Animals can sense many environment stimuli simultaneously and integrate these signals within the nervous system.However,the neural system and molecular mechanisms underlying are largely unknown.The insulin-like signal...Animals can sense many environment stimuli simultaneously and integrate these signals within the nervous system.However,the neural system and molecular mechanisms underlying are largely unknown.The insulin-like signaling pathway is known to regulate dauer formation,longevity and fat metabolism C.elegans.Here,we report that this pathway is also involved in interaction assay which is to observe the interaction between chemotaxis toward diacetyl and avoidance of cu2+ion.展开更多
The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficie...The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.展开更多
Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GP...Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.展开更多
Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic ris...Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.展开更多
This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which i...This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which inevitably involves an integrated learning of English and law.Secondly,it points out that the content of legal English reflects a combination of legal knowledge and English skills.Thirdly,it expounds on the difficulties that Chinese English majors are facing in the process of learning English and law simultaneously and furnishes some practical suggestions.展开更多
Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still ...Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.展开更多
Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-f...Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.展开更多
Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for in...Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.展开更多
Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, vari...Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, various communication activities performed on-line, and standard courses help mature students to boost their self-esteem, give them more autonomy, and enhance their motivation. Tandem learning, which is based on the principles of reciprocity and autonomy, is ideally suited for adult students. It helps overcome the problems connected to the affective reactions of adults, and at the same time, being content driven, offers a more natural setting to learn an L2 （second language） on a mutual supportive basis.展开更多
To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,trans...To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.展开更多
In recent years, the effective implementation of quality education has brought new opportunities for the development of education. Tao Xingzhi is a famous thinker and educator in our country. His thought of "inte...In recent years, the effective implementation of quality education has brought new opportunities for the development of education. Tao Xingzhi is a famous thinker and educator in our country. His thought of "integration of teaching, learning and doing" emphasizes that education needs to realize the effective unity of teaching, learning and doing. The rational application of the idea of "unity of teaching and doing" in higher vocational colleges can not only improve the quality and efficiency of the classroom, but also facilitate the smooth development of teaching reform. Therefore, this paper studies the application of Tao Xingzhi's thought of "integration of teaching, learning and doing" in higher vocational education reform, and puts forward relevant strategies, aiming to provide reference for higher vocational education teachers.展开更多
CLIL,which stands for Content and Language Integrated Learning,is an instructional approach that gives ample curricular and pedagogical attention to content and language outcomes in multilingual educational settings.I...CLIL,which stands for Content and Language Integrated Learning,is an instructional approach that gives ample curricular and pedagogical attention to content and language outcomes in multilingual educational settings.Increasingly,it is heralded as a way to responsibly enact top-down English-Medium-of-Instruction(EMI)policies at the university level,where teachers and students are tasked with developing their English proficiency while remaining competitive in the international job market.However,teachers and teacher educators hoping to implement this approach in their science,technology,engineering and mathematics(STEM)content courses face significant challenges.This article serves as an introduction to a vip-edited special issue that reports on several aspects related to a project of international collaboration called Project SCILLA,an acronym for“STEM Content Integrated with Language-Learning Activities”.We first provide a brief overview of the project,which was developed and carried out in collaboration between Michigan State University and a consortium of 10 rural universities in Kazakhstan as a way to support STEM educators who wish to adapt their teaching practices to Kazakhstan’s Ministry of Education.We then offer an overview of the six articles that comprise the special issue,and call for deliberate and dialogic international collaboration as a way to support teachers responding to language policy demands.展开更多
Chinese traditional culture is a culture that pursues rebirth and immortality in the spiritual world.Archaeological evidence in recent years shows that this pursuit of traditional Chinese culture has an exceptionally ...Chinese traditional culture is a culture that pursues rebirth and immortality in the spiritual world.Archaeological evidence in recent years shows that this pursuit of traditional Chinese culture has an exceptionally long cultural origin and development history.Selflessness and the public good have a long history of development,cultural tradition and social foundation in China.The greed of the inner world and the aggressive capital of the outer world are the common enemies of China's fine traditional culture and Marxist theory.The main object of understanding and practice in Chinese,Western and Marxist learning is ultimately the public-private relationship,which is a common major category in Chinese,Western and Marxist learning.展开更多
The optimal consensus problem for linear two-time-scale multi-agent systems under malicious attacks is studied in this paper.Firstly,an integral sliding mode function is devised to guide the system trajectory towards ...The optimal consensus problem for linear two-time-scale multi-agent systems under malicious attacks is studied in this paper.Firstly,an integral sliding mode function is devised to guide the system trajectory towards the sliding mode surface and the impact of attacks can be eliminated.Then,the optional consensus problem is reformulated as a zero-sum game problem between each agent and its neighbouring agents.Thus,the game algebraic Riccati equation with singu-larly perturbed parameter is formulated.Furthermore,to avoid the requirement of the system dynamics information,an integral reinforcement learning algorithm is presented to obtain the optimal control policy for multi-agent systems.Compared with existing learning methods,the obtained reinforcement learning algorithm is devoid of potential calculation error issues from singularly perturbed parameter.Meanwhile,the convergence of the proposed algorithm is ver-ified.Finally,a simulation example is provided to demonstrate the efficacy of the proposed control method.展开更多
The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure o...The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.展开更多
In order to help the operator perform the human-robot collaboration task and optimize the task performance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure wi...In order to help the operator perform the human-robot collaboration task and optimize the task performance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure with the inner loop and outer loop is first established.The tasks of the inner loop and outer loop are robot control and task optimization,respectively.An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed,which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator.Subsequently,the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force.The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model.The model includes the unknown dynamics of the operator and the task performance details.To relax the requirement of the system model,the integral reinforcement learning is employed to solve the linear quadratic regulator problem.Besides,an auxiliary force is designed to help the operator complete the specific task better.Compared with the traditional control scheme,the security performance and interaction performance of the human-robot collaboration system are improved.The effectiveness of the proposed method is verified through two numerical simulations.In addition,a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.展开更多
Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research si...Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.展开更多
As the torso is critical to the coordinated movement and flexibility of vertebrates,a 6-(Degree of Freedom)DOF bionic parallel torso with noteworthy motion space was designed in our previous work.To improve the compli...As the torso is critical to the coordinated movement and flexibility of vertebrates,a 6-(Degree of Freedom)DOF bionic parallel torso with noteworthy motion space was designed in our previous work.To improve the compliance of the parallel mechanism,a pair of virtual muscle models is constructed on both sides of the rotating joints of each link of the mechanism,and a bionic muscle control algorithm is introduced.By analyzing the control parameters of the muscle model,dynamic characteristics similar to those of biological muscle are obtained.An adaptive stiffness control is proposed to adaptively adjust the stiffness coefficient with the change in the external load of the parallel mechanism.The attitude closed-loop control can effectively keep the attitude angle unchanged when the position of the moving platform changes.The simulations and experiments are undertaken to validate compliant movements and the flexibility and adaptability of the parallel mechanism.展开更多
Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,wit...Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.展开更多
文摘Animals can sense many environment stimuli simultaneously and integrate these signals within the nervous system.However,the neural system and molecular mechanisms underlying are largely unknown.The insulin-like signaling pathway is known to regulate dauer formation,longevity and fat metabolism C.elegans.Here,we report that this pathway is also involved in interaction assay which is to observe the interaction between chemotaxis toward diacetyl and avoidance of cu2+ion.
基金supported by the National Key Research and Development Program of China(2023YFB3307801)the National Natural Science Foundation of China(62394343,62373155,62073142)+3 种基金Major Science and Technology Project of Xinjiang(No.2022A01006-4)the Programme of Introducing Talents of Discipline to Universities(the 111 Project)under Grant B17017the Fundamental Research Funds for the Central Universities,Science Foundation of China University of Petroleum,Beijing(No.2462024YJRC011)the Open Research Project of the State Key Laboratory of Industrial Control Technology,China(Grant No.ICT2024B70).
文摘The distillation process is an important chemical process,and the application of data-driven modelling approach has the potential to reduce model complexity compared to mechanistic modelling,thus improving the efficiency of process optimization or monitoring studies.However,the distillation process is highly nonlinear and has multiple uncertainty perturbation intervals,which brings challenges to accurate data-driven modelling of distillation processes.This paper proposes a systematic data-driven modelling framework to solve these problems.Firstly,data segment variance was introduced into the K-means algorithm to form K-means data interval(KMDI)clustering in order to cluster the data into perturbed and steady state intervals for steady-state data extraction.Secondly,maximal information coefficient(MIC)was employed to calculate the nonlinear correlation between variables for removing redundant features.Finally,extreme gradient boosting(XGBoost)was integrated as the basic learner into adaptive boosting(AdaBoost)with the error threshold(ET)set to improve weights update strategy to construct the new integrated learning algorithm,XGBoost-AdaBoost-ET.The superiority of the proposed framework is verified by applying this data-driven modelling framework to a real industrial process of propylene distillation.
基金supported by the National Key R&D Program of China(No.2023YFD2200400-01)the Fundamental Scientific Research Operation of Central-level Public Welfare Scientific Research Institutes(No.CAFYBB2023MA001).
文摘Gross primary production(GPP)is closely associated with processes such as photosynthesis and transpiration within ecosystems,which is a vital component of the global carbon-water-energy cycle.Accurate prediction of GPP in terrestrial ecosystems is essential for evaluating terrestrial carbon cycle processes.Machine learning(ML)models provide significant technical support in this domain.Presently,there is a deficiency of high-precision and robust GPP prediction variables and models.Challenges such as unclear contributions of predictive variables,extended model training durations,and limited robustness must be addressed.Solar-induced chlorophyll fluorescence(SIF),optimized multilayer perceptron neural networks,and ensemble learning models show the potential to overcome these challenges.This study aimed to develop an optimized multilayer perceptron neural network model and an ensemble learning model,while objectively assessing the capacity of SIF to predict GPP.Identifying robust models capable of enhancing the accuracy of GPP predictions was the ultimate goal.This study utilized continuous observations of SIF and meteorological data collected from 2020 to 2021 at a designated research observation station within the Populus plantation ecosystem of the Huanghuaihai agricultural protective forest system in Henan Province,China.By optimizing and evaluating the predictive accuracy and robustness of the models across different temporal scales(half-hourly and daily scales),a multi-layer perceptron(MLP)neural network optimization model based on the back propagation(BP)neural network(BPNN)algorithm(BP/MLP)and MLP and random forest(RF)integration(MLP-RF)ensemble models were constructed,utilizing SIF as the primary predictive variable for GPP.Both the BP/MLP(half-hourly scale model R^(2)=0.885,daily scale model R^(2)=0.921)and the MLP-RF(half-hourly scale model R^(2)=0.845,daily scale model R^(2)=0.914)models showed superior accuracy compared to the BPNN(half-hourly scale model R^(2)=0.841,daily scale model R^(2)=0.918)and the traditional RF(half-hourly scale model R^(2)=0.798,daily scale model R^(2)=0.867)models,with the BP/MLP model consistently outperforming the MLP-RF model.The BP/MLP model,which was optimized through particle swarm optimization(PSO),significantly enhanced the robustness of GPP predictions on a half-hourly scale and daily scale.Considering both half-hourly scale and daily scale in the PSO-BP/MLP modeling,the four indicators,light-use efficiency(LUE),photosynthetically active radiation(PAR),absorbed photosynthetically active radiation(APAR),and the variation in SIF with NIRvP(fSIF(NIRvP)),exhibited the potential for enhancing the accuracy of GPP predictions.This study employed a series of model optimization techniques to develop a GPP prediction model with enhanced performance that objectively evaluated the contributions of the predictive variables.This approach provided an innovative and effective method for assessing the carbon cycle in terrestrial ecosystems.
文摘Tian et al present a timely machine learning(ML)model integrating biochemical and novel traditional Chinese medicine(TCM)indicators(tongue edge redness,greasy coating)to predict hepatic steatosis in high metabolic risk patients.Their prospective cohort design and dual-feature selection(LASSO+RFE)culminating in an interpretable XGBoost model(area under the curve:0.82)represent a significant methodological advance.The inclusion of TCM diagnostics addresses metabolic dysfunction-associated fatty liver disease(MAFLD’s)multisystem heterogeneity-a key strength that bridges holistic medicine with precision analytics and underscores potential cost savings over imaging-dependent screening.However,critical limitations impede clinical translation.First,the model’s singlecenter validation(n=711)lacks external/generalizability testing across diverse populations,risking bias from local demographics.Second,MAFLD subtyping(e.g.,lean MAFLD,diabetic MAFLD)was omitted despite acknowledged disease heterogeneity;this overlooks distinct pathophysiologies and may limit utility in stratified care.Third,while TCM features ranked among the top predictors in SHAP analysis,their clinical interpretability remains nebulous without mechanistic links to metabolic dysregulation.To resolve these gaps,we propose external validation in multiethnic cohorts using the published feature set(e.g.,aspartate aminotransferase/alanine aminotransferase,low-density lipoprotein cholesterol,TCM tongue markers)to assess robustness.Subtype-specific modeling to capture MAFLD heterogeneity,potentially enhancing accuracy in highrisk subgroups.Probing TCM microbiome/metabolomic correlations to ground tongue phenotypes in biological pathways,elevating model credibility.Despite shortcomings,this work pioneers a low-cost screening paradigm.Future iterations addressing these issues could revolutionize early MAFLD detection in resource-limited settings.
文摘This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which inevitably involves an integrated learning of English and law.Secondly,it points out that the content of legal English reflects a combination of legal knowledge and English skills.Thirdly,it expounds on the difficulties that Chinese English majors are facing in the process of learning English and law simultaneously and furnishes some practical suggestions.
基金financial supports from the Fund of Science and Technology on Reactor Fuel and Materials Laboratory(JCKYS2019201074)the Affiliated Hospital of Putian University,the Shenzhen Fundamental Research Program(JCYJ20220531095404009)+1 种基金the Shenzhen Knowledge Innovation Plan-Fundamental Research(Discipline Distribution)(JCYJ20180507184623297)the Major Science and Technology Infrastructure Project of Material Genome Big-science Facilities Platform supported by Municipal Development and Reform Commission of Shenzhen。
文摘Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.
文摘Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.
基金Supported by the National Natural Science Foundation of China(61862018)the Subject of the Training Plan for Thousands of Young and Middle-aged Backbone Teachers in Guangxi Colleges and Universities(2020QGRW017)。
文摘Background Data-driven event analysis has gradually become the backbone of modern competitive sports analysis. Competitive sports data analysis tasks increasingly use computer vision and machine-learning models for intelligent data analysis. Existing sports visualization systems focus on the player–team data visualization, which is not intuitive enough for team season win–loss data and game time-series data visualization and neglects the prediction of all-star players. Methods This study used an interactive visualization system designed with parallel aggregated ordered hypergraph dynamic hypergraphs, Calliope visualization data story technology,and i Storyline narrative visualization technology to visualize the regular statistics and game time data of players and teams. NPIPVis includes dynamic hypergraphs of a team’s wins and losses and game plot narrative visualization components. In addition, an integrated learning-based all-star player prediction model, SRR-voting, which starts from the existing minority and majority samples, was proposed using the synthetic minority oversampling technique and Random Under Sampler methods to generate and eliminate samples of a certain size to balance the number of allstar and average players in the datasets. Next, a random forest algorithm was introduced to extract and construct the features of players and combined with the voting integrated model to predict the all-star players, using GridSearch CV, to optimize the hyperparameters of each model in integrated learning and then combined with five-fold cross-validation to improve the generalization ability of the model. Finally, the SHapley Additive ex Planations(SHAP) model was introduced to enhance the interpretability of the model. Results The experimental results of comparing the SRR-voting model with six common models show that the accuracy, F1-score, and recall metrics are significantly improved, which verifies the effectiveness and practicality of the SRR-voting model. Conclusions This study combines data visualization and machine learning to design a National Basketball Association data visualization system to help the general audience visualize game data and predict all-star players;this can also be extended to other sports events or related fields.
文摘Tandem learning via email and computer-mediated communication activities flanked standard frontal English lessons for Italian adult learners in Italy. It is here suggested that the combination of tandem learning, various communication activities performed on-line, and standard courses help mature students to boost their self-esteem, give them more autonomy, and enhance their motivation. Tandem learning, which is based on the principles of reciprocity and autonomy, is ideally suited for adult students. It helps overcome the problems connected to the affective reactions of adults, and at the same time, being content driven, offers a more natural setting to learn an L2 （second language） on a mutual supportive basis.
文摘To enhance the safety of road traffic operations,this paper proposed a model based on stacking integrated learning utilizing American road traffic accident statistics.Initially,the process involved data cleaning,transformation,and normalization.Subsequently,various classification models were constructed,including logistic regression,k-nearest neighbors,gradient boosting,decision trees,AdaBoost,and extra trees models.Evaluation metrics such as accuracy,precision,recall,F1 score,and Hamming loss were employed.Upon analysis,the passive-aggressive classifier model exhibited superior comprehensive indices compared to other models.Based on the model’s output results,an in-depth examination of the factors influencing traffic accidents was conducted.Additionally,measures and suggestions aimed at reducing the incidence of severe traffic accidents were presented.These findings served as a valuable reference for mitigating the occurrence of traffic accidents.
文摘In recent years, the effective implementation of quality education has brought new opportunities for the development of education. Tao Xingzhi is a famous thinker and educator in our country. His thought of "integration of teaching, learning and doing" emphasizes that education needs to realize the effective unity of teaching, learning and doing. The rational application of the idea of "unity of teaching and doing" in higher vocational colleges can not only improve the quality and efficiency of the classroom, but also facilitate the smooth development of teaching reform. Therefore, this paper studies the application of Tao Xingzhi's thought of "integration of teaching, learning and doing" in higher vocational education reform, and puts forward relevant strategies, aiming to provide reference for higher vocational education teachers.
基金funding from the U.S.-Kazakhstan University Partnerships program funded by the U.S.Mission to Kazakhstan and administered by American Councils[Award number SKZ100-19-CA-0149].
文摘CLIL,which stands for Content and Language Integrated Learning,is an instructional approach that gives ample curricular and pedagogical attention to content and language outcomes in multilingual educational settings.Increasingly,it is heralded as a way to responsibly enact top-down English-Medium-of-Instruction(EMI)policies at the university level,where teachers and students are tasked with developing their English proficiency while remaining competitive in the international job market.However,teachers and teacher educators hoping to implement this approach in their science,technology,engineering and mathematics(STEM)content courses face significant challenges.This article serves as an introduction to a vip-edited special issue that reports on several aspects related to a project of international collaboration called Project SCILLA,an acronym for“STEM Content Integrated with Language-Learning Activities”.We first provide a brief overview of the project,which was developed and carried out in collaboration between Michigan State University and a consortium of 10 rural universities in Kazakhstan as a way to support STEM educators who wish to adapt their teaching practices to Kazakhstan’s Ministry of Education.We then offer an overview of the six articles that comprise the special issue,and call for deliberate and dialogic international collaboration as a way to support teachers responding to language policy demands.
文摘Chinese traditional culture is a culture that pursues rebirth and immortality in the spiritual world.Archaeological evidence in recent years shows that this pursuit of traditional Chinese culture has an exceptionally long cultural origin and development history.Selflessness and the public good have a long history of development,cultural tradition and social foundation in China.The greed of the inner world and the aggressive capital of the outer world are the common enemies of China's fine traditional culture and Marxist theory.The main object of understanding and practice in Chinese,Western and Marxist learning is ultimately the public-private relationship,which is a common major category in Chinese,Western and Marxist learning.
基金funded by the National Natural Science Foundation of China[grant number 62103005]Open Fund Project of Key Laboratory of Multidisciplinary Management and Control of Complex Systems of Anhui Higher Education Institutes[grant number CS2022-03]+1 种基金The Scientific Research Projects in Colleges and Universities of Anhui Province[grant number 2022AH050308]The Youth Foundation of Anhui University of Technology[grant number QZ202107].
文摘The optimal consensus problem for linear two-time-scale multi-agent systems under malicious attacks is studied in this paper.Firstly,an integral sliding mode function is devised to guide the system trajectory towards the sliding mode surface and the impact of attacks can be eliminated.Then,the optional consensus problem is reformulated as a zero-sum game problem between each agent and its neighbouring agents.Thus,the game algebraic Riccati equation with singu-larly perturbed parameter is formulated.Furthermore,to avoid the requirement of the system dynamics information,an integral reinforcement learning algorithm is presented to obtain the optimal control policy for multi-agent systems.Compared with existing learning methods,the obtained reinforcement learning algorithm is devoid of potential calculation error issues from singularly perturbed parameter.Meanwhile,the convergence of the proposed algorithm is ver-ified.Finally,a simulation example is provided to demonstrate the efficacy of the proposed control method.
基金supported by the National Natural Science Foundation of China(No.62111530051)the Fundamental Research Funds for the Central Universities(No.3102017JC06002)the Shaanxi Science and Technology Program,China(No.2017KW-ZD-04).
文摘The libration control problem of space tether system(STS)for post-capture of payload is studied.The process of payload capture will cause tether swing and deviation from the nominal position,resulting in the failure of capture mission.Due to unknown inertial parameters after capturing the payload,an adaptive optimal control based on policy iteration is developed to stabilize the uncertain dynamic system in the post-capture phase.By introducing integral reinforcement learning(IRL)scheme,the algebraic Riccati equation(ARE)can be online solved without known dynamics.To avoid computational burden from iteration equations,the online implementation of policy iteration algorithm is provided by the least-squares solution method.Finally,the effectiveness of the algorithm is validated by numerical simulations.
基金the National Key R&D Program of China(No.2018YFB1308400)the Natural Science Foundation of Zhejiang Province(No.LY21F030018)。
文摘In order to help the operator perform the human-robot collaboration task and optimize the task performance,an adaptive control method based on optimal admittance parameters is proposed.The overall control structure with the inner loop and outer loop is first established.The tasks of the inner loop and outer loop are robot control and task optimization,respectively.An inner-loop robot controller integrated with barrier Lyapunov function and radial basis function neural networks is then proposed,which makes the robot with unknown dynamics securely behave like a prescribed robot admittance model sensed by the operator.Subsequently,the optimal parameters of the robot admittance model are obtained in the outer loop to minimize the task tracking error and interaction force.The optimization problem of the robot admittance model is transformed into a linear quadratic regulator problem by constructing the human-robot collaboration system model.The model includes the unknown dynamics of the operator and the task performance details.To relax the requirement of the system model,the integral reinforcement learning is employed to solve the linear quadratic regulator problem.Besides,an auxiliary force is designed to help the operator complete the specific task better.Compared with the traditional control scheme,the security performance and interaction performance of the human-robot collaboration system are improved.The effectiveness of the proposed method is verified through two numerical simulations.In addition,a practical human-robot collaboration experiment is carried out to demonstrate the performance of the proposed method.
文摘Network security problems bring many imperceptible threats to the integrity of data and the reliability of device services,so proposing a network intrusion detection model with high reliability is of great research significance for network security.Due to the strong generalization of invalid features during training process,it is more difficult for single autoencoder intrusion detection model to obtain effective results.A network intrusion detection model based on the Ensemble of Denoising Adversarial Autoencoder(EDAAE)was proposed,which had higher accuracy and reliability compared to the traditional anomaly detection model.Using the adversarial learning idea of Adversarial Autoencoder(AAE),the discriminator module was added to the original model,and the encoder part was used as the generator.The distribution of the hidden space of the data generated by the encoder matched with the distribution of the original data.The generalization of the model to the invalid features was also reduced to improve the detection accuracy.At the same time,the denoising autoencoder and integrated operation was introduced to prevent overfitting in the adversarial learning process.Experiments on the CICIDS2018 traffic dataset showed that the proposed intrusion detection model achieves an Accuracy of 95.23%,which out performs traditional self-encoders and other existing intrusion detection models methods in terms of overall performance.
基金the National Natural Science Foundation of China(Grant No.51605039)the Open Foundation of Shanghai Collaborative Innovation Center of Intelligent Manufacturing Robot Technology for Large Components,in part by the China Postdoctoral Science Foundation(Grant No.2018T111005)the Fundamental Research Funds for the Central Universities(Grant Nos.300102259308,300102259401,and 300102252503).
文摘As the torso is critical to the coordinated movement and flexibility of vertebrates,a 6-(Degree of Freedom)DOF bionic parallel torso with noteworthy motion space was designed in our previous work.To improve the compliance of the parallel mechanism,a pair of virtual muscle models is constructed on both sides of the rotating joints of each link of the mechanism,and a bionic muscle control algorithm is introduced.By analyzing the control parameters of the muscle model,dynamic characteristics similar to those of biological muscle are obtained.An adaptive stiffness control is proposed to adaptively adjust the stiffness coefficient with the change in the external load of the parallel mechanism.The attitude closed-loop control can effectively keep the attitude angle unchanged when the position of the moving platform changes.The simulations and experiments are undertaken to validate compliant movements and the flexibility and adaptability of the parallel mechanism.
基金from any funding agency in the public,commercial,or not-for-profit sectors.
文摘Online banking fraud occurs whenever a criminal can seize accounts and transfer funds from an individual’s online bank account.Successfully preventing this requires the detection of as many fraudsters as possible,without producing too many false alarms.This is a challenge for machine learning owing to the extremely imbalanced data and complexity of fraud.In addition,classical machine learning methods must be extended,minimizing expected financial losses.Finally,fraud can only be combated systematically and economically if the risks and costs in payment channels are known.We define three models that overcome these challenges:machine learning-based fraud detection,economic optimization of machine learning results,and a risk model to predict the risk of fraud while considering countermeasures.The models were tested utilizing real data.Our machine learning model alone reduces the expected and unexpected losses in the three aggregated payment channels by 15%compared to a benchmark consisting of static if-then rules.Optimizing the machine-learning model further reduces the expected losses by 52%.These results hold with a low false positive rate of 0.4%.Thus,the risk framework of the three models is viable from a business and risk perspective.
