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.

AI-Powered Innovations in High-Tech Research and Development: From Theory to Practice

This comparative review explores the dynamic and evolving landscape of artificial intelligence(AI)-powered innovations within high-tech research and development(R&D).It delves into both theoreticalmodels and practical applications across a broad range of industries,including biotechnology,automotive,aerospace,and telecom-munications.By examining critical advancements in AI algorithms,machine learning,deep learning models,simulations,and predictive analytics,the review underscores the transformative role AI has played in advancing theoretical research and shaping cutting-edge technologies.The review integrates both qualitative and quantitative data derived from academic studies,industry reports,and real-world case studies to showcase the tangible impacts of AI on product innovation,process optimization,and strategic decision-making.Notably,it discusses the challenges of integrating AI within complex industrial systems,such as ethical concerns,technical limitations,and the need for regulatory oversight.The findings reveal a mixed landscape where AI has significantly accelerated R&D processes,reduced costs,and enabled more precise simulations and predictions,but also highlighted gaps in knowledge transfer,skills adaptation,and cross-industry standardization.By bridging the gap between AI theory and practice,the review offers insights into the effectiveness,successes,and obstacles faced by organizations as they implement AI-driven solutions.Concluding with a forward-looking perspective,the review identifies emerging trends,future challenges,and promising opportunities inAI-poweredR&D,such as the rise of autonomous systems,AI-driven drug discovery,and sustainable energy solutions.It offers a holistic understanding of how AI is shaping the future of technological innovation and provides actionable insights for researchers,engineers,and policymakers involved in high-tech Research and Development(R&D).

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.

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.

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.

The Impact of Music Education on Children’s Cognitive Development:From Traditional Classrooms to Modern Digital Platforms

Music education has long been debated for its influence on children’s cognitive development,particularly regarding their thinking methods and adaptability.This article synthesizes research data to examine the cognitive benefits of music instruction,including increased IQ,language proficiency,memory,and attention.Traditional face-to-face training,while personalized and socially interactive,faces limitations such as budget constraints and accessibility.Modern digital platforms offer individualized learning paths with AI-driven feedback but may lack necessary interpersonal interaction.This paper proposes a hybrid approach to music education,integrating traditional and digital methods to maximize cognitive gains.Further research is recommended to explore the implementation of these integrated learning strategies in varied educational settings.

The Appeals,Dilemmas,and Pathways of Enabling Rural Teachers’Professional Development Through Emerging Technologies

With the rapid advancements in technology,especially in digitalization and intelligence,numerous modern technologies have poured into rural schools,effectively improving their informatization conditions.Nevertheless,these technologies remain detached from rural teachers,failing to significantly enhance the quality of education and teaching in rural areas.Rural education is a crucial aspect of ensuring balanced development in education.The question of how to enhance rural teachers’technological application abilities and fully leverage the positive role of technology in rural education and teaching has become a significant topic of current research on rural education issues.To better address this question,this study conducted a thorough examination of the specific appeals of rural teachers in the process of technology enablement.It was discovered that rural teachers generally face dilemmas such as insufficient technological application abilities,difficulties in obtaining quality teaching resources,and the lack of continuous technical support and update mechanisms.Based on these findings,specific pathways such as strengthening rural teacher training,optimizing the allocation of educational resources,and establishing mechanisms for continuous technical support and updates are proposed to aid in the high-quality development of rural education.

The Effectiveness of Group Cooperative Learning Method in Badminton Teaching in Colleges and Universities

In college badminton teaching,teachers utilize the group cooperative learning method,which not only helps to improve students’badminton skill level but also cultivates their teamwork spirit,communication skills,and self-management ability unconsciously.In view of this,this paper mainly describes the significance of applying the group cooperative learning method in college badminton teaching,analyzes the current problems in college badminton teaching,and aims to discover effective development strategies for group cooperative learning method in college badminton teaching in order to improve the effectiveness of college badminton teaching.

An Assessment of the Role of Art as a Resource for Learning and Development of the Child With Computer Applications

An assessment of the role of Art as a resource for learning and development of the child has become necessary now that education appears to be the yearning of all especially in the developing countries. This paper seeks to assess the role of Art in learning and development of a child. The paper stresses that the child needs Art to learn because children＇s attention is drawn first by pictures （Art works） before letters of Alphabet. According to the paper, children become more knowledgeable and creative as they participate in Art classes where they are given opportunities to express themselves while engaging in drawing and painting exercises. This leads to self-discovery and development in children. By this, entrepreneurial spirit is also imbibed. The paper also notes that children who would not ordinarily want to learn have found pleasure in learning with Art and computer. Here the use of cartoons and animations become appropriate in teaching the child. Children also become more adventurous and creative as they find pleasure in drawing with application software like Corel Draw, Microsoft Paint, and so on. Finally, the paper emphasizes the need for Art to be wholly infused in the curricular of schools especially the primaries. This will be a strong factor or agent of development in children. Here the creative and inventive spirit so developed and imbibed will lead to discoveries of future entrepreneurs, industrialists and technologists for national developments.

Learning and Talent Development Alignment——take Network Railway as an example

Learning and talent development(LTD)aims to enhance an organisation’s performance,the need for LTD to link to the organisation’s overall business strategy,which also be described as linkage/alignment,is more imperative than ever in this rapid changing environment.This paper aims to explore how Network Rail utilise LTD strategies to support its overall business strategies.

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.

On division management for environmental system of China: lessons from European local sustainable development

The concept of sustainable development, after being brousht forward, has become a shibboleth in the world, at national and local levels. In Europe, this concept is implemented from the local to tha nation, even to the Continent. The local sustainable development mainly consists in two factors： one is the renovating method continually invented by European Secretariot of ICLEI, and the other is that the local authorities towards sustainability are co-operated by the European Sustainable Cities and Towns Campaign. The coo-budget method is the outcome of tbese factors, For our country is a big country, on rapid progression of indastrialization and urbanization, with a large population and scarce resoarces per capita, it is practically significance to study us soon as possible the methodology. Experience from European local sustainable development may help us to resolve the handicap of departraent division in local a, tbority.

A Preliminary Study on Connectivism—Constructivism Learning Theory Based on Developmental Cognitive Neuroscience and Spiking Neural Network

Based on a new theoretical perspective, this paper attempts to unify the seemingly incompatible learning theories of Connectivism and Constructivism into a scientific theoretical framework. The Connectivism-Constructivism learning theory is not a simple superposition of the two theories. Instead, it absorbs the essence of the learning theory of Constructivism, Connectivism and Neo-Constructivism, and takes the two empirical scientific experimental results of developmental cognitive neuroscience and spiking neural network as the factual basis, and develops two theories from the perspective of development. Integration, to achieve the resolution of contradictions, complement each other, and then rebuild. This paper discusses Connectivism-Constructivism learning theory. The theory holds that the essence of knowledge is the connection between the subject and the environment. There are two forms: physical form and logical form. The only logical form can be realized and utilized by people. Learning can be divided into two stages: connection and construction. Connection is the premise, construction is the core, and the network action generated in the connection stage as a raw material is pruned, and processed by various systems in the construction stage to become psychological representation. When the psychological representation is used, the relevant network shaping is finished, and the meaningful network is formed, which completes the change of knowledge from physical form to logical form and from logical form to physical form. Therefore, learning is the process of constructing meaningful network. We should not only promote the students’ connection stage, but also help the students’ construction stage. The innovation and breakthrough contribution of this paper is that it is the first time to look at the topic of learning theory from a new research perspective. In order to explore a more convincing learning theoretical framework, this article takes the lead in seeking theoretical support and factual basis from developmental cognitive neuroscience and Spiking neural network. As a result, Connectivism learning theory and Constructivism learning theory are successfully integrated into a rather complete and effective theoretical framework to reconstruct Connectivism-Constructivism learning theory.

Automatic recognition of depression based on audio and video:A review

Depression is a common mental health disorder.With current depression detection methods,specialized physicians often engage in conversations and physiological examinations based on standardized scales as auxiliary measures for depression assessment.Non-biological markers-typically classified as verbal or non-verbal and deemed crucial evaluation criteria for depression-have not been effectively utilized.Specialized physicians usually require extensive training and experience to capture changes in these features.Advancements in deep learning technology have provided technical support for capturing non-biological markers.Several researchers have proposed automatic depression estimation(ADE)systems based on sounds and videos to assist physicians in capturing these features and conducting depression screening.This article summarizes commonly used public datasets and recent research on audio-and video-based ADE based on three perspectives:Datasets,deficiencies in existing research,and future development directions.

Application of Traffic Calming Devices in Developing Countries: Learning Lesson from Bangladesh

Traffic calming devices become more favorable for many cities to manage traffic in urban as well as neighborhood areas. Many developing cities already initiated the implementation, while the process has a wide variation in both quality and quantity. The objective of this study is to evaluate the justification of speed humps in urban roads and also to recommend a guideline for installation of speed humps as a traffic calming device in Dhaka city, Bangladesh. It was found that there are vast variations in the implementation, while general public also express an immense disparity of perception about devices quality and suitability and their agreement. Lesson learned from Dhaka City shows that selecting an appropriate placement and dimension of the traffic humps is an urgent requirement to be solved by government by providing standards, guidelines, and policy when there is an initiative to implement or improve traffic calming application.

Training and Rural Development in Some Countries of the European Union

The aim of this research was to value, using a multiple regression model, the role of knowledge to guarantee the development in rural areas of European Union countries over 10 years. The main question was to find out relationships among some variable, as the percentage of national Gross Domestic Product （GDP） used to improve the high training, and rural development in terms of agricultural labour units. The results underlined in 2001 as an high value of rural development, in terms of working force in agriculture, was identified in some countries of European Union characterised by a low value both in high training investments and also by a low value of Human Development Index, according to the definition of The Economist. The results in 2010 pointed out an inverse correlation among the dependent variable development in rural areas and the independent variables per capita GDP and national expenditure in advanced training, in percentage of national GDP. The learning by doing and by using, the introduction of advanced training in agriculture, using Long Life Learning measures of European Union, are important to improve the development of European rural areas but, sometimes, these actions are not perceived as something of useful.

Training and Development Strategies for Senior and Middle Level Managers with the Purpose of Learning Organizations

In the presence of dynamic organizational environment and a growing supply of‘knowledgeable employees’which require more professional managers to address their fast changing and increasing needs,senior and middle level managers are now required to keep up with the dynamic and learning environment more than ever.In order to train senior and middle level managers,the article has recommended four perspectives to encourage the development of learning manager.The first aspect for senior and middle level mangers is to integrate learning talents into their practices.The second point is to encourage managers to provide strong support for individuals and teams to develop a learning organization.The third point encourages learning managers and organizations to be composed into the culture of the organization.The last point advocates for more open and free dissemination of information and knowledge to be allowed within an organization.

Embryonic skeleton development and neonatal learning and memory ability of rats anesthetized with pentobarbital sodium: Differences of administration occasion and time

BACKGROUND: Generally speaking, anesthesia is often used in gravid body and it has been already proved that many kind of medicine can result in malformation. OBJECTIVE: To explore embryonic skeleton development and neonatal learning and memory of rats anesthetized with pentobarbital sodium in gravid rats. DESIGN: A randomized control trial. SETTING: Laboratory Animal Center of Xuzhou Medical College. MATERIALS: A total of 80 adult female SD rats, of clean grade and weighing 220-240 g, were selected in this study. The main reagents were detailed as follows: pentobarbital sodium (Shanghai Xingzhi Chemical Plant, batch number: 921019); MG-2 maze test apparatus (Zhangjiagang Biomedical Instrument Factory); somatotype microscope (Beijing Taike Instrument Co., Ltd.). METHODS: ① A total of 160 SD rats of half males and females were selected in this study. All rats were copulated. The day that the plug was checked out in the vagina next day was looked as the first day of pregnancy. Gravid rats were divided randomly into four groups, including early anesthesia group, second anesthesia group, late anesthesia group and control group with 20 in each group. Rats in the early anesthesia group were injected with 25 mg/kg soluble pentobarbitone on the 7th day of pregnancy for once; rats in the second anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 7th and the 14th days of pregnancy for once; rats in the late anesthesia group were anesthetized with 25 mg/kg soluble pentobarbitone on the 14th day of pregnancy for once; rats in the control group did not treat with anything. The time of anesthetizing was controlled in 3 to 4 hours and ether was absorbed while the time was not enough. ② Half of each group was sacrificed on day 20th of pregnancy and the fetus was taken out to be stained with alizarin red S. After stained, the fetal skeleton was examined. The learning and memorizing of one-month rats that were given birth by the rest gravid rats were tested through electric mare method. Determine their study ability according to their correct rate of 90% or above of arrival at the safe area in 20 s. After they finally learned to arrive at the safe area correctly, test them once more in 24 hours and record the correct rate of 15 times. MAIN OUTCOME MEASURES: The rate of malformation in fetus and ability of learning and memory in one-month rats. RESULTS: A total of 80 female rats were anesthetized in this experiment. Totally 490 immature rats were tested with maze testing machine and 196 fetuses were stained with alizarin red S to observe the development of their skeleton. However, one of the 80 female rats was led to death because of overdose. ① Malformation experiment: Learning ability of second anesthesia group was evidently different from the control group while the other two groups were not in the electric mare method. The fetal skeleton malformation rate of three experimental groups was 87.0%, 60.9% and 17.9%, respectively, while it was 5.6% in the control group. ② Electric mare method: Times of rats which arrived at the safe regions were respectively 49.0±31.0, 68.0±35.0, 47.0±31.0 and 44.0±21.0 in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there was significant difference between the second anesthesia group and the control group (P < 0.05). Exact rates of memory of rats were respectively (64.36±14.35)%, (62.15±18.33)%, (54.19±12.28)% and (68.24±15.91)% in early anesthesia group, second anesthesia group, late anesthesia group and control group; and then, there were no significant differences as compared with the control group (P > 0.05). CONCLUSION: The influence of anesthesia with pentobarbital sodium is obvious in fetal skeleton development and learning and memory ability.