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.

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.

Computational intelligence interception guidance law using online off-policy integral reinforcement learning

Missile interception problem can be regarded as a two-person zero-sum differential games problem,which depends on the solution of Hamilton-Jacobi-Isaacs(HJI)equa-tion.It has been proved impossible to obtain a closed-form solu-tion due to the nonlinearity of HJI equation,and many iterative algorithms are proposed to solve the HJI equation.Simultane-ous policy updating algorithm(SPUA)is an effective algorithm for solving HJI equation,but it is an on-policy integral reinforce-ment learning(IRL).For online implementation of SPUA,the dis-turbance signals need to be adjustable,which is unrealistic.In this paper,an off-policy IRL algorithm based on SPUA is pro-posed without making use of any knowledge of the systems dynamics.Then,a neural-network based online adaptive critic implementation scheme of the off-policy IRL algorithm is pre-sented.Based on the online off-policy IRL method,a computa-tional intelligence interception guidance(CIIG)law is developed for intercepting high-maneuvering target.As a model-free method,intercepting targets can be achieved through measur-ing system data online.The effectiveness of the CIIG is verified through two missile and target engagement scenarios.

The Impact of Industry-Education Integration on College Students’ Motivation to Learn English under the TPACK Framework

This paper explores the impact of industry-education integration on students’motivation in college English courses under the TPACK(Technological Pedagogical Content Knowledge)framework using a comprehensive approach combining quantitative and qualitative methods.Quantitative data analysis indicates a significant positive correlation between the perception of industry-education integration and the level of student learning motivation.There is also a clear association between the perception scores of TPACK framework integration and learning motivation.Qualitative data analysis reveals students’positive experiences and recognition of the TPACK framework integration in practical application projects.The study concludes that industry-education integration and the TPACK framework play a crucial role in enhancing students’learning motivation.It suggests optimizing teaching practices through faculty training,designing practical application projects,and promoting student interaction.This comprehensive analysis provides substantial guidance for the future development of English courses.

Curriculum Reform Practices in “Cosmetic Product Formulation Design and Preparation Technology” Under the Perspective of Integration of Industry, Education, and Research

With the upgrading of industries,the cosmetics industry has posed new requirements for technical talents.As a professional core course in cosmetic technology,“Cosmetic Product Formulation Design and Preparation Technology”serves as the foundation for cultivating students’abilities in cosmetic development and preparation.To foster high-quality skilled talents capable of adapting to the rapid growth of color cosmetics and to better promote the deep integration of scientific and technological industries with curriculum teaching,the teacher team embarked on active explorations and practical teaching research for curriculum teaching reform from four dimensions:strengthening top-level design,enriching teaching content,optimizing teaching design,and reforming assessment methods.These efforts have enhanced students’comprehensive vocational qualities and innovative consciousness,contributing to the teaching reform in higher vocational colleges under the integration of industry,education,and research.

Bloody Mahjong playing strategy based on the integration of deep learning and XGBoost

Bloody Mahjong is a kind of mahjong.It is very popular in China in recent years.It not only has the characteristics of mahjong's conventional state space,huge hidden information,complicated rules,and large randomness of hand cards but also has special rules such as Change three,Hu must lack at least one suit,and Continue playing after Hu.These rules increase the difficulty of research.These special rules are used as the input of the deep learning DenseNet model.DenseNet is used to extract the Mahjong situation features.The learned features are used as the input of the classification algorithm XGBoost,and then the XGBoost algorithm is used to derive the card strategy.Experiments show that the fusion model of deep learning and XGBoost proposed in this paper has higher accuracy than the single model using only one of them in the case of highdimensional sparse features.In the case of fewer training rounds,accuracy of the model can still reach 83%.In the games against real people,it plays like human.

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.

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.

On the Integrated Learning of English and Law

This paper centers on the integrated learning of English and law in China.Firstly,it outlines the importance of English in the solution of the ever increasing legal disputes between China and the outside world,which inevitably involves an integrated learning of English and law.Secondly,it points out that the content of legal English reflects a combination of legal knowledge and English skills.Thirdly,it expounds on the difficulties that Chinese English majors are facing in the process of learning English and law simultaneously and furnishes some practical suggestions.

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.

Design and Analysis of Integrated Predictive Iterative Learning Control for Batch Process Based on Two-dimensional System Theory

Based on the two-dimensional （2D） system theory, an integrated predictive iterative learning control （2D-IPILC） strategy for batch processes is presented. First, the output response and the error transition model predictions along the batch index can be calculated analytically due to the 2D Roesser model of the batch process. Then, an integrated framework of combining iterative learning control （ILC） and model predictive control （MPC） is formed reasonably. The output of feedforward ILC is estimated on the basis of the predefined process 2D model. By min- imizing a quadratic objective function, the feedback MPC is introduced to obtain better control performance for tracking problem of batch processes. Simulations on a typical batch reactor demonstrate that the satisfactory tracking performance as well as faster convergence speed can be achieved than traditional proportion type （P- t-we） ILC despite the model error and disturbances.

Machine Learning Security Defense Algorithms Based on Metadata Correlation Features

With the popularization of the Internet and the development of technology,cyber threats are increasing day by day.Threats such as malware,hacking,and data breaches have had a serious impact on cybersecurity.The network security environment in the era of big data presents the characteristics of large amounts of data,high diversity,and high real-time requirements.Traditional security defense methods and tools have been unable to cope with the complex and changing network security threats.This paper proposes a machine-learning security defense algorithm based on metadata association features.Emphasize control over unauthorized users through privacy,integrity,and availability.The user model is established and the mapping between the user model and the metadata of the data source is generated.By analyzing the user model and its corresponding mapping relationship,the query of the user model can be decomposed into the query of various heterogeneous data sources,and the integration of heterogeneous data sources based on the metadata association characteristics can be realized.Define and classify customer information,automatically identify and perceive sensitive data,build a behavior audit and analysis platform,analyze user behavior trajectories,and complete the construction of a machine learning customer information security defense system.The experimental results show that when the data volume is 5×103 bit,the data storage integrity of the proposed method is 92%.The data accuracy is 98%,and the success rate of data intrusion is only 2.6%.It can be concluded that the data storage method in this paper is safe,the data accuracy is always at a high level,and the data disaster recovery performance is good.This method can effectively resist data intrusion and has high air traffic control security.It can not only detect all viruses in user data storage,but also realize integrated virus processing,and further optimize the security defense effect of user big data.

Progress in clinical research on the integration of Chinese and Western medicines for treating primary liver cancer

Primary liver cancer(PLC) is one of the most common malignant tumors in China. PLC is characterized by insidious onset, rapid progress, poor quality of life, and short survival time. Notably, current treatment strategies remain unsatisfactory. Traditional Chinese medicines(TCM) have been used to treat a variety of diseases, including liver diseases, for more than 2000 years. In this study, we performed a review of the use frequency and clinical efficacy of TCM in treating PLC. Relevant literature from January 1, 2009, to January 1, 2021 was retrieved from network databases of China National Knowledge Infrastructure(CNKI), Chongqing VIP, Wanfang, PubMed, and SinoMed. The most frequently used TCM and their efficacy in PLC treatment were summarized. Based on the inclusion and exclusion criteria, 33 articles were selected. Overall, the efficacy of the combination of TCM and Western medicines in the treatment of PLC was higher than that in the control groups(i.e. treatment with Western medicines alone)(65.11% vs.44.31%, P <.05). Among the 33 selected articles, 11 were investigated for TCM preparation(marketed drugs) and 22 for TCM formulas. In total, 102 types of TCM(single herbs) were used to treat PLC. The top five most frequently used TCM were Poria(14.71%), Astragali radix(13.73%), Atractylodis Macrocephalae Rhizoma(12.75%), Bupleuri radix(12.75%), and Glycyrrhizae radix et Rhizoma(11.76%). Of the 102 types of TCM, tonics were the most frequently used categories, followed by heat-clearing medicines, blood-invigorating medicines, and stasis-resolving medicines. Of 207 papers, 174(84.06%) could not be subjected to statistical analysis due to research quality. Further high-quality research on herb sources, formula components and dosage, toxicology, and ethics of TCM is necessary. In conclusion, TCM play a promising role in the treatment and management of PLC, although further investigations are warranted.

Participatory and Integrated Research in Mountainous Regions of Thailand and Vietnam:Approaches and Lessons Learned

Participatory and integrated research approaches employed by a long-term Thai- Vietnamese-German collaborative research program, ‘The Uplands Program’, that address the vicious circles of resource scarcity, environmental degrada- tion and rural poverty in mountainous regions of northern Thailand and northern Vietnam are discussed in this paper. We present two examples from the Thai component of the research program to show how different disciplines and stakeholders need to cooperate at different scales to make meaningful scientific contributions towards sustainable land use and rural development in mountainous regions. The case of resource conservation in the Thai highlands shows that local and scientific knowledge, conven- tional surveys and participatory modeling can be creatively combined. Integrated research on the potential of integrating fruit trees and associated technologies into mountain farming systems suggests that natural scientists have to work alongsideeconomists and social scientists to avoid harmful effects of purely technology-driven and productivity- enhancing approaches. The success of new technologies cannot be measured solely by adoption rates and yield increases, but also needs to take into account their long-term impact on various groups of farmers and the ecological, economic and social trade-offs that they entail. Technical and institutional innovations need to go hand in hand to provide viable livelihood opportunities for smallholder farmers in mountain watersheds. The major lesson learned from the first six years of our research in the mountains of Thailand and Vietnam is that conventional and participatory approaches are not antagonistic; if scientists from various disciplines and research paradigms are open-minded, the combination of both approaches can produce meaningful results that cater for the needs of both the academic community and local stakeholders in mountain environments.

An integrated machine learning model for accurate and robust prediction of superconducting critical temperature

Discovering new superconductors via traditional trial-and-error experimental approaches is apparently a time-consuming process,and the correlations between the critical temperature(Tc) and material features are still obscure.The rise of machine learning(ML) technology provides new opportunities to speed up inefficient exploration processes,and could potentially uncover new hints on the unclear correlations.In this work,we utilize open-source materials data,ML models,and data mining methods to explore the correlation between the chemical features and Tcvalues of superconducting materials.To further improve the prediction accuracy,a new model is created by integrating three basic algorithms,showing an enhanced accuracy with the coefficient of determination(R2) score of 95.9 % and root mean square error(RMSE) of 6.3 K.The average marginal contributions of material features towards Tcvalues are estimated to determine the importance of various features during prediction processes.The results suggest that the range thermal conductivity plays a critical role in Tcprediction among all element features.Furthermore,the integrated ML model is utilized to screen out potential twenty superconducting materials with Tcvalues beyond 50.0 K.This study provides insights towards Tcprediction to accelerate the exploration of potential high-Tcsuperconductors.

Automated Machine Learning Algorithm Using Recurrent Neural Network to Perform Long-Term Time Series Forecasting

Long-term time series forecasting stands as a crucial research domain within the realm of automated machine learning(AutoML).At present,forecasting,whether rooted in machine learning or statistical learning,typically relies on expert input and necessitates substantial manual involvement.This manual effort spans model development,feature engineering,hyper-parameter tuning,and the intricate construction of time series models.The complexity of these tasks renders complete automation unfeasible,as they inherently demand human intervention at multiple junctures.To surmount these challenges,this article proposes leveraging Long Short-Term Memory,which is the variant of Recurrent Neural Networks,harnessing memory cells and gating mechanisms to facilitate long-term time series prediction.However,forecasting accuracy by particular neural network and traditional models can degrade significantly,when addressing long-term time-series tasks.Therefore,our research demonstrates that this innovative approach outperforms the traditional Autoregressive Integrated Moving Average(ARIMA)method in forecasting long-term univariate time series.ARIMA is a high-quality and competitive model in time series prediction,and yet it requires significant preprocessing efforts.Using multiple accuracy metrics,we have evaluated both ARIMA and proposed method on the simulated time-series data and real data in both short and long term.Furthermore,our findings indicate its superiority over alternative network architectures,including Fully Connected Neural Networks,Convolutional Neural Networks,and Nonpooling Convolutional Neural Networks.Our AutoML approach enables non-professional to attain highly accurate and effective time series forecasting,and can be widely applied to various domains,particularly in business and finance.

Integrating a flipped classroom and problem-based learning into ophthalmology education

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.

MUS Model:A Deep Learning-Based Architecture for IoT Intrusion Detection

In the face of the effective popularity of the Internet of Things(IoT),but the frequent occurrence of cybersecurity incidents,various cybersecurity protection means have been proposed and applied.Among them,Intrusion Detection System(IDS)has been proven to be stable and efficient.However,traditional intrusion detection methods have shortcomings such as lowdetection accuracy and inability to effectively identifymalicious attacks.To address the above problems,this paper fully considers the superiority of deep learning models in processing highdimensional data,and reasonable data type conversion methods can extract deep features and detect classification using advanced computer vision techniques to improve classification accuracy.TheMarkov TransformField(MTF)method is used to convert 1Dnetwork traffic data into 2D images,and then the converted 2D images are filtered by UnsharpMasking to enhance the image details by sharpening;to further improve the accuracy of data classification and detection,unlike using the existing high-performance baseline image classification models,a soft-voting integrated model,which integrates three deep learning models,MobileNet,VGGNet and ResNet,to finally obtain an effective IoT intrusion detection architecture:the MUS model.Four types of experiments are conducted on the publicly available intrusion detection dataset CICIDS2018 and the IoT network traffic dataset N_BaIoT,and the results demonstrate that the accuracy of attack traffic detection is greatly improved,which is not only applicable to the IoT intrusion detection environment,but also to different types of attacks and different network environments,which confirms the effectiveness of the work done.

Construction and optimization of traditional Chinese medicine constitution prediction models based on deep learning

Objective To cater to the demands for personalized health services from a deep learning per-spective by investigating the characteristics of traditional Chinese medicine(TCM)constitu-tion data and constructing models to explore new prediction methods.Methods Data from students at Chengdu University of Traditional Chinese Medicine were collected and organized according to the 24 solar terms from January 21,2020,to April 6,2022.The data were used to identify nine TCM constitutions,including balanced constitution,Qi deficiency constitution,Yang deficiency constitution,Yin deficiency constitution,phlegm dampness constitution,damp heat constitution,stagnant blood constitution,Qi stagnation constitution,and specific-inherited predisposition constitution.Deep learning algorithms were employed to construct multi-layer perceptron(MLP),long short-term memory(LSTM),and deep belief network(DBN)models for the prediction of TCM constitutions based on the nine constitution types.To optimize these TCM constitution prediction models,this study in-troduced the attention mechanism(AM),grey wolf optimizer(GWO),and particle swarm op-timization(PSO).The models’performance was evaluated before and after optimization us-ing the F1-score,accuracy,precision,and recall.Results The research analyzed a total of 31655 pieces of data.(i)Before optimization,the MLP model achieved more than 90%prediction accuracy for all constitution types except the balanced and Qi deficiency constitutions.The LSTM model's prediction accuracies exceeded 60%,indicating that their potential in TCM constitutional prediction may not have been fully realized due to the absence of pronounced temporal features in the data.Regarding the DBN model,the binary classification analysis showed that,apart from slightly underperforming in predicting the Qi deficiency constitution and damp heat constitution,with accuracies of 65%and 60%,respectively.The DBN model demonstrated considerable discriminative power for other constitution types,achieving prediction accuracy rates and area under the receiver op-erating characteristic(ROC)curve(AUC)values exceeding 70%and 0.78,respectively.This indicates that while the model possesses a certain level of constitutional differentiation abili-ty,it encounters limitations in processing specific constitutional features,leaving room for further improvement in its performance.For multi-class classification problem,the DBN model’s prediction accuracy rate fell short of 50%.(ii)After optimization,the LSTM model,enhanced with the AM,typically achieved a prediction accuracy rate above 75%,with lower performance for the Qi deficiency constitution,stagnant blood constitution,and Qi stagna-tion constitution.The GWO-optimized DBN model for multi-class classification showed an increased prediction accuracy rate of 56%,while the PSO-optimized model had a decreased accuracy rate to 37%.The GWO-PSO-DBN model,optimized with both algorithms,demon-strated an improved prediction accuracy rate of 54%.Conclusion This study constructed MLP,LSTM,and DBN models for predicting TCM consti-tution and improved them based on different optimisation algorithms.The results showed that the MLP model performs well,the LSTM and DBN models were effective in prediction but with certain limitations.This study also provided a new technology reference for the es-tablishment and optimisation strategies of TCM constitution prediction models,and a novel idea for the treatment of non-disease.