Model Agnostic Meta-Learning(MAML)-Based Ensemble Model for Accurate Detection of Wheat Diseases Using Vision Transformer and Graph Neural Networks

Wheat is a critical crop,extensively consumed worldwide,and its production enhancement is essential to meet escalating demand.The presence of diseases like stem rust,leaf rust,yellow rust,and tan spot significantly diminishes wheat yield,making the early and precise identification of these diseases vital for effective disease management.With advancements in deep learning algorithms,researchers have proposed many methods for the automated detection of disease pathogens;however,accurately detectingmultiple disease pathogens simultaneously remains a challenge.This challenge arises due to the scarcity of RGB images for multiple diseases,class imbalance in existing public datasets,and the difficulty in extracting features that discriminate between multiple classes of disease pathogens.In this research,a novel method is proposed based on Transfer Generative Adversarial Networks for augmenting existing data,thereby overcoming the problems of class imbalance and data scarcity.This study proposes a customized architecture of Vision Transformers(ViT),where the feature vector is obtained by concatenating features extracted from the custom ViT and Graph Neural Networks.This paper also proposes a Model AgnosticMeta Learning(MAML)based ensemble classifier for accurate classification.The proposedmodel,validated on public datasets for wheat disease pathogen classification,achieved a test accuracy of 99.20%and an F1-score of 97.95%.Compared with existing state-of-the-art methods,this proposed model outperforms in terms of accuracy,F1-score,and the number of disease pathogens detection.In future,more diseases can be included for detection along with some other modalities like pests and weed.

Olive Leaf Disease Detection via Wavelet Transform and Feature Fusion of Pre-Trained Deep Learning Models

Olive trees are susceptible to a variety of diseases that can cause significant crop damage and economic losses.Early detection of these diseases is essential for effective management.We propose a novel transformed wavelet,feature-fused,pre-trained deep learning model for detecting olive leaf diseases.The proposed model combines wavelet transforms with pre-trained deep-learning models to extract discriminative features from olive leaf images.The model has four main phases:preprocessing using data augmentation,three-level wavelet transformation,learning using pre-trained deep learning models,and a fused deep learning model.In the preprocessing phase,the image dataset is augmented using techniques such as resizing,rescaling,flipping,rotation,zooming,and contrasting.In wavelet transformation,the augmented images are decomposed into three frequency levels.Three pre-trained deep learning models,EfficientNet-B7,DenseNet-201,and ResNet-152-V2,are used in the learning phase.The models were trained using the approximate images of the third-level sub-band of the wavelet transform.In the fused phase,the fused model consists of a merge layer,three dense layers,and two dropout layers.The proposed model was evaluated using a dataset of images of healthy and infected olive leaves.It achieved an accuracy of 99.72%in the diagnosis of olive leaf diseases,which exceeds the accuracy of other methods reported in the literature.This finding suggests that our proposed method is a promising tool for the early detection of olive leaf diseases.

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

Foreign language teaching practice is developing rapidly,but research on foreign language teacher learning is currently relatively fragmented and unstructured.The book Foreign Language Teacher Learning,written by Professor Kang Yan from Capital Normal University,published in September 2022,makes a systematic introduction to foreign language teacher learning,which to some extent makes up for this shortcoming.Her book presents the lineage of foreign language teacher learning research at home and abroad,analyzes both theoretical and practical aspects,reviews the cuttingedge research results,and foresees the future development trend,painting a complete research picture for researchers in the field of foreign language teaching and teacher education as well as front-line teachers interested in foreign language teacher learning.This is an important inspiration for conducting foreign language teacher learning research in the future.And this paper makes a review of the book from aspects such as its content,major characteristics,contributions and limitations.

Research and Discussion on Flipped Classroom Combined with Case-Based Learning in Pharmacoeconomics Teaching

Objective:To explore the application effect of flipped classroom combined with case-based learning teaching methods in pharmacoeconomics teaching.Methods:The students majoring in clinical pharmacy in 2019 were selected as the study subjects,and the cost-effectiveness analysis of different dosage forms of Yinzhihuang in the treatment of neonatal jaundice was selected as the teaching case.The flipped classroom combined with case-based learning teaching method was used to carry out theoretical teaching to the students.After the course,questionnaires were distributed through the Sojump platform to evaluate the teaching effect.Results:The results of the questionnaire showed that 85.71%of the students believed that the flipped classroom combined with case-based learning teaching method was helpful in mobilizing the learning enthusiasm and initiative,and improving the comprehensive application ability of the knowledge of pharmacoeconomics.92.86%of the students think that it is conducive to the understanding and memorization of learning content,as well as the cultivation of teamwork,communication,etc.Conclusion:Flipped classroom combined with case-based learning teaching method can improve students’knowledge mastery,thinking skills,and practical application skills,as well as optimize and improve teachers’teaching levels.

A Deep Learning-Based Teaching Design for High School Geography Units:Taking the Example of Landforms of the Humanistic Education Edition

The traditional teaching methods of one-way cultivation of students can no longer meet the requirements of talent cultivation at this stage.The issue of how to promote students from passive acceptance to the independent cognitive understanding stage(i.e.deep learning)has become the focus of geography teaching.Therefore,under the guidance of deep learning theory,this paper takes the“landforms”knowledge unit of the Humanistic Education Edition as an example,improves the classroom teaching means through the unit teaching mode,reconstructs the“landforms”teaching unit,and explores the specific teaching of high school geography unit based on deep learning.This study provides a good example and guidelines for high school geography teaching and learning.

Analysis of the Role of Problem-Based Independent Learning Model in Teaching Cerebral Ischemic Stroke First Aid in Emergency Medicine

Objective:To analyze the effect of using a problem-based(PBL)independent learning model in teaching cerebral ischemic stroke(CIS)first aid in emergency medicine.Methods:90 interns in the emergency department of our hospital from May 2022 to May 2023 were selected for the study.They were divided into Group A(45,conventional teaching method)and Group B(45 cases,PBL independent learning model)by randomized numerical table method to compare the effects of the two groups.Results:The teaching effect indicators and student satisfaction scores in Group B were higher than those in Group A(P<0.05).Conclusion:The use of the PBL independent learning model in the teaching of CIS first aid can significantly improve the teaching effect and student satisfaction.

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.

The Application of Reinforcement Theory in the Review Stage of English Teaching and Learning in Chinese Higher Vocational and Technical Colleges

Reinforcement theory is a behavioral psychology theory proposed by Skinner,which has been widely applied in various fields such as management and education.Positive reinforcement and negative reinforcement are the two types of reinforcement.By adopting these two different reinforcement methods appropriately,human behavior can develop in a positive direction.In the review stage of English teaching and learning in Chinese higher vocational and technical colleges,the use of different reinforcement methods based on various classes,individuals,conditions,and environments can effectively promote or change the behavior of teachers and students,thereby improving the effectiveness of the review.

Strategies on Promoting Transformative Learning of College English Teachers

Based on the previous case study in promoting transformative learning of college English teachers,who participated in a three-month online training courses,the article made a further research and concluded four strategies on promoting transformative learning of college English Teachers.

Transformative Learning:Intercultural Adaptation of Chinese Teachers at the Confucius Institute in Spain

In recent years there has been a proliferation of studies demonstrating the value of teaching abroad as much for its benefits for the training and professional development of these teachers,as for its impact and beneficial effects on students.This article uses transformative learning as a theoretical framework to interpret the achievements associated with the experience of teaching abroad,and to identify and analyze different motivational factors,adjustments,changes,challenges,and perspectives of Chinese teachers linked to a Confucius Institute in Spain,through a qualitative analysis of narratives elicited through in-depth interviews and focus groups.It highlights the role and potential of the transnational,intercultural experience of these teachers as authentic actors in the part played by the Confucius Institutes in language teaching and the promotion of Chinese culture,indicating many issues including language difficulties,professional adjustment,ideas about education,beliefs of teachers and the management of the program.

UAV-Assisted Dynamic Avatar Task Migration for Vehicular Metaverse Services: A Multi-Agent Deep Reinforcement Learning Approach

Avatars, as promising digital representations and service assistants of users in Metaverses, can enable drivers and passengers to immerse themselves in 3D virtual services and spaces of UAV-assisted vehicular Metaverses. However, avatar tasks include a multitude of human-to-avatar and avatar-to-avatar interactive applications, e.g., augmented reality navigation,which consumes intensive computing resources. It is inefficient and impractical for vehicles to process avatar tasks locally. Fortunately, migrating avatar tasks to the nearest roadside units(RSU)or unmanned aerial vehicles(UAV) for execution is a promising solution to decrease computation overhead and reduce task processing latency, while the high mobility of vehicles brings challenges for vehicles to independently perform avatar migration decisions depending on current and future vehicle status. To address these challenges, in this paper, we propose a novel avatar task migration system based on multi-agent deep reinforcement learning(MADRL) to execute immersive vehicular avatar tasks dynamically. Specifically, we first formulate the problem of avatar task migration from vehicles to RSUs/UAVs as a partially observable Markov decision process that can be solved by MADRL algorithms. We then design the multi-agent proximal policy optimization(MAPPO) approach as the MADRL algorithm for the avatar task migration problem. To overcome slow convergence resulting from the curse of dimensionality and non-stationary issues caused by shared parameters in MAPPO, we further propose a transformer-based MAPPO approach via sequential decision-making models for the efficient representation of relationships among agents. Finally, to motivate terrestrial or non-terrestrial edge servers(e.g., RSUs or UAVs) to share computation resources and ensure traceability of the sharing records, we apply smart contracts and blockchain technologies to achieve secure sharing management. Numerical results demonstrate that the proposed approach outperforms the MAPPO approach by around 2% and effectively reduces approximately 20% of the latency of avatar task execution in UAV-assisted vehicular Metaverses.

A Deep Learning Approach for Forecasting Thunderstorm Gusts in the Beijing–Tianjin–Hebei Region

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China,and it is of great importance to correctly forecast them.At present,the forecasting of thunderstorm gusts is mainly based on traditional subjective methods,which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources.In this paper,we propose a deep learning method called Thunderstorm Gusts TransU-net(TGTransUnet)to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology(IUM)with a lead time of 1 to 6 h.To determine the specific range of thunderstorm gusts,we combine three meteorological variables:radar reflectivity factor,lightning location,and 1-h maximum instantaneous wind speed from automatic weather stations(AWSs),and obtain a reasonable ground truth of thunderstorm gusts.Then,we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture,which is based on convolutional neural networks and a transformer.The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training,validation,and testing datasets.Finally,the performance of TG-TransUnet is compared with other methods.The results show that TG-TransUnet has the best prediction results at 1–6 h.The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China.

Machine learning-assisted efficient design of Cu-based shape memory alloy with specific phase transition temperature

The martensitic transformation temperature is the basis for the application of shape memory alloys(SMAs),and the ability to quickly and accurately predict the transformation temperature of SMAs has very important practical significance.In this work,machine learning(ML)methods were utilized to accelerate the search for shape memory alloys with targeted properties(phase transition temperature).A group of component data was selected to design shape memory alloys using reverse design method from numerous unexplored data.Component modeling and feature modeling were used to predict the phase transition temperature of the shape memory alloys.The experimental results of the shape memory alloys were obtained to verify the effectiveness of the support vector regression(SVR)model.The results show that the machine learning model can obtain target materials more efficiently and pertinently,and realize the accurate and rapid design of shape memory alloys with specific target phase transition temperature.On this basis,the relationship between phase transition temperature and material descriptors is analyzed,and it is proved that the key factors affecting the phase transition temperature of shape memory alloys are based on the strength of the bond energy between atoms.This work provides new ideas for the controllable design and performance optimization of Cu-based shape memory alloys.

Enhancing Ulcerative Colitis Diagnosis:A Multi-Level Classification Approach with Deep Learning

The evaluation of disease severity through endoscopy is pivotal in managing patients with ulcerative colitis,a condition with significant clinical implications.However,endoscopic assessment is susceptible to inherent variations,both within and between observers,compromising the reliability of individual evaluations.This study addresses this challenge by harnessing deep learning to develop a robust model capable of discerning discrete levels of endoscopic disease severity.To initiate this endeavor,a multi-faceted approach is embarked upon.The dataset is meticulously preprocessed,enhancing the quality and discriminative features of the images through contrast limited adaptive histogram equalization(CLAHE).A diverse array of data augmentation techniques,encompassing various geometric transformations,is leveraged to fortify the dataset’s diversity and facilitate effective feature extraction.A fundamental aspect of the approach involves the strategic incorporation of transfer learning principles,harnessing a modified ResNet-50 architecture.This augmentation,informed by domain expertise,contributed significantly to enhancing the model’s classification performance.The outcome of this research endeavor yielded a highly promising model,demonstrating an accuracy rate of 86.85%,coupled with a recall rate of 82.11%and a precision rate of 89.23%.

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.

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.

Power Transformer Fault Diagnosis Using Random Forest and Optimized Kernel Extreme Learning Machine

Power transformer is one of the most crucial devices in power grid.It is significant to determine incipient faults of power transformers fast and accurately.Input features play critical roles in fault diagnosis accuracy.In order to further improve the fault diagnosis performance of power trans-formers,a random forest feature selection method coupled with optimized kernel extreme learning machine is presented in this study.Firstly,the random forest feature selection approach is adopted to rank 42 related input features derived from gas concentration,gas ratio and energy-weighted dissolved gas analysis.Afterwards,a kernel extreme learning machine tuned by the Aquila optimization algorithm is implemented to adjust crucial parameters and select the optimal feature subsets.The diagnosis accuracy is used to assess the fault diagnosis capability of concerned feature subsets.Finally,the optimal feature subsets are applied to establish fault diagnosis model.According to the experimental results based on two public datasets and comparison with 5 conventional approaches,it can be seen that the average accuracy of the pro-posed method is up to 94.5%,which is superior to that of other conventional approaches.Fault diagnosis performances verify that the optimum feature subset obtained by the presented method can dramatically improve power transformers fault diagnosis accuracy.

Research on the Digital Transformation of English Teaching in Vocational Education

With the advancement of technology,exploring the impact of digital transformation on vocational education English teaching has become crucial.This study aims to investigate the effectiveness of digital transformation in English teaching in vocational education in China by exploring students’and teachers’attitudes,views,and experiences on the use of digital technology in English teaching.This study employed a mixed method of qualitative and quantitative analysis.The research results indicate that digital transformation has had a positive impact on vocational education English teaching,as it enhances the teaching process,promotes communication and collaboration,and increases students’enthusiasm and participation.However,implementing digital transformation in vocational education English teaching also poses challenges,including a lack of resources,infrastructure,and training.This study provides an in-depth understanding of the advantages and challenges of digital transformation in vocational education English teaching and proposes strategies to improve the implementation of digital technology in this context.

Application of Cooperative Learning and Flipped Classrooms in University Basketball Courses in China

This study investigates the application of the teaching model combining cooperative learning and flipped classrooms in university basketball courses in China.By analyzing the advantages and disadvantages of the traditional basketball teaching model and students’satisfaction with the course,the necessity of implementing cooperative learning and flipped classrooms is proposed.The study planned in detail the implementation strategies before class,in the classroom,and after class,and compared them with the control group through an experimental design.The experimental results showed that the new teaching mode demonstrated significant advantages in terms of learning outcomes,student satisfaction,and teacher evaluation.This study provides a valuable reference for the future reform of the physical education curriculum.

A Deep Learning Ensemble Method for Forecasting Daily Crude Oil Price Based on Snapshot Ensemble of Transformer Model

The oil industries are an important part of a country’s economy.The crude oil’s price is influenced by a wide range of variables.Therefore,how accurately can countries predict its behavior and what predictors to employ are two main questions.In this view,we propose utilizing deep learning and ensemble learning techniques to boost crude oil’s price forecasting performance.The suggested method is based on a deep learning snapshot ensemble method of the Transformer model.To examine the superiority of the proposed model,this paper compares the proposed deep learning ensemble model against different machine learning and statistical models for daily Organization of the Petroleum Exporting Countries(OPEC)oil price forecasting.Experimental results demonstrated the outperformance of the proposed method over statistical and machine learning methods.More precisely,the proposed snapshot ensemble of Transformer method achieved relative improvement in the forecasting performance compared to autoregressive integrated moving average ARIMA(1,1,1),ARIMA(0,1,1),autoregressive moving average(ARMA)(0,1),vector autoregression(VAR),random walk(RW),support vector machine(SVM),and random forests(RF)models by 99.94%,99.62%,99.87%,99.65%,7.55%,98.38%,and 99.35%,respectively,according to mean square error metric.