Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision...Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.展开更多
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
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
Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness a...Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.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
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
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
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
Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-b...Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-based learning (PBL) is a pedagogic strategy to strengthen significant learning;then the objective was to establish PBL influence in nursing students’ experiences on drug use and abuse during COVID-19 contingency. Methods: Qualitative, phenomenological and descriptive paradigm, 12 female and male nursing students aged 20 - 24 years old from the 5<sup>th</sup> and 6<sup>th</sup> semesters participated. Information collection was through semi-structured interview and a deep one in four cases. A guide of questions about: How the pandemic impacted your life? How did you face it? And what did you learn during this process? Those questions were used. Qualitative data analysis was based on De Souza Minayo, and signed informed consent was obtained from participants. Results: Students’ experiences allowed four categories to emerge, with six sub-categories. Category I. Students’ experiences on drug use and abuse facing the sanitary contingency;Category II. Students’ skills development to identify a problem and design of appropriate solutions;Category III. Developing skills to favor interpersonal relationships;Category IV. Influence of PBL in nursing students’ experiences on drug use and abuse during the COVID-19 contingency. Conclusion: PBL favored analysis and thoughts in nursing students’ experiences on drug use and abuse during the COVID-19 contingency, they worked collaboratively, developed resilience to daily life situations, and implemented stress coping strategies with their significant learning, which diminished their risk behavior.展开更多
Background:Recently,WeChat application has been widely applied in the field of nursing education in China.Among them,WeChat as a platform combined with problem-based learning(PBL)teaching method is a commonly used met...Background:Recently,WeChat application has been widely applied in the field of nursing education in China.Among them,WeChat as a platform combined with problem-based learning(PBL)teaching method is a commonly used method in nursing teaching,however,no unanimous consensus on the effectiveness of the application of nursing teaching.Purpose:The purpose of this meta-analysis is to systematically evaluate the effectiveness of WeChat as a platform combined with PBL teaching method in nursing teaching for Chinese nursing students from four aspects:nursing students'performance,ability,learning interest and teaching satisfaction.Methods:The following English and Chinese databases were searched for relevant articles:PubMed,Embase,Cochrane library,Web of Science,China National Knowledge Infrastructure(CNKI),WanFang Database,VIP Database and Chinese Biomedical Literature Database(CBM).The search timeframe was set fromthe establishment of these databaseto August 2021.Two reviewers separately screened records,abstracted data,and assessed risk of bias in trials included.Review Manager Software 5.3 was used to analyze data.Results:A total of 11 randomized controlled trials(RCTs)were included.The results of this meta-analysis showed that WeChat combined with PBL teaching method can significantly improve theory examination(SMD=2.50,95%CI:1.69 to 3.30,P<0.001),operation skill(SMD=2.32,95%CI:1.47 to 3.18,P<0.001),comprehensive ability(SMD=3.18,95%CI:1.59 to 4.76,P<0.001),and learning interest(SMD=3.08,95%CI:1.44 to 4.72,P<0.001)in Chinese nursing students,and increase nursing students on teachers'teaching satisfaction(SMD=2.64,95%CI:0.39 to 4.89,P<0.001).Conclusion:WeChat combined with PBL teaching method can significantly enhance Chinese nursing students'knowledge mastery and application,improve their comprehensive ability and learning interest,and improve classroom teaching effect.College nursing educators should consider adopting WeChat combined with PBL teaching method in nursing education to fully demonstrate its application value.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
This quasi-experimental study aimed at looking into the effectiveness of PBL （problem-based learning） in improving the performance in Navigation 3 （terrestrial and coastal navigation） of BSMT （Bachelor of Science...This quasi-experimental study aimed at looking into the effectiveness of PBL （problem-based learning） in improving the performance in Navigation 3 （terrestrial and coastal navigation） of BSMT （Bachelor of Science in Marine Transportation） second year students at JBLFMU-Arevalo during the first semester of school year 2016-2017. The respondents of this research were the two sections comparable with each other who was enrolled in the subject Navigation 3. There were 60 student respondents composed of 30 in the experimental group and 30 in the control group. A validated three item teacher-made problem solving test with 10 points for each correct answer was used as an instrument. The dependent variable was the scores in Navigation 3 and independent variable was the PBL approach. The statistical tools used were mean, standard deviation, Mann-Whitney test, and Wilcoxon-Signed ranks test set at 0.05 level of significance. The effect size was computed to determine the effectiveness of the PBL approach in terms of students＇ performance in Navigation 3. Results showed that in the pretest, though the experimental group had a higher mean than the control group, the Mann-Whitney test showed that the mean scores of the two groups were comparable because the significant value was greater than 0.05. When the treatment was introduced, findings showed that there were significant differences in the Navigation 3 performance in the pretest and posttest of experimental and control groups as well as in the posttests of both groups. It could be inferred that the better performance of the experimental group could be attributed to the intervention where the students were actively involved in the learning process.展开更多
基金funded by the Beijing Engineering Research Center of Electric Rail Transportation.
文摘Effective partitioning is crucial for enabling parallel restoration of power systems after blackouts.This paper proposes a novel partitioning method based on deep reinforcement learning.First,the partitioning decision process is formulated as a Markov decision process(MDP)model to maximize the modularity.Corresponding key partitioning constraints on parallel restoration are considered.Second,based on the partitioning objective and constraints,the reward function of the partitioning MDP model is set by adopting a relative deviation normalization scheme to reduce mutual interference between the reward and penalty in the reward function.The soft bonus scaling mechanism is introduced to mitigate overestimation caused by abrupt jumps in the reward.Then,the deep Q network method is applied to solve the partitioning MDP model and generate partitioning schemes.Two experience replay buffers are employed to speed up the training process of the method.Finally,case studies on the IEEE 39-bus test system demonstrate that the proposed method can generate a high-modularity partitioning result that meets all key partitioning constraints,thereby improving the parallelism and reliability of the restoration process.Moreover,simulation results demonstrate that an appropriate discount factor is crucial for ensuring both the convergence speed and the stability of the partitioning training.
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.
基金funded by Princess Nourah bint Abdulrahman University Researchers Supporting Project number(PNURSP2025R104)Princess Nourah bint Abdulrahman University,Riyadh,Saudi Arabia.
文摘Modern intrusion detection systems(MIDS)face persistent challenges in coping with the rapid evolution of cyber threats,high-volume network traffic,and imbalanced datasets.Traditional models often lack the robustness and explainability required to detect novel and sophisticated attacks effectively.This study introduces an advanced,explainable machine learning framework for multi-class IDS using the KDD99 and IDS datasets,which reflects real-world network behavior through a blend of normal and diverse attack classes.The methodology begins with sophisticated data preprocessing,incorporating both RobustScaler and QuantileTransformer to address outliers and skewed feature distributions,ensuring standardized and model-ready inputs.Critical dimensionality reduction is achieved via the Harris Hawks Optimization(HHO)algorithm—a nature-inspired metaheuristic modeled on hawks’hunting strategies.HHO efficiently identifies the most informative features by optimizing a fitness function based on classification performance.Following feature selection,the SMOTE is applied to the training data to resolve class imbalance by synthetically augmenting underrepresented attack types.The stacked architecture is then employed,combining the strengths of XGBoost,SVM,and RF as base learners.This layered approach improves prediction robustness and generalization by balancing bias and variance across diverse classifiers.The model was evaluated using standard classification metrics:precision,recall,F1-score,and overall accuracy.The best overall performance was recorded with an accuracy of 99.44%for UNSW-NB15,demonstrating the model’s effectiveness.After balancing,the model demonstrated a clear improvement in detecting the attacks.We tested the model on four datasets to show the effectiveness of the proposed approach and performed the ablation study to check the effect of each parameter.Also,the proposed model is computationaly efficient.To support transparency and trust in decision-making,explainable AI(XAI)techniques are incorporated that provides both global and local insight into feature contributions,and offers intuitive visualizations for individual predictions.This makes it suitable for practical deployment in cybersecurity environments that demand both precision and accountability.
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Introduction: Nursing students’ experiences during the pandemic provoked social isolation, the way to learn and every context increasing their stress and anxiety leading to drug use and abuse, among others. Problem-based learning (PBL) is a pedagogic strategy to strengthen significant learning;then the objective was to establish PBL influence in nursing students’ experiences on drug use and abuse during COVID-19 contingency. Methods: Qualitative, phenomenological and descriptive paradigm, 12 female and male nursing students aged 20 - 24 years old from the 5<sup>th</sup> and 6<sup>th</sup> semesters participated. Information collection was through semi-structured interview and a deep one in four cases. A guide of questions about: How the pandemic impacted your life? How did you face it? And what did you learn during this process? Those questions were used. Qualitative data analysis was based on De Souza Minayo, and signed informed consent was obtained from participants. Results: Students’ experiences allowed four categories to emerge, with six sub-categories. Category I. Students’ experiences on drug use and abuse facing the sanitary contingency;Category II. Students’ skills development to identify a problem and design of appropriate solutions;Category III. Developing skills to favor interpersonal relationships;Category IV. Influence of PBL in nursing students’ experiences on drug use and abuse during the COVID-19 contingency. Conclusion: PBL favored analysis and thoughts in nursing students’ experiences on drug use and abuse during the COVID-19 contingency, they worked collaboratively, developed resilience to daily life situations, and implemented stress coping strategies with their significant learning, which diminished their risk behavior.
文摘Background:Recently,WeChat application has been widely applied in the field of nursing education in China.Among them,WeChat as a platform combined with problem-based learning(PBL)teaching method is a commonly used method in nursing teaching,however,no unanimous consensus on the effectiveness of the application of nursing teaching.Purpose:The purpose of this meta-analysis is to systematically evaluate the effectiveness of WeChat as a platform combined with PBL teaching method in nursing teaching for Chinese nursing students from four aspects:nursing students'performance,ability,learning interest and teaching satisfaction.Methods:The following English and Chinese databases were searched for relevant articles:PubMed,Embase,Cochrane library,Web of Science,China National Knowledge Infrastructure(CNKI),WanFang Database,VIP Database and Chinese Biomedical Literature Database(CBM).The search timeframe was set fromthe establishment of these databaseto August 2021.Two reviewers separately screened records,abstracted data,and assessed risk of bias in trials included.Review Manager Software 5.3 was used to analyze data.Results:A total of 11 randomized controlled trials(RCTs)were included.The results of this meta-analysis showed that WeChat combined with PBL teaching method can significantly improve theory examination(SMD=2.50,95%CI:1.69 to 3.30,P<0.001),operation skill(SMD=2.32,95%CI:1.47 to 3.18,P<0.001),comprehensive ability(SMD=3.18,95%CI:1.59 to 4.76,P<0.001),and learning interest(SMD=3.08,95%CI:1.44 to 4.72,P<0.001)in Chinese nursing students,and increase nursing students on teachers'teaching satisfaction(SMD=2.64,95%CI:0.39 to 4.89,P<0.001).Conclusion:WeChat combined with PBL teaching method can significantly enhance Chinese nursing students'knowledge mastery and application,improve their comprehensive ability and learning interest,and improve classroom teaching effect.College nursing educators should consider adopting WeChat combined with PBL teaching method in nursing education to fully demonstrate its application value.
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.
文摘This quasi-experimental study aimed at looking into the effectiveness of PBL （problem-based learning） in improving the performance in Navigation 3 （terrestrial and coastal navigation） of BSMT （Bachelor of Science in Marine Transportation） second year students at JBLFMU-Arevalo during the first semester of school year 2016-2017. The respondents of this research were the two sections comparable with each other who was enrolled in the subject Navigation 3. There were 60 student respondents composed of 30 in the experimental group and 30 in the control group. A validated three item teacher-made problem solving test with 10 points for each correct answer was used as an instrument. The dependent variable was the scores in Navigation 3 and independent variable was the PBL approach. The statistical tools used were mean, standard deviation, Mann-Whitney test, and Wilcoxon-Signed ranks test set at 0.05 level of significance. The effect size was computed to determine the effectiveness of the PBL approach in terms of students＇ performance in Navigation 3. Results showed that in the pretest, though the experimental group had a higher mean than the control group, the Mann-Whitney test showed that the mean scores of the two groups were comparable because the significant value was greater than 0.05. When the treatment was introduced, findings showed that there were significant differences in the Navigation 3 performance in the pretest and posttest of experimental and control groups as well as in the posttests of both groups. It could be inferred that the better performance of the experimental group could be attributed to the intervention where the students were actively involved in the learning process.
