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
Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of...Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.展开更多
Financial Accounting outsourcing in recent years has been the development of new outsourcing model with more and more enterprises management and CFOs of all ages. With the development of financial accounting outsourci...Financial Accounting outsourcing in recent years has been the development of new outsourcing model with more and more enterprises management and CFOs of all ages. With the development of financial accounting outsourcing services, it will lead to economic, social and development prospects that are also widely recognized. Not only small and medium companies are keen to own non-core financial accounting processes outsourced to agencies, large group has the entire basis of accounting processes tend to be outsourced to a qualified and experienced outsourcing service. Group companies or large multinationals are gradually re-integration of the financial sector, the basic financial accounting processes separate from the daily work out for outsourcing to focus resources on the development of core competencies.展开更多
User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-tr...User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.展开更多
Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent bioc...Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent biocompatibility in vivo and have significant advantages in the management of ischemic stroke.However,the uncertain distribution and rapid clearance of extracellular vesicles impede their delivery efficiency.By utilizing membrane decoration or by encapsulating therapeutic cargo within extracellular vesicles,their delivery efficacy may be greatly improved.Furthermore,previous studies have indicated that microvesicles,a subset of large-sized extracellular vesicles,can transport mitochondria to neighboring cells,thereby aiding in the restoration of mitochondrial function post-ischemic stroke.Small extracellular vesicles have also demonstrated the capability to transfer mitochondrial components,such as proteins or deoxyribonucleic acid,or their sub-components,for extracellular vesicle-based ischemic stroke therapy.In this review,we undertake a comparative analysis of the isolation techniques employed for extracellular vesicles and present an overview of the current dominant extracellular vesicle modification methodologies.Given the complex facets of treating ischemic stroke,we also delineate various extracellular vesicle modification approaches which are suited to different facets of the treatment process.Moreover,given the burgeoning interest in mitochondrial delivery,we delved into the feasibility and existing research findings on the transportation of mitochondrial fractions or intact mitochondria through small extracellular vesicles and microvesicles to offer a fresh perspective on ischemic stroke therapy.展开更多
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Strategic management of equipment system develop-ment must attach importance to effective strategic risk manage-ment.Aiming at the identification of strategic risk of equipment system development,firstly,the source of strategic risk of equip-ment system development is analyzed and classified.Based on this,a causal loop diagram of strategic risk of equipment sys-tem development based on system dynamics is established.The system dynamics analysis software Vensim PLE is used to carry out the risk influencing factors analysis,risk consequences ana-lysis,risk feedback loop identification and corresponding pre-control measures,and achieves a good risk identification effect.
文摘Financial Accounting outsourcing in recent years has been the development of new outsourcing model with more and more enterprises management and CFOs of all ages. With the development of financial accounting outsourcing services, it will lead to economic, social and development prospects that are also widely recognized. Not only small and medium companies are keen to own non-core financial accounting processes outsourced to agencies, large group has the entire basis of accounting processes tend to be outsourced to a qualified and experienced outsourcing service. Group companies or large multinationals are gradually re-integration of the financial sector, the basic financial accounting processes separate from the daily work out for outsourcing to focus resources on the development of core competencies.
基金supported by the National Natural Science Foundation of China(61671208).
文摘User-transformer relations are significant to electric power marketing,power supply safety,and line loss calculations.To get accurate user-transformer relations,this paper proposes an identification method for user-transformer relations based on improved quantum particle swarm optimization(QPSO)and Fuzzy C-Means Clustering.The main idea is:as energymeters at different transformer areas exhibit different zero-crossing shift features,we classify the zero-crossing shift data from energy meters through Fuzzy C-Means Clustering and compare it with that at the transformer end to identify user-transformer relations.The proposed method contributes in three main ways.First,based on the fuzzy C-means clustering algorithm(FCM),the quantum particle swarm optimization(PSO)is introduced to optimize the FCM clustering center and kernel parameters.The optimized FCM algorithm can improve clustering accuracy and efficiency.Since easily falls into a local optimum,an improved PSO optimization algorithm(IQPSO)is proposed.Secondly,considering that traditional FCM cannot solve the linear inseparability problem,this article uses a FCM(KFCM)that introduces kernel functions.Combinedwith the IQPSOoptimization algorithm used in the previous step,the IQPSO-KFCM algorithm is proposed.Simulation experiments verify the superiority of the proposed method.Finally,the proposed method is applied to transformer detection.The proposed method determines the class members of transformers and meters in the actual transformer area,and obtains results consistent with actual user-transformer relations.This fully shows that the proposed method has practical application value.
基金supported by the grants from University of Macao,China,Nos.MYRG2022-00221-ICMS(to YZ)and MYRG-CRG2022-00011-ICMS(to RW)the Natural Science Foundation of Guangdong Province,No.2023A1515010034(to YZ)。
文摘Ischemic stroke is a secondary cause of mortality worldwide,imposing considerable medical and economic burdens on society.Extracellular vesicles,serving as natural nanocarriers for drug delivery,exhibit excellent biocompatibility in vivo and have significant advantages in the management of ischemic stroke.However,the uncertain distribution and rapid clearance of extracellular vesicles impede their delivery efficiency.By utilizing membrane decoration or by encapsulating therapeutic cargo within extracellular vesicles,their delivery efficacy may be greatly improved.Furthermore,previous studies have indicated that microvesicles,a subset of large-sized extracellular vesicles,can transport mitochondria to neighboring cells,thereby aiding in the restoration of mitochondrial function post-ischemic stroke.Small extracellular vesicles have also demonstrated the capability to transfer mitochondrial components,such as proteins or deoxyribonucleic acid,or their sub-components,for extracellular vesicle-based ischemic stroke therapy.In this review,we undertake a comparative analysis of the isolation techniques employed for extracellular vesicles and present an overview of the current dominant extracellular vesicle modification methodologies.Given the complex facets of treating ischemic stroke,we also delineate various extracellular vesicle modification approaches which are suited to different facets of the treatment process.Moreover,given the burgeoning interest in mitochondrial delivery,we delved into the feasibility and existing research findings on the transportation of mitochondrial fractions or intact mitochondria through small extracellular vesicles and microvesicles to offer a fresh perspective on ischemic stroke therapy.
