Carbon-based hollow structured nanomaterials have become one of the hot areas for research and development of hollow structured nanomaterials due to their unique structure, excellent physicochemical properties and promising applications. The design and synthesis of novel carbon-based hollow structured nanomaterials are of great scientific significance and wide application value. The recent research on the synthesis, structure and functionalization of carbon-based hollow structured nanomaterials and their related applications are reviewed. The basic synthetic strategies of carbon-based hollow structure nanomaterials are briefly introduced, and the structural design, material functionalization and main applications of carbon-based hollow structure nanomaterials are described in detail. Finally, the current challenges and opportunities in the synthesis and application of carbon-based hollow structured nanomaterials are discussed.

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 study examines the effectiveness of Kazakhstan’s grant funding system in supporting research institutions and universities, focusing on the relationship between funding levels, expert evaluations, and research outputs. We analyzed 317 projects awarded grants in 2021, using parametric methods to assess publication outcomes in Scopus and Web of Science databases. Descriptive statistics for 1606 grants awarded between 2021 and 2023 provide additional insights into the broader funding landscape. The results highlight key correlations between funding, evaluation scores, and journal publication percentiles, with a notable negative correlation observed between international and national expert evaluations in specific scientific fields. A productivity analysis at the organizational level was conducted using non-parametric methods to evaluate institutional efficiency in converting funding into research output. Data were manually collected from the National Center of Science and Technology Evaluation and supplemented with publication data from Scopus and Web of Science, using unique grant numbers and principal investigators’ profiles. This comprehensive analysis contributes to the development of an analytical framework for improving research funding policies in Kazakhstan.

China’s annual government work report (GWR) contains terms with Chinese characteristics (TCC), reflecting unique policy frameworks. Translating these terms into English poses significant challenges due to cultural disparities between China and the West. This paper examines the English translation methods used for such terms, using the 2020 GWR as a case study, aiming to provide valuable insights for future translation practices.