Integration of Theory and Practice in Medical Morphology Curriculum in Postgraduate Training:A Flipped Classroom and Case-based Learning Exercise

Objective:The integration of training in theory and practice across the medical education spectrum is being encouraged to increase student understanding and skills in the sciences.This study aimed to determine the deciding factors that drive students'perceived advantages in class to improve precision education and the teaching model.Methods:A mixed strategy of an existing flipped classroom(FC)and a case-based learning(CBL)model was conducted in a medical morphology curriculum for 575 postgraduate students.The subjective learning evaluation of the individuals(learning time,engagement,study interest and concentration,and professional integration)was collected and analyzed after FC-CBL model learning.Results:The results from the general evaluation showed promising results of the medical morphology in the FC-CBL model.Students felt more engaged by instructors in person and benefited in terms of time-saving,flexible arrangements,and professional improvement.Our study contributed to the FC-CBL model in Research Design in postgraduate training in 4 categories:1)advancing a guideline of precision teaching according to individual characteristics;2)revealing whether a learning background is needed for a Research Design course to guide setting up a preliminary course;3)understanding the perceived advantages and their interfaces;and 4)barriers and/or improvement to implement the FC-CBL model in the Research Design class,such as a richer description of e-learning and hands-on practice.Conclusion:Undertaking a FC-CBL combined model could be a useful addition to pedagogy for medical morphology learning in postgraduate training.

Under "Integration of Doing, Learning and Teaching", Research on the Project-Based Teaching Innovation of "Landscape Planning and Design"

Based on the research on the project course theory of "integration of theory and practice" in higher vocational education and the analysis of practical teaching in colleges and universities at home and abroad, combined with literature research, case analysis, system theory and other research methods, the project-based teaching goal, model, content and means of "integration of doing, learning and teaching" in higher vocational education is explored, and the project-based teaching model of "Landscape Planning and Design" is discussed combined with the application of information-based teaching methods. So as to provide references for carrying out the project-based teaching in similar courses in higher vocational colleges and really achieve docking the actual post requirements with the course to provide the basis for achieving the purpose of cultivating skilled talents in higher vocational education.

Integrating WeChat-based Autonomous Learning Community into Improving Speaking--An Action Research Study in EFL

The purpose of this paper is to share the findings of an action research aiming at helping college students to improve their speaking by applying a WeChat-based autonomous learning community.WeChat is the most wide-spread social media platform in China.In this 10-week action research,a total of 16 participants in a Chinese university were involved.After identifying the incentive of participating in this WeChat Group speaking activity,most of which were related with pronunciation and a lack of speaking fluency practice,an action plan was developed and implemented.In this WeChat group,the participants received weekly learning material about pronunciation and speaking assignments accordingly,then they had one week to learn the pronunciation independently and prepare the oral assignments.Following this,participants submitted their voice recording in the WeChat group and were given feedback by an instructor in this group.Observations,questionnaires,and surveys were used to collect data.The results show a positive feedback from the learners on a WeChat-based autonomous learning community.The study observes(1)college EFL learners have a strong motivation;intrinsic motivation especially has a positive relationship with participants’performance;(2)students have a positive attitude towards WeChat-based autonomous learning community;(3)timely feedback from instructors is highly valued by language learners.

Research trends of machine learning in traditional medicine:a big-data based tenyear bibliometric analysis

Background:With the rapid development of the world’s technology,the connection and integration between traditional medicine and modern machine learning technology are increasingly close.In this study,we aimed to analyze publications on machine learning in traditional medicine by using bibliometric methods and explore global trends in the field.Methods:Relevant research on machine learning in traditional medicine extracted from the Web of Science Core Collection database.Bibliometric analysis and visualization were performed using the Bibliometrix package in R software.Global trends,source journals,authorship,and thematic keywords analysis were performed in this study.Results:From 2012 to 2022,a total of 282 publications on machine learning in traditional medicine were identified and analyzed.The average annual growth rate of the publications was 13.35%.China had the largest contribution in this field(53.9%),followed by the United States(32.6%).IEEE Access had the largest number of published articles,with a total of 15 articles.Calvin Yu-Chian Chen,Xiao-juan Hu and Jue Wang were the main researchers in this field.Shanghai University of Traditional Chinese Medicine and University of California,San Francisco were the main research institutions.Conclusion:This study provides information on research trends in machine learning in traditional medicine to better understand research hotspots and future developments in this field.According to current global trends,the number of publications in this field will gradually increase.China currently dominated the field.Applied research of machine learning techniques may be the next hot topic in this field and deserves further attention.

An Analysis of the Research Trends in Blended Learning Methods in Teaching College English Reading Courses

Curtis J.Bonk defined blended learning as a combination of face-to-face and computer-assisted online learning instruction since it was gradually formed after the emergence of the Internet[1,2].Breen pointed out that blended learning is a learning method that combines online and offline learning activities and resources[3].The blended learning approach is popular in school and e-learning.Moreover,it is one of the important trends in promoting higher education reform in the coming years.Therefore,it has attracted the attention of international researchers[4].In this paper,42 articles in Scopus(established by international institutions)and 30 articles in CNKI(found by Chinese mainland institutions)on the query of“college English reading blended learning”in the field of education were analyzed.Furthermore,CiteSpace software was used to analyze the research hotspots and trends with the keyword clusters and citations that occurred in the last two decades(1999 to 2022)to foresee future research prospects.

Machine learning technique for prediction of magnetocaloric effect in La(Fe,Si/Al)_(13)-based materials

Data-mining techniques using machine learning are powerful and efficient for materials design, possessing great potential for discovering new materials with good characteristics. Here, this technique has been used on composition design for La（Fe,Si/Al）（13）-based materials, which are regarded as one of the most promising magnetic refrigerants in practice. Three prediction models are built by using a machine learning algorithm called gradient boosting regression tree（GBRT） to essentially find the correlation between the Curie temperature（TC）, maximum value of magnetic entropy change（（？SM）（max））,and chemical composition, all of which yield high accuracy in the prediction of TC and（？SM）（max）. The performance metric coefficient scores of determination（R^2） for the three models are 0.96, 0.87, and 0.91. These results suggest that all of the models are well-developed predictive models on the challenging issue of generalization ability for untrained data, which can not only provide us with suggestions for real experiments but also help us gain physical insights to find proper composition for further magnetic refrigeration applications.

Researches on Foreign Language Learning Anxiety

Foreign language anxiety is one of the factors of affecting foreign language achievement. It is negatively associated with language skill learning. This article will show some researches on foreign language anxiety from certain aspects.

College English Teaching Study Conducted by Discovery Learning Theory and Action Research——Take College English Ⅱ for Instance

The unceasing revolution of the global economy and culture boosts the revolutionary step of the educational circle.Combining the creed of The Guide of College English Teaching in 2016 with the results of investigation and survey in colleges, a research group in the Institute of Foreign Languages of Hankou University comes up with a revolutionary trial scheme on College English teaching conducted by discovery learning theory, as well as a research method of action research, which is in hope of mending the problems and shortcomings of current College English teaching.

Advances in machine learning-and artificial intelligence-assisted material design of steels

With the rapid development of artificial intelligence technology and increasing material data,machine learning-and artificial intelligence-assisted design of high-performance steel materials is becoming a mainstream paradigm in materials science.Machine learning methods,based on an interdisciplinary discipline between computer science,statistics and material science,are good at discovering correlations between numerous data points.Compared with the traditional physical modeling method in material science,the main advantage of machine learning is that it overcomes the complex physical mechanisms of the material itself and provides a new perspective for the research and development of novel materials.This review starts with data preprocessing and the introduction of different machine learning models,including algorithm selection and model evaluation.Then,some successful cases of applying machine learning methods in the field of steel research are reviewed based on the main theme of optimizing composition,structure,processing,and performance.The application of machine learning methods to the performance-oriented inverse design of material composition and detection of steel defects is also reviewed.Finally,the applicability and limitations of machine learning in the material field are summarized,and future directions and prospects are discussed.

Forecasting Shark Attack Risk Using AI: A Deep Learning Approach

This study aimed to develop a predictive model utilizing available data to forecast the risk of future shark attacks, making this critical information accessible for everyday public use. Employing a deep learning/neural network methodology, the system was designed to produce a binary output that is subsequently classified into categories of low, medium, or high risk. A significant challenge encountered during the study was the identification and procurement of appropriate historical and forecasted marine weather data, which is integral to the model’s accuracy. Despite these challenges, the results of the study were startlingly optimistic, showcasing the model’s ability to predict with impressive accuracy. In conclusion, the developed forecasting tool not only offers promise in its immediate application but also sets a robust precedent for the adoption and adaptation of similar predictive systems in various analogous use cases in the marine environment and beyond.

Prediction and interpretation of photocatalytic NO removal on g-C_(3)N_(4)-based catalysts using machine learning

Predictive modeling of photocatalytic NO removal is highly desirable for efficient air pollution abatement.However,great challenges remain in precisely predicting photocatalytic performance and understanding interactions of diverse features in the catalytic systems.Herein,a dataset of g-C_(3) N_(4)-based catalysts with 255 data points was collected from peer-reviewed publications and machine learning(ML)model was proposed to predict the NO removal rate.The result shows that the Gradient Boosting Decision Tree(GBDT)demonstrated the greatest prediction accuracy with R 2 of 0.999 and 0.907 on the training and test data,respectively.The SHAP value and feature importance analysis revealed that the empirical categories for NO removal rate,in the order of importance,were catalyst characteristics>reaction process>preparation conditions.Moreover,the partial dependence plots broke the ML black box to further quantify the marginal contributions of the input features(e.g.,doping ratio,flow rate,and pore volume)to the model output outcomes.This ML approach presents a pure data-driven,interpretable framework,which provides new insights into the influence of catalyst characteristics,reaction process,and preparation conditions on NO removal.

A Study on the Causes of Students'Poor Learning Attitudes and the Social Psychological Countermeasures to Change Them

At this stage,with the continuous improvement of the quality of life and education,students at all levels of education are facing great pressure of learning and competition in life.If these pressures are not relieved orsolved in a timely manner,the lighter they affect daily life and learning,the more serious they cause mental health problems such as depression and anxiety,and even the horrible idea of ending their lives prematurely.Therefore,educators must pay great attention to students'learning status and mental health,and if students are found to have bad learning attitudes,they must promptly understand the causes of their balearning attitudes and,on that basis,develop and implement effective psychologicalguidance and mental health education programs to alleviate students'learning stress and mental health problems.In social psychology,students'behavior is the main influencing factor of learning mental health,behavior and mental health have a close relationship,through student behavior patterns and changes in learning attitudes can understand the psychological characteristics and health status of students.Therefore,many educators analyze the causes of students'poor learning attitudes based on socialpsychology,and develop and implement effective mental health education countermeasures to improve and transform poor learning attitudes.

Cultivation Path of Nursing Undergraduates’Scientific Research and Innovation Ability

Objective: The cultivation of the innovation ability and scientific research is one of the nursing learning objectives for undergraduate students. To explore the method and effect of training system of scientific research innovation ability of nursing undergraduates based on “3332”. Methods: Three course learning modules are constructed: stage-based course learning module, systematic project practice training module and comprehensive practice training module. A practical training platform for scientific research innovation projects is built, and undergraduate scientific research innovation ability training is carried out from both in-class and out-of-class lines. Results: Since 2017, the students have obtained 7 national innovation and entrepreneurship training programs, 52 university-level undergraduate scientific research projects, published more than 10 academic papers, and obtained 2 patent authorization. Conclusions: The training system of scientific research innovation ability of nursing undergraduates based on “3332” is conducive to the development of scientific research innovation ability of nursing students, and to cultivate nursing talents who can adapt to the development of the new era and have better post competence.

Analysis and Strategy Research on the Mathematics Learning Situation of the Postgraduate Candidates

This study takes the students who intend to study for graduate students as the research object,and makes a questionnaire survey on the mathematics learning situation and learning platform of the postgraduate candidates.Questionnaires were distributed online and offline,and a total of 326 valid questionnaires were collected.Based on 326 questionnaires,this paper analyzes the current learning situation of mathematics in the postgraduate entrance examination and studies the strategies based on the present situation,which points out the direction for the construction of mathematics learning and communication platform based on the needs of postgraduate entrance examination.

Researching the Research: Applying Machine Learning Techniques to Dissertation Classification

This research examines industry-based dissertation research in a doctoralcomputing program through the lens of machine learning algorithms todetermine if natural language processing-based categorization on abstractsalone is adequate for classification. This research categorizes dissertationby both their abstracts and by their full-text using the GraphLabCreate library from Apple’s Turi to identify if abstract analysis is anadequate measure of content categorization, which we found was not. Wealso compare the dissertation categorizations using IBM’s Watson Discoverydeep machine learning tool. Our research provides perspectiveson the practicality of the manual classification of technical documents;and, it provides insights into the: (1) categories of academic work createdby experienced fulltime working professionals in a Computing doctoralprogram, (2) viability and performance of automated categorization of theabstract analysis against the fulltext dissertation analysis, and (3) natuallanguage processing versus human manual text classification abstraction.

Empirical Research of Farmer College Students' Learning Ability and Learning Effect in the Digital Environment

In the course of training farmer college students,there have been a number of problems facing the modern distance education based on digital learning environment,such as weak learning adaptability and ability of farmers and diverse learning needs.Using the empirical methods of questionnaire survey and interview,this article conducts an analysis of the farmer college students' digital learning ability and learning effect,and based on the theoretical study and practical achievements,explores some aspects,such as the information literacy and entrepreneurship ability training for farmer college students,the reform of course teaching and practice teaching,and the building of digital learning platform and special resources.And finally some innovative ideas and recommendations are put forth.