As the most critical part of post-graduate education,the Chinese government launched Standard Resident Training in 2013 to solve the regional inequality of medical quality and meet the increasing social requirement fo...As the most critical part of post-graduate education,the Chinese government launched Standard Resident Training in 2013 to solve the regional inequality of medical quality and meet the increasing social requirement for better medical service.We integrated problem-based learning(PBL)and case-based learning(CBL)in the Endodontics Standard Resident Training.By evaluating with objective parameters including theoretical knowledge and clinical practice skill,and subjective parameters including questionnaire,it was found that PBL+CBL played a positive role in endodontic resident training with a significant difference(P<0.05).This combined training model is instructive for China’s resident training,and this result can provide a rudimentary reference to current postgraduate teaching reform.展开更多
In order to gain insight into the current research status and development trend of problem-based learning(PBL)in colleges and universities,this study employs the bibliometric method to conduct statistical and analytic...In order to gain insight into the current research status and development trend of problem-based learning(PBL)in colleges and universities,this study employs the bibliometric method to conduct statistical and analytical studies based on the examination of journal papers and review papers within the Web of Science(WOS)database.The objective is to provide a reference point for research in related fields.The findings indicate a sustained expansion in PBL research output at universities,with the United States accounting for most documents in the field,while European research institutions such as Aalborg University and Maastricht University are at the forefront.Nevertheless,the density of collaborative networks between authors is relatively low,and cross-institutional and interdisciplinary collaboration still requires further strengthening.The majority of research results are published in academic journals such as Academic Medicine and the International Journal of Sustainability in Higher Education.Presently,the focal point of PBL research in colleges and universities is undergoing a transition from a“single-discipline focus”to an“interdisciplinary integration.”This integration is profoundly intertwined with the nascent fields of modern educational technology and education for sustainable development,thereby offering a novel avenue for the advancement of pedagogical approaches and educational equity.展开更多
Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial i...Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.展开更多
Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of...Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.展开更多
Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has bee...Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has been extensively studied,there is still no consensus on the correlation between acoustic attenuation coefficient and sediment physical properties.Predicting the acoustic attenuation coefficient remains a challenging issue in sedimentary acoustic research.In this study,we propose a prediction method for the acoustic attenuation coefficient using machine learning algorithms,specifically the random forest(RF),support vector machine(SVR),and convolutional neural network(CNN)algorithms.We utilized the acoustic attenuation coefficient and sediment particle size data from 52 stations as training parameters,with the particle size parameters as the input feature matrix,and measured acoustic attenuation as the training label to validate the attenuation prediction model.Our results indicate that the error of the attenuation prediction model is small.Among the three models,the RF model exhibited the lowest prediction error,with a mean squared error of 0.8232,mean absolute error of 0.6613,and root mean squared error of 0.9073.Additionally,when we applied the models to predict the data collected at different times in the same region,we found that the models developed in this study also demonstrated a certain level of reliability in real prediction scenarios.Our approach demonstrates that constructing a sediment acoustic characteristics model based on machine learning is feasible to a certain extent and offers a novel perspective for studying sediment acoustic properties.展开更多
This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by cons...This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.展开更多
Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real...Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.展开更多
Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our ho...Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction.展开更多
Objective:Given the unique cultural background,way of life,and physical environment of the Tibetan Plateau,this study aims to investigate the effects of health education using problem-based learning(PBL)approaches on ...Objective:Given the unique cultural background,way of life,and physical environment of the Tibetan Plateau,this study aims to investigate the effects of health education using problem-based learning(PBL)approaches on the knowledge,attitude,practice,and coping skills of women with high-risk pregnancies in this region.Methods:76 high-risk pregnancy cases were enrolled at Xizang’s Linzhi People’s Hospital between September 2023 and April 2024.30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education.46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches.Two groups’performance on their health knowledge,attitude,practice and coping skills before and after interventions were evaluated,and patient satisfaction were measured at the end of the study.Results:There was no statistical significance(P P P Conclusions:Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge,attitude and practice and healthier coping skills.Also,it can improve patient sanctification.展开更多
This study focuses on the application of the“PBL(problem-based learning)+Flipped Classroom”teaching model in bilingual education,aiming to explore its potential to enhance the quality and effectiveness of bilingual ...This study focuses on the application of the“PBL(problem-based learning)+Flipped Classroom”teaching model in bilingual education,aiming to explore its potential to enhance the quality and effectiveness of bilingual teaching.PBL emphasizes learning through the resolution of real-world problems,while the Flipped Classroom advocates that students acquire basic knowledge through self-study before class,dedicating class time to in-depth discussions and practical activities.The integration of these two teaching models in bilingual education aims to stimulate students’interest in learning,improve their autonomous learning abilities,enhance critical thinking,and foster cross-cultural communication skills.Through literature review,case analysis,and empirical research,this study first examines the current applications and challenges of PBL and the Flipped Classroom in bilingual education.Subsequently,it elaborates on the specific implementation steps of the“PBL+Flipped Classroom”teaching model in bilingual education,including problem design,preview material provision,cooperative learning,classroom activities,and language support.A comparative experiment is then conducted to analyze the impact of this teaching model on students’learning motivation,academic performance,and cross-cultural communication skills.The results indicate that the“PBL+Flipped Classroom”teaching model significantly improves students’learning motivation and participation,enhances academic performance,and effectively boosts their cross-cultural communication skills.Furthermore,this model aids in cultivating students’autonomous learning abilities and critical thinking,providing an innovative and effective approach to bilingual education.This study offers new ideas and insights for the field of bilingual education,which is of great significance for promoting the innovation and development of bilingual teaching models.展开更多
The Veterinary Microbiology course is centered around the diagnosis and testing of pathogenic microorganisms,with the core value of“moral education and character development.”It reconstructs multidimensional teachin...The Veterinary Microbiology course is centered around the diagnosis and testing of pathogenic microorganisms,with the core value of“moral education and character development.”It reconstructs multidimensional teaching resources by integrating disciplinary achievements with clinical cases and implements a hybrid teaching approach combining virtual simulation and problem-based learning(PBL)through the“three stages+four models+three reflections”framework.Dual-qualification teachers employ various teaching methods,create a“six-in-one”model for ideological and political education,and conduct formative assessments based on the principles of diversified objectives and process emphasis.The hybrid teaching reform addresses issues such as fragmented knowledge,insufficient class hours,weak animal disease diagnostic abilities among students,limited application and expansion of knowledge points,and students’lack of proactive critical thinking skills.The application of hybrid teaching has shown significant advantages and effectiveness,providing a reference for teaching reform in similar microbiology courses.展开更多
Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese M...Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.展开更多
Mathematical modeling course has been one of the fast development courses in China since 1992,which mainly trains students’innovation ability.However,the teaching of mathematical modeling course is quite difficult si...Mathematical modeling course has been one of the fast development courses in China since 1992,which mainly trains students’innovation ability.However,the teaching of mathematical modeling course is quite difficult since it requires students to have a strong mathematical foundation,good ability to design algorithms,and programming skills.Besides,limited class hours and lack of interest in learning are the other reasons.To address these problems,according to the outcome-based education,we adopt the problem-based learning combined with a seminar mode in teaching.We customize cases related to computer and software engineering,start from simple problems in daily life,step by step deepen the difficulty,and finally refer to the professional application in computer and software engineering.Also,we focus on ability training rather than mathematical theory or programming language learning.Initially,we prepare the problem,related mathematic theory,and core code for students.Furtherly,we train them how to find the problem,and how to search the related mathematic theory and software tools by references for modeling and analysis.Moreover,we solve the problem of limited class hours by constructing an online resource learning library.After a semester of practical teaching,it has been shown that the interest and learning effectiveness of students have been increased and our reform plan has achieved good results.展开更多
Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recogn...Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.展开更多
Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content...Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.展开更多
Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection met...Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection methods during manufacturing is critical yet challenging.This study introduces a deep learning-based method for detecting lithium-plating-type defects using formation and capacity grading data,enabling accurate identification of defective batteries without additional equipment.First,lithiumplating-type defect batteries with various types and area ratios are fabricated.Formation and capacity grading data from 154 batteries(48 defective,106 normal)are collected to construct a dataset.Subsequently,a dual-task deep learning model is then developed,where the reconstruction task learns latent representations from the features,while the classification task identifies the defective batteries.Shapley value analysis further quantifies feature importance,revealing that defective batteries exhibit reduced coulombic efficiency(attributed to irreversible lithium loss)and elevated open-circuit voltage/K-values(linked to self-equalization effects).These findings align with the electrochemical mechanisms of lithium plating,enhancing the model's interpretability.Validated on statistically robust samples shows that the framework achieves a recall of 97.14%for defective batteries and an overall accuracy of 97.42%.This deep learning-driven solution provides a scalable and cost-effective quality control strategy for battery manufacturing.展开更多
The mechanical properties of solid oxide fuel cells(SOFCs)can limit their mechanical stability and lifespan.Understanding the correlation between the microstructure and mechanical properties of porous electrode is ess...The mechanical properties of solid oxide fuel cells(SOFCs)can limit their mechanical stability and lifespan.Understanding the correlation between the microstructure and mechanical properties of porous electrode is essential for enhancing the performance and durability of SOFCs.Accurate prediction of mechanical properties of porous electrode can be achieved by microscale finite element modeling based on three-dimensional(3D)microstructures,which requires expensive 3D tomography techniques and massive computational resources.In this study,we proposed a cost-effective alternative approach to access the mechanical properties of porous electrodes,with the elastic properties of La_(0.6)Sr_(0.4)Co_(0.2)Fe_(0.8)O_(3-δc)athode serving as a case study.Firstly,a stochastic modeling was used to reconstruct 3D microstructures from two-dimensional(2D)cross-sections as an alternative to expensive tomography.Then,the discrete element method(DEM)was used to predict the elastic properties of porous ceramics based on the discretized 3D microstructures reconstructed by stochastic modeling.Based on 2D microstructure and the elastic properties calculated by the DEM modeling of the 3D reconstructed porous microstructures,a convolutional neural network(CNN)based deep learning model was built to predict the elastic properties rapidly from 2D microstructures.The proposed combined framework can be implemented with limited computational resources and provide a basis for rapid prediction of mechanical properties and parameter estimation for multiscale modeling of SOFCs.展开更多
The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomen...The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomenological or empirical formulas.In this study,we present a physics-guided machine learning(ML)framework to model shear and craze in polymeric materials.The effects of all three principal stresses for the craze initiation are considered other than the maximum tensile principal stress solely in previous works.We implemented a finite element framework through a user-defined material subroutine and applied the constitutive model to the deformation in three polymers(PLA 4060D,PLA 3051D,and HIPS).The result shows that our ML-based model can predict the stress-strain and volume-strain responses at different strain rates with high accuracy.Notably,the ML-based approach needs no assumptions about yield criteria or hardening laws.This work highlights the potential of hybrid physics-ML paradigms to overcome the trade-offs between model complexity and accuracy in polymer mechanics,paving the way for computationally efficient and generalizable constitutive models for thermoplastic materials.展开更多
Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus o...Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.展开更多
基金supported by the Teaching Reform Project of Stomatology College of Chongqing Medical University(KQJ202215,KQJ202204)the Teaching Reform Project of Chongqing Medical University(JY20220317).
文摘As the most critical part of post-graduate education,the Chinese government launched Standard Resident Training in 2013 to solve the regional inequality of medical quality and meet the increasing social requirement for better medical service.We integrated problem-based learning(PBL)and case-based learning(CBL)in the Endodontics Standard Resident Training.By evaluating with objective parameters including theoretical knowledge and clinical practice skill,and subjective parameters including questionnaire,it was found that PBL+CBL played a positive role in endodontic resident training with a significant difference(P<0.05).This combined training model is instructive for China’s resident training,and this result can provide a rudimentary reference to current postgraduate teaching reform.
文摘In order to gain insight into the current research status and development trend of problem-based learning(PBL)in colleges and universities,this study employs the bibliometric method to conduct statistical and analytical studies based on the examination of journal papers and review papers within the Web of Science(WOS)database.The objective is to provide a reference point for research in related fields.The findings indicate a sustained expansion in PBL research output at universities,with the United States accounting for most documents in the field,while European research institutions such as Aalborg University and Maastricht University are at the forefront.Nevertheless,the density of collaborative networks between authors is relatively low,and cross-institutional and interdisciplinary collaboration still requires further strengthening.The majority of research results are published in academic journals such as Academic Medicine and the International Journal of Sustainability in Higher Education.Presently,the focal point of PBL research in colleges and universities is undergoing a transition from a“single-discipline focus”to an“interdisciplinary integration.”This integration is profoundly intertwined with the nascent fields of modern educational technology and education for sustainable development,thereby offering a novel avenue for the advancement of pedagogical approaches and educational equity.
文摘Bearing is an indispensable key component in mechanical equipment,and its working state is directly related to the stability and safety of the whole equipment.In recent years,with the rapid development of artificial intelligence technology,especially the breakthrough of deep learning technology,it provides a new idea for bearing fault diagnosis.Deep learning can automatically learn features from a large amount of data,has a strong nonlinear modeling ability,and can effectively solve the problems existing in traditional methods.Aiming at the key problems in bearing fault diagnosis,this paper studies the fault diagnosis method based on deep learning,which not only provides a new solution for bearing fault diagnosis but also provides a reference for the application of deep learning in other mechanical fault diagnosis fields.
文摘Non-technical losses(NTL)of electric power are a serious problem for electric distribution companies.The solution determines the cost,stability,reliability,and quality of the supplied electricity.The widespread use of advanced metering infrastructure(AMI)and Smart Grid allows all participants in the distribution grid to store and track electricity consumption.During the research,a machine learning model is developed that allows analyzing and predicting the probability of NTL for each consumer of the distribution grid based on daily electricity consumption readings.This model is an ensemble meta-algorithm(stacking)that generalizes the algorithms of random forest,LightGBM,and a homogeneous ensemble of artificial neural networks.The best accuracy of the proposed meta-algorithm in comparison to basic classifiers is experimentally confirmed on the test sample.Such a model,due to good accuracy indicators(ROC-AUC-0.88),can be used as a methodological basis for a decision support system,the purpose of which is to form a sample of suspected NTL sources.The use of such a sample will allow the top management of electric distribution companies to increase the efficiency of raids by performers,making them targeted and accurate,which should contribute to the fight against NTL and the sustainable development of the electric power industry.
基金funded by the Basic Scientific Fund for National Public Research Institutes of China(No.2022 S01)the National Natural Science Foundation of China(Nos.42176191,42049902,and U22A2012)+5 种基金the Shandong Provincial Natural Science Foundation,China(No.ZR2022YQ40)the National Key R&D Program of China(No.2021YFF0501202)the Southern Marine Science and Engineering Guangdong Laboratory(Zhuhai)(No.SML2023 SP232)the Fundamental Research Funds for the Central Universities,Sun Yat-sen University(No.241gqb006)Data acquisition and sample collections were supported by the National Natural Science Foundation of China Open Research Cruise(Cruise No.NORC2021-02+NORC2021301)funded by the Shiptime Sharing Project of the National Natural Science Foundation of China。
文摘Accurate acquisition and prediction of acoustic parameters of seabed sediments are crucial in marine sound propagation research.While the relationship between sound velocity and physical properties of sediment has been extensively studied,there is still no consensus on the correlation between acoustic attenuation coefficient and sediment physical properties.Predicting the acoustic attenuation coefficient remains a challenging issue in sedimentary acoustic research.In this study,we propose a prediction method for the acoustic attenuation coefficient using machine learning algorithms,specifically the random forest(RF),support vector machine(SVR),and convolutional neural network(CNN)algorithms.We utilized the acoustic attenuation coefficient and sediment particle size data from 52 stations as training parameters,with the particle size parameters as the input feature matrix,and measured acoustic attenuation as the training label to validate the attenuation prediction model.Our results indicate that the error of the attenuation prediction model is small.Among the three models,the RF model exhibited the lowest prediction error,with a mean squared error of 0.8232,mean absolute error of 0.6613,and root mean squared error of 0.9073.Additionally,when we applied the models to predict the data collected at different times in the same region,we found that the models developed in this study also demonstrated a certain level of reliability in real prediction scenarios.Our approach demonstrates that constructing a sediment acoustic characteristics model based on machine learning is feasible to a certain extent and offers a novel perspective for studying sediment acoustic properties.
文摘This article reviews the application and progress of deep learning in efficient numerical computing methods.Deep learning,as an important branch of machine learning,provides new ideas for numerical computation by constructing multi-layer neural networks to simulate the learning process of the human brain.The article explores the application of deep learning in solving partial differential equations,optimizing problems,and data-driven modeling,and analyzes its advantages in computational efficiency,accuracy,and adaptability.At the same time,this article also points out the challenges faced by deep learning numerical computation methods in terms of computational efficiency,interpretability,and generalization ability,and proposes strategies and future development directions for integrating with traditional numerical methods.
基金supported by National Key Research and Development Program of China(2023YFF0906100)National Natural Science Foundation of China(52408008)Key Research and Development Program of Jiangsu Province(BE2022833).
文摘Current damage detection methods based on model updating and sensitivity Jacobian matrixes show a low convergence ratio and computational efficiency for online calculations.The aim of this paper is to construct a real-time automated damage detection method by developing a theory-assisted adaptive mutiagent twin delayed deep deterministic(TA2-MATD3)policy gradient algorithm.First,the theoretical framework of reinforcement-learning-driven damage detection is established.To address the disadvantages of traditional mutiagent twin delayed deep deterministic(MATD3)method,the theory-assisted mechanism and the adaptive experience playback mechanism are introduced.Moreover,a historical residential house built in 1889 was taken as an example,using its 12-month structural health monitoring data.TA2-MATD3 was compared with existing damage detection methods in terms of the convergence ratio,online computing efficiency,and damage detection accuracy.The results show that the computational efficiency of TA2-MATD3 is approximately 117–160 times that of the traditional methods.The convergence ratio of damage detection on the training set is approximately 97%,and that on the test set is in the range of 86.2%–91.9%.In addition,the main apparent damages found in the field survey were identified by TA2-MATD3.The results indicate that the proposed method can significantly improve the online computing efficiency and damage detection accuracy.This research can provide novel perspectives for the use of reinforcement learning methods to conduct damage detection in online structural health monitoring.
文摘Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction.
基金Leading Specialist Construction Project-Department of the First Affiliated Hospital,Jinan University(2022225).
文摘Objective:Given the unique cultural background,way of life,and physical environment of the Tibetan Plateau,this study aims to investigate the effects of health education using problem-based learning(PBL)approaches on the knowledge,attitude,practice,and coping skills of women with high-risk pregnancies in this region.Methods:76 high-risk pregnancy cases were enrolled at Xizang’s Linzhi People’s Hospital between September 2023 and April 2024.30 patients admitted between September 2023 and December 2023 were selected as the control group and were performed with regular patient education.46 patients admitted between January 2024 and April 2024 were selected as the observation group and were performed regular patient education with problem-based learning approaches.Two groups’performance on their health knowledge,attitude,practice and coping skills before and after interventions were evaluated,and patient satisfaction were measured at the end of the study.Results:There was no statistical significance(P P P Conclusions:Health education with problem-based learning approaches is worth promoting as it can help high-risk pregnant women in plateau areas develop better health knowledge,attitude and practice and healthier coping skills.Also,it can improve patient sanctification.
基金Quality Engineering Project of Chuzhou University“A Study on the Application of the‘PBL+Flipped Classroom’Teaching Mode Integrated with Rain Classroom in Bilingual Teaching of International Enterprise Management”(2022jyc047)Scientific Research Projects of Higher Education Institutions in Anhui Province“Research on the Mechanism of Enabling the Dual-Chain Integration of‘Industry Chain–Talent Chain’in Anhui through New Quality Productivity”(2024AH052932)+1 种基金Education Science Planning Project of Jiangsu Province“Research on the Reform of Practical Teaching in Open Universities under Digital Transformation”(C/2023/01/126)Project of Social Science Foundation of Jiangsu Province“Research on the Path and Mechanism of Digital Empowerment for the Integration of‘Industry Chain-Talent Chain’in Jiangsu”(23GLD002)。
文摘This study focuses on the application of the“PBL(problem-based learning)+Flipped Classroom”teaching model in bilingual education,aiming to explore its potential to enhance the quality and effectiveness of bilingual teaching.PBL emphasizes learning through the resolution of real-world problems,while the Flipped Classroom advocates that students acquire basic knowledge through self-study before class,dedicating class time to in-depth discussions and practical activities.The integration of these two teaching models in bilingual education aims to stimulate students’interest in learning,improve their autonomous learning abilities,enhance critical thinking,and foster cross-cultural communication skills.Through literature review,case analysis,and empirical research,this study first examines the current applications and challenges of PBL and the Flipped Classroom in bilingual education.Subsequently,it elaborates on the specific implementation steps of the“PBL+Flipped Classroom”teaching model in bilingual education,including problem design,preview material provision,cooperative learning,classroom activities,and language support.A comparative experiment is then conducted to analyze the impact of this teaching model on students’learning motivation,academic performance,and cross-cultural communication skills.The results indicate that the“PBL+Flipped Classroom”teaching model significantly improves students’learning motivation and participation,enhances academic performance,and effectively boosts their cross-cultural communication skills.Furthermore,this model aids in cultivating students’autonomous learning abilities and critical thinking,providing an innovative and effective approach to bilingual education.This study offers new ideas and insights for the field of bilingual education,which is of great significance for promoting the innovation and development of bilingual teaching models.
基金Education Research and Reform Project of the Online Open Course Alliance in the Guangdong-Hong Kong-Macao Greater Bay Area in 2023(WGKM2023158)Research Topic of the Online Open Curriculum Steering Committee of Guangdong Province in 2022(2022ZXKC462)+3 种基金Foshan Philosophy and Social Science Planning Project in 2024(2024-GJ037)Innovation Project of Guangdong Graduate Education(2022JGXM129,2022JGXM128,2023ANLK-080)Demonstration Project of Ideological and Political Reform of Guangdong Education Department(Guangdong Higher Education Letter[2021]No.21)Guangdong Provincial Department of Education,Provincial First-Class Undergraduate Courses(Guangdong Higher Education Letter[2023]No.33)。
文摘The Veterinary Microbiology course is centered around the diagnosis and testing of pathogenic microorganisms,with the core value of“moral education and character development.”It reconstructs multidimensional teaching resources by integrating disciplinary achievements with clinical cases and implements a hybrid teaching approach combining virtual simulation and problem-based learning(PBL)through the“three stages+four models+three reflections”framework.Dual-qualification teachers employ various teaching methods,create a“six-in-one”model for ideological and political education,and conduct formative assessments based on the principles of diversified objectives and process emphasis.The hybrid teaching reform addresses issues such as fragmented knowledge,insufficient class hours,weak animal disease diagnostic abilities among students,limited application and expansion of knowledge points,and students’lack of proactive critical thinking skills.The application of hybrid teaching has shown significant advantages and effectiveness,providing a reference for teaching reform in similar microbiology courses.
文摘Objective: To explore the application effect of flipped classroom combined with problem-based learning teaching method in clinical skills teaching of standardized training for resident doctors of traditional Chinese Medicine. Methods: The study used the experimental control method. The study lasted from September to November 2022. The subjects of this study were 49 students of standardized training for resident doctors of traditional Chinese Medicine from grades 2020, 2021 and 2022 of Dazhou integrated TCM & Western Medicine Hospital. They were randomly divided into experiment group (25) and control group (24). The experiment group adopted flipped classroom combined with problem-based learning teaching method, and the control group adopted traditional teaching method. The teaching content was 4 basic clinical skill projects, including four diagnoses of traditional Chinese Medicine, cardiopulmonary resuscitation, dressing change procedure, acupuncture and massage. The evaluation method was carried out by comparing the students’ performance and a self-designed questionnaire was used to investigate the students’ evaluation of the teaching method. Results: The test scores of total scores in the experimental group (90.12 ± 5.89) were all higher than those in the control group (81.47 ± 7.96) (t = 4.53, P P Conclusions: The teaching process of the flipped classroom combined with problem-based learning teaching method is conducive to improving the efficiency of classroom teaching, cultivating students’ self-learning ability, and enhancing students’ willingness to learn.
基金supported in part by the 2023 Schoollevel Education and Teaching Reform Project of Guangdong Ocean University。
文摘Mathematical modeling course has been one of the fast development courses in China since 1992,which mainly trains students’innovation ability.However,the teaching of mathematical modeling course is quite difficult since it requires students to have a strong mathematical foundation,good ability to design algorithms,and programming skills.Besides,limited class hours and lack of interest in learning are the other reasons.To address these problems,according to the outcome-based education,we adopt the problem-based learning combined with a seminar mode in teaching.We customize cases related to computer and software engineering,start from simple problems in daily life,step by step deepen the difficulty,and finally refer to the professional application in computer and software engineering.Also,we focus on ability training rather than mathematical theory or programming language learning.Initially,we prepare the problem,related mathematic theory,and core code for students.Furtherly,we train them how to find the problem,and how to search the related mathematic theory and software tools by references for modeling and analysis.Moreover,we solve the problem of limited class hours by constructing an online resource learning library.After a semester of practical teaching,it has been shown that the interest and learning effectiveness of students have been increased and our reform plan has achieved good results.
文摘Advanced DriverAssistance Systems(ADAS)technologies can assist drivers or be part of automatic driving systems to support the driving process and improve the level of safety and comfort on the road.Traffic Sign Recognition System(TSRS)is one of themost important components ofADAS.Among the challengeswith TSRS is being able to recognize road signs with the highest accuracy and the shortest processing time.Accordingly,this paper introduces a new real time methodology recognizing Speed Limit Signs based on a trio of developed modules.Firstly,the Speed Limit Detection(SLD)module uses the Haar Cascade technique to generate a new SL detector in order to localize SL signs within captured frames.Secondly,the Speed Limit Classification(SLC)module,featuring machine learning classifiers alongside a newly developed model called DeepSL,harnesses the power of a CNN architecture to extract intricate features from speed limit sign images,ensuring efficient and precise recognition.In addition,a new Speed Limit Classifiers Fusion(SLCF)module has been developed by combining trained ML classifiers and the DeepSL model by using the Dempster-Shafer theory of belief functions and ensemble learning’s voting technique.Through rigorous software and hardware validation processes,the proposedmethodology has achieved highly significant F1 scores of 99.98%and 99.96%for DS theory and the votingmethod,respectively.Furthermore,a prototype encompassing all components demonstrates outstanding reliability and efficacy,with processing times of 150 ms for the Raspberry Pi board and 81.5 ms for the Nano Jetson board,marking a significant advancement in TSRS technology.
文摘Objective:To analyze the risk factors of anxiety in young hypertensive patients and build a prediction model to provide a scientific basis for clinical diagnosis and treatment.Methods:According to the research content,young hypertensive patients admitted to the hospital from January 2022 to December 2024 were selected as the research object and at least 950 patients were included according to the sample size calculation.According to the existence of anxiety,950 patients were divided into control group(n=650)and observation group(n=300),and the clinical data of all patients were collected for univariate analysis and multivariate Logistic regression analysis to get the risk factors of hypertension patients complicated with anxiety in.All patients were randomly divided into a training set(n=665)and a test set(n=285)according to the ratio of 7:3,and the evaluation efficiency of different prediction models was obtained by using machine learning algorithm.To evaluate the clinical application effect of the prediction model.Results:(1)Univariate analysis showed that age,BMI,education background,marital status,smoking,drinking,sleep disorder,family history of hypertension,history of diabetes,history of hyperlipidemia,history of cerebral infarction,and TC were important risk factors for young hypertensive patients complicated with anxiety.(2)Multivariate Logistic regression analysis showed that hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors for young hypertensive patients complicated with anxiety.(3)Extra Trees has the highest predictive power for young people with hypertension complicated with anxiety,while Decision-Tree has the lowest predictive power.Conclusion:Hypertension history,drinking history,coronary heart disease history,diabetes history,BMI,TC,and TG are important independent risk factors that affect the anxiety of young hypertensive patients.Extra Trees model has the best prediction efficiency among different groups of models.
基金supported by the National Natural Science Foundation of China(NSFC,52277223 and 51977131)the Shanghai Pujiang Programme(23PJD062)。
文摘Lithium-plating-type defects in lithium-ion batteries pose severe safety risks due to their potential to trigger thermal runaway.To prevent defective batteries from entering the market,developing in-line detection methods during manufacturing is critical yet challenging.This study introduces a deep learning-based method for detecting lithium-plating-type defects using formation and capacity grading data,enabling accurate identification of defective batteries without additional equipment.First,lithiumplating-type defect batteries with various types and area ratios are fabricated.Formation and capacity grading data from 154 batteries(48 defective,106 normal)are collected to construct a dataset.Subsequently,a dual-task deep learning model is then developed,where the reconstruction task learns latent representations from the features,while the classification task identifies the defective batteries.Shapley value analysis further quantifies feature importance,revealing that defective batteries exhibit reduced coulombic efficiency(attributed to irreversible lithium loss)and elevated open-circuit voltage/K-values(linked to self-equalization effects).These findings align with the electrochemical mechanisms of lithium plating,enhancing the model's interpretability.Validated on statistically robust samples shows that the framework achieves a recall of 97.14%for defective batteries and an overall accuracy of 97.42%.This deep learning-driven solution provides a scalable and cost-effective quality control strategy for battery manufacturing.
基金supported by the National Natural Science Foundation of China(Grant Nos.12172104 and 11932005)the Talent Recruitment Project of Guangdong(2021QN02L892)+3 种基金the Stable Supporting Fund of Shenzhen(GXWD20231130153335002)the Shccig-Qinling Program(SMYJY202300140C)the program of Innovation Team in Universities and Colleges in Guangdong(2021KCXTD006)Development and Reform Commission of Shenzhen(XMHT20220103004).
文摘The mechanical properties of solid oxide fuel cells(SOFCs)can limit their mechanical stability and lifespan.Understanding the correlation between the microstructure and mechanical properties of porous electrode is essential for enhancing the performance and durability of SOFCs.Accurate prediction of mechanical properties of porous electrode can be achieved by microscale finite element modeling based on three-dimensional(3D)microstructures,which requires expensive 3D tomography techniques and massive computational resources.In this study,we proposed a cost-effective alternative approach to access the mechanical properties of porous electrodes,with the elastic properties of La_(0.6)Sr_(0.4)Co_(0.2)Fe_(0.8)O_(3-δc)athode serving as a case study.Firstly,a stochastic modeling was used to reconstruct 3D microstructures from two-dimensional(2D)cross-sections as an alternative to expensive tomography.Then,the discrete element method(DEM)was used to predict the elastic properties of porous ceramics based on the discretized 3D microstructures reconstructed by stochastic modeling.Based on 2D microstructure and the elastic properties calculated by the DEM modeling of the 3D reconstructed porous microstructures,a convolutional neural network(CNN)based deep learning model was built to predict the elastic properties rapidly from 2D microstructures.The proposed combined framework can be implemented with limited computational resources and provide a basis for rapid prediction of mechanical properties and parameter estimation for multiscale modeling of SOFCs.
基金supported by the National Natural Science Foundation of China(NSFC)Excellent Research Group Program for“Multiscale Problems in Nonlinear Mechanics”(Grant No.12588201)。
文摘The inelastic behavior of thermoplastic polymers may involve shearing and crazing,and both depend on temperature and strain rate.Traditional constitutive models account for temperature and strain rate through phenomenological or empirical formulas.In this study,we present a physics-guided machine learning(ML)framework to model shear and craze in polymeric materials.The effects of all three principal stresses for the craze initiation are considered other than the maximum tensile principal stress solely in previous works.We implemented a finite element framework through a user-defined material subroutine and applied the constitutive model to the deformation in three polymers(PLA 4060D,PLA 3051D,and HIPS).The result shows that our ML-based model can predict the stress-strain and volume-strain responses at different strain rates with high accuracy.Notably,the ML-based approach needs no assumptions about yield criteria or hardening laws.This work highlights the potential of hybrid physics-ML paradigms to overcome the trade-offs between model complexity and accuracy in polymer mechanics,paving the way for computationally efficient and generalizable constitutive models for thermoplastic materials.
基金supported by the National Key R&D Program of China(2022YFD1401600)the National Science Foundation for Distinguished Young Scholars of Zhejang Province,China(LR23C140001)supported by the Key Area Research and Development Program of Guangdong Province,China(2018B020205003 and 2020B0202090001).
文摘Inferring phylogenetic trees from molecular sequences is a cornerstone of evolutionary biology.Many standard phylogenetic methods(such as maximum-likelihood[ML])rely on explicit models of sequence evolution and thus often suffer from model misspecification or inadequacy.The on-rising deep learning(DL)techniques offer a powerful alternative.Deep learning employs multi-layered artificial neural networks to progressively transform input data into more abstract and complex representations.DL methods can autonomously uncover meaningful patterns from data,thereby bypassing potential biases introduced by predefined features(Franklin,2005;Murphy,2012).Recent efforts have aimed to apply deep neural networks(DNNs)to phylogenetics,with a growing number of applications in tree reconstruction(Suvorov et al.,2020;Zou et al.,2020;Nesterenko et al.,2022;Smith and Hahn,2023;Wang et al.,2023),substitution model selection(Abadi et al.,2020;Burgstaller-Muehlbacher et al.,2023),and diversification rate inference(Voznica et al.,2022;Lajaaiti et al.,2023;Lambert et al.,2023).In phylogenetic tree reconstruction,PhyDL(Zou et al.,2020)and Tree_learning(Suvorov et al.,2020)are two notable DNN-based programs designed to infer unrooted quartet trees directly from alignments of four amino acid(AA)and DNA sequences,respectively.
