In order to understand the finishing effect of Waterborne Acrylic Paint under different painting methods and amount, bamboo-laminated lumber for furniture was coated with waterborne acrylic paint, then the effects of different painting methods and amount on the drying rate, smoothness, hardness, adhesion and wear resistance of the paint film were investigated. Further, the mechanism of film formation was described by thermal property analysis using thermogravimetry and differential scanning calorimeter. The results show that different painting methods have little effect on film properties, the drying time of primer and topcoat are not affected by them, which is 8/8.5 min for primer surface/solid and 6.5/7 min for topcoats. The film surface hardness and adhesion can reach B and 0 grade, the best wear resistance of the film is 51.24 mg·100 r⁻¹ when using one-layer primer one-layer topcoat. Different coating amount has great influence on film properties, the drying speed of the film increases with the increase of the painting amount. The film properties reach the best when the painting amount is 80 g/m², while too little painting amount leads to the decrease of hardness, and too much leads to the wear resistance weaken. Thermal analysis of the primer and topcoat show that water decomposition occurs at 100 ℃ and thermal decomposition of organic components occur at 350 ℃. Topcoats have better thermal stability than primers higher than that of topcoat, the topcoat displayed better thermal stability than the primer.

This paper contributes to a long-standing debate in development practice: under what conditions can externally established participatory groups engage in the collective management of services beyond the life of a project? Using 10 years of panel data on water point functionality from Indonesia’s rural water program, the Program for Community-Based Water Supply and Sanitation, the paper explored the determinants of subnational variation in infrastructure sustainability. It then investigated positive and negative deviance cases to answer why some communities successfully engaged in system management despite being located in difficult conditions as per quantitative findings and vice versa. The findings show that differences in the implementation of community participation, driven by local social relations between frontline service providers, that is, village authorities and water user groups, explain sustainable management. This initial condition of state-society relations influences how the project is initiated, kicking off negative or positive reinforcing pathways, leading to community collective action or exit. The paper concludes that the relationships between frontline government representatives and community actors are important and are an underexamined aspect of the ability of external projects to generate successful community-led management of public goods.

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.