Compare and Contrast Communicative Language Teaching and Task-based Learning

Communicative Language Teaching(CLT)and Task-based(TBL)Learning as two different teaching approaches have been discussed for a long time.According to two classes respectively applying CLT and TBL introduced by Larsen-Freeman and Anderson(2011),some similarities and differences between CLT and TBL could be summarized.

Task-based Learning for Teaching Middle School English Learners in China

Task-based teaching has been introduced to many EFL educational settings and has become widely used within those contexts.As a new English teaching approach,it has been evaluated by educators.This essay discusses task-based language teaching within the context of EFL students in a middle school in China.A discription of the context,problems of introducing this approach in this context is demonstrated,followed by an overview of this approach and an evaluation including its advantages and disadvantages weighed by scholars,as well as their opinions on"tasks"and"grammar".The end of this essay provides a discussion of solutions for the problems of using this ap proach within this context.

Exploration on English Task-based Learning Teaching Mode——from a perspective of New Curriculum Reform

Task-based learning teaching （TBLT） is a recent popular language teaching mode which is based on learning tasks as the center,promoting learners to acquire language knowledge in the course of fi nishing learning tasks, and educating their language communication abilities.As a matter of fact, it is further development and improvement of the communicative language teaching （CLT）. On the base of theories of TBLT,this paper simply explores connotation and types of learning tasks, design procedures and principles of learning tasks and common problemsdesigning and fulfi lling learning tasks.

Inspires effective alternatives to backpropagation:predictive coding helps understand and build learning

Artificial neural networks are capable of machine learning by simulating the hiera rchical structure of the human brain.To enable learning by brain and machine,it is essential to accurately identify and correct the prediction errors,referred to as credit assignment(Lillicrap et al.,2020).It is critical to develop artificial intelligence by understanding how the brain deals with credit assignment in neuroscience.

Early identification of stroke through deep learning with multi-modal human speech and movement data

Early identification and treatment of stroke can greatly improve patient outcomes and quality of life.Although clinical tests such as the Cincinnati Pre-hospital Stroke Scale(CPSS)and the Face Arm Speech Test(FAST)are commonly used for stroke screening,accurate administration is dependent on specialized training.In this study,we proposed a novel multimodal deep learning approach,based on the FAST,for assessing suspected stroke patients exhibiting symptoms such as limb weakness,facial paresis,and speech disorders in acute settings.We collected a dataset comprising videos and audio recordings of emergency room patients performing designated limb movements,facial expressions,and speech tests based on the FAST.We compared the constructed deep learning model,which was designed to process multi-modal datasets,with six prior models that achieved good action classification performance,including the I3D,SlowFast,X3D,TPN,TimeSformer,and MViT.We found that the findings of our deep learning model had a higher clinical value compared with the other approaches.Moreover,the multi-modal model outperformed its single-module variants,highlighting the benefit of utilizing multiple types of patient data,such as action videos and speech audio.These results indicate that a multi-modal deep learning model combined with the FAST could greatly improve the accuracy and sensitivity of early stroke identification of stroke,thus providing a practical and powerful tool for assessing stroke patients in an emergency clinical setting.

Machine learning applications in healthcare clinical practice and research

Machine learning(ML)is a type of artificial intelligence that assists computers in the acquisition of knowledge through data analysis,thus creating machines that can complete tasks otherwise requiring human intelligence.Among its various applications,it has proven groundbreaking in healthcare as well,both in clinical practice and research.In this editorial,we succinctly introduce ML applications and present a study,featured in the latest issue of the World Journal of Clinical Cases.The authors of this study conducted an analysis using both multiple linear regression(MLR)and ML methods to investigate the significant factors that may impact the estimated glomerular filtration rate in healthy women with and without non-alcoholic fatty liver disease(NAFLD).Their results implicated age as the most important determining factor in both groups,followed by lactic dehydrogenase,uric acid,forced expiratory volume in one second,and albumin.In addition,for the NAFLD-group,the 5th and 6th most important impact factors were thyroid-stimulating hormone and systolic blood pressure,as compared to plasma calcium and body fat for the NAFLD+group.However,the study's distinctive contribution lies in its adoption of ML methodologies,showcasing their superiority over traditional statistical approaches(herein MLR),thereby highlighting the potential of ML to represent an invaluable advanced adjunct tool in clinical practice and research.

Recombinant chitinase-3-like protein 1 alleviates learning and memory impairments via M2 microglia polarization in postoperative cognitive dysfunction mice

Postoperative cognitive dysfunction is a seve re complication of the central nervous system that occurs after anesthesia and surgery,and has received attention for its high incidence and effect on the quality of life of patients.To date,there are no viable treatment options for postoperative cognitive dysfunction.The identification of postoperative cognitive dysfunction hub genes could provide new research directions and therapeutic targets for future research.To identify the signaling mechanisms contributing to postoperative cognitive dysfunction,we first conducted Gene Ontology and Kyoto Encyclopedia of Genes and Genomes pathway enrichment analyses of the Gene Expression Omnibus GSE95426 dataset,which consists of mRNAs and long non-coding RNAs differentially expressed in mouse hippocampus3 days after tibial fracture.The dataset was enriched in genes associated with the biological process"regulation of immune cells,"of which Chill was identified as a hub gene.Therefore,we investigated the contribution of chitinase-3-like protein 1 protein expression changes to postoperative cognitive dysfunction in the mouse model of tibial fractu re surgery.Mice were intraperitoneally injected with vehicle or recombinant chitinase-3-like protein 124 hours post-surgery,and the injection groups were compared with untreated control mice for learning and memory capacities using the Y-maze and fear conditioning tests.In addition,protein expression levels of proinflammatory factors(interleukin-1βand inducible nitric oxide synthase),M2-type macrophage markers(CD206 and arginase-1),and cognition-related proteins(brain-derived neurotropic factor and phosphorylated NMDA receptor subunit NR2B)were measured in hippocampus by western blotting.Treatment with recombinant chitinase-3-like protein 1 prevented surgery-induced cognitive impairment,downregulated interleukin-1βand nducible nitric oxide synthase expression,and upregulated CD206,arginase-1,pNR2B,and brain-derived neurotropic factor expression compared with vehicle treatment.Intraperitoneal administration of the specific ERK inhibitor PD98059 diminished the effects of recombinant chitinase-3-like protein 1.Collectively,our findings suggest that recombinant chitinase-3-like protein 1 ameliorates surgery-induced cognitive decline by attenuating neuroinflammation via M2 microglial polarization in the hippocampus.Therefore,recombinant chitinase-3-like protein1 may have therapeutic potential fo r postoperative cognitive dysfunction.

Regulator of G protein signaling 6 mediates exercise-induced recovery of hippocampal neurogenesis,learning,and memory in a mouse model of Alzheimer’s disease

Hippocampal neuronal loss causes cognitive dysfunction in Alzheimer’s disease.Adult hippocampal neurogenesis is reduced in patients with Alzheimer’s disease.Exercise stimulates adult hippocampal neurogenesis in rodents and improves memory and slows cognitive decline in patients with Alzheimer’s disease.However,the molecular pathways for exercise-induced adult hippocampal neurogenesis and improved cognition in Alzheimer’s disease are poorly understood.Recently,regulator of G protein signaling 6(RGS6)was identified as the mediator of voluntary running-induced adult hippocampal neurogenesis in mice.Here,we generated novel RGS6fl/fl;APP_(SWE) mice and used retroviral approaches to examine the impact of RGS6 deletion from dentate gyrus neuronal progenitor cells on voluntary running-induced adult hippocampal neurogenesis and cognition in an amyloid-based Alzheimer’s disease mouse model.We found that voluntary running in APP_(SWE) mice restored their hippocampal cognitive impairments to that of control mice.This cognitive rescue was abolished by RGS6 deletion in dentate gyrus neuronal progenitor cells,which also abolished running-mediated increases in adult hippocampal neurogenesis.Adult hippocampal neurogenesis was reduced in sedentary APP_(SWE) mice versus control mice,with basal adult hippocampal neurogenesis reduced by RGS6 deletion in dentate gyrus neural precursor cells.RGS6 was expressed in neurons within the dentate gyrus of patients with Alzheimer’s disease with significant loss of these RGS6-expressing neurons.Thus,RGS6 mediated voluntary running-induced rescue of impaired cognition and adult hippocampal neurogenesis in APP_(SWE) mice,identifying RGS6 in dentate gyrus neural precursor cells as a possible therapeutic target in Alzheimer’s disease.

On the Task-based Collaborative Learning

Task-based language teaching(TBLT) has been a prevalent teaching practice in the TEFL field in the recent years and its momentum for striving to be the legitimate one has never ceased. The present study tries to provide a theoretical foundation for its application in the communicative learning approach of English as the second language(ESL),namely the collaborative learning mode.

The Application of Task-based Language Learning in English Teaching

Task-based Language Teaching(TBLT) proved to be an influential teaching approach in second language acquisition recent years. Yet, TBLT came into being with certain culture background, it can't be applied randomly and mechanically. This paper precisely introduces the task-based language learning and reviews the literature on the basic pedagogical theories of TBLT,then briefly introduces the framework of Willis for TBLT, and takes a small size group of middle school students as an example.It mainly aims to discuss the application of Task-based language learning in the classroom and through analyzing the disadvantages of task-based language teaching to find out a successful approach to implement TBLT.

A Study on English Reading Teaching With Cooperative Learning By Means of Task-Based Teaching in Higher Vocational Education

Higher Vocational Education(HVE) is an important component of higher education in our country.The English Curriculum Standard of HVE considers promoting the students' reading comprehension as one of the most important goals in English teaching.Task-based CL emphasizes the importance of education as a vehicle for teaching citizens to live cooperatively in a social democracy and teachers are not the only sources of knowledge and students should be able to help each other and learn in an interdependent way.The heart of cooperative learning is student-centered.Therefore,task-based CL can overcome the shortcomings in traditional teaching models,and the goal of improving reading comprehension can be achieved for the teachers of reading by realizing the mental process of reading comprehension and how prior knowledge can enhance a reader's interaction with the text through CL.This thesis consists of five parts.Part one,two and three briefly introduces the background and significance of the study,definitions and models of cooperative learning and reading comprehension.Part four and five are the key sections of the thesis,which introduces the hypotheses,subjects,instrument,procedure of the study and the analysis of the results of the study.Part five draws the conclusion of the study.

Task-based Teaching and Learning in English Listening Class

In technical college English listening class,task-based teaching and learning method can not only create harmonious environment for students' learning,but also motivate students' enthusiasm in listening class,thus students can benefit a great deal in listening class and the listening can be carried out successfully.

Task-Based Teaching and Learning in Business English Writing

Business English,as a dependent discipline,has received great attention nowadays.To conduct researches on Business English teaching is quite essential,and it is of importance to design new teaching methods to improve the quality of Business English classes.This thesis aims to design effective task-based activities which involve students in the building and creation of Business English classes.Through the task-based activities,students learn how to write business materials positively and actively.

Autonomous and Incidental Learning of Vocabulary Through Task-based Language Teaching

This study mainly discussed the effects of three tasks of translating authentic business report on L2 vocabulary learning.160 students were chosen from different majors by a pre-task proficiency test.The findings revealed that task 3 was the optimum task in vo-cabulary gain and direct vocabulary learning had a more facilitated power than incidental vocabulary learning in this translation task forthe learners with the lowest level of vocabulary.This study also suggested that the caution of need and evaluation needed to be adjustedand paid for the learners with the lowest vocabulary level.

Conducting the Task-based Approach With Context Aware Mobile Situated Learning on Learning English for College Students＇ Learning Performance

Learning English with specific purpose （ESP） initiatives domain knowledge and language ability with English learning to meet future job demands （Leroux ＆ Lafleur, 1995; Khan ＆ Khan, 2015）. In the information age, the innovative technologies of mobile devices make the dramatical changes in ways of teaching and learning （Atkinson, 2011; Bierstaker, Janvrin ＆ Lowe, 2014, Pittaway, 2012; Yang ＆ Che, 2015）. The focus of this study aims to examine ESP college students＇ English learning performance by using Context Aware Mobile Situated Learning （CAMSL） in Tourism and Hospitality Management field and other majors. The mixed research method is conducted for data collection and analysis. The quantitative data are collected by examining students＇ learning performance; the qualitative data are allowed to understand the students＇ perspective toward their role in using the CAMSL with Tourism related content. Eight-three students are randomly selected and divided into two groups： 42 students are assigned in the experimental group A （CAMSL）, and 41 students are assigned in control group. Two groups of students receive pretest and posttest to examine their English performance. Besides, twenty students from group A and B are selected for online survey. The survey is mailed directly to students＇ email account. Results represented by using CAMSL show the significant improvement on students＇ learning performance. In addition, the survey data indicate the benefits of using the CAMSL help students enhance their academic discourse, develop their learned knowledge to represent their profession, use English properly to speak themselves up, and provide the effectiveness of obtain domain knowledge in future workplace.

Study on the Application of Goal-oriented Task-based“Teaching, Learning and Doing”Integrated BE Teaching Mode

This study aims to investigate the effectiveness of applying a new teaching mode of Goal-oriented Task-based Approach(GOTBA) into the teaching of Business English. A comparative study of Experimental Class and Control Class was adopted in the research. Instruments include tests and questionnaires and interview. Results show that GOTBA is significantly effective to increase the learners' learning motivation so to promote their BE competence.

基于改进Q-Learning的移动机器人路径规划算法

随着移动机器人在生产生活中的深入应用,其路径规划能力也需要向快速性和环境适应性兼备发展。为解决现有移动机器人使用强化学习方法进行路径规划时存在的探索前期容易陷入局部最优、反复搜索同一区域,探索后期收敛率低、收敛速度慢的问题,本研究提出一种改进的Q-Learning算法。该算法改进Q矩阵赋值方法,使迭代前期探索过程具有指向性,并降低碰撞的情况;改进Q矩阵迭代方法,使Q矩阵更新具有前瞻性,避免在一个小区域中反复探索;改进随机探索策略,在迭代前期全面利用环境信息,后期向目标点靠近。在不同栅格地图仿真验证结果表明,本文算法在Q-Learning算法的基础上,通过上述改进降低探索过程中的路径长度、减少抖动并提高收敛的速度,具有更高的计算效率。

M-learning结合CBL在消化科规培教学中的探讨及应用

目的探究移动学习平台(M-learning,ML)结合案例教学(Case-based Learning,CBL)在消化科住院医师规范化培训(简称规培)教学中的应用效果。方法选取2021年1月—2023年1月于上海市徐汇区中心医院消化科参加规培学习的80名医师作为研究对象,将其按照随机数表法分为研究组和对照组,每组40名。对照组给予传统讲授式教学法,研究组给予M-learning结合CBL教学法,对比两组医师的理论考试成绩、实践技能考试成绩和学习满意度。结果研究组的理论成绩和实践技能考试成绩均高于对照组,差异具有统计学意义(P均<0.05);研究组的学习满意度明显高于对照组,差异具有统计学意义(P<0.05)。结论将Mlearning结合CBL教学法应用于消化科规培教学中,不仅能够提升医师的理论考试成绩和实践技能考试成绩,还能够有效提高医师学习满意度。

基于Q-Learning的航空器滑行路径规划研究

针对传统算法规划航空器滑行路径准确度低、不能根据整体场面运行情况进行路径规划的问题,提出一种基于Q-Learning的路径规划方法。通过对机场飞行区网络结构模型和强化学习的仿真环境分析,设置了状态空间和动作空间,并根据路径的合规性和合理性设定了奖励函数,将路径合理性评价值设置为滑行路径长度与飞行区平均滑行时间乘积的倒数。最后,分析了动作选择策略参数对路径规划模型的影响。结果表明,与A*算法和Floyd算法相比,基于Q-Learning的路径规划在滑行距离最短的同时,避开了相对繁忙的区域,路径合理性评价值高。

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.