The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

Automatic detection of breast lesions in automated 3D breast ultrasound with cross-organ transfer learning

Background Deep convolutional neural networks have garnered considerable attention in numerous machine learning applications,particularly in visual recognition tasks such as image and video analyses.There is a growing interest in applying this technology to diverse applications in medical image analysis.Automated three dimensional Breast Ultrasound is a vital tool for detecting breast cancer,and computer-assisted diagnosis software,developed based on deep learning,can effectively assist radiologists in diagnosis.However,the network model is prone to overfitting during training,owing to challenges such as insufficient training data.This study attempts to solve the problem caused by small datasets and improve model detection performance.Methods We propose a breast cancer detection framework based on deep learning(a transfer learning method based on cross-organ cancer detection)and a contrastive learning method based on breast imaging reporting and data systems(BI-RADS).Results When using cross organ transfer learning and BIRADS based contrastive learning,the average sensitivity of the model increased by a maximum of 16.05%.Conclusion Our experiments have demonstrated that the parameters and experiences of cross-organ cancer detection can be mutually referenced,and contrastive learning method based on BI-RADS can improve the detection performance of the model.

Surveying on English Mobile Learning Among University Students: Current State and Influencing Factors

Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has become mainstream to utilize mobile devices for English learning among university students’English learning.The current study aims to examine the current situation and influencing factors of university students’English mobile learning.98 university students in one university of Shanghai participated the study and the questionnaire was used to collect the data.The results indicated that most university students already have electronic devices to support mobile learning.Personal factors,environmental factors,digital literacy,and technological capabilities are the main factors affecting university students’English mobile learning.The current study has implications for learners,teachers,and software developers.Learners should adjust their learning motivation,play an active role,and fully utilize the mobile platform to obtain resources and improve learning efficiency.Teachers should incorporate the advantages of mobile teaching and promote categorized and tiered teaching.Software developers should add new functions on the basis of meeting the basic needs of learners and continuously innovate the mobile learning platform.

Design and Research of an Intelligent Learning System for University Physics

In order to break through the limitations of traditional teaching,realize the integration of online and offline teaching,and optimize the intelligent learning experience of university physics,this paper proposes the design of an intelligent learning system for university physics based on cloud computing platforms,and applies this system to teaching environment of university physics.It successfully integrates emerging technologies such as cloud computing,machine learning,and situational awareness,integrates learning context awareness,intelligent recording and broadcasting,resource sharing,learning performance prediction,and content planning and recommendation,and comprehensively improves the quality of university physics teaching.It can optimize the teaching process and deepen intelligent teaching reform,aiming at providing references for the teaching practice of university physics.

Exploration of University English Teachers’Acceptance and Willingness to Use Learning Management System Data Analysis Tools

This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.

Developing Universities into Learning Organizations

A number of companies and organizations consider that it is necessary todevelop into learning organizations in order to meet the challenges of rapidly changing world. Aftera review on the literature on the learning organization, there is no question that the concept isboth attractive and complex. There appears to be more consensus about that becoming a learningorganization is more of a journey than a destination. Senge identifies five key disciplines thatKelp organization to become a learning organization, and the disciplines mean commitment, focus,and practice. In recent years the concept of the learning organization is translated into theeducation sector. Today, more than ever, more and more people see education as the highest form ofleverage to improve society. As the highest form of the education sector, universities must try todevelop into learning organizations. But the process will be neither easy nor swift, and we shouldview the process not as a task to be completed, but as the ongoing work. Effective change andimprovement can only happen by conducting long-term practice involving teachers, administrators,parents, and students who have a common vision and work and live with a learning culture.

Organizational Learning as Moderating Variable Between Human Resource Information System and Public Universities＇ Performance

This study was conducted to see if organizational performance is affected by human resource information system （HRIS） and organizational learning capability. HRIS examined in this study consists of performance appraisal and career management. Data were collected at the public universities located in West Sumatra using questionnaires as the main data collection tool in quantitative approach. Data were analysed using the Statistical Program for Social Science （SPSS）. HRIS was measured by using the concept of behavior and found that independent variables significantly related to organizational performance. These results supported that the organizational learning capability as moderating variable influenced the relationship between HRIS and organizational performance. The model stresses the importance of HRIS which supports the organizational performance at public universities. The results of this study indicate that at public universities in West Sumatra, the improvement of HRIS will lead to higher levels of organizational performance. Results of this study are expected to provide benefits to all stakeholders who have an interest in higher education, especially in information technology and performance.

Evolution of pore systems in low-maturity oil shales during thermal upgrading--Quantified by dynamic SEM and machine learning

In-situ upgrading by heating is feasible for low-maturity shale oil,where the pore space dynamically evolves.We characterize this response for a heated substrate concurrently imaged by SEM.We systematically follow the evolution of pore quantity,size(length,width and cross-sectional area),orientation,shape(aspect ratio,roundness and solidity)and their anisotropy—interpreted by machine learning.Results indicate that heating generates new pores in both organic matter and inorganic minerals.However,the newly formed pores are smaller than the original pores and thus reduce average lengths and widths of the bedding-parallel pore system.Conversely,the average pore lengths and widths are increased in the bedding-perpendicular direction.Besides,heating increases the cross-sectional area of pores in low-maturity oil shales,where this growth tendency fluctuates at<300℃ but becomes steady at>300℃.In addition,the orientation and shape of the newly-formed heating-induced pores follow the habit of the original pores and follow the initial probability distributions of pore orientation and shape.Herein,limited anisotropy is detected in pore direction and shape,indicating similar modes of evolution both bedding-parallel and bedding-normal.We propose a straightforward but robust model to describe evolution of pore system in low-maturity oil shales during heating.

Short-term displacement prediction for newly established monitoring slopes based on transfer learning

This study makes a significant progress in addressing the challenges of short-term slope displacement prediction in the Universal Landslide Monitoring Program,an unprecedented disaster mitigation program in China,where lots of newly established monitoring slopes lack sufficient historical deformation data,making it difficult to extract deformation patterns and provide effective predictions which plays a crucial role in the early warning and forecasting of landslide hazards.A slope displacement prediction method based on transfer learning is therefore proposed.Initially,the method transfers the deformation patterns learned from slopes with relatively rich deformation data by a pre-trained model based on a multi-slope integrated dataset to newly established monitoring slopes with limited or even no useful data,thus enabling rapid and efficient predictions for these slopes.Subsequently,as time goes on and monitoring data accumulates,fine-tuning of the pre-trained model for individual slopes can further improve prediction accuracy,enabling continuous optimization of prediction results.A case study indicates that,after being trained on a multi-slope integrated dataset,the TCN-Transformer model can efficiently serve as a pretrained model for displacement prediction at newly established monitoring slopes.The three-day average RMSE is significantly reduced by 34.6%compared to models trained only on individual slope data,and it also successfully predicts the majority of deformation peaks.The fine-tuned model based on accumulated data on the target newly established monitoring slope further reduced the three-day RMSE by 37.2%,demonstrating a considerable predictive accuracy.In conclusion,taking advantage of transfer learning,the proposed slope displacement prediction method effectively utilizes the available data,which enables the rapid deployment and continual refinement of displacement predictions on newly established monitoring slopes.

Total organic carbon content logging prediction based on machine learning:A brief review

The total organic carbon content usually determines the hydrocarbon generation potential of a formation.A higher total organic carbon content often corresponds to a greater possibility of generating large amounts of oil or gas.Hence,accurately calculating the total organic carbon content in a formation is very important.Present research is focused on precisely calculating the total organic carbon content based on machine learning.At present,many machine learning methods,including backpropagation neural networks,support vector regression,random forests,extreme learning machines,and deep learning,are employed to evaluate the total organic carbon content.However,the principles and perspectives of various machine learning algorithms are quite different.This paper reviews the application of various machine learning algorithms to deal with total organic carbon content evaluation problems.Of various machine learning algorithms used for TOC content predication,two algorithms,the backpropagation neural network and support vector regression are the most commonly used,and the backpropagation neural network is sometimes combined with many other algorithms to achieve better results.Additionally,combining multiple algorithms or using deep learning to increase the number of network layers can further improve the total organic carbon content prediction.The prediction by backpropagation neural network may be better than that by support vector regression;nevertheless,using any type of machine learning algorithm improves the total organic carbon content prediction in a given research block.According to some published literature,the determination coefficient(R^(2))can be increased by up to 0.46 after using machine learning.Deep learning algorithms may be the next breakthrough direction that can significantly improve the prediction of the total organic carbon content.Evaluating the total organic carbon content based on machine learning is of great significance.

Empowerment of Artificial Intelligence in Teaching Reform of Ideological and Political Courses in Universities

The rapid development of artificial intelligence(AI)technology has brought new opportunities and challenges to the field of education.As an important link in cultivating students’comprehensive quality and socialist core values,it is necessary to carry out continuous teaching reform and innovation in ideological and political courses in colleges and universities.Based on the concept of AI empowering the teaching reform of ideological and political courses,this study aims to explore how to use artificial intelligence technology to improve the teaching effect and learning experience of ideological and political courses.The research first analyzes the application status of artificial intelligence technology in education,and then discusses the application potential of artificial intelligence in ideological and political courses.Subsequently,the teaching reform strategy of ideological and political courses based on artificial intelligence is proposed,including the use of virtual reality technology,the application of intelligent auxiliary teaching tools to enhance personalized teaching,and the construction of an intelligent learning management system.Lastly,a case analysis is conducted to explore the implementation effect of the teaching reform of ideological and political courses in universities.The results showed that the application of artificial intelligence technology can effectively improve the teaching effect and learning experience of ideological and political courses,and provide new ideas and methods for the teaching reform of ideological and political courses in universities.

Predicting power conversion efficiency of binary organic solar cells based on Y6 acceptor by machine learning

Organic solar cells(OSCs)are a promising photovoltaic technology for practical applications.However,the design and synthesis of donor materials molecules based on traditional experimental trial-anderror methods are often complex and expensive in terms of money and time.Machine learning(ML)can effectively learn from data sets and build reliable models to predict the performance of materials with reasonable accuracy.Y6 has become the landmark high-performance OSC acceptor material.We collected the power conversion efficiency(PCE)of small molecular donors and polymer donors based on the Y6 acceptor and calculated their molecule structure descriptors.Then we used six types of algorithms to develop models and compare the predictive performance with the coefficient of determination(R^(2))and Pearson correlation coefficient(r)as the metrics.Among them,decision tree-based algorithms showed excellent predictive capability,especially the Gradient Boosting Regression Tree(GBRT)models based on small molecular donors and polymer donors exhibited that the values of R2are 0.84 and 0.69 for the testing set,respectively.Our work provides a strategy to predict PCEs rapidly,and discovers the influence of the descriptors,thereby being expected to screen high-performance donor material molecules.

Gasification of Organic Waste:Parameters,Mechanism and Prediction with the Machine Learning Approach

Gasification of organic waste represents one of the most effective valorization pathways for renewable energy and resources recovery,while this process can be affected by multi-factors like temperature,feedstock,and steam content,making the product’s prediction problematic.With the popularization and promotion of artificial intelligence such as machine learning(ML),traditional artificial neural networks have been paid more attention by researchers from the data science field,which provides scientific and engineering communities with flexible and rapid prediction frameworks in the field of organic waste gasification.In this work,critical parameters including temperature,steam ratio,and feedstock during gasification of organic waste were reviewed in three scenarios including steam gasification,air gasification,and oxygen-riched gasification,and the product distribution and involved mechanism were elaborated.Moreover,we presented the details of ML methods like regression analysis,artificial neural networks,decision trees,and related methods,which are expected to revolutionize data analysis and modeling of the gasification of organic waste.Typical outputs including the syngas yield,composition,and HHVs were discussed with a better understanding of the gasification process and ML application.This review focused on the combination of gasification and ML,and it is of immediate significance for the resource and energy utilization of organic waste.

Estimation of soil organic matter in the Ogan-Kuqa River Oasis, Northwest China, based on visible and near-infrared spectroscopy and machine learning

Visible and near-infrared(vis-NIR)spectroscopy technique allows for fast and efficient determination of soil organic matter(SOM).However,a prior requirement for the vis-NIR spectroscopy technique to predict SOM is the effective removal of redundant information.Therefore,this study aims to select three wavelength selection strategies for obtaining the spectral response characteristics of SOM.The SOM content and spectral information of 110 soil samples from the Ogan-Kuqa River Oasis were measured under laboratory conditions in July 2017.Pearson correlation analysis was introduced to preselect spectral wavelengths from the preprocessed spectra that passed the 0.01 level significance test.The successive projection algorithm(SPA),competitive adaptive reweighted sampling(CARS),and Boruta algorithm were used to detect the optimal variables from the preselected wavelengths.Finally,partial least squares regression(PLSR)and random forest(RF)models combined with the optimal wavelengths were applied to develop a quantitative estimation model of the SOM content.The results demonstrate that the optimal variables selected were mainly located near the range of spectral absorption features(i.e.,1400.0,1900.0,and 2200.0 nm),and the CARS and Boruta algorithm also selected a few visible wavelengths located in the range of 480.0–510.0 nm.Both models can achieve a more satisfactory prediction of the SOM content,and the RF model had better accuracy than the PLSR model.The SOM content prediction model established by Boruta algorithm combined with the RF model performed best with 23 variables and the model achieved the coefficient of determination(R2)of 0.78 and the residual prediction deviation(RPD)of 2.38.The Boruta algorithm effectively removed redundant information and optimized the optimal wavelengths to improve the prediction accuracy of the estimated SOM content.Therefore,combining vis-NIR spectroscopy with machine learning to estimate SOM content is an important method to improve the accuracy of SOM prediction in arid land.

Automated File Labeling for Heterogeneous Files Organization Using Machine Learning

File labeling techniques have a long history in analyzing the anthological trends in computational linguistics.The situation becomes worse in the case of files downloaded into systems from the Internet.Currently,most users either have to change file names manually or leave a meaningless name of the files,which increases the time to search required files and results in redundancy and duplications of user files.Currently,no significant work is done on automated file labeling during the organization of heterogeneous user files.A few attempts have been made in topic modeling.However,one major drawback of current topic modeling approaches is better results.They rely on specific language types and domain similarity of the data.In this research,machine learning approaches have been employed to analyze and extract the information from heterogeneous corpus.A different file labeling technique has also been used to get the meaningful and`cohesive topic of the files.The results show that the proposed methodology can generate relevant and context-sensitive names for heterogeneous data files and provide additional insight into automated file labeling in operating systems.

A Universal Activation Function for Deep Learning

Recently,deep learning has achieved remarkable results in fields that require human cognitive ability,learning ability,and reasoning ability.Activation functions are very important because they provide the ability of artificial neural networks to learn complex patterns through nonlinearity.Various activation functions are being studied to solve problems such as vanishing gradients and dying nodes that may occur in the deep learning process.However,it takes a lot of time and effort for researchers to use the existing activation function in their research.Therefore,in this paper,we propose a universal activation function(UA)so that researchers can easily create and apply various activation functions and improve the performance of neural networks.UA can generate new types of activation functions as well as functions like traditional activation functions by properly adjusting three hyperparameters.The famous Convolutional Neural Network(CNN)and benchmark datasetwere used to evaluate the experimental performance of the UA proposed in this study.We compared the performance of the artificial neural network to which the traditional activation function is applied and the artificial neural network to which theUA is applied.In addition,we evaluated the performance of the new activation function generated by adjusting the hyperparameters of theUA.The experimental performance evaluation results showed that the classification performance of CNNs improved by up to 5%through the UA,although most of them showed similar performance to the traditional activation function.

Research on the Development Model of University Archives Cultural Products Based on Deep Learning

The products of an archival culture in colleges and universities are the final result of the development of archival cultural resources,and the development of archival cultural effects in colleges and universities should be an important part of improving the artistic level of libraries.The existing RippleNet model doesn’t consider the influence of key nodes on recommendation results,and the recommendation accuracy is not high.Therefore,based on the RippleNet model,this paper introduces the influence of complex network nodes into the model and puts forward the Cn RippleNet model.The performance of the model is verified by experiments,which provide a theoretical basis for the promotion and recommendation of its cultural products of universarchives,solve the problem that RippleNet doesn’t consider the influence of key nodes on recommendation results,and improve the recommendation accuracy.This paper also combs the development course of archival cultural products in detail.Finally,based on the Cn-RippleNet model,the cultural effect of university archives is recommended and popularized.

Automatic delineation of organs at risk in non-small cell lung cancer radiotherapy based on deep learning networks

Objective To introduce an end-to-end automatic segmentation method for organs at risk(OARs)in chest computed tomography(CT)images based on dense connection deep learning and to provide an accurate auto-segmentation model to reduce the workload on radiation oncologists.Methods CT images of 36 lung cancer cases were included in this study.Of these,27 cases were randomly selected as the training set,six cases as the validation set,and nine cases as the testing set.The left and right lungs,cord,and heart were auto-segmented,and the training time was set to approximately 5 h.The testing set was evaluated using geometric metrics including the Dice similarity coefficient(DSC),95%Hausdorff distance(HD95),and average surface distance(ASD).Thereafter,two sets of treatment plans were optimized based on manually contoured OARs and automatically contoured OARs,respectively.Dosimetric parameters including Dmax and Vx of the OARs were obtained and compared.Results The proposed model was superior to U-Net in terms of the DSC,HD95,and ASD,although there was no significant difference in the segmentation results yielded by both networks(P>0.05).Compared to manual segmentation,auto-segmentation significantly reduced the segmentation time by nearly 40.7%(P<0.05).Moreover,the differences in dose-volume parameters between the two sets of plans were not statistically significant(P>0.05).Conclusion The bilateral lung,cord,and heart could be accurately delineated using the DenseNet-based deep learning method.Thus,feature map reuse can be a novel approach to medical image auto-segmentation.

Investigation and reflection on students learning satisfaction in local application-oriented universities

In the process of the transformation and development of the local applied undergraduate colleges, how to achieve the goal of cultivating high-quality applied talents is one of the most difficult problems in the sustainable development. The local application of H City University as an example, by using a self-designed questionnaire to investigate the students＇ learning satisfaction, according to the process of the development of the local undergraduate colleges and universities application problems, provide objective basis and reference for the reform and development.

Building a Model for Teaching Korean in Universities under the Vision of Experiential Learning

The rapid development of the economy and the continuous improvement of the education system made the state begin to pay more attention to the talents in the education sector,especially in the context of the development of economic globalization,the demand for talents in foreign languages is increasing.China and South Korea are closely connected,so the demand for Korean language talents in our country is increasing,and many universities have established Korean language majors,and is constantly exploring teaching models and methods to enhance Korean language teaching,among which,experiential teaching being the university’s Korean language teaching is the important ways and means.This paper mainly analyzes the construction of the model of Korean language teaching in universities under experiential learning.