Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosec...Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.展开更多
Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs label...Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.展开更多
Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer p...Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer patterns helps thefirm to develop efficient decisions and in turn,helps to optimize the enter-prise’s business and maximizes consumer satisfaction correspondingly.To con-duct an effective assessment about the customers,Naive Bayes(also called Simple Bayes),a machine learning model is utilized.However,the efficacious of the simple Bayes model is utterly relying on the consumer data used,and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied.However,in practice,the NB pre-mise is not true in consumer data,and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results.In this work,an ensem-ble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier.In ensemble variable selection,two different strategies are applied:one is based upon data perturbation(or homogeneous ensemble,same feature selector is applied to a different subsamples derived from the same learning set)and the other one is based upon function perturbation(or heterogeneous ensemble different feature selector is utilized to the same learning set).Further-more,the feature set captured from both ensemble strategies is applied to NB indi-vidually and the outcome obtained is computed.Finally,the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.展开更多
Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution,the parti...Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution,the particle interactions,and utilizing homogenization with ensemble volume average approach.The matrix material,spherical particles with radius a1,and spherical particles with radius a2,are denoted as the 0th phase,the 1st phase,and the 2nd phase,respectively.Particularly,the two inhomogeneity phases are different particle sizes and the same elastic material properties.Improved higher-order(in ratio of spherical particle sizes to the distance between the centers of spherical particles)bounds on effective elastic properties of 3-phase particle reinforced proposed Formulation II and Formulation I derive composites.As a special case,i.e.,particle size of the 1st phase is the same as that of the 2nd phase,the proposed formulations reduce to 2-phase formulas.Our theoretical predictions demonstrate excellent agreement with selected experimental data.In addition,several numerical examples are presented to demonstrate the competence of the proposed frameworks.展开更多
The theoretical superheat limit of liquids in homogeneous nucleate boiling is determined. A new hypothesis to define the superheat limit is proposed on the basis of the fluctuation theory in statistical thermodynamics...The theoretical superheat limit of liquids in homogeneous nucleate boiling is determined. A new hypothesis to define the superheat limit is proposed on the basis of the fluctuation theory in statistical thermodynamics. Using Gibbs canonical and grand canonical ensemble formulas, the superheat limit are derived. The numerical results are in good agreement with those in literature.展开更多
Because of the use of different limiting procedures, there are two conflict conclusions on wave function. One is that a wave function can describe a single particle and the other is that it can only describe an ensemb...Because of the use of different limiting procedures, there are two conflict conclusions on wave function. One is that a wave function can describe a single particle and the other is that it can only describe an ensemble in the classical limit. In this paper the limiting procedures have been compared. We put the synthesized limit n→∞, (?)→0 but keep E(n,(?)) to be the actual measured value of energy. Under this limit condition, we can not only prove that a wave function can only describe an ensemble, but also can make clear the dynamical behavior of the particles of the ensemble. Calculations show that the reason why quantum mechanics cannot describe a particle is not because of the motion equation but because of the definition of state for the particle.展开更多
The thermal conductivity of complex fluid materials （dusty plasmas） has been explored through novel Evan-Gillan homogeneous non-equilibrium molecular dynamic （HNEMD） algorithm. The thermal conductivity coefficient...The thermal conductivity of complex fluid materials （dusty plasmas） has been explored through novel Evan-Gillan homogeneous non-equilibrium molecular dynamic （HNEMD） algorithm. The thermal conductivity coefficient obtained from HNEMD is dependent on various plasma parameters （T,k）. The proposed algorithm gives accurate results with fast convergence and small size effect over a wide range of plasma parameters. The cross microscopic heat energy current is discussed in association with variation of temperature （1/Г） and external perturbations （Pz）. The thermal conductivity obtained from HNEMD simulations is found to be very good agreement and more reliable than previously known numerical techniques of equilibrium molecular dynarnic, nonequilibrium molecular dynamic simulations. Our new investigations point to an effective conclusion that the thermal conductivity of complex dusty plasmas is dependent on an extensive range of plasma coupling （P） and screening parameter （k） and it varies by the alteration in these parameters. It is also shown that a different approach is used for computations of thermal conductivity in 2D complex plasmas and can be appropriate method for behaviors of complex systems.展开更多
基金funded by the National Key R&D Program of China(2021YFD1400200)the Taishan Scholar Constructive Engineering Foundation of Shandong,China(tstp20221135)。
文摘Potato cyst nematodes(PCNs)are a significant threat to potato production,having caused substantial damage in many countries.Predicting the future distribution of PCN species is crucial to implementing effective biosecurity strategies,especially given the impact of climate change on pest species invasion and distribution.Machine learning(ML),specifically ensemble models,has emerged as a powerful tool in predicting species distributions due to its ability to learn and make predictions based on complex data sets.Thus,this research utilised advanced machine learning techniques to predict the distribution of PCN species under climate change conditions,providing the initial element for invasion risk assessment.We first used Global Climate Models to generate homogeneous climate predictors to mitigate the variation among predictors.Then,five machine learning models were employed to build two groups of ensembles,single-algorithm ensembles(ESA)and multi-algorithm ensembles(EMA),and compared their performances.In this research,the EMA did not always perform better than the ESA,and the ESA of Artificial Neural Network gave the highest performance while being cost-effective.Prediction results indicated that the distribution range of PCNs would shift northward with a decrease in tropical zones and an increase in northern latitudes.However,the total area of suitable regions will not change significantly,occupying 16-20%of the total land surface(18%under current conditions).This research alerts policymakers and practitioners to the risk of PCNs’incursion into new regions.Additionally,this ML process offers the capability to track changes in the distribution of various species and provides scientifically grounded evidence for formulating long-term biosecurity plans for their control.
基金financially supported by the National Natural Science Foundation of China(Grant No.42002134)China Postdoctoral Science Foundation(Grant No.2021T140735)Science Foundation of China University of Petroleum,Beijing(Grant Nos.2462020XKJS02 and 2462020YXZZ004).
文摘Typically, relationship between well logs and lithofacies is complex, which leads to low accuracy of lithofacies identification. Machine learning (ML) methods are often applied to identify lithofacies using logs labelled by rock cores. However, these methods have accuracy limits to some extent. To further improve their accuracies, practical and novel ensemble learning strategy and principles are proposed in this work, which allows geologists not familiar with ML to establish a good ML lithofacies identification model and help geologists familiar with ML further improve accuracy of lithofacies identification. The ensemble learning strategy combines ML methods as sub-classifiers to generate a comprehensive lithofacies identification model, which aims to reduce the variance errors in prediction. Each sub-classifier is trained by randomly sampled labelled data with random features. The novelty of this work lies in the ensemble principles making sub-classifiers just overfitting by algorithm parameter setting and sub-dataset sampling. The principles can help reduce the bias errors in the prediction. Two issues are discussed, videlicet (1) whether only a relatively simple single-classifier method can be as sub-classifiers and how to select proper ML methods as sub-classifiers;(2) whether different kinds of ML methods can be combined as sub-classifiers. If yes, how to determine a proper combination. In order to test the effectiveness of the ensemble strategy and principles for lithofacies identification, different kinds of machine learning algorithms are selected as sub-classifiers, including regular classifiers (LDA, NB, KNN, ID3 tree and CART), kernel method (SVM), and ensemble learning algorithms (RF, AdaBoost, XGBoost and LightGBM). In this work, the experiments used a published dataset of lithofacies from Daniudi gas field (DGF) in Ordes Basin, China. Based on a series of comparisons between ML algorithms and their corresponding ensemble models using the ensemble strategy and principles, conclusions are drawn: (1) not only decision tree but also other single-classifiers and ensemble-learning-classifiers can be used as sub-classifiers of homogeneous ensemble learning and the ensemble can improve the accuracy of the original classifiers;(2) the ensemble principles for the introduced homogeneous and heterogeneous ensemble strategy are effective in promoting ML in lithofacies identification;(3) in practice, heterogeneous ensemble is more suitable for building a more powerful lithofacies identification model, though it is complex.
文摘Executing customer analysis in a systemic way is one of the possible solutions for each enterprise to understand the behavior of consumer patterns in an efficient and in-depth manner.Further investigation of customer patterns helps thefirm to develop efficient decisions and in turn,helps to optimize the enter-prise’s business and maximizes consumer satisfaction correspondingly.To con-duct an effective assessment about the customers,Naive Bayes(also called Simple Bayes),a machine learning model is utilized.However,the efficacious of the simple Bayes model is utterly relying on the consumer data used,and the existence of uncertain and redundant attributes in the consumer data enables the simple Bayes model to attain the worst prediction in consumer data because of its presumption regarding the attributes applied.However,in practice,the NB pre-mise is not true in consumer data,and the analysis of these redundant attributes enables simple Bayes model to get poor prediction results.In this work,an ensem-ble attribute selection methodology is performed to overcome the problem with consumer data and to pick a steady uncorrelated attribute set to model with the NB classifier.In ensemble variable selection,two different strategies are applied:one is based upon data perturbation(or homogeneous ensemble,same feature selector is applied to a different subsamples derived from the same learning set)and the other one is based upon function perturbation(or heterogeneous ensemble different feature selector is utilized to the same learning set).Further-more,the feature set captured from both ensemble strategies is applied to NB indi-vidually and the outcome obtained is computed.Finally,the experimental outcomes show that the proposed ensemble strategies perform efficiently in choosing a steady attribute set and increasing NB classification performance efficiently.
基金This work was in part sponsored by the 2015-2016 California State University Long Beach Research,Scholarship and Creative Activity(RSCA)Award。
文摘Higher-order multiscale structures are proposed to predict the effective elastic properties of 3-phase particle reinforced composites by considering the probabilistic spherical particles spatial distribution,the particle interactions,and utilizing homogenization with ensemble volume average approach.The matrix material,spherical particles with radius a1,and spherical particles with radius a2,are denoted as the 0th phase,the 1st phase,and the 2nd phase,respectively.Particularly,the two inhomogeneity phases are different particle sizes and the same elastic material properties.Improved higher-order(in ratio of spherical particle sizes to the distance between the centers of spherical particles)bounds on effective elastic properties of 3-phase particle reinforced proposed Formulation II and Formulation I derive composites.As a special case,i.e.,particle size of the 1st phase is the same as that of the 2nd phase,the proposed formulations reduce to 2-phase formulas.Our theoretical predictions demonstrate excellent agreement with selected experimental data.In addition,several numerical examples are presented to demonstrate the competence of the proposed frameworks.
基金supported by the National Key Research and Development Program of China (Grant No.2022YFB4602000)Liaoning Open Competition Science and Technology Major Project (Grant No.2022JH1/10400043)+1 种基金the National Natural Science Foundation of China (Grant No.12302184)Shanghai Pujiang Talent Program (Grant No.22PJ1413800).
基金Project supported by the National Natural Science Foundation of China.
文摘The theoretical superheat limit of liquids in homogeneous nucleate boiling is determined. A new hypothesis to define the superheat limit is proposed on the basis of the fluctuation theory in statistical thermodynamics. Using Gibbs canonical and grand canonical ensemble formulas, the superheat limit are derived. The numerical results are in good agreement with those in literature.
基金the National Natural Science Foundation of China
文摘Because of the use of different limiting procedures, there are two conflict conclusions on wave function. One is that a wave function can describe a single particle and the other is that it can only describe an ensemble in the classical limit. In this paper the limiting procedures have been compared. We put the synthesized limit n→∞, (?)→0 but keep E(n,(?)) to be the actual measured value of energy. Under this limit condition, we can not only prove that a wave function can only describe an ensemble, but also can make clear the dynamical behavior of the particles of the ensemble. Calculations show that the reason why quantum mechanics cannot describe a particle is not because of the motion equation but because of the definition of state for the particle.
文摘The thermal conductivity of complex fluid materials （dusty plasmas） has been explored through novel Evan-Gillan homogeneous non-equilibrium molecular dynamic （HNEMD） algorithm. The thermal conductivity coefficient obtained from HNEMD is dependent on various plasma parameters （T,k）. The proposed algorithm gives accurate results with fast convergence and small size effect over a wide range of plasma parameters. The cross microscopic heat energy current is discussed in association with variation of temperature （1/Г） and external perturbations （Pz）. The thermal conductivity obtained from HNEMD simulations is found to be very good agreement and more reliable than previously known numerical techniques of equilibrium molecular dynarnic, nonequilibrium molecular dynamic simulations. Our new investigations point to an effective conclusion that the thermal conductivity of complex dusty plasmas is dependent on an extensive range of plasma coupling （P） and screening parameter （k） and it varies by the alteration in these parameters. It is also shown that a different approach is used for computations of thermal conductivity in 2D complex plasmas and can be appropriate method for behaviors of complex systems.
