Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

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.

Study of The Technical Index of Online Learning Behavior Analysis of Nursing Majors on The Superstar Platform Based on The Kirkpatrick Evaluation Model

Objective:To analyze the technical indexes of students’online learning behavior analysis based on Kirkman’s evaluation model,sort out the basic indexes of online learning behavior,and extract scientific and efficient evaluation indexes of online learning effect through statistical analysis.Methods:The online learning behavior data of Physiology of nursing students from 2021-2023 and the first semester of 22 nursing classes(3 and 4)were collected and analyzed.The preset learning behavior indexes were analyzed by multi-dimensional analysis and a correlation analysis was conducted between the indexes and the final examination scores to screen for the dominant important indexes for online learning effect evaluation.Results:The study found that the demand for online learning of nursing students from 2021-2023 increased and the effect was statistically significant.Compared with the stage assessment results,the online learning effect was statistically significant.Conclusion:The main indicators for evaluating and classifying online learning behaviors were summarized.These two indicators can help teachers predict which part of students need learning intervention,optimize the teaching process,and help students improve their learning behavior and academic performance.

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.

Review of China Mobile English Learning： Achievements and Shortcomings -Based on statistical analysis of literature for 10 years （2004-2014） in CNKI （China National Knowledge Infrastructure）

The CNKI includes 153 pieces of paper for 10-year period of 2004-2014 about mobile English learning. We conducted a statistical analysis of 10 years of research among mobile English learning achievements and shortcomings and summarized in order to provide advice and reference for study in the future.

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.

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.

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.

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.

Machine learning-based approach:global trends,research directions,and regulatory standpoints

The field of machine learning(ML)is sufficiently young that it is still expanding at an accelerating pace,lying at the crossroads of computer science and statistics,and at the core of artificial intelligence(AI)and data science.Recent progress in ML has been driven both by the development of new learning algorithms theory,and by the ongoing explosion in the availability of vast amount of data(often referred to as"big data")and low-cost computation.The adoption of ML-based approaches can be found throughout science,technology and industry,leading to more evidence-based decision-making across many walks of life,including healthcare,biomedicine,manufacturing,education,financial modeling,data governance,policing,and marketing.Although the past decade has witnessed the increasing interest in these fields,we are just beginning to tap the potential of these ML algorithms for studying systems that improve with experience.In this paper,we present a comprehensive view on geo worldwide trends(taking into account China,the USA,Israel,Italy,the UK,and the Middle East)of ML-based approaches highlighting the rapid growth in the last 5 years attributable to the introduction of related national policies.Furthermore,based on the literature review,we also discuss the potential research directions in this field,summarizing some popular application areas of machine learning technology,such as healthcare,cyber-security systems,sustainable agriculture,data governance,and nanotechnology,and suggest that the"dissemination of research"in the ML scientific community has undergone the exceptional growth in the time range of 2018–2020,reaching a value of 16,339 publications.Finally,we report the challenges and the regulatory standpoints for managing ML technology.Overall,we hope that this work will help to explain the geo trends of ML approaches and their applicability in various real-world domains,as well as serve as a reference point for both academia and industry professionals,particularly from a technical,ethical and regulatory point of view.

Educational Practices in the Model of Music Learning Theory of E. Edwin Gordon： An Observational Research

This paper analyzes the supervision activity, to which educators and teachers enrolled with AIGAM （Gordon Italian Association for the Musical Learning） are subject to every year and intends to verify the application of those principles expressed in the learning model of the MLT （Music Learning Theory） developed by educational psychologist E. Edwin Gordon （1989, 1999, 2000, 2001, 2007） and promoted internationally by various institutions and organizations specifically accredited. It describes the influence of the videotaped supervision on the process, functions of monitoring, and evaluation of educational practices, starting with an empirical model that has guided the interventions in a study of supervision on training aimed at consolidating and developing professional skills in music education in early childhood. This paper sought to understand： the kind of practices, interactions, communications developing during an educational actions, the existence of a consistent relationship between the principles expressed in the MLT and their application, the type and benefits of supervision performed by of video recording on stakeholders in terms of change in professional behavior, and finally whether the active supervision could be comparable with other kinds of approaches.

Developing Theoretical Relativistic Framework for Research in Open and Flexible Learning： A New Trend in Educational Research

The purpose of research study was to develop a theoretical relativistic framework for research in open and flexible learning environment because it is a new dimension in the field of education. Developing a theoretical relativistic framework for any research study is first and prime step in walking on the track to reach the distinction set by the researcher. Open and flexible learning is a new trend in education, enriched with ICT-use as a basic demand of the 21st century generation in all parts of the globe. So, it requires a theoretical framework for its initiation, implementation, development and evaluation which is relatively developed and advanced from the existing framework. In any research study the literature review is carried out in order to develop, build or construct a theoretical framework. The researcher of the study has observed while attending the international conference on ODL （AAOU, 2013） that most of the studies require theoretical underpinning for ICT-use in education. The researcher assume that being a new trend in education to use ICT for teaching learning purpose; it requires conceptual clarity and theoretical background of the user and researcher, because, without theory the practice is wastage of money, time and energy and it becomes ineffective and requires relatively new conceptual development. So, the problem stated by the researcher for the study was： Developing theoretical relativistic framework for research in open and flexible learning： A new trend in educational research. The objective of the study was integrating the interrelated concepts to build a pnemonological network for identifying the constructs in ICT-rich open and flexible learning environment. The study was significant because it provided theoretical background for conducting research in ICT-use for teaching and learning through open and flexible systems; whether blended or online learning and training. The methodology used by the researcher was qualitative and interpretive because there were reviewing of literature and meta-analysis for building the framework. The data were analyzed and interpreted by the researcher for the findings and drawing conclusions. On the basis of conclusions the researcher has made suggestions and recommendations for conducting research in open and flexible learning environment by using this theoretical relativistic framework. The framework was named as Virtual Learning Environment Framework （VLEF）.

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.

AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).

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.

Brain Research on Mental Disorders: A Criticism

Objective: To expose the problems and inherent limitations of neuroscience-based brain research on mental disorders. Method: Discussion of the theory underlying brain research on mental disorders, followed by a systematic evaluation of typical studies. Results: The fundamental problem is that brain researchers fail to differentiate between biological mental disorders in which brain processes cause the disorder (notably schizophrenia, bipolar disorder, and melancholic depression) and learned mental disorders in which brain processes mediate but do not cause the disorder (which is the case with reactive depression, reactive anxiety, OCD, and PTSD). Researchers have been unsuccessful in identifying mechanisms in the brain that cause biological mental disorders, and will never be able to locate the innumerable specific neural connections that mediate learned mental disorders. Moreover, the author’s review of typical studies in this field shows that they have serious problems with theory, measurement, and data analysis, and that their findings cannot be trusted. Conclusions: Neuroscience-based brain research on mental disorders, unlike other neurological research, has been an expensive failure and it is not worth continuing.