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
Background:This study explored the value of integrating problem-based learning(PBL)and team-based learning(TBL)methods into plastic and reconstructive surgery clinical practice.By addressing the challenges faced in tr...Background:This study explored the value of integrating problem-based learning(PBL)and team-based learning(TBL)methods into plastic and reconstructive surgery clinical practice.By addressing the challenges faced in traditional teachings,this study aimed to enhance educational outcomes and prepare students for real-world surgical scenarios,thereby improving patient care in this specialized field.Methods:Fifty undergraduate students majoring in clinical medicine at the Shanghai Jiao Tong University School of Medicine were selected as research subjects.They were randomly divided into experimental and control groups.The experimental group received the combined PBL-TBL teaching method,whereas the control group received the traditional teaching.The teaching effect was evaluated based on student satisfaction and academic performance.Results:The student satisfaction in the experimental group was higher than that of the control group(P<0.05).Subjective scoring for academic performance by instructors was higher in the experimental group than in the control group(P<0.05).Conclusion:The PBL and TBL combination had a significant effect when applied in plastic and reconstructive surgery clinical practice.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
Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL...Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.展开更多
In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,a...In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.展开更多
To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mix...To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mixture Separator(MSMS).This architecture consists of a multi-user encoder,a channel layer,and a separation decoder,allowing it to handle multiple functions simultaneously,including encoding,modulation,signal separation,demodulation,and decoding.The MSMS receiver effectively enables the separation of numerous user signals,making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals,thus significantly enhancing communication security.The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design.The design of a single receiver reduces system costs and improves spectrum efficiency.The MSMS outperforms traditional Space-time Block Coding(STBC)strategies regarding separation performance,particularly in Block Error Rate(BLER)metrics.Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation.Moreover,this study extends the MSMS framework from a two-user scenario to a three-user scenario,further demonstrating the flexibility and scalability of the proposed architecture.展开更多
The shear adhesive strength at the clay‒metal interfaces serves as a critical parameter for evaluating the soil adhesion and metal interface mudding phenomena.However,its rapid determination remains challenging becaus...The shear adhesive strength at the clay‒metal interfaces serves as a critical parameter for evaluating the soil adhesion and metal interface mudding phenomena.However,its rapid determination remains challenging because of the demanding requirements for high-precision instrumentation and complex calibration procedures.In this study,an integrated framework was presented that combined physical experiments,theoretical approaches,and machine learning to enable the autonomous determination of the shear adhesive strength of soil under multiple influencing factors.We developed an improved particle swarm optimization-optimized ordinary kriging(IPOK)surrogate testing method to enhance the limited experimental datasets,and a lightweight residual neural network(RLNet)was then used for effective intra-and extra-domain predictions.A comprehensive model discussion,comparison,and interpretability analysis were conducted.The results from 64 physical experiments considering the consistency index,normal stress,clay content,rotation rate,and disc material effectively characterized the shear adhesion behaviour of kaolin.The IPOK surrogate experiments successfully replicated the physical data points while enriching the dataset details.The RLNet model trained with IPOK data achieved superior prediction performance,with a root mean square error of 7.491 and a determination coefficient of 0.927 in 16 orthogonal validation tests,and high similarity was attained between the predicted and measured values.A detailed model discussion analysis confirmed the superiority of the IPOK-RLNet framework.This methodology provides a cost-effective rapid analysis technique for assessing clay‒metal interface shear adhesion,significantly reducing laboratory testing requirements and experimental costs while increasing engineering efficiency.展开更多
Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a b...Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.展开更多
Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-eng...Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.展开更多
Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black ...Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.展开更多
Purpose:To investigate nursing students’knowledge of and attitudes about problem-based learning(PBL).Methods:A total of 1200 students were surveyed at eight nursing colleges in Hunan Province.Results:In all,1037 vali...Purpose:To investigate nursing students’knowledge of and attitudes about problem-based learning(PBL).Methods:A total of 1200 students were surveyed at eight nursing colleges in Hunan Province.Results:In all,1037 valid questionnaires were returned,for an effective return rate of 86.4%.Some 54.4%of the students learned that PBL was a pedagogical method from teachers,and 27.8%of the students had participated in PBL courses.Almost all of students(97.6%)were interested in PBL,and 66.7%of survey participants believed that students who were not good at solving problems would have difficulty in PBL courses.Conclusion:Nursing educators should guide students to adapt to new learning approaches,and encourage students to participate in the teaching reform to promote students’autonomous learning ability,innovation ability,and comprehensive ability.展开更多
This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mech...This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mechanism to train the model.In the training process,the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix,and then the model is trained based on the indirect labels.The final prediction result of the model conforms to the physical mechanism between indirect label and label,and also meets the constraints of the indirect label.The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained.Finally,the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.展开更多
Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known e...Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known education methods that can be integrated into ophthalmology education to improve students' competence level and promote active learning. Methods: We used a mixed teaching methodology that integrated a FC and PBL into a 1-week ophthalmology clerkship for 72 fourth-year medical students. The course includes two major sessions: FC session and PBL session, relying on clinical and real-patient cases. Written examinations were set up to assess students' academic performance and questionnaires were designed to evaluate their perceptions. Results: The post-course examination results were higher than the pre-course results, and many students gained ophthalmic knowledge and learning skills to varying levels. Comparison of pre-and post-course questionnaires indicated that interests in ophthalmology increased and more students expressed desires to be eye doctors. Most students were satisfied with the new method, while some suggested the process should be slower and the communication with their teacher needed to strengthen.Conclusions: FC and PBL are complementary methodologies. Utilizing the mixed teaching meth of FC and PBL was successful in enhancing academic performance, student satisfactions and promoting active learning.展开更多
Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and cou...Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.展开更多
Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effectiv...Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effective PBL problems.The purpose of this paper is to provide a possible solution to the formulation of PBL problems for computer science courses,which is to reimplement open-source projects(ROSP).A case is demonstrated by showing how ROSP was adopted in a practical intercourse-level PBL course module.This paper contributes to a new PBL problem formulation method for promoting PBL in a practical way for Chinese universities.展开更多
In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is...In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is even challenging for numerical solvers due to the instability. We display the preciseness of the solution domain delivered by deep learning models and the low cost of deploying this model for complex physics problems, showing the versatile character of this method and bringing it to new areas. This will require more allocation points and more careful design of the deep learning model architectures and residual neural network can be a potential candidate.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
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.
基金supported by grants from the National Natural Science Foundation of China(grant nos.82472554 and 82202449)the Fund for Excellent Young Scholars of Shanghai Ninth People’s Hospital,Shanghai Jiao Tong University School of Medicine(grant no.JYYQ006).
文摘Background:This study explored the value of integrating problem-based learning(PBL)and team-based learning(TBL)methods into plastic and reconstructive surgery clinical practice.By addressing the challenges faced in traditional teachings,this study aimed to enhance educational outcomes and prepare students for real-world surgical scenarios,thereby improving patient care in this specialized field.Methods:Fifty undergraduate students majoring in clinical medicine at the Shanghai Jiao Tong University School of Medicine were selected as research subjects.They were randomly divided into experimental and control groups.The experimental group received the combined PBL-TBL teaching method,whereas the control group received the traditional teaching.The teaching effect was evaluated based on student satisfaction and academic performance.Results:The student satisfaction in the experimental group was higher than that of the control group(P<0.05).Subjective scoring for academic performance by instructors was higher in the experimental group than in the control group(P<0.05).Conclusion:The PBL and TBL combination had a significant effect when applied in plastic and reconstructive surgery clinical practice.
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.
基金supported by National Natural Science Foundation of China(62227818,12204239,62275121)Youth Foundation of Jiangsu Province(BK20220946)+1 种基金Fundamental Research Funds for the Central Universities(30923011024)Open Research Fund of Jiangsu Key Laboratory of Spectral Imaging&Intelligent Sense(JSGP202201).
文摘Deep learning(DL)is making significant inroads into biomedical imaging as it provides novel and powerful ways of accurately and efficiently improving the image quality of photoacoustic microscopy(PAM).Off-the-shelf DL models,however,do not necessarily obey the fundamental governing laws of PAM physical systems,nor do they generalize well to scenarios on which they have not been trained.In this work,a physics-embedded degeneration learning(PEDL)approach is proposed to enhance the image quality of PAM with a self-attention enhanced U-Net network,which obtains greater physical consistency,improves data efficiency,and higher adaptability.The proposed method is demonstrated on both synthetic and real datasets,including animal experiments in vivo(blood vessels of mouse's ear and brain).And the results show that compared with previous DL methods,the PEDL algorithm exhibits good performance in recovering PAM images qualitatively and quantitatively.It overcomes the challenges related to training data,accuracy,and robustness which a typical data-driven approach encounters,whose exemplary application envisions to provide a new perspective for existing DL tools of enhanced PAM.
文摘In the rapidly evolving landscape of digital transformation and industrial integration,higher education faces the challenge of cultivating applied talents equipped with interdisciplinary knowledge,engineering skills,and innovative thinking.Traditional teaching models often fail to bridge the gap between theoretical knowledge and practical application,resulting in passive learning and limited problem-solving capabilities.This paper proposes a three-dimensional integrated teaching model centered on“Information Technology-Domain Knowledge-Outcome Production”(the“2+2+2”credit framework)to address these challenges.Drawing on constructivist theories,Bloom’s Taxonomy,and the CDIO model,the framework uses real projects to drive learning,facilitating the seamless integration of theoretical teaching and practical innovation.The model emphasizes tiered teaching objectives and interdisciplinary pathways,supported by dynamic assessment systems that track students’growth in knowledge,skills,and abilities.Applied in smart health and financial technology domains,this approach enhances students’comprehensive capabilities,aligning educational outcomes with industry demands.This study offers replicable strategies for educational reform in new engineering disciplines,aiming to transform students into proactive innovators and versatile talents.
基金supported by the National Social Science Foundation of China under Grant 2022-SKJJ-B-112。
文摘To tackle the physical layer security challenges in wireless communication,this paper introduces a multiuser architecture that leverages single-channel blind source separation,centered around a Multi-source Signal Mixture Separator(MSMS).This architecture consists of a multi-user encoder,a channel layer,and a separation decoder,allowing it to handle multiple functions simultaneously,including encoding,modulation,signal separation,demodulation,and decoding.The MSMS receiver effectively enables the separation of numerous user signals,making it exceedingly difficult for unauthorized eavesdroppers to extract valuable information from the mixed signals,thus significantly enhancing communication security.The MSMS can address the challenges of few-shot sample training and achieve joint optimization during transmission by employing a deep learning-based network design.The design of a single receiver reduces system costs and improves spectrum efficiency.The MSMS outperforms traditional Space-time Block Coding(STBC)strategies regarding separation performance,particularly in Block Error Rate(BLER)metrics.Modulation constellation diagrams further analyze the effectiveness of multi-source signal mixture separation.Moreover,this study extends the MSMS framework from a two-user scenario to a three-user scenario,further demonstrating the flexibility and scalability of the proposed architecture.
基金financial support pro-vided by National Natural Science Foundation of China(Grant No.52178402).
文摘The shear adhesive strength at the clay‒metal interfaces serves as a critical parameter for evaluating the soil adhesion and metal interface mudding phenomena.However,its rapid determination remains challenging because of the demanding requirements for high-precision instrumentation and complex calibration procedures.In this study,an integrated framework was presented that combined physical experiments,theoretical approaches,and machine learning to enable the autonomous determination of the shear adhesive strength of soil under multiple influencing factors.We developed an improved particle swarm optimization-optimized ordinary kriging(IPOK)surrogate testing method to enhance the limited experimental datasets,and a lightweight residual neural network(RLNet)was then used for effective intra-and extra-domain predictions.A comprehensive model discussion,comparison,and interpretability analysis were conducted.The results from 64 physical experiments considering the consistency index,normal stress,clay content,rotation rate,and disc material effectively characterized the shear adhesion behaviour of kaolin.The IPOK surrogate experiments successfully replicated the physical data points while enriching the dataset details.The RLNet model trained with IPOK data achieved superior prediction performance,with a root mean square error of 7.491 and a determination coefficient of 0.927 in 16 orthogonal validation tests,and high similarity was attained between the predicted and measured values.A detailed model discussion analysis confirmed the superiority of the IPOK-RLNet framework.This methodology provides a cost-effective rapid analysis technique for assessing clay‒metal interface shear adhesion,significantly reducing laboratory testing requirements and experimental costs while increasing engineering efficiency.
基金jointly funded by the National Science and Technology Major Project of the Ministry of Science and Technology of China(2018AAA0102900)the"New Generation Artificial Intelligence"Key Field Research and Development Plan of Guangdong Province(2021B0101410002)。
文摘Humans achieve cognitive development through continuous interaction with their environment,enhancing both perception and behavior.However,current robots lack the capacity for human-like action and evolution,posing a bottleneck to improving robotic intelligence.Existing research predominantly models robots as one-way,static mappings from observations to actions,neglecting the dynamic processes of perception and behavior.This paper introduces a novel approach to robot cognitive learning by considering physical properties.We propose a theoretical framework wherein a robot is conceptualized as a three-body physical system comprising a perception-body(P-body),a cognition-body(C-body),and a behavior-body(B-body).Each body engages in physical dynamics and operates within a closed-loop interaction.Significantly,three crucial interactions connect these bodies.The C-body relies on the Pbody's extracted states and reciprocally offers long-term rewards,optimizing the P-body's perception policy.In addition,the C-body directs the B-body's actions through sub-goals,and subsequent P-body-derived states facilitate the C-body's cognition dynamics learning.At last,the B-body would follow the sub-goal generated by the C-body and perform actions conditioned on the perceptive state from the P-body,which leads to the next interactive step.These interactions foster the joint evolution of each body,culminating in optimal design.To validate our approach,we employ a navigation task using a four-legged robot,D'Kitty,equipped with a movable global camera.Navigational prowess demands intricate coordination of sensing,planning,and D'Kitty's motion.Leveraging our framework yields superior task performance compared with conventional methodologies.In conclusion,this paper establishes a paradigm shift in robot cognitive learning by integrating physical interactions across the P-body,C-body,and B-body,while considering physical properties.Our framework's successful application to a navigation task underscores its efficacy in enhancing robotic intelligence.
基金supported in part by the National Science and Technology Major Project of China(No.2019-I-0019-0018)the National Natural Science Foundation of China(Nos.61890920,61890921,12302065 and 12172073).
文摘Partial Differential Equations(PDEs)are model candidates of soft sensing for aero-engine health management units.The existing Physics-Informed Neural Networks(PINNs)have made achievements.However,unmeasurable aero-engine driving sources lead to unknown PDE driving terms,which weaken PINNs feasibility.To this end,Physically Informed Hierarchical Learning followed by Recurrent-Prediction Term(PIHL-RPT)is proposed.First,PIHL is proposed for learning nonhomogeneous PDE solutions,in which two networks NetU and NetG are constructed.NetU is for learning solutions satisfying PDEs;NetG is for learning driving terms to regularize NetU training.Then,we propose a hierarchical learning strategy to optimize and couple NetU and NetG,which are integrated into a data-physics-hybrid loss function.Besides,we prove PIHL-RPT can iteratively generate a series of networks converging to a function,which can approximate a solution to well-posed PDE.Furthermore,RPT is proposed for prediction improvement of PIHL,in which network NetU-RP is constructed to compensate for information loss caused by data sampling and driving sources’immeasurability.Finally,artificial datasets and practical vibration process datasets from our wear experiment platform are used to verify the feasibility and effectiveness of PIHL-RPT based soft sensing.Meanwhile,comparisons with relevant methods,discussions,and PIHL-RPT based health monitoring example are given.
文摘Similar to many fields of sciences,recent deep learning advances have been applied extensively in geosciences for both small-and large-scale problems.However,the necessity of using large training data and the’black box’nature of learning have limited them in practice and difficult to interpret.Furthermore,including the governing equations and physical facts in such methods is also another challenge,which entails either ignoring the physics or simplifying them using unrealistic data.To address such issues,physics informed machine learning methods have been developed which can integrate the governing physics law into the learning process.In this work,a 1-dimensional(1 D)time-dependent seismic wave equation is considered and solved using two methods,namely Gaussian process(GP)and physics informed neural networks.We show that these meshless methods are trained by smaller amount of data and can predict the solution of the equation with even high accuracy.They are also capable of inverting any parameter involved in the governing equation such as wave velocity in our case.Results show that the GP can predict the solution of the seismic wave equation with a lower level of error,while our developed neural network is more accurate for velocity(P-and S-wave)and density inversion.
文摘Purpose:To investigate nursing students’knowledge of and attitudes about problem-based learning(PBL).Methods:A total of 1200 students were surveyed at eight nursing colleges in Hunan Province.Results:In all,1037 valid questionnaires were returned,for an effective return rate of 86.4%.Some 54.4%of the students learned that PBL was a pedagogical method from teachers,and 27.8%of the students had participated in PBL courses.Almost all of students(97.6%)were interested in PBL,and 66.7%of survey participants believed that students who were not good at solving problems would have difficulty in PBL courses.Conclusion:Nursing educators should guide students to adapt to new learning approaches,and encourage students to participate in the teaching reform to promote students’autonomous learning ability,innovation ability,and comprehensive ability.
基金partially funded by the National Natural Science Foundation of China (Grants 51520105005 and U1663208)
文摘This study proposes a supervised learning method that does not rely on labels.We use variables associated with the label as indirect labels,and construct an indirect physics-constrained loss based on the physical mechanism to train the model.In the training process,the model prediction is mapped to the space of value that conforms to the physical mechanism through the projection matrix,and then the model is trained based on the indirect labels.The final prediction result of the model conforms to the physical mechanism between indirect label and label,and also meets the constraints of the indirect label.The present study also develops projection matrix normalization and prediction covariance analysis to ensure that the model can be fully trained.Finally,the effect of the physics-constrained indirect supervised learning is verified based on a well log generation problem.
基金supported by National Natural Science Foundation of China for Young Scientist (81200686, 81400426)Research Fund for the Doctoral Program of Higher Education of China (20120171120108)+1 种基金Natural Science Foundation of Guangdong Province, China(S2011040005378)Fundamental Research Funds for the Central Universities (11ykpy65, 15ykpy31)
文摘Background: Ophthalmology is an important medical science subject, but it is given with insufficient attention in undergraduate medical education. Flipped classroom(FC) and problem-based learning(PBL) are well-known education methods that can be integrated into ophthalmology education to improve students' competence level and promote active learning. Methods: We used a mixed teaching methodology that integrated a FC and PBL into a 1-week ophthalmology clerkship for 72 fourth-year medical students. The course includes two major sessions: FC session and PBL session, relying on clinical and real-patient cases. Written examinations were set up to assess students' academic performance and questionnaires were designed to evaluate their perceptions. Results: The post-course examination results were higher than the pre-course results, and many students gained ophthalmic knowledge and learning skills to varying levels. Comparison of pre-and post-course questionnaires indicated that interests in ophthalmology increased and more students expressed desires to be eye doctors. Most students were satisfied with the new method, while some suggested the process should be slower and the communication with their teacher needed to strengthen.Conclusions: FC and PBL are complementary methodologies. Utilizing the mixed teaching meth of FC and PBL was successful in enhancing academic performance, student satisfactions and promoting active learning.
基金supported by the National Key Research and Development Program (2022YFF0609504)the National Natural Science Foundation of China (61974126,51902273,62005230,62001405)the Natural Science Foundation of Fujian Province of China (No.2021J06009)
文摘Perovskite solar cells(PsCs)have developed tremendously over the past decade.However,the key factors influencing the power conversion efficiency(PCE)of PSCs remain incompletely understood,due to the complexity and coupling of these structural and compositional parameters.In this research,we demon-strate an effective approach to optimize PSCs performance via machine learning(ML).To address chal-lenges posed by limited samples,we propose a feature mask(FM)method,which augments training samples through feature transformation rather than synthetic data.Using this approach,squeeze-and-excitation residual network(SEResNet)model achieves an accuracy with a root-mean-square-error(RMSE)of 0.833%and a Pearson's correlation coefficient(r)of 0.980.Furthermore,we employ the permu-tation importance(PI)algorithm to investigate key features for PCE.Subsequently,we predict PCE through high-throughput screenings,in which we study the relationship between PCE and chemical com-positions.After that,we conduct experiments to validate the consistency between predicted results by ML and experimental results.In this work,ML demonstrates the capability to predict device performance,extract key parameters from complex systems,and accelerate the transition from laboratory findings to commercialapplications.
基金This research was financially supported by the PBL Research and Application Project of Northeastern University(Grant No.PBL-JX2021yb029,PBL-JX2021yb027).
文摘Recently,Problem-Based Learning(PBL)has been regarded as a possible way towards effective educational changes in Chinese universities.However,problems have been exposed in the adoption of PBL,such as choosing effective PBL problems.The purpose of this paper is to provide a possible solution to the formulation of PBL problems for computer science courses,which is to reimplement open-source projects(ROSP).A case is demonstrated by showing how ROSP was adopted in a practical intercourse-level PBL course module.This paper contributes to a new PBL problem formulation method for promoting PBL in a practical way for Chinese universities.
文摘In this article, a physics aware deep learning model is introduced for multiphase flow problems. The deep learning model is shown to be capable of capturing complex physics phenomena such as saturation front, which is even challenging for numerical solvers due to the instability. We display the preciseness of the solution domain delivered by deep learning models and the low cost of deploying this model for complex physics problems, showing the versatile character of this method and bringing it to new areas. This will require more allocation points and more careful design of the deep learning model architectures and residual neural network can be a potential candidate.
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.
