Towards Lessening Learners’Aversive Emotions and Promoting Their Mental Health:Developing and Validating a Measurement of English Speaking Demotivation in the Chinese EFL Context

While a plethora of studies has been conducted to explore demotivation and its impact on mental health in second language(L2)education,scanty research focuses on demotivation in L2 speaking learning.Particularly,little research explores the measures to quantify L2 speaking demotivation.The present two-phase study attempts to develop and validate an English Speaking Demotivation Scale(ESDS).To this end,an independent sample of 207 Chinese tertiary learners of English as a Foreign Language(EFL)participated in the development phase,and another group of 188 Chinese EFL learners was recruited for the validation of the scale.Exploratory Factor Analysis(EFA)and Confirmatory Factor Analysis(CFA)were employed to determine the factor structure of the scale.The EFA results revealed a six-factor solution with Teacher-related Factors in Learning Spoken English(TFLSE),Interest and Valence in Learning Spoken English(IVLSE),Self-efficacy in Learning Spoken English(SELSE),Negative Peer Influence in Learning Spoken English(NPILSE),Undesirable Environment for Learning Spoken English(UELSE),and Negative Influence of Assessment and Learning Materials in speaking class(NIALM).In the validation phase,Confirmatory Factor Analysis(CFA)was performed to validate the internal structure of the scale.The CFA results showed that the model fits the data well.Overall,the ESDS is a robust and trustworthy psychometric tool that could be utilized to examine L2 speaking demotivation.Implications for diminishing EFL learners’demotivation,lessening their aversive emotions and promoting their mental health are also discussed.

The Impact of Comment Style on Learners’ Essay Revisions

This paper investigates how the type of written feedback provided by teachers affects learners’ ability to revise essays. A total of 120 participants participated in two rounds of essay composition and feedback delivery. The findings indicate that learners who receive comments in the form of questions are more prone to make revisions compared to those receiving statements or imperatives. These results hold significant implications for educators and the ways they utilize written feedback to enhance learners’ writing skills.

Analysis of Young Learners’Listening Skills

The importance of listening as one of the communication skills cannot be overstated.However,in many areas,listening is neglected.For young learners,learning to listen is even more beneficial for their future studies,therefore,listening skills should be given due importance.

Enhancing Learners’Performance in Grade 7 Mathematics Through 50-30-20 Exercise

Assessment exercises constitute a crucial component of the teaching and learning process,serving the purpose of gauging the degree to which learning objectives have been accomplished.This study aims to assess the mathematics performance of Grade 7 learners using the 50-30-20 exercise.Specifically,this study seeks to determine the learners’pre-test and post-test mean scores,identify significant differences between the pre-test and post-test results,evaluate learners’exercises,and propose enhanced exercises.The research employs a quasi-experimental design,with 40 Grade 7 learners in the school year 2023-2024 as participants,selected through purposive non-random sampling.Statistical data analysis involves the use of mean,standard deviation,paired t-test,and Cohen’s D effect size.Ethical considerations were paramount,as evidenced by a letter of authorization from the school head outlining the strict adherence to voluntary participation,informed parental consent,anonymity,confidentiality,risk mitigation,results-sharing protocols,and the commitment to keeping research data confidential.The data yielded a remarkable outcome:the experimental group exhibited improvement in both the pre-test and post-test.This result substantiates the initial objective of the study,showcasing a noteworthy and favorable performance among the participants.Consequently,it suggests that a majority of the participants strongly agree that the 50-30-20 exercises contribute to enhancing their understanding and problem-solving skills,as well as their ability to grasp mathematical concepts and improve their overall performance in mathematics.Therefore,the 50-30-20 exercises not only facilitated students in understanding mathematics lessons but were also aligned with the Department of Education’s development plan.

Entangled relationship:Chinese parents’involvement in young learners’English tests

Young learners’English(YLE)tests have become increasingly prevalent among and important to Chinese English learners and their parents.In China,parents are actively involved in their children’s education and test-taking decisions,and their participation has given rise to a series of social impacts.Although parent involvement has received increasing public and academic attention in newspaper and scholarly articles,there exists no thorough analysis of current research on parents’roles in young learners’test-taking.To address this gap,we systemically analyzed Chinese newspaper articles and academic articles between 2011 and 2021 to elicit Chinese parents’roles in YLE test-taking.Our thematic analysis of the literature identifies the ways in which parents influence their children’s test-taking experiences,and how the tests affect this influence.Specifically,our results elicit 1)parents’engagement in YLE testing;2)parents’understanding of the intended uses of YLE tests;3)parents’perception and consequences of their involvement in YLE testing;and 4)parents’backgrounds.This analysis reveals the popularity of YLE tests among young learners and their parents in China and public concern over test-related issues.It also broadens our understanding of YLE testing impact and suggests future potential research directions.

Expert Experience and Data-Driven Based Hybrid Fault Diagnosis for High-SpeedWire Rod Finishing Mills

The reliable operation of high-speed wire rod finishing mills is crucial in the steel production enterprise.As complex system-level equipment,it is difficult for high-speed wire rod finishing mills to realize fault location and real-time monitoring.To solve the above problems,an expert experience and data-driven-based hybrid fault diagnosis method for high-speed wire rod finishing mills is proposed in this paper.First,based on its mechanical structure,time and frequency domain analysis are improved in fault feature extraction.The approach of combining virtual value,peak value with kurtosis value index,is adopted in time domain analysis.Speed adjustment and side frequency analysis are proposed in frequency domain analysis to obtain accurate component characteristic frequency and its corresponding sideband.Then,according to time and frequency domain characteristics,fault location based on expert experience is proposed to get an accurate fault result.Finally,the proposed method is implemented in the equipment intelligent diagnosis system.By taking an equipment fault on site,for example,the effectiveness of the proposed method is illustrated in the system.

Language Transfer Manifested in English Idiom Acquisition by Chinese EFL Learners

Idiom acquisition plays an important role in the process of second language learning and acquisition.Proper understanding of linguistic transfer in terms of English idiom acquisition is helpful for EFL learners to improve the efficiency of learning English and also facilitates the process of English teaching.This study examines the Chinese learner’s language transfer features manifested in their understanding of English idioms which are classified into five different types according to their linguistic forms and conceptual bases.The results suggest that equivalent linguistic forms and equivalent conceptual bases of idioms are helpful to cause positive transfer while different conceptual bases of idioms may result in negative cross-linguistic transfer.This article may contribute to the development of language transfer theory and provide pedagogical implications of how English idioms should be learned by students and taught by English teachers.

Relationships Between Chinese EFL Learners’IELTS Band Score and Motivated Strategies for Learning

EFL learners,who study English as a foreign language,often use different learning strategies in the learning process,with positive and negative results in their academic performance.Whether these learning strategies are effective or not are questions to be explored.So,the author examined the relationship between Chinese EFL learners’test results and the use of motivated strategies for learning in English learning.Participants are students who have taken a high-stake standardized English proficiency test:IELTS(International English Language Testing System)with band score obtained.The results show that students’performance on high-stakes assessment is significantly associated with the use of motivated learning strategies like internal value in sharp contrast to test anxiety,which bears no relevance.Interviews are then implemented to candidates with different levels of English proficiency to figure out other related factors contributing to the test results.

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.

A Study on the Different Levels of“Attention”in the Processing of English Subjunctive Mood by Chinese Learners

“Attention”is a very vital cognitive mechanism.Cognitive psychology considers“attention”as the focus of mental activity and consciousness on an object.People engage their senses and memory to actively process the object in front of them.Theories of second language acquisition suggest that a large amount of input is the basis for language acquisition.At the same time,what is actively noticed by the learner can be effectively internalized.Thus,“attention”is also a non-linguistic factor that plays a significant role in language acquisition.The subjunctive mood is a major challenge in the teaching of English grammar.Generally speaking,the verbs in a sentence are used to distinguish between subjunctive and declarative sentences.In this paper,experimental utterances were written based on previous research by using verbs that are easy to cause ambiguity and confusion,and then set up the reinforced and non-reinforced attention conditions with different levels of“attention”to investigate whether there are different results and effects on subjects’comprehension of sentences under different levels of“attention”conditions.Under the different levels of“attention”,the subjects were examined to see whether there were different results in comprehension of the sentences and the impact of the different levels of“attention”.It was found that subjects in the reinforced“attention”condition comprehended sentences better,answered questions faster,and answered questions more correctly than subjects in the non-reinforced condition.This study also shed some light on English second language grammar teaching.

Effects of Mobile-Assisted Language Learning on Young Learners’Linguistic Skills

Mobile-assisted language learning(MALL)has been regarded as an excellent tool in the field of language acquisition as modern technology develops.The popularity of using mobile devices in education makes it possible for people to learn languages through platforms like tablets and smartphones from anywhere and at any time.However,the application of MALL in a collaborative student-centered environment has received comparatively little attention.Since spoken fluency and vocabulary size are the two crucial components of language proficiency,this study aims to investigate if young learners can improve their native language level through learning online beyond the classroom.The quantitative data reveal that MALL does make a difference in students’linguistic skills.The results show that the incorporation of mobile applications into language learning could better help learners achieve the learning outcomes and improve their communication skills than simply using conventional methods.In addition,the data of the questionnaire exposed some issues that need to be continuously improved.The viable suggestions are also discussed to share ideas about building a more sustainable learning environment in a data-driven age.

A Comparative Study on Verb-Noun Collocation Errors of Chinese EFL Learners with Different Language Proficiency

This study presents a comparative analysis of verb-noun collocation errors made by Chinese EFL learners with different language proficiency.Linguistic data from two sub-corpora of CLEC(Chinese Learner English Corpus,Gui&Yang,1999)consist of the compositions written freely by Chinese senior high school students(ST2)and the 3rd-and 4th-year English majors of Chinese universities(ST6).The study results show that four types of verb-noun collocation errors are frequently made due to incorrect L1 translation,misuse of relative synonyms,misuse of delexical verbs,and misuse of part of speech;the number of errors decreases with the development of language proficiency,especially errors by incorrect L1 translation and relative synonyms.And for both ST2 and ST6 EFL learners,errors resulted from incorrect L1 translation account for nearly a half with the same high-frequency words.It is also found that ST6 learners’competence in collocation and synonym identification doesn’t grow with the increase of their vocabulary,as is shown in the case of overusing delexical verbs in collocation,the same error made by ST2 learners,who have a significantly weaker command of delexical verbs.Implications of the findings are also discussed with the purpose of raising the awareness of collocation pedagogy and enhancing EFL learners’collocation competence.

A comparative study of data-driven battery capacity estimation based on partial charging curves

With its generality and practicality, the combination of partial charging curves and machine learning(ML) for battery capacity estimation has attracted widespread attention. However, a clear classification,fair comparison, and performance rationalization of these methods are lacking, due to the scattered existing studies. To address these issues, we develop 20 capacity estimation methods from three perspectives:charging sequence construction, input forms, and ML models. 22,582 charging curves are generated from 44 cells with different battery chemistry and operating conditions to validate the performance. Through comprehensive and unbiased comparison, the long short-term memory(LSTM) based neural network exhibits the best accuracy and robustness. Across all 6503 tested samples, the mean absolute percentage error(MAPE) for capacity estimation using LSTM is 0.61%, with a maximum error of only 3.94%. Even with the addition of 3 m V voltage noise or the extension of sampling intervals to 60 s, the average MAPE remains below 2%. Furthermore, the charging sequences are provided with physical explanations related to battery degradation to enhance confidence in their application. Recommendations for using other competitive methods are also presented. This work provides valuable insights and guidance for estimating battery capacity based on partial charging curves.

Data-Driven Learning Control Algorithms for Unachievable Tracking Problems

For unachievable tracking problems, where the system output cannot precisely track a given reference, achieving the best possible approximation for the reference trajectory becomes the objective. This study aims to investigate solutions using the Ptype learning control scheme. Initially, we demonstrate the necessity of gradient information for achieving the best approximation.Subsequently, we propose an input-output-driven learning gain design to handle the imprecise gradients of a class of uncertain systems. However, it is discovered that the desired performance may not be attainable when faced with incomplete information.To address this issue, an extended iterative learning control scheme is introduced. In this scheme, the tracking errors are modified through output data sampling, which incorporates lowmemory footprints and offers flexibility in learning gain design.The input sequence is shown to converge towards the desired input, resulting in an output that is closest to the given reference in the least square sense. Numerical simulations are provided to validate the theoretical findings.

Data-driven casting defect prediction model for sand casting based on random forest classification algorithm

The complex sand-casting process combined with the interactions between process parameters makes it difficult to control the casting quality,resulting in a high scrap rate.A strategy based on a data-driven model was proposed to reduce casting defects and improve production efficiency,which includes the random forest(RF)classification model,the feature importance analysis,and the process parameters optimization with Monte Carlo simulation.The collected data includes four types of defects and corresponding process parameters were used to construct the RF model.Classification results show a recall rate above 90% for all categories.The Gini Index was used to assess the importance of the process parameters in the formation of various defects in the RF model.Finally,the classification model was applied to different production conditions for quality prediction.In the case of process parameters optimization for gas porosity defects,this model serves as an experimental process in the Monte Carlo method to estimate a better temperature distribution.The prediction model,when applied to the factory,greatly improved the efficiency of defect detection.Results show that the scrap rate decreased from 10.16% to 6.68%.

Data-driven diagnosis of high temperature PEM fuel cells based on the electrochemical impedance spectroscopy: Robustness improvement and evaluation

Utilizing machine learning techniques for data-driven diagnosis of high temperature PEM fuel cells is beneficial and meaningful to the system durability. Nevertheless, ensuring the robustness of diagnosis remains a critical and challenging task in real application. To enhance the robustness of diagnosis and achieve a more thorough evaluation of diagnostic performance, a robust diagnostic procedure based on electrochemical impedance spectroscopy (EIS) and a new method for evaluation of the diagnosis robustness was proposed and investigated in this work. To improve the diagnosis robustness: (1) the degradation mechanism of different faults in the high temperature PEM fuel cell was first analyzed via the distribution of relaxation time of EIS to determine the equivalent circuit model (ECM) with better interpretability, simplicity and accuracy;(2) the feature extraction was implemented on the identified parameters of the ECM and extra attention was paid to distinguishing between the long-term normal degradation and other faults;(3) a Siamese Network was adopted to get features with higher robustness in a new embedding. The diagnosis was conducted using 6 classic classification algorithms—support vector machine (SVM), K-nearest neighbor (KNN), logistic regression (LR), decision tree (DT), random forest (RF), and Naive Bayes employing a dataset comprising a total of 1935 collected EIS. To evaluate the robustness of trained models: (1) different levels of errors were added to the features for performance evaluation;(2) a robustness coefficient (Roubust_C) was defined for a quantified and explicit evaluation of the diagnosis robustness. The diagnostic models employing the proposed feature extraction method can not only achieve the higher performance of around 100% but also higher robustness for diagnosis models. Despite the initial performance being similar, the KNN demonstrated a superior robustness after feature selection and re-embedding by triplet-loss method, which suggests the necessity of robustness evaluation for the machine learning models and the effectiveness of the defined robustness coefficient. This work hopes to give new insights to the robust diagnosis of high temperature PEM fuel cells and more comprehensive performance evaluation of the data-driven method for diagnostic application.

Noise-Tolerant ZNN-Based Data-Driven Iterative Learning Control for Discrete Nonaffine Nonlinear MIMO Repetitive Systems

Aiming at the tracking problem of a class of discrete nonaffine nonlinear multi-input multi-output(MIMO) repetitive systems subjected to separable and nonseparable disturbances, a novel data-driven iterative learning control(ILC) scheme based on the zeroing neural networks(ZNNs) is proposed. First, the equivalent dynamic linearization data model is obtained by means of dynamic linearization technology, which exists theoretically in the iteration domain. Then, the iterative extended state observer(IESO) is developed to estimate the disturbance and the coupling between systems, and the decoupled dynamic linearization model is obtained for the purpose of controller synthesis. To solve the zero-seeking tracking problem with inherent tolerance of noise,an ILC based on noise-tolerant modified ZNN is proposed. The strict assumptions imposed on the initialization conditions of each iteration in the existing ILC methods can be absolutely removed with our method. In addition, theoretical analysis indicates that the modified ZNN can converge to the exact solution of the zero-seeking tracking problem. Finally, a generalized example and an application-oriented example are presented to verify the effectiveness and superiority of the proposed process.

A hybrid physics-informed data-driven neural network for CO_(2) storage in depleted shale reservoirs

To reduce CO_(2) emissions in response to global climate change,shale reservoirs could be ideal candidates for long-term carbon geo-sequestration involving multi-scale transport processes.However,most current CO_(2) sequestration models do not adequately consider multiple transport mechanisms.Moreover,the evaluation of CO_(2) storage processes usually involves laborious and time-consuming numerical simulations unsuitable for practical prediction and decision-making.In this paper,an integrated model involving gas diffusion,adsorption,dissolution,slip flow,and Darcy flow is proposed to accurately characterize CO_(2) storage in depleted shale reservoirs,supporting the establishment of a training database.On this basis,a hybrid physics-informed data-driven neural network(HPDNN)is developed as a deep learning surrogate for prediction and inversion.By incorporating multiple sources of scientific knowledge,the HPDNN can be configured with limited simulation resources,significantly accelerating the forward and inversion processes.Furthermore,the HPDNN can more intelligently predict injection performance,precisely perform reservoir parameter inversion,and reasonably evaluate the CO_(2) storage capacity under complicated scenarios.The validation and test results demonstrate that the HPDNN can ensure high accuracy and strong robustness across an extensive applicability range when dealing with field data with multiple noise sources.This study has tremendous potential to replace traditional modeling tools for predicting and making decisions about CO_(2) storage projects in depleted shale reservoirs.

Research Nexus and Implications of Learner Identity in Foreign Language Education

One of the most obvious markers of an individual’s identity is their language.However,in some ways,the relationship between identity and language acquisition seems to be missed.In fact,a lot of studies have shown that identity may influence the reasons behind language acquisition,especially in bilingual or multilingual societies.Learning English can provide EFL(English as a Foreign Language)students with a good sense of identity and encourage them to practice their agency,which can improve learning efficiency and effectiveness.This paper examines how the efficacy and results of language learning are influenced by the learner’s identity.

China's English Learners' Verbal Phrase Acquisition Research Based on the Corpus——“at last”and“in the end”as an Example

This study is a comparative research based on corpus. This article compares China's English learners with English na?tive speakers about the use of "at last" and "in the end" from three aspects: frequency of use, location, semantic prosody. The study shows that the use of the two words are greatly different between China's English learners and English native speakers. In addition, this study discusses about causes of obvious differences and teaching enlightenments.