A Study on the Instructional Design of Interdisciplinary Thematic Learning in Middle School Physical Education Based on the CASES-T Model

With the deepening of educational reform,interdisciplinary thematic learning,as an emerging educational model,has become a focus of attention in the field of educational research.Based on the STEM(science,technology,engineering,and mathematics)education concept and CASES-T(Content,Activity,Situation,Evaluation,Strategy-Target)model,this study provides a theoretical basis for the teaching design and implementation of interdisciplinary thematic learning in middle school physical education.Through the analysis of specific interdisciplinary thematic learning cases,it aims to provide theoretical support and practical guidance for the reform of middle school physical education through the CASES-T model-based interdisciplinary thematic teaching design research in middle school physical education,in order to enhance students’learning effects,cultivate core literacy in physical education,and promote students’all-round development.

Machine learning applications in stroke medicine:advancements,challenges,and future prospectives

Stroke is a leading cause of disability and mortality worldwide,necessitating the development of advanced technologies to improve its diagnosis,treatment,and patient outcomes.In recent years,machine learning techniques have emerged as promising tools in stroke medicine,enabling efficient analysis of large-scale datasets and facilitating personalized and precision medicine approaches.This abstract provides a comprehensive overview of machine learning’s applications,challenges,and future directions in stroke medicine.Recently introduced machine learning algorithms have been extensively employed in all the fields of stroke medicine.Machine learning models have demonstrated remarkable accuracy in imaging analysis,diagnosing stroke subtypes,risk stratifications,guiding medical treatment,and predicting patient prognosis.Despite the tremendous potential of machine learning in stroke medicine,several challenges must be addressed.These include the need for standardized and interoperable data collection,robust model validation and generalization,and the ethical considerations surrounding privacy and bias.In addition,integrating machine learning models into clinical workflows and establishing regulatory frameworks are critical for ensuring their widespread adoption and impact in routine stroke care.Machine learning promises to revolutionize stroke medicine by enabling precise diagnosis,tailored treatment selection,and improved prognostication.Continued research and collaboration among clinicians,researchers,and technologists are essential for overcoming challenges and realizing the full potential of machine learning in stroke care,ultimately leading to enhanced patient outcomes and quality of life.This review aims to summarize all the current implications of machine learning in stroke diagnosis,treatment,and prognostic evaluation.At the same time,another purpose of this paper is to explore all the future perspectives these techniques can provide in combating this disabling disease.

The Effects of Integrating Language Learning Strategy into EFL Instruction for Non-English Major Students in Vocational Technical Colleges

Language learning strategies play an important role in language learning. Students in vocational technical colleges have been regarded as low English proficiency learners due to their weak foundation in learning English. To explore the solution, the present study employed a specific survey of language learning strategy use and tried to conduct a one-semester language learning strategy training, aiming to investigate the effects of integrating language learning strategy into EFL instruction for non-English major students in vocational technical colleges. We found after the instruction, the difference in the frequency of overall strategy use between the experimental group and the control group was significant indicating that the students could be trained with language learning strategies to enhance the frequency of their strategy use. After language learning strategy instruction, the experimental group didn＇t perform significantly better than the control group in terms of their English language proficiency. However, the results of this present study still supported this viewpoint that the strategy instruction had positive impacts to some extent if properly conducted, for it had positive effects on enhancing students＇ language learning strategy use.

On the Instruction Design of the Course Business English Writing Based on Blended Learning Model

Blended learning based on information technology,emphasizes students’central position and teachers’leading role in teaching and learning.In view of this,the author attempts to apply the model to the instruction of the course Business English Writing.In the instructional design,the author navigates students to enhance the input of sample texts on the basis of webbased autonomous learning and organizes cooperative learning so as to increase students’writing practice,thus improving their research ability and communicative skills.Consequently,the two major problems—deficiency in language input because of limited class hours and lack of interaction owing to the large size of classes can be effectively solved.

The Effectiveness of Explicit Vocabulary Instruction on Productive Vocabulary Learning in Writing Among Intermediate School Learners in Saudi Arabia

This study aimed to identify the effectiveness of explicit vocabulary instruction on productive vocabulary learning in writing among intermediate school learners in Saudi Arabia,verify the existence of any statistically significant differences at the significance level of 0.01 between the mean scores of the post-test of the control group and the experimental group in the vocabulary test,and verify the existence of any statistically significant differences at the significance level of 0.01 between the mean scores of the pre-and post-test of the experimental group in the vocabulary test in favor of the post-test.The study community consisted of all intermediate school students in the Kingdom of Saudi Arabia,while the study sample included 30 students.The experimental method was adopted to identify the effectiveness of explicit vocabulary instruction on productive vocabulary learning in writing and the achievement test was used as the study tool.The study reached many results including there were no statistically significant differences at the significance level of 0.01 between the mean scores of the post-test of the control group and the experimental group in the vocabulary test and there were no statistically significant differences at the significance level of 0.01 between the mean scores of the pre-and post-test of the experimental group in the vocabulary test in favor of the post-test.According to these results,some recommendations and proposals were made including training English teachers at the intermediate school level in the Kingdom of Saudi Arabia,on explicit vocabulary instruction and conducting future researches on the trends of English teachers at the intermediate school level,in the Kingdom of Saudi Arabia,regarding the adoption of explicit vocabulary instruction while teaching the English language.

Research on the Relationship Between Learning Motivation and Neural Activity in the Learning Process of Instructional Video:A NIRS Study

As the intrinsic driving force to promote learner’s learning,learning motivation is one of the key factors that affect learning engagement and efficiency.In terms of optimizing instructional videos and strengthening learning effects,it is particularly important to understand the cognitive neural mechanism and influencing factors of the changes of learning motivation.By using the near-infrared spectrometer technology,the paper has collected the state of neural activity while learners were learning different instructional videos,and has analyzed the relationship between the learning motivation of instructional videos and the state of neural activity in the learning process from the angle of cognitive neuroscience.It is found that both the intrinsic and extrinsic learning motivation of instructional videos will affect the state of neural activity in the learning process;the learning process will also affect the intensity of learning motivation,while the preparation of fine instructional videos will also cause the transfer of learning motivation.

The Development and Influence of RCPS Instructions to Promote Students＇ Scientific Learning Motivation and Scientific Concept Learning

This research aims at developing RCPS （revised creative problem solving） teaching model, besides the authors designed the instructions of chemical reaction to promote eight grade students＇ scientific learning motivation and scientific concept learning. We adopted quasi-experiment study, the experimental group and controlled group all 28 students were chose, go on the parameter is analyzed together compared with textbook instructions, scale of scientific learning motivation and test of scientific conception learning were used for the two groups in prior test and post test, then they used statistical ANCOVA （analysis of covariance） to analyze the differences between the two teaching models. The result of this study finds that RCPS teaching model improved student＇s scientific learning motivation and learning scientific concept was superior to textbook instructions in controlled group, p = 0.001 （〈 0.01）, and all with high experimental treatment effects （〉 0.14）. The study also proposes that when RCPS teaching model was applied to scientific concept teaching, RCPS teaching model should be joined the conception introducing stage, and pay attention to students＇ scientific learning motivation.

An Empirical Study on Classroom Learning Anxiety of Secondary Vocational English Learners in Content-based Instruction

This research aims to study the relationship between content-based instruction and secondary vocational English learners.Two classes in one secondary vocational school were chosen as participants.The result shows that CBI teaching has a negative correlation with English learning anxiety and has an impact on alleviating students' anxiety.

The effect of scaffolding instruction on learning among nursing students in China:a meta-analysis

Objective:To systematically evaluate the influence of scaffolding instruction on the learning effect of nursing students in China.Methods:Through the databases of CNKI,Wanfang,VIP,CBM,PubMed,Cochrane Library,Embase and Web of Science,the randomized controlled trials of scaffolding instruction in nursing field were collected from the establishment of the databases to the publication in March 2022.After literature screening,data extraction and quality evaluation,meta-analysis was performed using RevMan5.3 and Stata16.0 software.Results:Nineteen articles were included,with a total of 2,340 nursing students.Meta-analysis results showed that,compared with the traditional teaching method,the scaffolding instruction was significantly better in nursing students’theoretical examination scores(SMD=1.37,95%CI:1.00 to 1.75,P<0.001),operation skill scores(SMD=1.83,95%CI:1.23 to 2.42,P<0.001),communication ability(SMD=1.51,95%CI:0.70 to 2.32,P=0.0003)and teaching satisfaction(P<0.05).Conclusion:Scaffolding instruction can improve the theoretical and operational scores of nursing students,and it also has a positive impact on communication ability and teaching satisfaction.This teaching method deserves to be applied and promoted in the field of nursing education.

The Influence of Combining Computer-Assisted Language Learning With Instruction on Chinese College Students’L2 Pragmatic Ability

In the area of computer-assisted language learning(CALL),although a number of studies have adopted various CALL-based devices(e.g.,blogs,gaming,and synthetic environments)to foster second language(L2)acquisition,the vital component of instruction has received little attention.The present study explored the usefulness of CALL-based communication in conjunction with instruction on EFL learners’L2 pragmatic development.Sixty-two EFL students from a university in China were recruited for the current research.The experimental group communicated with a native English speaker through synchronous messaging via Skype and had two instructional sessions pertinent to compliment responses,while the control group interacted with a native English speaker via Skype without having the teaching intervention.Findings from an independent samples t-test demonstrated that the experimental group produced significantly more proper compliment responses in the immediate posttest than the control group(p<.001).Moreover,a significant difference(p<.001)was found for the experimental group between the preintervention and delayed post-intervention mean scores,suggesting that CALL coupled with teaching intervention had a long-term impact on learners’L2 pragmatic development.These findings enrich our understanding of the beneficial and lasting influence of combining CALL with instruction on EFL students’pragmatic development.In addition,pedagogical implications for deploying CALL paired with L2 pragmatics instruction are provided.

Promoting Local Culture Learning in the Context of Foreign Language Instruction

Foreign language learning is also culture learning,which is a dynamic process of bridging the connections between the local culture and the foreign culture.Based on a survey of 667 teachers of English and 278 students in colleges and universities in China,this paper first reviews the current research on the promotion of Chinese culture into the classroom teaching in the context of foreign language learning and then explores future prospects in this area of teaching.The discussion starts with the discussion on these major questions in the process of incorporating local culture in the form of telling stories about China,i.e.,“why need we tell the stories”,“who tells the stories”,“who leads in telling the stories”,“how can we tell the stories”,“what can we tell in the stories”,“where can we tell the stories”,and“how well can we tell the stories”.It continues proposing to pay attention to interdisciplinary research on discourse construction,communication modes and evaluation of communicative effectiveness,and also the design of foreign language curriculum and teaching materials.It concludes that incorporating local culture into the foreign language instruction is of great help to widen students’horizons,cultivate their language proficiency,enhance their culture awareness,strengthen their confidence,and realize shared appreciation of civilization with the people from other cultural backgrounds.

PERCEPOLIS: Pervasive Cyberinfrastructure for Personalized Learning and Instructional Support

This paper describes PERCEPOLIS, an educational platform that leverages technological advances, in particular in pervasive computing, to facilitate personalized learning in higher education, while supporting a networked curricular model. Fundamental to PERCEPOLIS is the modular approach to course development. Blended instruction, where students are responsible for perusing certain learning objects outside of class, used in conjunction with the cyberinfrastructure will allow the focus of face-to-face meetings to shift from lecture to active learning, interactive problem-solving, and reflective instructional tasks. The novelty of PERCEPOLIS lies in its ability to leverage pervasive and ubiquitous computing and communication through the use of intelligent software agents that use a student’s academic profile and interests, as well as supplemental information such as his or her learning style, to customize course content. Assessments that gauge the student’s mastery of concepts are used to allow self-paced progression through the course. Furthermore, the cyberinfrastructure facilitates the collection of data on student performance and learning at a resolution that far exceeds what is currently available. We believe that such an infrastructure will accelerate the acquisition of knowledge and skills critical to professional engineering practice, while facilitating the study of how this acquisition comes about, yielding insights that may lead to significant changes in pedagogy.

Multimodal Machine Learning Guides Low Carbon Aeration Strategies in Urban Wastewater Treatment

The potential for reducing greenhouse gas(GHG)emissions and energy consumption in wastewater treatment can be realized through intelligent control,with machine learning(ML)and multimodality emerging as a promising solution.Here,we introduce an ML technique based on multimodal strategies,focusing specifically on intelligent aeration control in wastewater treatment plants(WWTPs).The generalization of the multimodal strategy is demonstrated on eight ML models.The results demonstrate that this multimodal strategy significantly enhances model indicators for ML in environmental science and the efficiency of aeration control,exhibiting exceptional performance and interpretability.Integrating random forest with visual models achieves the highest accuracy in forecasting aeration quantity in multimodal models,with a mean absolute percentage error of 4.4%and a coefficient of determination of 0.948.Practical testing in a full-scale plant reveals that the multimodal model can reduce operation costs by 19.8%compared to traditional fuzzy control methods.The potential application of these strategies in critical water science domains is discussed.To foster accessibility and promote widespread adoption,the multimodal ML models are freely available on GitHub,thereby eliminating technical barriers and encouraging the application of artificial intelligence in urban wastewater treatment.

Deploying Hybrid Ensemble Machine Learning Techniques for Effective Cross-Site Scripting(XSS)Attack Detection

Cross-Site Scripting(XSS)remains a significant threat to web application security,exploiting vulnerabilities to hijack user sessions and steal sensitive data.Traditional detection methods often fail to keep pace with the evolving sophistication of cyber threats.This paper introduces a novel hybrid ensemble learning framework that leverages a combination of advanced machine learning algorithms—Logistic Regression(LR),Support Vector Machines(SVM),eXtreme Gradient Boosting(XGBoost),Categorical Boosting(CatBoost),and Deep Neural Networks(DNN).Utilizing the XSS-Attacks-2021 dataset,which comprises 460 instances across various real-world trafficrelated scenarios,this framework significantly enhances XSS attack detection.Our approach,which includes rigorous feature engineering and model tuning,not only optimizes accuracy but also effectively minimizes false positives(FP)(0.13%)and false negatives(FN)(0.19%).This comprehensive methodology has been rigorously validated,achieving an unprecedented accuracy of 99.87%.The proposed system is scalable and efficient,capable of adapting to the increasing number of web applications and user demands without a decline in performance.It demonstrates exceptional real-time capabilities,with the ability to detect XSS attacks dynamically,maintaining high accuracy and low latency even under significant loads.Furthermore,despite the computational complexity introduced by the hybrid ensemble approach,strategic use of parallel processing and algorithm tuning ensures that the system remains scalable and performs robustly in real-time applications.Designed for easy integration with existing web security systems,our framework supports adaptable Application Programming Interfaces(APIs)and a modular design,facilitating seamless augmentation of current defenses.This innovation represents a significant advancement in cybersecurity,offering a scalable and effective solution for securing modern web applications against evolving threats.

Selective and Adaptive Incremental Transfer Learning with Multiple Datasets for Machine Fault Diagnosis

The visions of Industry 4.0 and 5.0 have reinforced the industrial environment.They have also made artificial intelligence incorporated as a major facilitator.Diagnosing machine faults has become a solid foundation for automatically recognizing machine failure,and thus timely maintenance can ensure safe operations.Transfer learning is a promising solution that can enhance the machine fault diagnosis model by borrowing pre-trained knowledge from the source model and applying it to the target model,which typically involves two datasets.In response to the availability of multiple datasets,this paper proposes using selective and adaptive incremental transfer learning(SA-ITL),which fuses three algorithms,namely,the hybrid selective algorithm,the transferability enhancement algorithm,and the incremental transfer learning algorithm.It is a selective algorithm that enables selecting and ordering appropriate datasets for transfer learning and selecting useful knowledge to avoid negative transfer.The algorithm also adaptively adjusts the portion of training data to balance the learning rate and training time.The proposed algorithm is evaluated and analyzed using ten benchmark datasets.Compared with other algorithms from existing works,SA-ITL improves the accuracy of all datasets.Ablation studies present the accuracy enhancements of the SA-ITL,including the hybrid selective algorithm(1.22%-3.82%),transferability enhancement algorithm(1.91%-4.15%),and incremental transfer learning algorithm(0.605%-2.68%).These also show the benefits of enhancing the target model with heterogeneous image datasets that widen the range of domain selection between source and target domains.

TLERAD: Transfer Learning for Enhanced Ransomware Attack Detection

Ransomware has emerged as a critical cybersecurity threat,characterized by its ability to encrypt user data or lock devices,demanding ransom for their release.Traditional ransomware detection methods face limitations due to their assumption of similar data distributions between training and testing phases,rendering them less effective against evolving ransomware families.This paper introduces TLERAD(Transfer Learning for Enhanced Ransomware Attack Detection),a novel approach that leverages unsupervised transfer learning and co-clustering techniques to bridge the gap between source and target domains,enabling robust detection of both known and unknown ransomware variants.The proposed method achieves high detection accuracy,with an AUC of 0.98 for known ransomware and 0.93 for unknown ransomware,significantly outperforming baseline methods.Comprehensive experiments demonstrate TLERAD’s effectiveness in real-world scenarios,highlighting its adapt-ability to the rapidly evolving ransomware landscape.The paper also discusses future directions for enhancing TLERAD,including real-time adaptation,integration with lightweight and post-quantum cryptography,and the incorporation of explainable AI techniques.

Direct Instruction by an Experienced Surgeon Can Shorten the Learning Curve for Laparoscopic-Assisted Distal Gastrectomy

Aim: Laparoscopy-assisted distal gastrectomy (LADG) with regional lymph node dissection is a treatment option for patient with early gastric cancer. However, LADG is a technically complex and advanced procedure, which is challenging for inexperienced surgeons. In this report, we retrospectively evaluated the learning curve for LADG of a single surgeon with no previous experience in LADG and the usefulness of direct instruction by a surgeon experienced in LADG in shortening the learning curve. Patients and Methods: This study was analyzed 80 consecutive patients, who underwent LADG by a single surgeon (first assistant in 10 cases and operator in 70 cases) between January 2008 and December 2012. Patients were divided into 3 sequential groups of 10 (training period), 30 (learning period), and 40 (operating period) cases in each group. Median operation time and estimated blood loss for these 3 groups were determined. Other learning indicators, including transfusion requirement, postoperative complications, number of lymph node harvested, and rate of conversion open gastrectomy, were also evaluated. Results: During the training period, median operation time and estimated blood loss were 219.5 min and 83.0 ml, respectively. During the learning period, the operation time was significantly longer than that of training period. In the operating period, the operation time was significantly lesser than that during the learning period. However, the operation time was not different from that during the training period and reached a plateau. The estimated blood loss during the operating period was significantly lesser than that during the learning period. The difference in the number of lymph nodes retrieved between each group was not significant. Conclusions: Direct instructions by an experienced surgeon can decrease the number of cases required for learning. Because LADG is technically more complex than other laparoscopic procedures, standardization of LADG and an effective training system for performing it should be established.

Developing Learning Strategies: Strategy Instruction

Learning strategies are critical in the process of learning, knowing, and thinking. Without strategies, nobody can reach competence, to master certain knowledge and skills which makes him/her an independent learner. The purose of this paper is trying to demonstrate an overview of leaning strategies based on the researches and studies have been done in the field with emphasis on strategy instruction for increasing reading comprehension and writing instruction.

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.

On Vocabulary Learning Mode—the Integration of Incidental Learning and Explicit Instructions

This article is mainly to review and comment on the article written by Sonbul, S.,& Schmitt, N.in 2009 which is con?cerned with the additional efficacy of putting explicit instruction as post process to incidental learning in obtaining vocabulary as well as testing the vocabulary knowledge of the form-meaning link at three different levels (meaning recognition, meaning recall, and meaning recognition).