BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depress...BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.展开更多
Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species...Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures.This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z.vivipara,N.natrix,V.berus,A.colchica,and protected in Latvia C.austriaca,E.orbicularis,L.agilis inhabiting northern(model territory Latvia)and southern(model territory Ukraine)part of their European range.The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change.We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future(by 2050)climates for marginal reptilepopulations.Using cluster analysis,we showed that this herpeto-complex can be divided into“widespread species”and“forest species”.For all forest species we predicted a climate-driven reduction in their distribution range both North(Latvia)and South(Ukraine).The most vulnerable populations of“forest species”tend to be located in the South of their range,as a consequence of northward shifts by 2050.By 2050 the greatest reduction in range is predicted for currently widely spread Z.vivipara(by 1.4 times)and V.berus(by 2.2 times).In terms of designing an effective protected-area network,these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found,and confirms the relevance of abioticmulti-factor GIS-modelling for achieving this goal.展开更多
A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies.The DNS protocol is one of the important ways to implement a covert channel.DNS co...A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies.The DNS protocol is one of the important ways to implement a covert channel.DNS covert channels are easily used by attackers for malicious purposes.Therefore,an effective detection approach of the DNS covert channels is significant for computer systems and network securities.Aiming at the difficulty of the DNS covert channel identification,we propose a DNS covert channel detection method based on a stacking model.The stacking model is evaluated on a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively.Besides,it can identify unknown covert channel traffic.The area under the curve(AUC)of the proposed method reaches 0.9901,which outperforms existing detection methods.展开更多
The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion dete...The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.展开更多
Rockburst is a frequently encountered hazard during the production of deep gold mines.Accurate prediction of rockburst is an important measure to prevent rockburst in gold mines.This study considers seven indicators t...Rockburst is a frequently encountered hazard during the production of deep gold mines.Accurate prediction of rockburst is an important measure to prevent rockburst in gold mines.This study considers seven indicators to evaluate rockburst at four deep gold mines.Field research and rock tests were performed at two gold mines in China to collect these seven indicators and rockburst cases.The collected database was oversampled by the synthetic minority oversampling technique(SMOTE)to balance the categories of rockburst datasets.Stacking models combining tree-based models and logistic regression(LR)were established by the balanced database.Rockburst datasets from another two deep gold mines were implemented to verify the applicability of the predictive models.The stacking model combining extremely randomized trees and LR based on SMOTE(SMOTE-ERT-LR)was the best model,and it obtained a training accuracy of 100%and an evaluation accuracy of 100%.Moreover,model evaluation suggested that SMOTE can enhance the prediction performance for weak rockburst,thereby improving the overall performance.Finally,sensitivity analysis was performed for SMOTE-ERT-LR.The results indicated that the SMOTE-ERT-LR model can achieve satisfactory performance when only depth,maximum tangential stress index,and linear elastic energy index were available.展开更多
Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In t...Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In this study,a stacking machine learning(ML)model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents,leveraging cone penetration data.The proposed stacking model incorporates three ML algorithms as the base regression models:K-nearest neighbors(KNN),support vector regression(SVR),and extreme gradient boosting(XGBoost).A dataset comprising 119 in-situ anchor pullout tests,where the cone penetration data were measured,is utilized to train and assess the stacking model performance.Three metrics,i.e.,coefficient of determination(R2),mean absolute error(MAE),and root mean square error(RMSE),are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach.The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20%error line.Thus,the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.展开更多
This study employs a stacking ensemble learning framework to establish a regression model for predicting the tribological properties of amide-based lubricating grease and determining the optimal additive ratios.Melami...This study employs a stacking ensemble learning framework to establish a regression model for predicting the tribological properties of amide-based lubricating grease and determining the optimal additive ratios.Melamine cyanuric acid(MCA)was selected as the thickener,and three extreme-pressure anti-wear additives were used to prepare the lubricating grease.The tribological performance was tested using an MFT-R4000 reciprocating friction and wear machine.Based on the tribological experimental data,the synthetic minority oversampling technique(SMOTE)was utilized for data augmentation,and a stacking ensemble algorithm with Bayesian optimization of hyperparameters was used to construct a predictive model for tribological performance.Subsequently,within this model framework,single and multi-objective optimization models were developed,and the fruit fly algorithm was employed to find the optimal additive combination ratios,which were experimentally validated.The results demonstrated that the learning framework based on the stacking ensemble model could effectively predict the tribological properties of amide-based lubricating grease in small sample datasets,with the R2 for the average friction coefficient prediction reaching 0.9939 and for the wear scar width prediction reaching 0.9535.In the experimental validation of the optimal additive ratios,the relative error of the friction coefficient ratio scheme was 0.51%,and the relative error of the wear scar width was 1.10%.This finding suggests that the learning framework provides a novel approach for predicting the performance of amide-based lubricating grease and studying additive combinations.展开更多
A series of polyimide(PI)films derived from pyromellitic dianhydride(PMDA)and 4,4'-oxydianiline(ODA)were prepared with the employment of chemical pre-imidization,and the pre-imidization degree(pre-ID)was found inf...A series of polyimide(PI)films derived from pyromellitic dianhydride(PMDA)and 4,4'-oxydianiline(ODA)were prepared with the employment of chemical pre-imidization,and the pre-imidization degree(pre-ID)was found influential on structures and properties of the films obtained.Specifically,a certain degree of chemical imidization could promote the in-plane orientation of molecular chains inside the film,which then enhanced the mechanical strength and reduced the coefficient of thermal expansion(CTE)of the films.Further,such pre-imidization process could expand the internal space gap inside the films,thereby lowering their dielectric constant and glass transition temperature.Our study provides a new approach for preparing high-performance PI films through chemical imidization.展开更多
Uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are e...Uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS.This study collected 1080 datasets consisting of SH,P-wave velocity,PLS,and UCS.All datasets were integrated into three categories(sedimentary,igneous,and metamorphic rocks)according to lithology.Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types.Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types.UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks.Nonetheless,the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks.The developed predictive models can be applied to quickly predict UCS at engineering sites,which benefits the rapid and intelligent classification of rock masses.Moreover,the importance of SH,P-wave velocity,and PLS were analyzed for the estimation of UCS.SH was a reliable indicator for UCS evaluation across various rock types.P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks,but it was not reliable for assessing the UCS of metamorphic rocks.展开更多
Yunnan's biodiversity is under considerable pressure and subtropical evergreen broad-leaved forests in this area have become increasingly fragmented through agriculture,logging,planting of economic plants,mining a...Yunnan's biodiversity is under considerable pressure and subtropical evergreen broad-leaved forests in this area have become increasingly fragmented through agriculture,logging,planting of economic plants,mining activities and changing environment.The aims of the study are to investigate climate changeinduced changes of subtropical evergreen broad-leaved forests in Yunnan and identify areas of current species richness centers for conservation preparation.Stacked species distribution models were created to generate ensemble forecasting of species distributions,alpha diversity and beta diversity for Yunnan's subtropical evergreen broad-leaved forests in both current and future climate scenarios.Under stacked species distribution models in rapid climate changes scenarios,changes of water-energy dynamics may possibly reduce beta diversity and increase alpha diversity.This point provides insight for future conservation of evergreen broad-leaved forest in Yunnan,highlighting the need to fully consider the problem of vegetation homogenization caused by transformation of water-energy dynamics.展开更多
Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.Whi...Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.展开更多
Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes ...Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.展开更多
Although bimodal copper(Cu)pastes can balance sintering driving force and material stability,they still require higher temperatures and pressures to achieve densification due to the lack of an optimal particle stackin...Although bimodal copper(Cu)pastes can balance sintering driving force and material stability,they still require higher temperatures and pressures to achieve densification due to the lack of an optimal particle stacking model and systematic optimization design.Here,a multimodal nonspherical particle stacking model was developed using computer simulation technology,resulting in sphere-flake hybrid Cu pastes with high stacking density,enhanced sintering driving force,and low shrinkage.The design principles for bimodal Cu pastes are further refined by synergistically combining computer simulations with experimental validation.The optimized bimodal ratio achieved a joint shear strength of 42.51 MPa under pressureless sintering at 250℃ for 15 min.Moreover,the Cu paste demonstrated compatibility with Ni/Ag/Au metallization and large-area sintering.It is believed that the computer-designed sphere-flake hybrid pastes offer high potential for high-power electronics packaging.展开更多
Heavy metal contamination in waste incineration fly ash poses serious environmental and public health risks,necessitating efficient and precise detection methods.Traditional techniques require complex sample preparati...Heavy metal contamination in waste incineration fly ash poses serious environmental and public health risks,necessitating efficient and precise detection methods.Traditional techniques require complex sample preparation and lengthy analysis,limiting their suitability for on-site or real-time monitoring.To address this,this study proposes a rapid detection method using visible and near-infrared reflectance spectroscopy to improve efficiency and reduce costs.Zn(zinc)and Pb(lead)spectral characteristics were analyzed through first-order differentiation(FD),second-order differentiation(SD),de-trending(DT),and logarithm of the reciprocal(LogInv)transformations,followed by continuous wavelet transform(CWT)to extract key bands(max|r|=0.78).A stacking model integrating partial least squares regression(PLSR),back-propagation neural network(BPNN),support vector regression(SVR),random forest(RF),and extreme gradient boosting(XGBoost)was developed to optimize spectral transformation and inversion modeling.Stacking outperformed individual models,achieving the highest accuracy for Zn(R^(2)=0.748)and Pb(R^(2)=0.735)with CWT-SD and CWT-FD transformation.BPNN exhibited overfitting in small samples,whereas PLSR was constrained by linear assumptions.In contrast,stacking combines the strengths of all the base models,improving accuracy and stability.This study elucidates the spectral characteristics of fly ash and validates the effectiveness of stacking in hyperspectral heavy metal prediction.The findings provide theoretical and technical support for efficient,non-destructive detection,with promising applications in waste incineration management and environmental monitoring.展开更多
An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumpt...An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.展开更多
A junctionless transistor is emerging as a most promising device for the future technology in the decananometer regime. To explore and exploit the behavior completely, the understanding of gate tunneling current is of...A junctionless transistor is emerging as a most promising device for the future technology in the decananometer regime. To explore and exploit the behavior completely, the understanding of gate tunneling current is of great importance. In this paper we have explored the gate tunneling current of a double gate junctionless transistor（DGJLT） for the first time through an analytical model, to meet the future requirement of expected high-k gate dielectric material that could replace SiO2. We therefore present the high-k gate stacked architecture of the DGJLT to minimize the gate tunneling current. This paper also demonstrates the impact of conduction band offset,workfunction difference and k-values on the tunneling current of the DGJLT.展开更多
A novel compact model for on-chip stacked transformers is presented.The proposed model topology gives a clear distinction to the eddy current,resistive and capacitive losses of the primary and secondary coils in the s...A novel compact model for on-chip stacked transformers is presented.The proposed model topology gives a clear distinction to the eddy current,resistive and capacitive losses of the primary and secondary coils in the substrate.A method to analytically determine the non-ideal parasitics between the primary coil and substrate is provided.The model is further verified by the excellent match between the measured and simulated S-parameters on the extracted parameters for a 1：1 stacked transformer manufactured in a commercial RF-CMOS technology.展开更多
As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarde...As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.展开更多
Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has bee...Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.展开更多
For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In t...For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.展开更多
文摘BACKGROUND There is a lack of literature discussing the utilization of the stacking ensemble algorithm for predicting depression in patients with heart failure(HF).AIM To create a stacking model for predicting depression in patients with HF.METHODS This study analyzed data on 1084 HF patients from the National Health and Nutrition Examination Survey database spanning from 2005 to 2018.Through univariate analysis and the use of an artificial neural network algorithm,predictors significantly linked to depression were identified.These predictors were utilized to create a stacking model employing tree-based learners.The performances of both the individual models and the stacking model were assessed by using the test dataset.Furthermore,the SHapley additive exPlanations(SHAP)model was applied to interpret the stacking model.RESULTS The models included five predictors.Among these models,the stacking model demonstrated the highest performance,achieving an area under the curve of 0.77(95%CI:0.71-0.84),a sensitivity of 0.71,and a specificity of 0.68.The calibration curve supported the reliability of the models,and decision curve analysis confirmed their clinical value.The SHAP plot demonstrated that age had the most significant impact on the stacking model's output.CONCLUSION The stacking model demonstrated strong predictive performance.Clinicians can utilize this model to identify highrisk depression patients with HF,thus enabling early provision of psychological interventions.
文摘Reptile fauna should be considered a conservation objective,especially in respect of the impacts of climate change on their distribution and range’s dynamics.Investigating the environmental drivers of reptile species richness and identifying their suitable habitats is a fundamental prerequisite to setting efficient long-term conservation measures.This study focused on geographical patterns and estimations of species richness for herpetofauna widely spread Z.vivipara,N.natrix,V.berus,A.colchica,and protected in Latvia C.austriaca,E.orbicularis,L.agilis inhabiting northern(model territory Latvia)and southern(model territory Ukraine)part of their European range.The ultimate goal was to designate a conservation network that will meet long-term goals for survival of the target species in the context of climate change.We used stacked species distribution models for creating maps depicting the distribution of species richness under current and future(by 2050)climates for marginal reptilepopulations.Using cluster analysis,we showed that this herpeto-complex can be divided into“widespread species”and“forest species”.For all forest species we predicted a climate-driven reduction in their distribution range both North(Latvia)and South(Ukraine).The most vulnerable populations of“forest species”tend to be located in the South of their range,as a consequence of northward shifts by 2050.By 2050 the greatest reduction in range is predicted for currently widely spread Z.vivipara(by 1.4 times)and V.berus(by 2.2 times).In terms of designing an effective protected-area network,these results permit to identify priority conservation areas where the full ensemble of selected reptile species can be found,and confirms the relevance of abioticmulti-factor GIS-modelling for achieving this goal.
基金National Key Research and Development Project(2016QY04W0901)and(2016QY04W0903).
文摘A covert channel is an information channel that is used by the computer process to exfiltrate data through bypassing security policies.The DNS protocol is one of the important ways to implement a covert channel.DNS covert channels are easily used by attackers for malicious purposes.Therefore,an effective detection approach of the DNS covert channels is significant for computer systems and network securities.Aiming at the difficulty of the DNS covert channel identification,we propose a DNS covert channel detection method based on a stacking model.The stacking model is evaluated on a campus network and the experimental results show that the detection based on the stacking model can detect the DNS covert channels effectively.Besides,it can identify unknown covert channel traffic.The area under the curve(AUC)of the proposed method reaches 0.9901,which outperforms existing detection methods.
文摘The number of attacks is growing tremendously in tandem with the growth of internet technologies.As a result,protecting the private data from prying eyes has become a critical and tough undertaking.Many intrusion detection solutions have been offered by researchers in order to decrease the effect of these attacks.For attack detection,the prior system has created an SMSRPF(Stacking Model Significant Rule Power Factor)classifier.To provide creative instance detection,the SMSRPF combines the detection of trained classifiers such as DT(Decision Tree)and RF(Random Forest).Nevertheless,it does not generate any accuratefindings that are adequate.The suggested system has built an EWF(Ensemble Wrapper Filter)feature selection with SMSRPF classifier for attack detection so as to overcome this problem.The UNSW-NB15 dataset is used as an input in this proposed research project.Specifically,min–max normalization approach is used to pre-process the incoming data.The feature selection is then carried out using EWF.Based on the selected features,SMSRPF classifiers are utilized to detect the attacks.The SMSRPF is integrated with the trained classi-fiers such as DT and RF to create creative instance detection.After that,the testing data is classified using MCAR(Multi-Class Classification based on Association Rules).The SRPF judges the rules correctly even when the confidence and the lift measures fail.Regarding accuracy,precision,recall,f-measure,computation time,and error,the experimental findings suggest that the new system outperforms the prior systems.
基金financial support from Postdoctoral Fellowship Program of China Postdoctoral Science Foundation(Grant No.GZC20232935)the National Natural Science Foundation of China(Grant No.52125903).
文摘Rockburst is a frequently encountered hazard during the production of deep gold mines.Accurate prediction of rockburst is an important measure to prevent rockburst in gold mines.This study considers seven indicators to evaluate rockburst at four deep gold mines.Field research and rock tests were performed at two gold mines in China to collect these seven indicators and rockburst cases.The collected database was oversampled by the synthetic minority oversampling technique(SMOTE)to balance the categories of rockburst datasets.Stacking models combining tree-based models and logistic regression(LR)were established by the balanced database.Rockburst datasets from another two deep gold mines were implemented to verify the applicability of the predictive models.The stacking model combining extremely randomized trees and LR based on SMOTE(SMOTE-ERT-LR)was the best model,and it obtained a training accuracy of 100%and an evaluation accuracy of 100%.Moreover,model evaluation suggested that SMOTE can enhance the prediction performance for weak rockburst,thereby improving the overall performance.Finally,sensitivity analysis was performed for SMOTE-ERT-LR.The results indicated that the SMOTE-ERT-LR model can achieve satisfactory performance when only depth,maximum tangential stress index,and linear elastic energy index were available.
基金support provided by National Natural Science Foundation of China(Grant No.52108297)Postdoctoral Research Foundation of China(Grant No.2021M692742)+2 种基金Special Support Project of China Postdoctoral Foundation(Grant No.2023T160560)Qin Chuang Yuan Imported High-level Innovation and Entrepreneurship Talent Project(Grant No.OCYRCXM-2022-29)Fundamental Research Funds for the Central Universities,CHD(Grant No.300102212301).
文摘Small ground anchors are widely used to fix securing tents in disaster relief efforts.Given the urgent nature of rescue operations,it is crucial to obtain prompt and accurate estimations of their pullout capacity.In this study,a stacking machine learning(ML)model is developed for the rapid estimation of pullout capacity offered by small ground anchors used for temporary tents,leveraging cone penetration data.The proposed stacking model incorporates three ML algorithms as the base regression models:K-nearest neighbors(KNN),support vector regression(SVR),and extreme gradient boosting(XGBoost).A dataset comprising 119 in-situ anchor pullout tests,where the cone penetration data were measured,is utilized to train and assess the stacking model performance.Three metrics,i.e.,coefficient of determination(R2),mean absolute error(MAE),and root mean square error(RMSE),are employed to evaluate the predictive accuracy of the proposed model and compare its performance against four popular ML models and an empirical formula to highlight the advantages of the proposed stacking approach.The results affirm that the proposed stacking model outperforms other ML models and the empirical approach as achieving higher R2 and lower MAE and RMSE and more predicted data points falling within 20%error line.Thus,the proposed stacking model holds promising potential as a solution for efficiently predicting the pullout capacity of small ground anchors.
基金support extended for this academic work by the Beijing Natural Science Foundation(No.2232066)the Open Project Foundation of State Key Laboratory of Solid Lubrication(No.LSL-2212).
文摘This study employs a stacking ensemble learning framework to establish a regression model for predicting the tribological properties of amide-based lubricating grease and determining the optimal additive ratios.Melamine cyanuric acid(MCA)was selected as the thickener,and three extreme-pressure anti-wear additives were used to prepare the lubricating grease.The tribological performance was tested using an MFT-R4000 reciprocating friction and wear machine.Based on the tribological experimental data,the synthetic minority oversampling technique(SMOTE)was utilized for data augmentation,and a stacking ensemble algorithm with Bayesian optimization of hyperparameters was used to construct a predictive model for tribological performance.Subsequently,within this model framework,single and multi-objective optimization models were developed,and the fruit fly algorithm was employed to find the optimal additive combination ratios,which were experimentally validated.The results demonstrated that the learning framework based on the stacking ensemble model could effectively predict the tribological properties of amide-based lubricating grease in small sample datasets,with the R2 for the average friction coefficient prediction reaching 0.9939 and for the wear scar width prediction reaching 0.9535.In the experimental validation of the optimal additive ratios,the relative error of the friction coefficient ratio scheme was 0.51%,and the relative error of the wear scar width was 1.10%.This finding suggests that the learning framework provides a novel approach for predicting the performance of amide-based lubricating grease and studying additive combinations.
基金This work was financially supported by the National Key Research and Development Program of China(No.2017YFB0308103)the National Natural Science Foundation of China(No.51773007)the Fundamental Research Funds for the Central Universities(No.XK1802-2).
文摘A series of polyimide(PI)films derived from pyromellitic dianhydride(PMDA)and 4,4'-oxydianiline(ODA)were prepared with the employment of chemical pre-imidization,and the pre-imidization degree(pre-ID)was found influential on structures and properties of the films obtained.Specifically,a certain degree of chemical imidization could promote the in-plane orientation of molecular chains inside the film,which then enhanced the mechanical strength and reduced the coefficient of thermal expansion(CTE)of the films.Further,such pre-imidization process could expand the internal space gap inside the films,thereby lowering their dielectric constant and glass transition temperature.Our study provides a new approach for preparing high-performance PI films through chemical imidization.
基金supported by the National Natural Science Foundation of China (No.52374153 and No.52074349)the Fundamental Research Funds for the Central Universities of Central South University (No.2023zzts0726).
文摘Uniaxial compressive strength(UCS)of rock is an essential parameter in geotechnical engineering.Point load strength(PLS),P-wave velocity,and Schmidt hammer rebound number(SH)are more easily obtained than UCS and are extensively applied for the indirect estimation of UCS.This study collected 1080 datasets consisting of SH,P-wave velocity,PLS,and UCS.All datasets were integrated into three categories(sedimentary,igneous,and metamorphic rocks)according to lithology.Stacking models combined with tree-based models and linear regression were developed based on the datasets of three rock types.Model evaluation showed that the stacking model combined with random forest and linear regression was the optimal model for three rock types.UCS of metamorphic rocks was less predictable than that of sedimentary and igneous rocks.Nonetheless,the proposed stacking models can improve the predictive performance for UCS of metamorphic rocks.The developed predictive models can be applied to quickly predict UCS at engineering sites,which benefits the rapid and intelligent classification of rock masses.Moreover,the importance of SH,P-wave velocity,and PLS were analyzed for the estimation of UCS.SH was a reliable indicator for UCS evaluation across various rock types.P-wave velocity was a valid parameter for evaluating the UCS of igneous rocks,but it was not reliable for assessing the UCS of metamorphic rocks.
基金Acknowledgments The authors thank Ming-Gang Zhang and Katharina Filz for suggestions about problem of multicollinearity and thank Damien Georges for suggestions about modeling.
文摘Yunnan's biodiversity is under considerable pressure and subtropical evergreen broad-leaved forests in this area have become increasingly fragmented through agriculture,logging,planting of economic plants,mining activities and changing environment.The aims of the study are to investigate climate changeinduced changes of subtropical evergreen broad-leaved forests in Yunnan and identify areas of current species richness centers for conservation preparation.Stacked species distribution models were created to generate ensemble forecasting of species distributions,alpha diversity and beta diversity for Yunnan's subtropical evergreen broad-leaved forests in both current and future climate scenarios.Under stacked species distribution models in rapid climate changes scenarios,changes of water-energy dynamics may possibly reduce beta diversity and increase alpha diversity.This point provides insight for future conservation of evergreen broad-leaved forest in Yunnan,highlighting the need to fully consider the problem of vegetation homogenization caused by transformation of water-energy dynamics.
基金funding this work through the Research Group Program under the Grant Number:(R.G.P.2/382/44).
文摘Many plant species have a startling degree of morphological similarity,making it difficult to split and categorize them reliably.Unknown plant species can be challenging to classify and segment using deep learning.While using deep learning architectures has helped improve classification accuracy,the resulting models often need to be more flexible and require a large dataset to train.For the sake of taxonomy,this research proposes a hybrid method for categorizing guava,potato,and java plumleaves.Two new approaches are used to formthe hybridmodel suggested here.The guava,potato,and java plum plant species have been successfully segmented using the first model built on the MobileNetV2-UNET architecture.As a second model,we use a Plant Species Detection Stacking Ensemble Deep Learning Model(PSD-SE-DLM)to identify potatoes,java plums,and guava.The proposed models were trained using data collected in Punjab,Pakistan,consisting of images of healthy and sick leaves from guava,java plum,and potatoes.These datasets are known as PLSD and PLSSD.Accuracy levels of 99.84%and 96.38%were achieved for the suggested PSD-SE-DLM and MobileNetV2-UNET models,respectively.
基金supported by GuangDong Basic and Applied Basic Research Foundation(2021A1515110215)Guangdong Academy of Sciences(2022GDASZH-2022010105)+2 种基金the National Science Foundation of China(42101239)the Guangzhou Basic Research Program(2022GZQN31)the Guangzhou Basic and Applied Research Project(202201010296).
文摘Stacking is the process of overlaying inferred species potential distributions for multiple species based on outputs of bioclimatic envelope models(BEMs).The approach can be used to investigate patterns and processes of species richness.If data limitations on individual species distributions are inevitable,but how do they affect inferences of patterns and processes of species richness?We investigate the influence of different data sources on estimated species richness gradients in China.We fitted BEMs using species distributions data for 334 bird species obtained from(1)global range maps,(2)regional checklists,(3)museum records and surveys,and(4)citizen science data using presence-only(Mahalanobis distance),presence-background(MAXENT),and presence–absence(GAM and BRT)BEMs.Individual species predictions were stacked to generate species richness gradients.Here,we show that different data sources and BEMs can generate spatially varying gradients of species richness.The environmental predictors that best explained species distributions also differed between data sources.Models using citizen-based data had the highest accuracy,whereas those using range data had the lowest accuracy.Potential richness patterns estimated by GAM and BRT models were robust to data uncertainty.When multiple data sets exist for the same region and taxa,we advise that explicit treatments of uncertainty,such as sensitivity analyses of the input data,should be conducted during the process of modeling.
基金supported by the National Natural Science Foundation of China(No.52201289)the Natural Science Foundation of Jiangsu Province(No.BK20221095)+1 种基金China Postdoctoral Science Foundation(No.2022M720055)the Fundamental Research Funds for the Central Universities(No.JUSRP202406005).
文摘Although bimodal copper(Cu)pastes can balance sintering driving force and material stability,they still require higher temperatures and pressures to achieve densification due to the lack of an optimal particle stacking model and systematic optimization design.Here,a multimodal nonspherical particle stacking model was developed using computer simulation technology,resulting in sphere-flake hybrid Cu pastes with high stacking density,enhanced sintering driving force,and low shrinkage.The design principles for bimodal Cu pastes are further refined by synergistically combining computer simulations with experimental validation.The optimized bimodal ratio achieved a joint shear strength of 42.51 MPa under pressureless sintering at 250℃ for 15 min.Moreover,the Cu paste demonstrated compatibility with Ni/Ag/Au metallization and large-area sintering.It is believed that the computer-designed sphere-flake hybrid pastes offer high potential for high-power electronics packaging.
基金supported by the National Key R&D Program of China(No.2024YFC3909000)the Major Scientific and Technological Innovation Projects of Hangzhou(No.2022AIZD0163)。
文摘Heavy metal contamination in waste incineration fly ash poses serious environmental and public health risks,necessitating efficient and precise detection methods.Traditional techniques require complex sample preparation and lengthy analysis,limiting their suitability for on-site or real-time monitoring.To address this,this study proposes a rapid detection method using visible and near-infrared reflectance spectroscopy to improve efficiency and reduce costs.Zn(zinc)and Pb(lead)spectral characteristics were analyzed through first-order differentiation(FD),second-order differentiation(SD),de-trending(DT),and logarithm of the reciprocal(LogInv)transformations,followed by continuous wavelet transform(CWT)to extract key bands(max|r|=0.78).A stacking model integrating partial least squares regression(PLSR),back-propagation neural network(BPNN),support vector regression(SVR),random forest(RF),and extreme gradient boosting(XGBoost)was developed to optimize spectral transformation and inversion modeling.Stacking outperformed individual models,achieving the highest accuracy for Zn(R^(2)=0.748)and Pb(R^(2)=0.735)with CWT-SD and CWT-FD transformation.BPNN exhibited overfitting in small samples,whereas PLSR was constrained by linear assumptions.In contrast,stacking combines the strengths of all the base models,improving accuracy and stability.This study elucidates the spectral characteristics of fly ash and validates the effectiveness of stacking in hyperspectral heavy metal prediction.The findings provide theoretical and technical support for efficient,non-destructive detection,with promising applications in waste incineration management and environmental monitoring.
基金supported by the Renewable Energy Organization of Iran (SANA)
文摘An appropriate mathematical model can help researchers to simulate,evaluate,and control a proton exchange membrane fuel cell (PEMFC) stack system.Because a PEMFC is a nonlinear and strongly coupled system,many assumptions and approximations are considered during modeling.Therefore,some differences are found between model results and the real performance of PEMFCs.To increase the precision of the models so that they can describe better the actual performance,opti-mization of PEMFC model parameters is essential.In this paper,an artificial bee swarm optimization algorithm,called ABSO,is proposed for optimizing the parameters of a steady-state PEMFC stack model suitable for electrical engineering applications.For studying the usefulness of the proposed algorithm,ABSO-based results are compared with the results from a genetic algo-rithm (GA) and particle swarm optimization (PSO).The results show that the ABSO algorithm outperforms the other algorithms.
文摘A junctionless transistor is emerging as a most promising device for the future technology in the decananometer regime. To explore and exploit the behavior completely, the understanding of gate tunneling current is of great importance. In this paper we have explored the gate tunneling current of a double gate junctionless transistor（DGJLT） for the first time through an analytical model, to meet the future requirement of expected high-k gate dielectric material that could replace SiO2. We therefore present the high-k gate stacked architecture of the DGJLT to minimize the gate tunneling current. This paper also demonstrates the impact of conduction band offset,workfunction difference and k-values on the tunneling current of the DGJLT.
文摘A novel compact model for on-chip stacked transformers is presented.The proposed model topology gives a clear distinction to the eddy current,resistive and capacitive losses of the primary and secondary coils in the substrate.A method to analytically determine the non-ideal parasitics between the primary coil and substrate is provided.The model is further verified by the excellent match between the measured and simulated S-parameters on the extracted parameters for a 1：1 stacked transformer manufactured in a commercial RF-CMOS technology.
文摘As a widely used rock excavation method in civil and mining construction works, the blasting operations and theinduced side effects are always investigated by the existing studies. The occurrence of flyrock is regarded as one ofthe most important issues induced by blasting operations, since the accurate prediction of which is crucial fordelineating safety zone. For this purpose, this study developed a flyrock prediction model based on 234 sets ofblasting data collected from Sugun Copper Mine site. A stacked multiple kernel support vector machine (stackedMK-SVM) model was proposed for flyrock prediction. The proposed stacked structure can effectively improve themodel performance by addressing the importance level of different features. For comparison purpose, 6 othermachine learning models were developed, including SVM, MK-SVM, Lagragian Twin SVM (LTSVM), ArtificialNeural Network (ANN), Random Forest (RF) and M5 Tree. This study implemented a 5-fold cross validationprocess for hyperparameters tuning purpose. According to the evaluation results, the proposed stacked MK-SVMmodel achieved the best overall performance, with RMSE of 1.73 and 1.74, MAE of 0.58 and 1.08, VAF of 98.95and 99.25 in training and testing phase, respectively.
基金supported by Guangdong Basic and Applied Basic Research Foundation(Grant No.2023B1515040022)the National Natural Science Foundation of China(Grant Nos.42177270 and 42207340).
文摘Soil extracellular electron transfer(EET)is a pivotal biological process within the realm of soil.Unfortunately,EET suffers from a lack of predictive models.Herein,an intricately crafted machine learning model has been developed for the purpose of predicting soil EET by using the physicochemical properties of soil as independent input variables and the EET capabilities in terms of current density(j_(max))and Coulombic charge(C_(out))as dependent output variables.An autoencoder ensemble stacking(AES)model was developed to address the aforementioned issue by integrating support vector machine,multilayer perceptron,extreme gradient boosting,and light gradient boosting machine algorithms as the stacking algorithms.With 10-fold crossvalidation,the AES model exhibited notable improvements in predicting j_(max)and C_(out),with average test R^(2)values of 0.83 and 0.84,respectively,surpassing those of single machine learning(ML)models and the basic ensemble model.By utilizing partial correlation plots(PDPs),Shapley Additive explanations(SHAP)values,and SHAP decision plots,we quantitatively explained the impact and contribution of the input molecules on the AES model’s predictions of j_(max)and C_(out).In the context of the SHAP method for the AES model,total carbon(TC)was identified as the most correlated descriptor for j_(max),while total organic carbon(TOC)stood out as the most relevant descriptor for C_(out).In the prediction tasks of j_(max)and C_(out)within the AES model,employing a multitask ML approach allowed the model to benefit from the shared information of input variables,thereby enhancing its overall generalizability.This study provides a feasible tool for the prediction of soil EET from soil physiochemical properties and an advanced understanding of the relationship between soil physiochemical properties and EET capability.
基金Supported by the Open Project of Key Laboratory of Audio and Video Restoration and Evaluation(2021KFKT005)。
文摘For the task of content retrieval,analysis and generation of film and television scene images in the field of intelligent editing,fine-grained emotion recognition and prediction of images is of great significance.In this paper,the fusion of traditional perceptual features,art features and multi-channel deep learning features are used to reflect the emotion expression of different levels of the image.In addition,the integrated learning model with stacking architecture based on linear regression coefficient and sentiment correlations,which is called the LS-stacking model,is proposed according to the factor association between multi-dimensional emotions.The experimental results prove that the mixed feature and LS-stacking model can predict well on the 16 emotion categories of the self-built image dataset.This study improves the fine-grained recognition ability of image emotion by computers,which helps to increase the intelligence and automation degree of visual retrieval and post-production system.
