Understanding the creep behaviors and mechanisms of Mg-Gd-Zn alloys via machine learning

Mg-Gd-Zn based alloys have better creep resistance than other Mg alloys and attract more attention at elevated temperatures.However,the multiple alloying elements and various heat treatment conditions,combined with complex microstructural evolution during creep tests,bring great challenges in understanding and predicting creep behaviors.In this study,we proposed to predict the creep properties and reveal the creep mechanisms of Mg-Gd-Zn based alloys by machine learning.On the one hand,the minimum creep rates were effectively predicted by using a support vector regression model.The complex and nonmonotonic effects of test temperature,test stress,alloying elements,and heat treatment conditions on the creep properties were revealed.On the other hand,the creep stress exponents and creep activation energies were calculated by machine learning to analyze the variation of creep mechanisms,based on which the constitutive equations of Mg-Gd-Zn based alloys were obtained.This study introduces an efficient method to comprehend creep behaviors through machine learning,offering valuable insights for the future design and selection of Mg alloys.

Decoding topological XYZ^(2) codes with reinforcement learning based on attention mechanisms

Quantum error correction, a technique that relies on the principle of redundancy to encode logical information into additional qubits to better protect the system from noise, is necessary to design a viable quantum computer. For this new topological stabilizer code-XYZ^(2) code defined on the cellular lattice, it is implemented on a hexagonal lattice of qubits and it encodes the logical qubits with the help of stabilizer measurements of weight six and weight two. However topological stabilizer codes in cellular lattice quantum systems suffer from the detrimental effects of noise due to interaction with the environment. Several decoding approaches have been proposed to address this problem. Here, we propose the use of a state-attention based reinforcement learning decoder to decode XYZ^(2) codes, which enables the decoder to more accurately focus on the information related to the current decoding position, and the error correction accuracy of our reinforcement learning decoder model under the optimisation conditions can reach 83.27% under the depolarizing noise model, and we have measured thresholds of 0.18856 and 0.19043 for XYZ^(2) codes at code spacing of 3–7 and 7–11, respectively. our study provides directions and ideas for applications of decoding schemes combining reinforcement learning attention mechanisms to other topological quantum error-correcting codes.

Enhancing Image Description Generation through Deep Reinforcement Learning:Fusing Multiple Visual Features and Reward Mechanisms

Image description task is the intersection of computer vision and natural language processing,and it has important prospects,including helping computers understand images and obtaining information for the visually impaired.This study presents an innovative approach employing deep reinforcement learning to enhance the accuracy of natural language descriptions of images.Our method focuses on refining the reward function in deep reinforcement learning,facilitating the generation of precise descriptions by aligning visual and textual features more closely.Our approach comprises three key architectures.Firstly,it utilizes Residual Network 101(ResNet-101)and Faster Region-based Convolutional Neural Network(Faster R-CNN)to extract average and local image features,respectively,followed by the implementation of a dual attention mechanism for intricate feature fusion.Secondly,the Transformer model is engaged to derive contextual semantic features from textual data.Finally,the generation of descriptive text is executed through a two-layer long short-term memory network(LSTM),directed by the value and reward functions.Compared with the image description method that relies on deep learning,the score of Bilingual Evaluation Understudy(BLEU-1)is 0.762,which is 1.6%higher,and the score of BLEU-4 is 0.299.Consensus-based Image Description Evaluation(CIDEr)scored 0.998,Recall-Oriented Understudy for Gisting Evaluation(ROUGE)scored 0.552,the latter improved by 0.36%.These results not only attest to the viability of our approach but also highlight its superiority in the realm of image description.Future research can explore the integration of our method with other artificial intelligence(AI)domains,such as emotional AI,to create more nuanced and context-aware systems.

Research on the Lifting Path of Data Literacy Ability of Applied University Teachers under the Perspective of Organizational Learning

With the arrival of the big data era, the modern higher education model has undergone radical changes, and higher requirements have been put forward for the data literacy of college teachers. The paper first analyzes the connotation of teacher data literacy, and then combs through the status quo and dilemmas of teachers’ data literacy ability in applied universities. The paper proposes to enhance the data literacy ability of teachers from the perspective of organizational learning. Through building a digital culture, building a data-driven teaching environment, and constructing an interdisciplinary learning community to further promote the application of the theory and practice of datafication inside and outside the organization, and ultimately improve the quality of teaching.

Exploration of University English Teachers’Acceptance and Willingness to Use Learning Management System Data Analysis Tools

This study investigates university English teachers’acceptance and willingness to use learning management system(LMS)data analysis tools in their teaching practices.The research employs a mixed-method approach,combining quantitative surveys and qualitative interviews to understand teachers’perceptions and attitudes,and the factors influencing their adoption of LMS data analysis tools.The findings reveal that perceived usefulness,perceived ease of use,technical literacy,organizational support,and data privacy concerns significantly impact teachers’willingness to use these tools.Based on these insights,the study offers practical recommendations for educational institutions to enhance the effective adoption of LMS data analysis tools in English language teaching.

A Survey into Teachers＇ Roles in Web-based College English Autonomous Learning

The paper, with the backdrop of web-based autonomous learning put forward by the recent college English teaching reform, aims to explore teachers＇ roles in this learning process in students＇ perception through the means of questionnaires and interviews. It further analyzes the possible reasons why students perceive their teachers＇ roles in such a way, in the hope of providing some implications for web-based college English autonomous learning.

Machine learning applications on lunar meteorite minerals:From classification to mechanical properties prediction

Amid the scarcity of lunar meteorites and the imperative to preserve their scientific value,nondestructive testing methods are essential.This translates into the application of microscale rock mechanics experiments and scanning electron microscopy for surface composition analysis.This study explores the application of Machine Learning algorithms in predicting the mineralogical and mechanical properties of DHOFAR 1084,JAH 838,and NWA 11444 lunar meteorites based solely on their atomic percentage compositions.Leveraging a prior-data fitted network model,we achieved near-perfect classification scores for meteorites,mineral groups,and individual minerals.The regressor models,notably the KNeighbor model,provided an outstanding estimate of the mechanical properties—previously measured by nanoindentation tests—such as hardness,reduced Young’s modulus,and elastic recovery.Further considerations on the nature and physical properties of the minerals forming these meteorites,including porosity,crystal orientation,or shock degree,are essential for refining predictions.Our findings underscore the potential of Machine Learning in enhancing mineral identification and mechanical property estimation in lunar exploration,which pave the way for new advancements and quick assessments in extraterrestrial mineral mining,processing,and research.

Language teachers should develop students' vocabulary learning skills

It is self-evident that vocabulary work is the most essential and necessary task in foreign language accepition. English language teachers should develop students’ vocabulary learning skills.

An Incentive Mechanism for Federated Learning:A Continuous Zero-Determinant Strategy Approach

As a representative emerging machine learning technique, federated learning(FL) has gained considerable popularity for its special feature of “making data available but not visible”. However, potential problems remain, including privacy breaches, imbalances in payment, and inequitable distribution.These shortcomings let devices reluctantly contribute relevant data to, or even refuse to participate in FL. Therefore, in the application of FL, an important but also challenging issue is to motivate as many participants as possible to provide high-quality data to FL. In this paper, we propose an incentive mechanism for FL based on the continuous zero-determinant(CZD) strategies from the perspective of game theory. We first model the interaction between the server and the devices during the FL process as a continuous iterative game. We then apply the CZD strategies for two players and then multiple players to optimize the social welfare of FL, for which we prove that the server can keep social welfare at a high and stable level. Subsequently, we design an incentive mechanism based on the CZD strategies to attract devices to contribute all of their high-accuracy data to FL.Finally, we perform simulations to demonstrate that our proposed CZD-based incentive mechanism can indeed generate high and stable social welfare in FL.

Prediction of lime utilization ratio of dephosphorization in BOF steelmaking based on online sequential extreme learning machine with forgetting mechanism

The machine learning models of multiple linear regression(MLR),support vector regression(SVR),and extreme learning ma-chine(ELM)and the proposed ELM models of online sequential ELM(OS-ELM)and OS-ELM with forgetting mechanism(FOS-ELM)are applied in the prediction of the lime utilization ratio of dephosphorization in the basic oxygen furnace steelmaking process.The ELM model exhibites the best performance compared with the models of MLR and SVR.OS-ELM and FOS-ELM are applied for sequential learning and model updating.The optimal number of samples in validity term of the FOS-ELM model is determined to be 1500,with the smallest population mean absolute relative error(MARE)value of 0.058226 for the population.The variable importance analysis reveals lime weight,initial P content,and hot metal weight as the most important variables for the lime utilization ratio.The lime utilization ratio increases with the decrease in lime weight and the increases in the initial P content and hot metal weight.A prediction system based on FOS-ELM is applied in actual industrial production for one month.The hit ratios of the predicted lime utilization ratio in the error ranges of±1%,±3%,and±5%are 61.16%,90.63%,and 94.11%,respectively.The coefficient of determination,MARE,and root mean square error are 0.8670,0.06823,and 1.4265,respectively.The system exhibits desirable performance for applications in actual industrial pro-duction.

Reinforcement learning based adaptive control for uncertain mechanical systems with asymptotic tracking

This paper mainly focuses on the development of a learning-based controller for a class of uncertain mechanical systems modeled by the Euler-Lagrange formulation.The considered system can depict the behavior of a large class of engineering systems,such as vehicular systems,robot manipulators and satellites.All these systems are often characterized by highly nonlinear characteristics,heavy modeling uncertainties and unknown perturbations,therefore,accurate-model-based nonlinear control approaches become unavailable.Motivated by the challenge,a reinforcement learning(RL)adaptive control methodology based on the actor-critic framework is investigated to compensate the uncertain mechanical dynamics.The approximation inaccuracies caused by RL and the exogenous unknown disturbances are circumvented via a continuous robust integral of the sign of the error(RISE)control approach.Different from a classical RISE control law,a tanh(·)function is utilized instead of a sign(·)function to acquire a more smooth control signal.The developed controller requires very little prior knowledge of the dynamic model,is robust to unknown dynamics and exogenous disturbances,and can achieve asymptotic output tracking.Eventually,co-simulations through ADAMS and MATLAB/Simulink on a three degrees-of-freedom(3-DOF)manipulator and experiments on a real-time electromechanical servo system are performed to verify the performance of the proposed approach.

Systematizing Teacher Development:A Review of Foreign Language Teacher Learning

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

Prediction of single cell mechanical properties in microchannels based on deep learning

Traditional methods for measuring single-cell mechanical characteristics face several challenges,including lengthy measurement times,low throughput,and a requirement for advanced technical skills.To overcome these challenges,a novel machine learning(ML)approach is implemented based on the convolutional neural networks(CNNs),aiming at predicting cells'elastic modulus and constitutive equations from their deformations while passing through micro-constriction channels.In the present study,the computational fluid dynamics technology is used to generate a dataset within the range of the cell elastic modulus,incorporating three widely-used constitutive models that characterize the cellular mechanical behavior,i.e.,the Mooney-Rivlin(M-R),Neo-Hookean(N-H),and Kelvin-Voigt(K-V)models.Utilizing this dataset,a multi-input convolutional neural network(MI-CNN)algorithm is developed by incorporating cellular deformation data as well as the time and positional information.This approach accurately predicts the cell elastic modulus,with a coefficient of determination R^(2)of 0.999,a root mean square error of 0.218,and a mean absolute percentage error of 1.089%.The model consistently achieves high-precision predictions of the cellular elastic modulus with a maximum R^(2)of 0.99,even when the stochastic noise is added to the simulated data.One significant feature of the present model is that it has the ability to effectively classify the three types of constitutive equations we applied.The model accurately and reliably predicts single-cell mechanical properties,showcasing a robust ability to generalize.We demonstrate that incorporating deformation features at multiple time points can enhance the algorithm's accuracy and generalization.This algorithm presents a possibility for high-throughput,highly automated,real-time,and precise characterization of single-cell mechanical properties.

Effects of Perceived Teacher Support on Student Behavioral Engagement in the Blended Learning Environment:Learning Experience as a Mediator

Blended learning(BL)has been widely adopted to improve students’academic achievements in higher education.However,its success relies mainly on student engagement,which plays an essential role in active learning and provides a rich understanding of students’experiences.The study utilized three self-designed scales-the Teacher Support Scale,Student Engagement Scale,and Student Learning Experience Scale-to gauge and examine the impact and relationship between perceived teacher support,student behavioral engagement,and the intermediary role of learning experiences.A cohort of 899 college students undertaking the obligatory College English course through BL modes across five Chinese universities actively participated by completing a comprehensive questionnaire.The results showed significant correlations between perceived teacher support,learning experience,and behavioral engagement.Perceived teacher support significantly predicted students’behavioral engagement,with socio-affective support exerting the most substantial predictive effects.All predictive effects were partially mediated by learning experience(learning mode,online resources,overall LMS-based learning,interaction with their instructor and peers,and learning outcome).The influence of perceived teacher support on behavioral engagement differed between students who reported the most positive(vs.negative)learning experiences.Suggestions for further research are offered for consideration.

Exploring the Long-Term Mechanism of the Construction of College Teachers’Ethics in the New Era Under the Concept of“Sanquan Education”

This paper takes the long-term mechanism exploration of the construction of teachers’ethics in the new era under the concept of“Sanquan education,”aiming to explore how to establish a long-term mechanism of the construction to meet the needs of college education development under the background of the new era.A series of targeted measures and suggestions are put forward,including improving the system and mechanism,strengthening the education and training of teachers’ethics,and establishing the evaluation and assessment mechanism.The implementation of these measures can promote the formation of the long-term mechanism of the construction of teachers’ethics in colleges and universities,improve teachers’ethics and behavior quality,and provide strong support for the development of education in colleges and universities.

The mismatch of beliefs about learning English between Chinese university students and teachers

The present investigation aims to explore the existence of differences of beliefs about learning English held by Chinese non-English major university students and their teachers of English in the southwest of China. Two questionnaires were used to collect the data. Chi-square （Z2） tests were used to analyze the quantitative data from both students＇ and teachers＇ questionnaires. The results of the chi-square （Z2） tests revealed that 34 out of 44 （plus 2 additional） items of beliefs varied significantly between teachers and students.

Strategies on Promoting Transformative Learning of College English Teachers

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

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

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

A Case Study on Anxiety of University English Teachers as Visiting Scholars in Their Collaborative Learning

The study is to explore the test anxiety of three university English teachers in their collaborative learning community when they do their visiting scholar program.The research questions are about the representations of test anxiety of the college English teacher collaborative learning community as visiting scholars,and the interaction between anxiety and their collaborative learning.The main data collection includes deep interview,field observation,online chatting,etc.The research finds that at the three stages of their learning,the participant shows various representations of test anxiety from cognitive,affective and behavioral perspectives,and collaborative learning community facilitates the participant have better execution in learning,more pressure released and much cognitive enhancement achieved.In contrast,lack of collaborative learning environment in her working university and the specific anxiety for marriage in female participant are two additional findings in the study,which may prove the positive effect of collaborative learning on anxiety from another point of view.