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.

Fostering independent learning in english education for undergraduate students

The enhancement of students'independent learning acumen is a cornerstone objective within college English education.Elevating students'proficiency in independent learning necessitates a thorough understanding of its significance and the implementation of effective cultivation strategies.By fostering an environment conducive to independent learning and imparting essential skills,students can proficiently navigate autonomous learning endeavors,thereby preparing themselves for a lifetime of scholarly pursuits.This paper delves into the multifaceted dimensions of independent learning,elucidates pertinent cultivation strategies,and underscores the imperative of equipping students with the tools necessary for lifelong learning and academic success.Through this comprehensive approach,students can develop the resilience and adaptability needed to excel in their academic endeavors and beyond.

Identifying influencing factors and characterizing key issues in urban sustainable development capacity through machine learning

In response to the United Nations Sustainable Development Goals and China’s“Dual Carbon”Goals(DCGs means the goals of“Carbon Peak and carbon neutrality”),this paper from the perspective of the construction of China’s Innovation Demonstration Zones for Sustainable Development Agenda(IDZSDAs),combines carbon emission-related metrics to construct a comprehensive assessment system for Urban Sustainable Development Capacity(USDC).After obtaining USDC assessment results through the assessment system,an approach combining Least Absolute Shrinkage and Selection Operator(LASSO)regression and Random Forest(RF)based on machine learning is proposed for identifying influencing factors and characterizing key issues.Combining Coupling Coordination Degree(CCD)analysis,the study further summarizes the systemic patterns and future directions of urban sustainable development.A case study on the IDZSDAs from 2015 to 2022 reveals that:(1)the combined identification method based on machine learning and CCD models effectively quantifies influencing factors and key issues in the urban sustainable development process;(2)the correspondence between influencing factors and key subsystems identified by the LASSO-RF combination model is generally consistent with the development situations in various cities;and(3)the machine learning-based combined recognition method is scalable and dynamic.It enables decision-makers to accurately identify influencing factors and characterize key issues based on actual urban development needs.

Research into the Application of Cooperative Learning to English Reading Teaching of Art Majors in Independent Colleges

This paper first stated some related theories of cooperative learning and English reading teaching.And then an applicationof cooperative learning in English reading teaching of art majors in independent college is implemented based on these literature re-views.The aim of the experiment is to explore the impact of the application of cooperative learning strategies.It mainly discussedthe following questions:(1)Can the group cooperative learning improve students'reading achievement?(2)What factors contributeto the success/failure of cooperative learning?(3)What are the favorite cooperative learning models in reading classes?The experi-ment proved that cooperative learning does not truly improve art students’reading achievement in independent college,but it real-ly has a positive role in promoting the students'learning interest,motivation and emotional attitude.The experiment also indicatesthat cooperative learning is still a valuable teaching strategy for applying,and GI,LT,TGT and other cooperative learning modelare welcomed by classmates.

“WE DID IT OURSELVES”INDEPENDENT LEARNING IN MIDDLE SCHOOLS

Recently,I was standing in the corridor of a middle school,and I heard the following ex-change between a student and a teacher who,like so many teachers these days,was hurrying,ina harassed sort of way,from one classroom to another,her arms filled with exercise books andother educational paraphernalia."Excuse me,Miss!"said the student."I don’t quite understand the third conditional!""Good question!"replied the teacher."Why don’t you look it up in the grammar notes at theback of the book?"Saying which,she swept off to her next class.The question arises:was this teacher a good teacher,or a bad teacher?I hope that we shall inthe course of the next hour or so seek to establish an answer to this question!

Recognition and interfere deceptive behavior based on inverse reinforcement learning and game theory

In real-time strategy(RTS)games,the ability of recognizing other players’goals is important for creating artifical intelligence(AI)players.However,most current goal recognition methods do not take the player’s deceptive behavior into account which often occurs in RTS game scenarios,resulting in poor recognition results.In order to solve this problem,this paper proposes goal recognition for deceptive agent,which is an extended goal recognition method applying the deductive reason method(from general to special)to model the deceptive agent’s behavioral strategy.First of all,the general deceptive behavior model is proposed to abstract features of deception,and then these features are applied to construct a behavior strategy that best matches the deceiver’s historical behavior data by the inverse reinforcement learning(IRL)method.Final,to interfere with the deceptive behavior implementation,we construct a game model to describe the confrontation scenario and the most effective interference measures.

Anomaly Detection of UAV State Data Based on Single-Class Triangular Global Alignment Kernel Extreme Learning Machine

Unmanned Aerial Vehicles(UAVs)are widely used and meet many demands in military and civilian fields.With the continuous enrichment and extensive expansion of application scenarios,the safety of UAVs is constantly being challenged.To address this challenge,we propose algorithms to detect anomalous data collected from drones to improve drone safety.We deployed a one-class kernel extreme learning machine(OCKELM)to detect anomalies in drone data.By default,OCKELM uses the radial basis(RBF)kernel function as the kernel function of themodel.To improve the performance ofOCKELM,we choose a TriangularGlobalAlignmentKernel(TGAK)instead of anRBF Kernel and introduce the Fast Independent Component Analysis(FastICA)algorithm to reconstruct UAV data.Based on the above improvements,we create a novel anomaly detection strategy FastICA-TGAK-OCELM.The method is finally validated on the UCI dataset and detected on the Aeronautical Laboratory Failures and Anomalies(ALFA)dataset.The experimental results show that compared with other methods,the accuracy of this method is improved by more than 30%,and point anomalies are effectively detected.

Reflections on English Listening Autonomous Learning in the Independent College

The self-access learning center emphasizes the principle and significance of student-centeredness rather than teacher-centeredness,which is the focus of the reform of college English in the independent college.To implement belief training program and assist students to grow into proper autonomous learners is the suggestion given when students lack beliefs and motivations.

Online Batch Process Monitoring Based on Just-in-Time Learning and Independent Component Analysis

A new method was developed for batch process monitoring in this paper. In the devdopad method, just-in-time learning （ JITL ） and independent component analysis （ICA） were integrated to build JITL-ICA monitoring scheme. JITL was employed to tackle with the characteristics of batch process such as inherent time- varying dynamics, multiple operating phases, and especially the case of uneven length stage. According to new coming test data, the most correlated segmentation was obtained from batch-wise unfolded training data by JITL. Then, ICA served as the principal components extraction approach. Therefore, the non.Gaussian distributed data can also be addressed under this modeling framework. The effectiveness and superiority of JITL-ICA based monitoring method was demonstrated by fed-batch penicillin fermentation.

A Constrained Local Neighborhood Approach for Efficient Markov Blanket Discovery in Undirected Independent Graphs

When learning the structure of a Bayesian network,the search space expands significantly as the network size and the number of nodes increase,leading to a noticeable decrease in algorithm efficiency.Traditional constraint-based methods typically rely on the results of conditional independence tests.However,excessive reliance on these test results can lead to a series of problems,including increased computational complexity and inaccurate results,especially when dealing with large-scale networks where performance bottlenecks are particularly evident.To overcome these challenges,we propose a Markov blanket discovery algorithm based on constrained local neighborhoods for constructing undirected independence graphs.This method uses the Markov blanket discovery algorithm to refine the constraints in the initial search space,sets an appropriate constraint radius,thereby reducing the initial computational cost of the algorithm and effectively narrowing the initial solution range.Specifically,the method first determines the local neighborhood space to limit the search range,thereby reducing the number of possible graph structures that need to be considered.This process not only improves the accuracy of the search space constraints but also significantly reduces the number of conditional independence tests.By performing conditional independence tests within the local neighborhood of each node,the method avoids comprehensive tests across the entire network,greatly reducing computational complexity.At the same time,the setting of the constraint radius further improves computational efficiency while ensuring accuracy.Compared to other algorithms,this method can quickly and efficiently construct undirected independence graphs while maintaining high accuracy.Experimental simulation results show that,this method has significant advantages in obtaining the structure of undirected independence graphs,not only maintaining an accuracy of over 96%but also reducing the number of conditional independence tests by at least 50%.This significant performance improvement is due to the effective constraint on the search space and the fine control of computational costs.

Efficient Selection of Linearly Independent Atomic Features for Accurate Machine Learning Potentials

Machine learning potentials are promising in atomistic simulations due to their comparable accuracy to first-principles theory but much lower computational cost.However,the reliability,speed,and transferability of atomistic machine learning potentials depend strongly on the way atomic configurations are represented.A wise choice of descriptors used as input for the machine learning program is the key for a successful machine learning representation.Here we develop a simple and efficient strategy to automatically select an optimal set of linearly-independent atomic features out of a large pool of candidates,based on the correlations that are intrinsic to the training data.Through applications to the construction of embedded atom neural network potentials for several benchmark molecules with less redundant linearly-independent embedded density descriptors,we demonstrate the efficiency and accuracy of this new strategy.The proposed algorithm can greatly simplify the initial selection of atomic features and vastly improve the performance of the atomistic machine learning potentials.

Personalizing Independent Learning:An Applied Study on Military English Course Based on the Flipped Classroom

The flipped classroom is currently a buzzword in higher education around the world. This method is more student-centered and learning-centered than traditional teaching;it personalizes students’ learning and facilitates their understanding, especially when they do autonomous learning with micro classes during their off-class time. This paper provides the findings and conclusions of a case study about flipped classroom practice of a military English course,an ESP course. The study shows the students prefer blended learning to fully online learning or the traditional approach and they are more engaged in their independent learning period. Moreover, it reports project-based learning and problem-based learning can facilitate students’ autonomous learning and realize pedagogical purposes. It is argued that the flipped classroom approach encourages ESP classroom learning, as well as ESP independent learning.

Independent Learning and Language Cognition in English Poetry Teaching

English poetry is beautiful in form and rich in meaning,whereas the teaching of English poetry is as difficult as poetry creation,which calls for not only enthusiasm and inspiration, but also attractive teaching method and supportive theoretical framework. With the framework of cognitive theory, this paper illustrates the teaching practice of one of Robert Frost’s poems,in the course of which the cognitive pattern is embedded,and tries to explore the cognition-based teaching of English poetry such as the analysis of metaphor by the use of space mapping theory. It begins with the analysis of the poetic structure,which is ensued by students’ active construction of the poem’s meaning based on cognitive interpretation. Finally,students are cultivated to express their own opinions while thinking critically about the poem so as to improve their independent learning ability.

On Goals for ESL Learning and Teaching

ESL learning can cultivate learners' language skills,communicative abilities,and develop different factors including emotional attitude,cultural awareness and learning strategies. This paper has analyzed two individual goals for ESL teaching accordingly,one is the understanding of foreign culture,and another is academic subject.

基于M-learning的远程自主学习模型建构

随着信息技术的快速发展，人们对知识的渴求更加强烈。为了适应当前终身学习的大趋势，移动学习应运而生。文章阐述了关于移动学习的几种不同概念表述，并根据现有技术条件下移动学习的运行方案，提出了基于M-learning的远程自主学习模型。

Autonomy and Foreign Language Learning

Independent language learning is one aspect of what the research literature calls learner autonomy, in other words, the ability and willingness of learners to study foreign languages on their own. This article illustrates the strategies I used to design, carry out and sustain my independent language learning project.

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.

The Effectiveness of Self-regulated Learning Strategies on Chinese College Students' English Learning

The purpose of this paper is to argue the effectiveness of self-regulated learning in English education in Chinese college classroom instruction. A study is given to show whether the introduction of self-regulated learning can help improve Chinese college students' English learning, and help them perform better in the National English test-CET-4 (College English Test Level-4,).

The Study of Word Partnership in Learning English

To get a sense of how vocabulary learning counts, as the core of college students' in-depth English learning, the questfor word partnership is a good place to start.Influenced by English learning habits well-trenched in early times, learners' under-standing of vocabulary learning is still confined to fixed phrases. It is trying to get rid of the interference of the mother tongue,which leads to Chinglish. This paper aims to elaborate on the collocation of word pairs,and to master and apply the collocationsthrough specific examples, which does wonder for the cultivation of learners' English thinking.

Effective Paths of Junior Middle School English Autonomous Learning Under the Background of “Internet+”

Under the background of“Internet+”learning method,junior high school English independent learning is individualized,autonomous,and interactive.“Internet+”learning can not only improve students’learning efficiency and learning quality,but also promote their comprehensive development.Therefore,in this paper,we analyzed the significance of developing junior high school English autonomous learning under the background of“Internet+,”explained the theoretical basis of autonomous learning,and how the concept of“Internet+”can be integrated into English teaching.