In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies an...In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.展开更多
Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision u...Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.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
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
The disconnection between teaching,learning,and evaluation is particularly pronounced in traditional high school chemistry teaching.To align with the demands of the new curriculum standards for talent development,it i...The disconnection between teaching,learning,and evaluation is particularly pronounced in traditional high school chemistry teaching.To align with the demands of the new curriculum standards for talent development,it is essential to implement reforms and innovations in teaching methods.This paper initially elucidates the integrated concept of teaching,learning,and evaluation,as well as its practical significance in the classroom.Subsequently,it explores the effective teaching design centered on the theme of iron and its compounds,actively investigating the implementation approach of the integration principle of teaching,learning,and evaluation in classroom.Furthermore,the paper emphasizes the pivotal role of the evaluation part in fostering the professional development of teachers and enhancing the core competencies of students,ultimately aiming to achieve high efficiency and quality in chemistry classroom teaching.展开更多
This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in...This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in the new junior high school English textbook published by Shanghai Education Press.Based on two rounds of action research,a micro-project design framework is constructed,which includes“unit micro-project design”,“micro-project-based unit teaching”,“micro-project achievement display”,and“evaluation and reflection”.Practice shows that with the guidance of task lists and scaffolding support,this framework effectively promotes the integration of subject knowledge and the development of students’core competence,providing a transferable implementation paradigm for integrated unit teaching.展开更多
The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the probl...The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately,a soft sensor based on ensemble local modeling was proposed.Firstly,outliers of process data are removed by local outlier factor.After standardization and transformation,normal data that can be used in the model are obtained.Next,in order to avoid redundant variables participating in modeling and reducing performance of models,feature selection was applied combing the mechanism of hot rolling process and mutual information among variables.Then,features of samples were extracted by supervised local preserving projection,and a prediction model was constructed by Gaussian process regression based on just-in-time learning(JITL).Other JITL-based models,such as support vector regression and gradient boosting regression tree models,keep all variables and make up for the lost information during dimension reduction.Finally,the soft sensor was developed by integrating individual models through stacking method.Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.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
This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential g...This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.展开更多
Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sen...Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.展开更多
The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities...The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.展开更多
The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within thi...The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within this topic,three key variables are identified:the dependent variable(DV),the independent variable(IV),and the mediating variable(MV).Specifically,the DV refers to English autonomous learning,the IV refers to motivation,and the MV refers to learning strategy.The research establishes that the MV(learning strategy)is an integral component of information processing theory(IPT).Consequently,the dissertation incorporates integrative and instrumental motivation theories alongside IPT as its foundational theoretical framework.This paper aims to explore the theoretical framework of the PhD dissertation in detail,focusing on the interplay of these three theories.展开更多
Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the in...Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.展开更多
With the rapid development of artificial intelligence technology,deep learning,as one of its core technologies,occupies an important position in the cultivation of applied talents.Based on the concept of integration o...With the rapid development of artificial intelligence technology,deep learning,as one of its core technologies,occupies an important position in the cultivation of applied talents.Based on the concept of integration of industry and education,this paper proposes a systematic teaching reform plan to address the issues of disconnection between theory and practice,single teaching methods,and insufficient practical resources in the deep learning courses for professional master’s students at our university.Through deep cooperation with Huawei Cloud Technologies Co.,Ltd.,we introduce cutting-edge theoretical content(such as GoogleNet,ResNet,Transformer,BERT,etc.),update practical cases(covering computer vision,natural language processing,and smart manufacturing),and adopt a case-led comprehensive teaching method combined with the online and offline hybrid practical platform ModelArts to promote the close integration of theory and practice.Simultaneously,a diversified evaluation system with practice as the core is constructed to comprehensively assess students’practical abilities and project execution levels.The research in this paper provides a valuable reference for the innovation of teaching modes and the cultivation of practical abilities in deep learning courses in higher education institutions.展开更多
The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack sys...The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.展开更多
This paper delves into the challenges and opportunities in the current educational system and proposes an innovative talent cultivation model that integrates science,industry,and education.Through an analysis of issue...This paper delves into the challenges and opportunities in the current educational system and proposes an innovative talent cultivation model that integrates science,industry,and education.Through an analysis of issues such as problems with university construction mechanisms,inadequate alignment between schools and enterprises,the disconnection between theory and practice,and a lack of awareness of innovation and entrepreneurship education,this paper explores a model using geography-related majors in higher education as an example.It discusses talent cultivation strategies based on innovation,professionalism,and practical education.Additionally,this paper explores a new teaching practice model for research-based learning curriculum design,as well as the construction and implementation of the curriculum system.展开更多
Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model ...Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.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
With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensu...With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.展开更多
Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The...Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The CBL method was applied to the experimental group,and the traditional approach for the control group.After the lecture,a questionnaire survey was conducted to evaluate the teaching effect in the two groups.Results The CBL method efficiently increased the students’interest in learning and autonomous learning ability,enhanced their ability to solve clinical problems with basic theoretic knowledge and cultivated their clinical thinking ability.Conclusion The CBL method can improve the quality of clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.展开更多
基金co-supported by the National Natural Science Foundation of China(Nos.92371201 and 52192633)the Natural Science Foundation of Shaanxi Province of China(No.2022JC-03)the Aeronautical Science Foundation of China(No.ASFC-20220019070002)。
文摘In multiple Unmanned Aerial Vehicles(UAV)systems,achieving efficient navigation is essential for executing complex tasks and enhancing autonomy.Traditional navigation methods depend on predefined control strategies and trajectory planning and often perform poorly in complex environments.To improve the UAV-environment interaction efficiency,this study proposes a multi-UAV integrated navigation algorithm based on Deep Reinforcement Learning(DRL).This algorithm integrates the Inertial Navigation System(INS),Global Navigation Satellite System(GNSS),and Visual Navigation System(VNS)for comprehensive information fusion.Specifically,an improved multi-UAV integrated navigation algorithm called Information Fusion with MultiAgent Deep Deterministic Policy Gradient(IF-MADDPG)was developed.This algorithm enables UAVs to learn collaboratively and optimize their flight trajectories in real time.Through simulations and experiments,test scenarios in GNSS-denied environments were constructed to evaluate the effectiveness of the algorithm.The experimental results demonstrate that the IF-MADDPG algorithm significantly enhances the collaborative navigation capabilities of multiple UAVs in formation maintenance and GNSS-denied environments.Additionally,it has advantages in terms of mission completion time.This study provides a novel approach for efficient collaboration in multi-UAV systems,which significantly improves the robustness and adaptability of navigation systems.
基金co-supported by the National Natural Science Foundation of China(No.62103432)the China Postdoctoral Science Foundation(No.284881)the Young Talent fund of University Association for Science and Technology in Shaanxi,China(No.20210108)。
文摘Exo-atmospheric vehicles are constrained by limited maneuverability,which leads to the contradiction between evasive maneuver and precision strike.To address the problem of Integrated Evasion and Impact(IEI)decision under multi-constraint conditions,a hierarchical intelligent decision-making method based on Deep Reinforcement Learning(DRL)was proposed.First,an intelligent decision-making framework of“DRL evasion decision”+“impact prediction guidance decision”was established:it takes the impact point deviation correction ability as the constraint and the maximum miss distance as the objective,and effectively solves the problem of poor decisionmaking effect caused by the large IEI decision space.Second,to solve the sparse reward problem faced by evasion decision-making,a hierarchical decision-making method consisting of maneuver timing decision and maneuver duration decision was proposed,and the corresponding Markov Decision Process(MDP)was designed.A detailed simulation experiment was designed to analyze the advantages and computational complexity of the proposed method.Simulation results show that the proposed model has good performance and low computational resource requirement.The minimum miss distance is 21.3 m under the condition of guaranteeing the impact point accuracy,and the single decision-making time is 4.086 ms on an STM32F407 single-chip microcomputer,which has engineering application value.
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.
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.
文摘The disconnection between teaching,learning,and evaluation is particularly pronounced in traditional high school chemistry teaching.To align with the demands of the new curriculum standards for talent development,it is essential to implement reforms and innovations in teaching methods.This paper initially elucidates the integrated concept of teaching,learning,and evaluation,as well as its practical significance in the classroom.Subsequently,it explores the effective teaching design centered on the theme of iron and its compounds,actively investigating the implementation approach of the integration principle of teaching,learning,and evaluation in classroom.Furthermore,the paper emphasizes the pivotal role of the evaluation part in fostering the professional development of teachers and enhancing the core competencies of students,ultimately aiming to achieve high efficiency and quality in chemistry classroom teaching.
基金this paper is funded by Project Information:2023 Guangdong Undergraduate Colleges and Universities Teaching Quality and Teaching Reform Project Construction Project,Project Name:Action Research on Whole-area Nurturing of English Reading Teaching in Universities,Secondary and Primary Schools under the Perspective of Discipline Nurturing.Project serial number:895.
文摘This study focuses on exploring the practical path of Micro Project-Based Learning from the perspective of integrated unit teaching,aiming to address the issue of integrating the Project Section with other sections in the new junior high school English textbook published by Shanghai Education Press.Based on two rounds of action research,a micro-project design framework is constructed,which includes“unit micro-project design”,“micro-project-based unit teaching”,“micro-project achievement display”,and“evaluation and reflection”.Practice shows that with the guidance of task lists and scaffolding support,this framework effectively promotes the integration of subject knowledge and the development of students’core competence,providing a transferable implementation paradigm for integrated unit teaching.
基金the National Natural Science Foundation of China(NSFC)under Grants 61773053,61873024Fundamental Research Funds for the China Central Universities of USTB(FRF-TP-19-049A1Z)the National Key R&D Program of China(No.2017YFB0306403).
文摘The mechanical properties of hot rolled strip are the key index of product quality,and the soft sensing of them is an important decision basis for the control and optimization of hot rolling process.To solve the problem that it is difficult to measure the mechanical properties of hot rolled strip in time and accurately,a soft sensor based on ensemble local modeling was proposed.Firstly,outliers of process data are removed by local outlier factor.After standardization and transformation,normal data that can be used in the model are obtained.Next,in order to avoid redundant variables participating in modeling and reducing performance of models,feature selection was applied combing the mechanism of hot rolling process and mutual information among variables.Then,features of samples were extracted by supervised local preserving projection,and a prediction model was constructed by Gaussian process regression based on just-in-time learning(JITL).Other JITL-based models,such as support vector regression and gradient boosting regression tree models,keep all variables and make up for the lost information during dimension reduction.Finally,the soft sensor was developed by integrating individual models through stacking method.Superiority and reliability of proposed soft sensors were verified by actual process data from a real hot rolling process.
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.
文摘This paper investigates the traffic offloading optimization challenge in Space-Air-Ground Integrated Networks(SAGIN)through a novel Recursive Multi-Agent Proximal Policy Optimization(RMAPPO)algorithm.The exponential growth of mobile devices and data traffic has substantially increased network congestion,particularly in urban areas and regions with limited terrestrial infrastructure.Our approach jointly optimizes unmanned aerial vehicle(UAV)trajectories and satellite-assisted offloading strategies to simultaneously maximize data throughput,minimize energy consumption,and maintain equitable resource distribution.The proposed RMAPPO framework incorporates recurrent neural networks(RNNs)to model temporal dependencies in UAV mobility patterns and utilizes a decentralized multi-agent reinforcement learning architecture to reduce communication overhead while improving system robustness.The proposed RMAPPO algorithm was evaluated through simulation experiments,with the results indicating that it significantly enhances the cumulative traffic offloading rate of nodes and reduces the energy consumption of UAVs.
基金supported by the National Natural Science Foundation of China(52272177,12204010)the Foundation for the Introduction of High-Level Talents of Anhui University(S020118002/097)+1 种基金the University Synergy Innovation Program of Anhui Province(GXXT-2023-066)the Scientific Research Project of Anhui Provincial Higher Education Institution(2023AH040008)。
文摘Flexible electronics face critical challenges in achieving monolithic three-dimensional(3D)integration,including material compatibility,structural stability,and scalable fabrication methods.Inspired by the tactile sensing mechanism of the human skin,we have developed a flexible monolithic 3D-integrated tactile sensing system based on a holey MXene paste,where each vertical one-body unit simultaneously functions as a microsupercapacitor and pressure sensor.The in-plane mesopores of MXene significantly improve ion accessibility,mitigate the self-stacking of nanosheets,and allow the holey MXene to multifunctionally act as a sensing material,an active electrode,and a conductive interconnect,thus drastically reducing the interface mismatch and enhancing the mechanical robustness.Furthermore,we fabricate a large-scale device using a blade-coating and stamping method,which demonstrates excellent mechanical flexibility,low-power consumption,rapid response,and stable long-term operation.As a proof-of-concept application,we integrate our sensing array into a smart access control system,leveraging deep learning to accurately identify users based on their unique pressing behaviors.This study provides a promising approach for designing highly integrated,intelligent,and flexible electronic systems for advanced human-computer interactions and personalized electronics.
文摘The rapid growth of biomedical data,particularly multi-omics data including genomes,transcriptomics,proteomics,metabolomics,and epigenomics,medical research and clinical decision-making confront both new opportunities and obstacles.The huge and diversified nature of these datasets cannot always be managed using traditional data analysis methods.As a consequence,deep learning has emerged as a strong tool for analysing numerous omics data due to its ability to handle complex and non-linear relationships.This paper explores the fundamental concepts of deep learning and how they are used in multi-omics medical data mining.We demonstrate how autoencoders,variational autoencoders,multimodal models,attention mechanisms,transformers,and graph neural networks enable pattern analysis and recognition across all omics data.Deep learning has been found to be effective in illness classification,biomarker identification,gene network learning,and therapeutic efficacy prediction.We also consider critical problems like as data quality,model explainability,whether findings can be repeated,and computational power requirements.We now consider future elements of combining omics with clinical and imaging data,explainable AI,federated learning,and real-time diagnostics.Overall,this study emphasises the need of collaborating across disciplines to advance deep learning-based multi-omics research for precision medicine and comprehending complicated disorders.
基金Swan College of Central South University of Forestry and Technology Teaching Reform Research Project“The Impact of Teachers’Task-Based Teaching Method on English Interpreting Learning among University Students in Hunan,China”(SWXYJGPJ27)Swan College of Central South University of Forestry and Technology Scientific Research Project“The Impact of Integrative Motivation and Instrumental Motivation on English Autonomous Learning among University Students in Hunan,China--A Mediating Role of Learning Strategy”(SYXY202441)。
文摘The research topic of the author’s PhD dissertation is“The Impact of Motivation Cultivation on English Autonomous Learning among University Students in Hunan,China—A Mediating Role of Learning Strategy.”Within this topic,three key variables are identified:the dependent variable(DV),the independent variable(IV),and the mediating variable(MV).Specifically,the DV refers to English autonomous learning,the IV refers to motivation,and the MV refers to learning strategy.The research establishes that the MV(learning strategy)is an integral component of information processing theory(IPT).Consequently,the dissertation incorporates integrative and instrumental motivation theories alongside IPT as its foundational theoretical framework.This paper aims to explore the theoretical framework of the PhD dissertation in detail,focusing on the interplay of these three theories.
文摘Under the tide of economic globalization,college English teaching should not only focus on the improvement of language ability,but also on the cultivation of students’critical thinking ability.This paper takes the integration of language learning and critical thinking ability as the breakthrough point,explores the college English teaching mode under the background of the integration of the two,analyzes the current situation and disadvantages of the separation of the two in the current teaching,and puts forward the integration path from the aspects of curriculum design,teacher training,evaluation system,and so on.With the help of activities such as creating real language situations,carrying out debates and critical reading,it helps students strengthen the improvement of logical analysis and critical thinking ability in their gradual learning,realize the coordinated development of language learning and critical thinking ability,and cultivate compound talents with both language literacy and critical thinking ability for the society.
基金Ministry of Education Industry-Education Cooperation and Collaborative Education Project“Tool Abnormality Detection in the Field of Smart Manufacturing Based on Ascend AI Cloud Services”(241100007143750)Dalian Jiaotong University Undergraduate Teaching Quality and Education Teaching Reform Research Project“Research on the Reform of the Curriculum System of Software Engineering Integrating Artificial Intelligence Under the Background of New Engineering”(010702)。
文摘With the rapid development of artificial intelligence technology,deep learning,as one of its core technologies,occupies an important position in the cultivation of applied talents.Based on the concept of integration of industry and education,this paper proposes a systematic teaching reform plan to address the issues of disconnection between theory and practice,single teaching methods,and insufficient practical resources in the deep learning courses for professional master’s students at our university.Through deep cooperation with Huawei Cloud Technologies Co.,Ltd.,we introduce cutting-edge theoretical content(such as GoogleNet,ResNet,Transformer,BERT,etc.),update practical cases(covering computer vision,natural language processing,and smart manufacturing),and adopt a case-led comprehensive teaching method combined with the online and offline hybrid practical platform ModelArts to promote the close integration of theory and practice.Simultaneously,a diversified evaluation system with practice as the core is constructed to comprehensively assess students’practical abilities and project execution levels.The research in this paper provides a valuable reference for the innovation of teaching modes and the cultivation of practical abilities in deep learning courses in higher education institutions.
基金funded by the Deanship of Postgraduate Studies and Scientific Research at Majmaah University,grant number[R-2025-1637].
文摘The integration of visual elements,such as emojis,into educational content represents a promising approach to enhancing student engagement and comprehension.However,existing efforts in emoji integration often lack systematic frameworks capable of addressing the contextual and pedagogical nuances required for effective implementation.This paper introduces a novel framework that combines Data-Driven Error-Correcting Output Codes(DECOC),Long Short-Term Memory(LSTM)networks,and Multi-Layer Deep Neural Networks(ML-DNN)to identify optimal emoji placements within computer science course materials.The originality of the proposed system lies in its ability to leverage sentiment analysis techniques and contextual embeddings to align emoji recommendations with both the emotional tone and learning objectives of course content.A meticulously annotated dataset,comprising diverse topics in computer science,was developed to train and validate the model,ensuring its applicability across a wide range of educational contexts.Comprehensive validation demonstrated the system’s superior performance,achieving an accuracy of 92.4%,precision of 90.7%,recall of 89.3%,and an F1-score of 90.0%.Comparative analysis with baselinemodels and relatedworks confirms themodel’s ability tooutperformexisting approaches inbalancing accuracy,relevance,and contextual appropriateness.Beyond its technical advancements,this framework offers practical benefits for educators by providing an Artificial Intelligence-assisted(AI-assisted)tool that facilitates personalized content adaptation based on student sentiment and engagement patterns.By automating the identification of appropriate emoji placements,teachers can enhance digital course materials with minimal effort,improving the clarity of complex concepts and fostering an emotionally supportive learning environment.This paper contributes to the emerging field of AI-enhanced education by addressing critical gaps in personalized content delivery and pedagogical support.Its findings highlight the transformative potential of integrating AI-driven emoji placement systems into educational materials,offering an innovative tool for fostering student engagement and enhancing learning outcomes.The proposed framework establishes a foundation for future advancements in the visual augmentation of educational resources,emphasizing scalability and adaptability for broader applications in e-learning.
文摘This paper delves into the challenges and opportunities in the current educational system and proposes an innovative talent cultivation model that integrates science,industry,and education.Through an analysis of issues such as problems with university construction mechanisms,inadequate alignment between schools and enterprises,the disconnection between theory and practice,and a lack of awareness of innovation and entrepreneurship education,this paper explores a model using geography-related majors in higher education as an example.It discusses talent cultivation strategies based on innovation,professionalism,and practical education.Additionally,this paper explores a new teaching practice model for research-based learning curriculum design,as well as the construction and implementation of the curriculum system.
基金Supported in part by the State Key Development Program for Basic Research of China(2012CB720505)the National Natural Science Foundation of China(61174105,60874049)
文摘Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.
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With the rapid advancements in edge computing and artificial intelligence,federated learning(FL)has gained momentum as a promising approach to collaborative data utilization across organizations and devices,while ensuring data privacy and information security.In order to further harness the energy efficiency of wireless networks,an integrated sensing,communication and computation(ISCC)framework has been proposed,which is anticipated to be a key enabler in the era of 6G networks.Although the advantages of pushing intelligence to edge devices are multi-fold,some challenges arise when incorporating FL into wireless networks under the umbrella of ISCC.This paper provides a comprehensive survey of FL,with special emphasis on the design and optimization of ISCC.We commence by introducing the background and fundamentals of FL and the ISCC framework.Subsequently,the aforementioned challenges are highlighted and the state of the art in potential solutions is reviewed.Finally,design guidelines are provided for the incorporation of FL and ISCC.Overall,this paper aims to contribute to the understanding of FL in the context of wireless networks,with a focus on the ISCC framework,and provide insights into addressing the challenges and optimizing the design for the integration of FL into future 6G networks.
文摘Objective To explore the application and the effect of the case based learning(CBL)method in clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.Methods The CBL method was applied to the experimental group,and the traditional approach for the control group.After the lecture,a questionnaire survey was conducted to evaluate the teaching effect in the two groups.Results The CBL method efficiently increased the students’interest in learning and autonomous learning ability,enhanced their ability to solve clinical problems with basic theoretic knowledge and cultivated their clinical thinking ability.Conclusion The CBL method can improve the quality of clinical probation teaching of the integrated curriculum of hematology among eight-year-program medical students.
