Application of machine learning and deep learning in geothermal resource development: Trends and perspectives

This study delves into the latest advancements in machine learning and deep learning applications in geothermal resource development,extending the analysis up to 2024.It focuses on artificial intelligence's transformative role in the geothermal industry,analyzing recent literature from Scopus and Google Scholar to identify emerging trends,challenges,and future opportunities.The results reveal a marked increase in artificial intelligence(AI)applications,particularly in reservoir engineering,with significant advancements observed post‐2019.This study highlights AI's potential in enhancing drilling and exploration,emphasizing the integration of detailed case studies and practical applications.It also underscores the importance of ongoing research and tailored AI applications,in light of the rapid technological advancements and future trends in the field.

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.

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.

Surveying on English Mobile Learning Among University Students: Current State and Influencing Factors

Mobile learning integrates mobile technology with digital learning,offering flexible,personalized content and portable equipment.It enables access to rich content and enhances learning efficiency.Therefore,it has become mainstream to utilize mobile devices for English learning among university students’English learning.The current study aims to examine the current situation and influencing factors of university students’English mobile learning.98 university students in one university of Shanghai participated the study and the questionnaire was used to collect the data.The results indicated that most university students already have electronic devices to support mobile learning.Personal factors,environmental factors,digital literacy,and technological capabilities are the main factors affecting university students’English mobile learning.The current study has implications for learners,teachers,and software developers.Learners should adjust their learning motivation,play an active role,and fully utilize the mobile platform to obtain resources and improve learning efficiency.Teachers should incorporate the advantages of mobile teaching and promote categorized and tiered teaching.Software developers should add new functions on the basis of meeting the basic needs of learners and continuously innovate the mobile learning platform.

Evaluation of Chinese Learning Efficiency of International Students in China:Comparing by Dimension

This paper introduces the Value Engineering method to calculate the value coefficient of Chinese learning efficiency for 377 international students by dimension.Results suggest that attention should be paid to male,European and American,rural,introverted,and“work or education needs”international students;Give full play to the driving role of the reference-type,explore the space for improving the learning efficiency of the improvement-type and attention-type,and pay attention to the problems of problem-type international students.

Evaluation of Chinese Learning Efficiency of International Students in China

This paper investigates the related factors of Chinese learning engagement and performance of 377 students from three universities in Guangzhou of China,employs the value engineering method,and calculates the value coefficient of learning efficiency.From the value coefficient of Chinese language learning,it proves that over 60%of international students studying in China have a higher efficiency in learning Chinese.

Efficacy of Online Learning and Influence on Skin and Eyes Among College Students

To evaluate the efficacy of online learning and explore the impact of long-term use of electronic products on facial skin as well as eyes.A cross-sectional survey was conducted to 180 sophomores in Xi′an Jiaotong University by cluster random sampling from September to October 2021.The questionnaire covering study condition,skin lesion and Ocular Surface Disease Index.χ_(2) test was used to compare the facial skin condition among different groups,and spearman correlation test was used to test the correlation of rank data.During online education,students′learning pressure is reduced,their autonomy is improved,and the learning efficiency is reduced.There were differences in the incidence of facial itching and papules among different groups.Duration of use of electronic products was positively correlated with the facial itching,with an r value of 0.231(P<0.05);the proportion of pigmentation in non-blue light protection groups(12.8%)was higher than that in blue light protection groups(1.7%),the difference was statistically significant(χ_(2)=8.384,P<0.05).The prevalence of dry eye among college students is 66.7%,and the proportion of moderate to severe dry eye is 34.5%.The study autonomy has been improved during online teaching.Long-term use of electronic products and no blue light protection have an impact on facial skin.Students should enhance the knowledge of skin-care and eye-care and develop better habits.

An Investigation on the Influence of Middle School Students'Grammar Learning Self-efficacy on Their Learning Performance

In middleschool English teaching,grammar instruction has always been an important and challenging aspect.Due to the strong influence of grammar explanation in Chinese,learning English grammar may be affected by negative transfer from their native language,which could pose certain obstacles to English acquisition.Self-efficacy,as a major emotional factor influencing learners'internal regulation strategies,has attracted wide attention from researchers both domestically and internationally for its impact on learning performance.High self-efficacy helps learners develop a more positive mindset towards learning,thereby contributing to improved learning performance.Therefore,this study focuses on investigating the relationship between grammatical self-efficacy and learning performance of English grammar among middle school students.This research contributes to a complementary understanding of grammatical self-efficacy among educators,enhancing their teaching skills,and effectively guiding students to learn English grammar with a more positive and healthy mindset,presenting a future grammar instruction in middle school English teaching.

Results of the virtual simulation teaching method on autonomous learning competencies of undergraduate nursing students

Background:With the continuous development of information technology,most universities use mobile teaching platforms for classroom teaching.With the help of the national virtual simulation experimental teaching project sharing platform,students can enhance self-directed learning through the virtual simulation operations of the project.Purpose:To explore the application of virtual simulation experiment in teaching the fundamentals of nursing practice based on the Platform of the National Virtual Simulation Experiment Teaching Project during the COVID-19 pandemic analyze the impact of this teaching method on the autonomous learning ability of undergraduate nursing students.Methods:Convenience sampling was used to select 121 nursing undergraduates from Y University’s School of Nursing;the online teaching of fundamentals of nursing practice was conducted to the students.After taking the course,questionnaires were distributed to the undergraduate nursing students to collect their perceptions regarding the use of the virtual simulation experiment platform and autonomous learning competencies.Results:Most students expressed their preference for the virtual simulation teaching platform,and their satisfaction with the project evaluation was high 83.05%.They hoped to promote the application in future experimental teaching.Undergraduate nursing students believed that the virtual simulation teaching platform was conducive to cultivating clinical thinking ability,could stimulate learning interest,enhanced autonomous learning competencies.Conclusion:During the pandemic,the virtual simulation teaching platform for a lecture on in nursing education has achieved good results in both the aspects of teaching and student learning.Teachers efficiently used their training time and reduced their teaching burden.Moreover,the laboratory cost was also reduced.For undergraduate nursing students,the system was conducive to cultivating clinical thinking ability,stimulating their interest in learning,enhancing their learning and comprehension abilities and learning initiative.

Curriculum Development and Teachers for Cultivating OECD Student Agency in Japan:From an Examination of Educational Thought,Educational System,History,and Contemporary Science and Mathematics Education Practices

The purpose of this paper is to examine the development of a curriculum in Japan to foster the agency(OECD Student Agency)necessary for children living in the future,as outlined in the OECD Education2030 Project,and the role of teachers who implement this curriculum.Therefore,I aim to achieve this goal by taking an overview of educational thought,educational systems and history,and examining several initiatives in the OECD Project and the situation of a Japanese high school that practices science and mathematics education.As a result of analyzing previous research and the narratives obtained from interviews with Teacher A(pseudonym)of a certain F High School(pseudonym),it was found that school education should not simply teach subjects,but should aim to enable student to help student exercise agency in the future.It was suggested that it is not necessary to add content to the curriculum,but rather to prepare to provide students with competencies that will enable them to use the content in various contexts,situations,and circumstances,suggesting that in order to discover such important competencies,teachers and other related parties are required to implement curricula that allow students to slowly and carefully explore the competencies surrounding the content,and to conceive and build curricula that include both hidden and intended curriculum that allows student to reflect on the curriculum they have achieved.

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.

Interpretable machine learning optimization(InterOpt)for operational parameters:A case study of highly-efficient shale gas development

An algorithm named InterOpt for optimizing operational parameters is proposed based on interpretable machine learning,and is demonstrated via optimization of shale gas development.InterOpt consists of three parts:a neural network is used to construct an emulator of the actual drilling and hydraulic fracturing process in the vector space(i.e.,virtual environment);:the Sharpley value method in inter-pretable machine learning is applied to analyzing the impact of geological and operational parameters in each well(i.e.,single well feature impact analysis):and ensemble randomized maximum likelihood(EnRML)is conducted to optimize the operational parameters to comprehensively improve the efficiency of shale gas development and reduce the average cost.In the experiment,InterOpt provides different drilling and fracturing plans for each well according to its specific geological conditions,and finally achieves an average cost reduction of 9.7%for a case study with 104 wells.

Machine learning inspired workflow to revise field development plan under uncertainty

We present an efficient and risk-informed closed-loop field development (CLFD) workflow for recurrently revising the field development plan (FDP) using the accrued information. To make the process practical, we integrated multiple concepts of machine learning, an intelligent selection process to discard the worst FDP options and a growing set of representative reservoir models. These concepts were combined and used with a cluster-based learning and evolution optimizer to efficiently explore the search space of decision variables. Unlike previous studies, we also added the execution time of the CLFD workflow and worked with more realistic timelines to confirm the utility of a CLFD workflow. To appreciate the importance of data assimilation and new well-logs in a CLFD workflow, we carried out researches at rigorous conditions without a reduction in uncertainty attributes. The proposed CLFD workflow was implemented on a benchmark analogous to a giant field with extensively time-consuming simulation models. The results underscore that an ensemble with as few as 100 scenarios was sufficient to gauge the geological uncertainty, despite working with a giant field with highly heterogeneous characteristics. It is demonstrated that the CLFD workflow can improve the efficiency by over 85% compared to the previously validated workflow. Finally, we present some acute insights and problems related to data assimilation for the practical application of a CLFD workflow.

Deep learning framework for multi‐round service bundle recommendation in iterative mashup development

Recent years have witnessed the rapid development of service‐oriented computing technologies.The boom of Web services increases software developers'selection burden in developing new service‐based systems such as mashups.Timely recommending appropriate component services for developers to build new mashups has become a fundamental problem in service‐oriented software engineering.Existing service recom-mendation approaches are mainly designed for mashup development in the single‐round scenario.It is hard for them to effectively update recommendation results according to developers'requirements and behaviours(e.g.instant service selection).To address this issue,the authors propose a service bundle recommendation framework based on deep learning,DLISR,which aims to capture the interactions among the target mashup to build,selected(component)services,and the following service to recommend.Moreover,an attention mechanism is employed in DLISR to weigh selected services when rec-ommending a candidate service.The authors also design two separate models for learning interactions from the perspectives of content and invocation history,respectively,and a hybrid model called HISR.Experiments on a real‐world dataset indicate that HISR can outperform several state‐of‐the‐art service recommendation methods to develop new mashups iteratively.

A Feature Learning-Based Model for Analyzing Students’ Performance in Supportive Learning

Supportive learning plays a substantial role in providing a quality edu-cation system.The evaluation of students’performance impacts their deeper insight into the subject knowledge.Specifically,it is essential to maintain the baseline foundation for building a broader understanding of their careers.This research concentrates on establishing the students’knowledge relationship even in reduced samples.Here,Synthetic Minority Oversampling TEchnique(SMOTE)technique is used for pre-processing the missing value in the provided input dataset to enhance the prediction accuracy.When the initial processing is not done substantially,it leads to misleading prediction accuracy.This research concentrates on modelling an efficient classifier model to predict students’perfor-mance.Generally,the online available student dataset comprises a lesser amount of sample,and k-fold cross-validation is performed to balance the dataset.Then,the relationship among the students’performance(features)is measured using the auto-encoder.The stacked Long Short Term Memory(s-LSTM)is used to learn the previous feedback connection.The stacked model handles the provided data and the data sequence for understanding the long-term dependencies.The simula-tion is done in the MATLAB 2020a environment,and the proposed model shows a better trade-off than the existing approaches.Some evaluation metrics like pre-diction accuracy,sensitivity,specificity,AUROC,F1-score and recall are evalu-ated using the proposed model.The performance of the s?LSTM model is compared with existing approaches.The proposed model gives 89% accuracy,83% precision,86%recall,and 87%F-score.The proposed model outperforms the existing systems in terms of the earlier metrics.

The Influence of Self-esteem on College Students’Learning Burnout:A Mediation Model

We measured 324 college students’self-esteem,motivation for achievement,and learning burnout,and also we examined the inner mechanism of self-esteem’s influence on students’learning burnout.The results show:(1)There is a negative effect of self-esteem on college students’learning burnout;(2)Motivation of pursuing succeed plays partial mediating effect between self-esteem and learning burnout,but mediating effect of avoiding failure motivation between them is not significant.The study reveals that:the degree of learning burnout can be reduced by improving college students’level of self-esteem.

Research on the Influencing Factors of College Students’Learning Experience in Livestreaming Courses

The learning experience of online courses has always been a hot topic.As livestreaming courses are online courses with real-time interaction between instructors and students,its learning experience directly affects the learning behavior and effect.The evaluation indicators related to online course learning experience were combined with the attitudes of scholars,enterprise practitioners,and learners in livestreaming courses.When the questionnaire was designed,the exploratory and confirmatory factors were analyzed,the Questionnaire on Learning Experience of College Students in Livestreaming Courses was developed to evaluate the learning experience of college students attending livestreaming courses.Last but not the least,based on the survey data,important factors affecting college students’learning experience in livestreaming courses,including course content,learning environment,course interaction,and learning incentives were discussed and analyzed;strategies to optimize the learning experience in livestreaming courses were proposed.

The impact of just-in-time teaching model on nursing students'learning in the background of"Internet+":a meta-analysis

Objective:To systematically evaluate the impact of just-in-time teaching methods on nursing students’learning outcomes.Methods:Through the databases of CNKI,Wanfang,VIP,CBM,PubMed,Cochrane Library,Embase and Web of Science,the randomized controlled trials of just-in-time teaching in nursing field were collected from the establishment of the databases to the publication in November 2022.After literature screening,data extraction and quality evaluation,meta-analysis was performed using RevMan5.3 and Stata16.0 software.Results:Twenty articles were included,with a total of 1,865 nursing students.Meta-analysis results showed that compared with the traditional teaching method,the just-in-time teaching method had statistically significant differences in nursing students’theoretical examination scores(standardized mean difference(SMD)=1.26,95%confidence interval(CI):1.03 to 1.48,P<0.001),operational skills scores(SMD=1.56,95%CI:1.21 to 1.90,P<0.001),self-learning ability(SMD=0.76,95%CI:0.61 to 0.91,P<0.001)and just-in-time teaching method satisfaction(P<0.05).Conclusion:The just-in-time teaching method combines the advantages of traditional teaching and networked teaching,improves nursing students’theoretical performance,skill operation level and teaching satisfaction,and cultivates nursing students’self-learning ability.

A Study on the Factors Influencing Learning Burnout Among College Students in an Online Teaching Environment

With the advancement of economic globalization,mobile networks and media technology are developing rapidly.Media information is manufactured and spread at any time,and people can better understand global information with the help of the media.In the Internet era,college students,overwhelmed by complex information and a lack of information discernment,are susceptible to indulging in the curated online world presented by others.At the same time,negative information such as false,violent,and pornographic also spread rapidly in various media.These phenomena impact students’media literacy and affect their mental health,thereby leading to learning burnout.This study analyzes the current situation of learning burnout among university students,explores the effective path of improving online education of college students,and provides a theoretical basis for reducing the burnout of college students and assisting students in developing a positive mentality.

The Effect of Inquiry-Based Learning Strategy on EFL Tenth-Grade Students’ Reading Comprehension

This study examined the potential effect of Inquiry-Based Learning Strategy(IBL)on the tenth-grade students’reading comprehension.Two groups and a quasi-experimental design were used.Two complete sections of grade 10 students from a public Secondary School for Girls in Irbid was randomly assigned by the researcher.The experimental group of 30 students was chosen first,and then the control group of 30 students.A pre-post reading comprehension test was designed before and after the study in order to fulfill its goals.Additionally,the experimental group was taught using the IBL strategy,whereas the control group was taught using the traditional teaching methods recommended in the tenth-grade Teacher’s Book.According to the findings,there were significant statistical differences favoring the experimental group over the control group.In light of the findings,the researcher recommended employing the IBL strategy to students with various levels and EFL skills.