Machine learning-based comparison of factors influencing estimated glomerular filtration rate in Chinese women with or without nonalcoholic fatty liver

BACKGROUND The prevalence of non-alcoholic fatty liver(NAFLD)has increased recently.Subjects with NAFLD are known to have higher chance for renal function impairment.Many past studies used traditional multiple linear regression(MLR)to identify risk factors for decreased estimated glomerular filtration rate(eGFR).However,medical research is increasingly relying on emerging machine learning(Mach-L)methods.The present study enrolled healthy women to identify factors affecting eGFR in subjects with and without NAFLD(NAFLD+,NAFLD-)and to rank their importance.AIM To uses three different Mach-L methods to identify key impact factors for eGFR in healthy women with and without NAFLD.METHODS A total of 65535 healthy female study participants were enrolled from the Taiwan MJ cohort,accounting for 32 independent variables including demographic,biochemistry and lifestyle parameters(independent variables),while eGFR was used as the dependent variable.Aside from MLR,three Mach-L methods were applied,including stochastic gradient boosting,eXtreme gradient boosting and elastic net.Errors of estimation were used to define method accuracy,where smaller degree of error indicated better model performance.RESULTS Income,albumin,eGFR,High density lipoprotein-Cholesterol,phosphorus,forced expiratory volume in one second(FEV1),and sleep time were all lower in the NAFLD+group,while other factors were all significantly higher except for smoking area.Mach-L had lower estimation errors,thus outperforming MLR.In Model 1,age,uric acid(UA),FEV1,plasma calcium level(Ca),plasma albumin level(Alb)and T-bilirubin were the most important factors in the NAFLD+group,as opposed to age,UA,FEV1,Alb,lactic dehydrogenase(LDH)and Ca for the NAFLD-group.Given the importance percentage was much higher than the 2nd important factor,we built Model 2 by removing age.CONCLUSION The eGFR were lower in the NAFLD+group compared to the NAFLD-group,with age being was the most important impact factor in both groups of healthy Chinese women,followed by LDH,UA,FEV1 and Alb.However,for the NAFLD-group,TSH and SBP were the 5th and 6th most important factors,as opposed to Ca and BF in the NAFLD+group.

A deep-learning-based approach for seismic surface-wave dispersion inversion(SfNet)with application to the Chinese mainland

Surface-wave tomography is an important and widely used method for imaging the crust and upper mantle velocity structure of the Earth.In this study,we proposed a deep learning(DL)method based on convolutional neural network(CNN),named SfNet,to derive the vS model from the Rayleigh wave phase and group velocity dispersion curves.Training a network model usually requires large amount of training datasets,which is labor-intensive and expensive to acquire.Here we relied on synthetics generated automatically from various spline-based vS models instead of directly using the existing vS models of an area to build the training dataset,which enhances the generalization of the DL method.In addition,we used a random sampling strategy of the dispersion periods in the training dataset,which alleviates the problem that the real data used must be sampled strictly according to the periods of training dataset.Tests using synthetic data demonstrate that the proposed method is much faster,and the results for the vS model are more accurate and robust than those of conventional methods.We applied our method to a dataset for the Chinese mainland and obtained a new reference velocity model of the Chinese continent(ChinaVs-DL1.0),which has smaller dispersion misfits than those from the traditional method.The high accuracy and efficiency of our DL approach makes it an important method for vS model inversions from large amounts of surface-wave dispersion data.

Evaluation of Chinese Learning Efficiency of International Students in China:Comparing by Dimension

This paper introduces the Value Engineering method to calculate the value coefficient of Chinese learning efficiency for 377 international students by dimension.Results suggest that attention should be paid to male,European and American,rural,introverted,and“work or education needs”international students;Give full play to the driving role of the reference-type,explore the space for improving the learning efficiency of the improvement-type and attention-type,and pay attention to the problems of problem-type international students.

The Impact of Classroom Evaluation on Chinese Learning Motivation:A Comparative Study of Oral Feedback and Written Comments

Classroom evaluation plays a critical role in shaping students’learning experiences,influencing not only their academic performance but also their motivation and engagement.In the context of primary Chinese language learning,oral feedback and written comments are two prevalent evaluation methods.This paper explores how these different types of feedback impact students’motivation,learning outcomes,and participation.By comparing the immediacy of oral feedback with the systematic nature of written comments,this study seeks to provide insights into how educators can utilize classroom evaluations more effectively to foster motivation in Chinese language learners.The findings indicate that both feedback methods have unique strengths,and a balanced approach may optimize learning outcomes.

Evaluation of Chinese Learning Efficiency of International Students in China

This paper investigates the related factors of Chinese learning engagement and performance of 377 students from three universities in Guangzhou of China,employs the value engineering method,and calculates the value coefficient of learning efficiency.From the value coefficient of Chinese language learning,it proves that over 60%of international students studying in China have a higher efficiency in learning Chinese.

Exploring 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

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.

A Novel Momentum-Based Measure for Online Portfolio Algorithm

In recent years, digital investment portfolios have become a significant area of interest in the field of machine learning. To tackle the issue of neglecting the momentum effect in risk asset prices within the follow-the-winner strategy and to evaluate the significance of this effect, a novel measure of risk asset price momentum trend is introduced for online investment portfolio research. Firstly, a novel approach is introduced to quantify the momentum trend effect, which is determined by the product of the slope of the linear regression model and the absolute value of the linear correlation coefficient. Secondly, a new investment portfolio optimization problem is established based on the prediction of future returns. Thirdly, the Lagrange multiplier method is used to obtain the analytical solution of the optimization model, and the soft projection optimization algorithm is used to map the analytical solution to obtain the investment portfolio of the model. Finally, experiments are conducted on five benchmark datasets and compared with popular investment portfolio algorithms. The empirical findings indicate that the algorithm we are introduced is capable of generating higher investment returns, thereby establishing its efficacy for the management of the online investment portfolios.

Research and Analysis of Grammatical Error Correction Technology for Chinese Documents

With the widespread use of Chinese globally, the number of Chinese learners has been increasing, leading to various grammatical errors among beginners. Additionally, as domestic efforts to develop industrial information grow, electronic documents have also proliferated. When dealing with numerous electronic documents and texts written by Chinese beginners, manually written texts often contain hidden grammatical errors, posing a significant challenge to traditional manual proofreading. Correcting these grammatical errors is crucial to ensure fluency and readability. However, certain special types of text grammar or logical errors can have a huge impact, and manually proofreading a large number of texts individually is clearly impractical. Consequently, research on text error correction techniques has garnered significant attention in recent years. The advent and advancement of deep learning have paved the way for sequence-to-sequence learning methods to be extensively applied to the task of text error correction. This paper presents a comprehensive analysis of Chinese text grammar error correction technology, elaborates on its current research status, discusses existing problems, proposes preliminary solutions, and conducts experiments using judicial documents as an example. The aim is to provide a feasible research approach for Chinese text error correction technology.

Interface Design and Functional Optimization of Chinese Learning Apps Based on User Experience

This research paper investigates the interface design and functional optimization of Chinese learning apps through the lens of user experience.With the increasing popularity of Chinese language learning apps in the era of rapid mobile internet development,users'demands for enhanced interface design and interaction experience have grown significantly.The study aims to explore the influence of user feedback on the design and functionality of Chinese learning apps,proposing optimization strategies to improve user experience and learning outcomes.By conducting a comprehensive literature review,utilizing methods such as surveys and user interviews for data collection,and analyzing user feedback,this research identifies existing issues in the interface design and interaction experience of Chinese learning apps.The results present user opinions,feedback analysis,identified problems,improvement directions,and specific optimization strategies.The study discusses the potential impact of these optimization strategies on enhancing user experience and learning outcomes,compares findings with previous research,addresses limitations,and suggests future research directions.In conclusion,this research contributes to enriching the design theory of Chinese learning apps,offering practical optimization recommendations for developers,and supporting the continuous advancement of Chinese language learning apps.

基于Swin Transformer和CNN的汉字书法教学系统

针对日益增长的汉字书法学习需求,将滑动窗口自注意力(Swin Transformer,ST)模型和卷积神经网络(Convolutional Neural Network,CNN)模型相结合,提出手写体汉字识别ST-CNN模型,进而开发了汉字书法教学系统。实测结果表明,ST-CNN模型识别准确率约为91.6%,较传统的ST模型提升了约0.5个百分点,较传统的CNN模型与ST模型,在收敛速度上分别提升了约10和30个百分点,开发的汉字书法教学系统性能良好。

Research on the Training Mode of “the Combination of College Students’ Extracurricular English Autonomous Learning and Chinese Culture” Based on WeChat Platform

In today’s college English extracurricular learning,college students often neglect the combination of English language learning and the input of Chinese traditional cultural knowledge,paying too much attention to Western culture learning,ignoring their traditional culture and lead to be speechless when encountering cultural exchange activities.The author believes that college students should pay attention to the input of Chinese traditional cultural content in the process of independent English learning outside the classroom.The creative team of college students led by the author through the WeChat platform,in Xinjiang Agricultural University,conducted a combination of Chinese culture input and English autonomous learning,aiming at strengthening the effectiveness of English learning,improving the self-learning ability and intercultural communication skills.

基于Conformer的端到端中英文管制语音识别

将语音识别技术应用到空中交通管理系统中可以提高飞行安全并降低管制员的工作负荷,目前已有的管制语音识别技术在中英文识别上效果较差,因此提出了一种基于Conformer-CTC/Attention的中英文管制语音识别框架。该方法使用基于改进的Conformer共享编码器对输入序列进行语言分类并以参数有效的方式对音频序列的局部和全局相依性进行建模,添加了语种分类模块来判断输入语音序列的语种,还采用了CTC解码器和注意力解码器联合解码的多任务建模方法。最后在建立的民航数据集对所提出的框架进行验证,试验结果表明,Conformer-CTC/Attention(Language-Category)相对于基线模型错误率降低,识别效果达到预期。

FullChinese at MIT:A Non-Disruptive Integration of Technology for Intermediate Students

We describe here ten years of development of a Chinese learning technology and five years of practical experience in integrating this technology in MIT classrooms for intermediate-high and advanced-low students.Key results are as follows:There is no need to disrupt the classroom experience(both for the teacher and the students);Technology provides a sharp increase in learning efficiency and motivation,as confirmed by students;and This overall improvement in learning is achieved by focusing on the efficiency of personal study time.The most salient type of feedback from students falls into two categories:“I wouldn’t have been able to take a class at that level without FullChinese,”and“The use of technology allowed me to prepare for class two to three times faster.”Results were achieved through a slow iterative process during which our learning technology evolved to solve observed needs in acquiring complex new material.

Mechanisms Influencing Learning Gains Under Information Security: Structural Equation Modeling with Mediating Effect

With the expanding enrollments in higher education,the quality of col-lege education and the learning gains of students have attracted much attention.It is important to study the influencing factors and mechanisms of individual stu-dents’acquisition of learning gains to improve the quality of talent cultivation in colleges.However,in the context of information security,the original data of learning situation surveys in various universities involve the security of educa-tional evaluation data and daily privacy of teachers and students.To protect the original data,data feature mining and correlation analyses were performed at the model level.This study selected 12,181 pieces of data from X University,which participated in the Chinese College Student Survey(CCSS)from 2018 to 2021.A confirmatory factor analysis was conducted and a structural equation modeling was conducted using AMOS 24.0.Through hypothesis testing,this study explored the mechanisms that influence learning gains from the per-spectives of student involvement,teacher involvement,and school support.The results indicated that the quality of student involvement has an important mediat-ing effect on learning gains and that a supportive campus environment has the greatest influence on learning gains.Establishing positive emotional communica-tions between teachers and students is a more direct and effective method than improving the teaching level to improve the quality of student involvement.This study discusses the implications of these results on the research and practice of connotative development in higher education.

Topology and Semantic Information Fusion Classification Network Based on Hyperspectral Images of Chinese Herbs

Most methods for classifying hyperspectral data only consider the local spatial relation-ship among samples,ignoring the important non-local topological relationship.However,the non-local topological relationship is better at representing the structure of hyperspectral data.This paper proposes a deep learning model called Topology and semantic information fusion classification network(TSFnet)that incorporates a topology structure and semantic information transmis-sion network to accurately classify traditional Chinese medicine in hyperspectral images.TSFnet uses a convolutional neural network(CNN)to extract features and a graph convolution network(GCN)to capture potential topological relationships among different types of Chinese herbal medicines.The results show that TSFnet outperforms other state-of-the-art deep learning classification algorithms in two different scenarios of herbal medicine datasets.Additionally,the proposed TSFnet model is lightweight and can be easily deployed for mobile herbal medicine classification.

Re-exploring Challenges and Strategies of English Learning for Chinese English-Learners

Chinese English-learners always face many challenges when they try to acquire English language,not only in China's Mainland,but in Chinese communities overseas.Despite the prevalent challenge,such as cultural differences,the present paper tries to re-explore and refine these challenges and strategies,analyze the specific factors,such as age,accent,and thinking difference by using cognitive perspective and sociocultural perspective.To enhance English teaching and learning,it also makes deep discussions and provides some suggestions for future use.

A Survey on Chinese Sign Language Recognition:From Traditional Methods to Artificial Intelligence

Research on Chinese Sign Language(CSL)provides convenience and support for individuals with hearing impairments to communicate and integrate into society.This article reviews the relevant literature on Chinese Sign Language Recognition(CSLR)in the past 20 years.Hidden Markov Models(HMM),Support Vector Machines(SVM),and Dynamic Time Warping(DTW)were found to be the most commonly employed technologies among traditional identificationmethods.Benefiting from the rapid development of computer vision and artificial intelligence technology,Convolutional Neural Networks(CNN),3D-CNN,YOLO,Capsule Network(CapsNet)and various deep neural networks have sprung up.Deep Neural Networks(DNNs)and their derived models are integral tomodern artificial intelligence recognitionmethods.In addition,technologies thatwerewidely used in the early days have also been integrated and applied to specific hybrid models and customized identification methods.Sign language data collection includes acquiring data from data gloves,data sensors(such as Kinect,LeapMotion,etc.),and high-definition photography.Meanwhile,facial expression recognition,complex background processing,and 3D sign language recognition have also attracted research interests among scholars.Due to the uniqueness and complexity of Chinese sign language,accuracy,robustness,real-time performance,and user independence are significant challenges for future sign language recognition research.Additionally,suitable datasets and evaluation criteria are also worth pursuing.

Remote Learning in Higher Education During the Pandemic: A Study of the Experiences of Chinese International Students at United Kingdom and United States Universities in 2020/21

During the COVID-19 pandemic crisis,many universities around the world made a drastic change by transferring most of their offline classes to emergency remote learning(ERL).The aim of this study was to explore how Chinese students,who studied in United Kingdom(UK)and United States(US)universities during the 2020/21 academic year,perceive their experiences of remote learning.As the UK and the US have two relatively advanced education systems,the arrangements of their universities for ERL and their support for international students are worth exploring.Moreover,during the ERL,a portion of Chinese students had online classes in their home countries instead of the country in which their universities are located.Therefore,semi-structured interviews were carried out to explore the academic experiences and social interaction of students who studied in UK and US universities,while remaining in China.The data were analyzed using the thematic analysis method.The findings showed that ERL was perceived negatively by students despite its flexibility in areas of academic learning experiences and social interaction.

Teaching Reform of “Nursing of Traditional Chinese Medicine” Course Based on OBE Concept

Traditional Chinese medicine(TCM)nursing is one of the important disciplines in TCM.It is based on TCM theory and combined with modern nursing theory and technology,aiming to provide comprehensive and individualized nursing services.With the changes in the medical environment and the continuous improvement of people’s health needs,the teaching of TCM nursing is facing new challenges and opportunities.This paper aims to discuss the teaching reform of TCM nursing course based on the concept of outcome-based education(OBE)or student-centered teaching.The significance of this study is to provide theoretical basis and practical guidance for the teaching reform of TCM nursing course,and to promote the development and progress of TCM nursing education.Through the teaching reform based on OBE concept,we can better cultivate TCM nursing talents with innovative spirit and practical skills,and contribute to the development of TCM.

Deep learning-based recognition of stained tongue coating images

Objective To build a dataset encompassing a large number of stained tongue coating images and process it using deep learning to automatically recognize stained tongue coating images.Methods A total of 1001 images of stained tongue coating from healthy students at Hunan University of Chinese Medicine and 1007 images of pathological(non-stained)tongue coat-ing from hospitalized patients at The First Hospital of Hunan University of Chinese Medicine withlungcancer;diabetes;andhypertensionwerecollected.Thetongueimageswererandomi-zed into the training;validation;and testing datasets in a 7:2:1 ratio.A deep learning model was constructed using the ResNet50 for recognizing stained tongue coating in the training and validation datasets.The training period was 90 epochs.The model’s performance was evaluated by its accuracy;loss curve;recall;F1 score;confusion matrix;receiver operating characteristic(ROC)curve;and precision-recall(PR)curve in the tasks of predicting stained tongue coating images in the testing dataset.The accuracy of the deep learning model was compared with that of attending physicians of traditional Chinese medicine(TCM).Results The training results showed that after 90 epochs;the model presented an excellent classification performance.The loss curve and accuracy were stable;showing no signs of overfitting.The model achieved an accuracy;recall;and F1 score of 92%;91%;and 92%;re-spectively.The confusion matrix revealed an accuracy of 92%for the model and 69%for TCM practitioners.The areas under the ROC and PR curves were 0.97 and 0.95;respectively.Conclusion The deep learning model constructed using ResNet50 can effectively recognize stained coating images with greater accuracy than visual inspection of TCM practitioners.This model has the potential to assist doctors in identifying false tongue coating and prevent-ing misdiagnosis.