The integration of Big Earth Data and Artificial Intelligence (AI) has revolutionized geological and mineral mapping by delivering enhanced accuracy, efficiency, and scalability in analyzing large-scale remote sensing datasets. This study appraisals the application of advanced AI techniques, including machine learning and deep learning models such as Convolutional Neural Networks (CNNs), to multispectral and hyperspectral data for the identification and classification of geological formations and mineral deposits. The manuscript provides a critical analysis of AI’s capabilities, emphasizing its current significance and potential as demonstrated by organizations like NASA in managing complex geospatial datasets. A detailed examination of selected AI methodologies, criteria for case selection, and ethical and social impacts enriches the discussion, addressing gaps in the responsible application of AI in geosciences. The findings highlight notable improvements in detecting complex spatial patterns and subtle spectral signatures, advancing the generation of precise geological maps. Quantitative analyses compare AI-driven approaches with traditional techniques, underscoring their superiority in performance metrics such as accuracy and computational efficiency. The study also proposes solutions to challenges such as data quality, model transparency, and computational demands. By integrating enhanced visual aids and practical case studies, the research underscores its innovations in algorithmic breakthroughs and geospatial data integration. These contributions advance the growing body of knowledge in Big Earth Data and geosciences, setting a foundation for responsible, equitable, and impactful future applications of AI in geological and mineral mapping.

This paper describes the significance, content, progress and corresponding basic theory and experimental research methods of micron/nanometer scale thermal science and engineering, which is one of the latest cutting-edge disciplines, and analyzes the effects of micron nanometer devices on the scale effect series of challenging hot issues, discussed the corresponding emergence of some new phenomena and new concepts, pointed out that the micron/nano thermal science aspects of the recent development of several types of theory and experimental technology success and shortcomings, and summed up a number for the exploration of the new ways and new directions, especially on some typical micron/nano-thermal devices and micro-scale biological heat transfer in some important scientific issues and their engineering applications were introduced.

This study adapts traditional service blueprint methodologies for technology-driven coopetition networks, where companies simultaneously collaborate and compete. Integrating insights from service science, we developed an enhanced service blueprint framework with three key components: the cyber frontstage Lane for digital interactions, the physical backstage Lane for physical operations, and the support stage lane for supporting processes. Empirical validation in the Portuguese stone sector demonstrated the framework’s effectiveness in identifying network dysfunctions and its ease of use for industry professionals. Feedback highlights its relevance in capturing the complexities of modern digital coopetition and managing interactions and resources. This research underscores the necessity of updating service blueprint methods to optimize service delivery and value co-creation in digitally evolving sectors.

In order to create the possibility of economic breakthrough development, remove economic institutional bottlenecks, release resources, and develop the economy quickly and sustainably in Vietnam in the coming time, it is impossible not to mention solutions to improve the quality, create breakthroughs in training and fostering talents. This is one of the important solutions in the context that the Party and State require the application and development of science and technology more and more extensively in all fields and all sectors in Vietnam. The article focuses on researching the the political basis, legal basis, and practical basis for training, fostering, attracting and employing talents in Vietnam. Meanwhile, statistics on undergraduate and postgraduate training in the period of 2016–2022, the training level of the workforce and the Global Talent Competitiveness Index show that Vietnam has not achieved many positive changes in training, fostering, attracting and employing talents as expected. The article is approached from many different aspects, including the perspective of leaders and managers at the head of state agencies, the perspective of businesses and the perspective of the university teaching staff and scientific research workers themselves. On that basis, the article points out the key contents that need addressing so as to build solutions to improve quality, create breakthroughs in training, fostering, attracting and employing talents in Vietnam in the context of international integration and science and technology development. The main contributions of the article focus on the identification of the concept of “talent”, the criteria for determining “talent” and the renewal of awareness of policies and laws on training, fostering, attracting, employing, introducing and recommending talents.

Despite the surge of publication of chatbots in the recent years in the field of education, we have little to know how this area has been researched so far, and the metrics of this type of research is still not known. To address such gap, this article offers a descriptive bibliometric study of chatbot research in education, aiming at presenting bibliometric analysis on articles on chatbots in education that were published in journals indexed in the Web of Science (WOS) database specifically Social Science Citation Index (SSCI) and Science Citation Index Expanded (SCIE) between 2016 and 2023. Descriptive bibliometric analysis was used to examine the data gathered from the chosen publications. including the annual number of articles and citations, the most productive author, countries with the highest publication output, productive affiliations, funding organizations, and publication sources. The bulk of the articles on chatbots in education, according to our dataset, were published between 2016 and 2023. The United States of America tops the list of countries regarding research productivity. The United Kingdom and China were ranked as most second and third productive countries, in terms of publication outputs. “Luke Kutszik Fryer emerged as the most productive author in this research domain in terms of the number of publications.” The University of Hong Kong had the highest number of publications among affiliations, indicating their significant contribution to the field. Additionally, the journal “Computers in Human Behavior” stood out with the highest number of publications per year, highlighting its relevance in publishing research on chatbots in education. This research offers valuable insights and a roadmap for prospective researchers, pinpointing critical areas where success can be attained in the study of chatbots in education.