regions inside an input affect the output is beyond the traditional GSA techniques.To fully address this issue,in this work,two regional moment-independent importance measures,Regional Importance Measure based on Probability Density Function（RIMPDF） and Regional Importance Measure based on Cumulative Distribution Function（RIMCDF）,are introduced to find out the contributions of specific regions of an input to the whole output distribution.The two regional importance measures prove to be reasonable supplements of the traditional GSA techniques.The ideas of RIMPDF and RIMCDF are applied in two engineering examples to demonstrate that the regional moment-independent importance analysis can add more information concerning the contributions of model inputs.
基金Sponsored by the Shanxi Provincial College Teaching Reform Innovation Funding Project(Grant No.201901d111270)the Natural Science Foundation of Shanxi Province(Grant No.201701d11127)。
文摘To gain a more comprehensive understanding and evaluate foam aluminum's performance,researchers have introduced various characterization indicators.However,the current understanding of the significance of these indicators in analyzing foam aluminum's performance is limited.This study employs the Generalized Regression Neural Network(GRNN)method to establish a model that links foam aluminum's microstructure characterization data with its mechanical properties.Through the GRNN model,researchers extracted four of the most crucial features and their corresponding weight values from the 13 pore characteristics of foam aluminum.Subsequently,a new characterization formula,called“Wang equivalent porosity”(WEP),was developed by using residual weights assigned to the feature weights,and four parameter coefficients were obtained.This formula aims to represent the relationship between foam aluminum's microstructural features and its mechanical performance.Furthermore,the researchers conducted model verification using compression data from 11 sets of foam aluminum.The validation results showed that among these 11 foam aluminum datasets,the Gibson-Ashby formula yielded anomalous results in two cases,whereas WEP exhibited exceptional stability without any anomalies.In comparison to the Gibson-Ashby formula,WEP demonstrated an 18.18%improvement in evaluation accuracy.
基金Project(51904333) supported by the National Natural Science Foundation of China。
文摘In order to understand the influence of brittleness and confining stress on rock cuttability,the indentation tests were carried out by a conical pick on the four types of rocks.Then,the experimental results were utilized to take regression analysis.The eight sets of normalized regression models were established for reflecting the relationships of peak indentation force(PIF)and specific energy(SE)with brittleness index and uniaxial confining stress.The regression analyses present that these regression models have good prediction performance.The regressive results indicate that brittleness indices and uniaxial confining stress conditions have non-linear effects on the rock cuttability that is determined by PIF and SE.Finally,the multilayer perceptual neural network was used to measure the importance weights of brittleness index and uniaxial confining stress upon the influence for rock cuttability.The results indicate that the uniaxial confining stress is more significant than brittleness index for influencing the rock cuttability.
基金the National Key Research and Development Program of China-2023 Key Special Project(No.2023YFC2907400)the National Natural Science Foundation of China(Grant No.52104109)the Natural Science Foundation of Hunan Province,China(No.2022JJ40602).
文摘The accurate prediction of peak particle velocity(PPV)is essential for effectively managing blastinduced vibrations in mining operations.This study presents a novel PPV prediction method based on the social network search and LightGBM(SNS-LightGBM)deep gradient cooperative learning framework.The SNS algorithm enhances LightGBM’s learning process by optimizing hyperparameters through global search capabilities and balancing model complexity to improve generalization.To assess its performance,five baseline machine learning models and a hybrid model combining SNS-LightGBM were developed for comparison.The predictive performance of these models was evaluated using metrics such as coefficient of determination(R^(2)),mean absolute error(MAE),mean absolute percentage error(MAPE),mean squared error(MSE),and root mean squared error(RMSE).The results indicate that the SNSLightGBM model substantially improves both the accuracy and stability of PPV predictions.The SNS-LightGBM model outperformed all other models,achieving an R^(2) of 0.975,MAE of 0.086,MAPE of 0.071,MSE of 0.019,and RMSE of 0.138.Additionally,a feature importance analysis revealed that distance and charge weight are the most significant factors influencing PPV,far surpassing other parameters.These findings offer valuable insights for improving the precision of blast vibration prediction and optimizing blasting designs.
基金the National Natural Science Foundation of China(No.22108052)the High-end chemicals and cutting-edge new materials Technology Innovation Center of Hefei(HCHC202309).
文摘The solid oxide electrolysis cell(SOEC)holds great promise to efficiently convert renewable energy into hydrogen.However,traditional modeling methods are limited to a specific or reported SOEC system.Therefore,four machine learning models are developed to predict the performance of SOEC processes of various types,operating parameters,and feed conditions.The impact of these features on the SOEC's outputs is explained by the Shapley additive explanations and partial dependency plot analyses.The preferredmodel is integratedwith a genetic algorithmto determine the optimal values of each input feature.Results show the improved extreme gradient enhanced regression(XGBoost)algorithm is the core of the machine learning model of the process since it has the highest R^(2)(>0.95)in the three outputs.The electrolytic cell descriptors have a greater impact on the system performance,contributing up to 54.5%.The effective area,voltage,and temperature are the three most influential factors in the SOEC system,contributing 21.6%,16.6%,and 13.0%to its performance.High temperature,high pressure,and low effective area are the most favorable conditions for H_(2)production rate.After conducting multi-objective optimization,the optimal current intensity and hydrogen production rate were determined to be 1.61 A/cm^(2)and 1.174 L/(h⋅cm^(2)).
文摘China’s car market consists of two plates——domestic made (A) and imported (B) cars. The market has experienced a transition process from the past "B strong v.s. A weak" to today’s "A strong v.s. B weak".
基金partially supported by the National Natural Science Foundation of China(grant number 62272044)the Ministry of Science and Technology of the People’s Republic of China with the STI2030-Major Projects(grant number 2021ZD0201900)+5 种基金the Teli Young Fellow Program from the Beijing Institute of Technology,Chinathe Grants-in-Aid for Scientific Research(grant number 20H00569)from the Ministry of Education,Culture,Sports,Science and Technology(MEXT),Japanthe JSPS KAKENHI(grant number 20H00569),Japanthe JST Mirai Program(grant number 21473074),Japanthe JST MOONSHOT Program(grant number JPMJMS229B),Japanthe BIT Research and Innovation Promoting Project(grant number 2023YCXZ014).
文摘Cardiovascular diseases are a prominent cause of mortality,emphasizing the need for early prevention and diagnosis.Utilizing artificial intelligence(AI)models,heart sound analysis emerges as a noninvasive and universally applicable approach for assessing cardiovascular health conditions.However,real-world medical data are dispersed across medical institutions,forming“data islands”due to data sharing limitations for security reasons.To this end,federated learning(FL)has been extensively employed in the medical field,which can effectively model across multiple institutions.Additionally,conventional supervised classification methods require fully labeled data classes,e.g.,binary classification requires labeling of positive and negative samples.Nevertheless,the process of labeling healthcare data is timeconsuming and labor-intensive,leading to the possibility of mislabeling negative samples.In this study,we validate an FL framework with a naive positive-unlabeled(PU)learning strategy.Semisupervised FL model can directly learn from a limited set of positive samples and an extensive pool of unlabeled samples.Our emphasis is on vertical-FL to enhance collaboration across institutions with different medical record feature spaces.Additionally,our contribution extends to feature importance analysis,where we explore 6 methods and provide practical recommendations for detecting abnormal heart sounds.The study demonstrated an impressive accuracy of 84%,comparable to outcomes in supervised learning,thereby advancing the application of FL in abnormal heart sound detection.
文摘Landslides pose a significant threat to the lives and livelihoods of marginalised communities residing in rural areas and the delicate ecological balance of the environment.Implementing advanced technologies is crucial for improving hazard risk assessment and enhancing preparedness measures in regions characterised by diverse topography and complex geological formations.Geospatial applications and modelling techniques have emerged as indispensable in mitigating landslide risks,particularly in environmentally sensitive areas.This study presents a comprehensive approach to landslide susceptibility mapping in the Nilgiri District of Tamil Nadu,India,leveraging the power of Artificial Neural Networks(ANNs)and integrating multi-dimensional geospatial datasets.Integrating ANN-based modelling and geospatial techniques offers significant advantages in terms of statistical robustness,reproducibility,and the ability to analyze the complex interplay of factors influencing landslide hazards quantitatively.The methodology involves rigorous pre-processing and integrating spatial data,including landslide event occurrences as the dependent variable and ten independent parameters influencing landslide susceptibility.These parameters encompass elevation,slope aspect,slope degree,distance to roads,land use patterns,geomorphology,lithology,drainage density,lineament density,and rainfall distribution.Feature extraction and selection techniques are employed to effectively model the complex interactions between these factors and landslide occurrences.This process identifies the most relevant variables influencing landslide susceptibility,enhancing the model's predictive capabilities.The state-of-the-art ANNs are trained using historical landslide occurrence data and the selected influencing factors,enabling the development of a robust and accurate landslide susceptibility model.The performance of the developed model is rigorously evaluated using a comprehensive suite of metrics,including accuracy,precision,and the Area under the Receiver Operating Characteristic(ROC)curve.Preliminary results indicate that the ANN-based landslide susceptibility model outperforms traditional zonation methods,demonstrating higher accuracy and reliability in predicting landslideprone areas.The resulting Landslide Susceptibility Map(LSM)categorises the study area into five distinct hazard zones,ranging from very high(664.1 km^(2)),high(598.9 km^(2)),moderate(639.7 km^(2)),low(478.9 km^(2))and to very low(170.9 km^(2)).Notably,the eastern and central regions of the district emerge as particularly vulnerable to landslide occurrences.The study's findings have far-reaching implications for disaster risk reduction efforts,landuse planning,and sustainable development strategies in the ecologically sensitive Nilgiri District and beyond.
文摘In this study some soil phosphorous sorption parameters(PSPs)by using different machine learning models(Cubist(Cu),random forest(RF),support vector machines(SVM)and Gaussian process regression(GPR))were predicted.The results showed that using the topographic attributes as the sole auxiliary variables was not adequate for predicting the PSPs.However,remote sensing data and its combination with soil properties were reliably used to predict PSPs(R^(2)=0.41 for MBC by RF model,R^(2)=0.49 for PBC by Cu model,R^(2)=0.37 for SPR by Cu model,and R^(2)=0.38 for SBC by RF model).The lowest RMSE values were obtained for MBC by RF model,PBC by SVM model,SPR by Cubist model and SBC by RF model.The results also showed that remote sensing data as the easily available datasets could reliably predict PSPs in the given study area.The outcomes of variable importance analysis revealed that among the soil properties cation exchange capacity(CEC)and clay content,and among the remote sensing indices B5/B7,Midindex,Coloration index,Saturation index,and OSAVI were the most imperative factors for predicting PSPs.Further studies are recommended to use other proximally sensed data to improve PSPs prediction to precise decision-making throughout the landscape.
