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In recent years, the demand for the protection and restoration of cultural heritage has become increasingly urgent. Particularly for paper-based cultural relics such as ancient books and paintings, their restoration is especially important due to their unique nature and susceptibility to environmental damage. Among various restoration materials, calcium hydroxide (Ca(OH)2) has been widely studied and applied in the protection of paper-based cultural relics. However, commercial Ca(OH)2 materials have issues such as a large particle size and slow carbonation. In order to address these issues, this study employed carboxymethyl cellulose (CMC) as a support, on which nano-sized Ca(OH)2 crystals were grown in situ on its surface, followed by loading with TiO2 nanoparticles, successfully preparing a multifunctional paper-based cultural relic restoration material with reinforcement, deacidification, anti-aging, and antimicrobial properties. It is worth noting that by in situ growing Ca(OH)2 on the surface of CMC, particle size control, uniform dispersion, and the fixation of Ca(OH)2 can be achieved. CMC is used to enhance the mechanical strength of the paper, Ca(OH)2 is used for deacidification, and TiO2 is used for anti-aging and antimicrobial purposes. This study provides a new approach and method for the restoration of paper-based cultural relics, simplifying traditional multi-step processes and avoiding potential impacts on the cultural relics from multiple repairs. [ABSTRACT FROM AUTHOR]

The rise in sea levels, driven by global climate change, poses a significant threat to cultural heritage in coastal regions. Traditional risk assessment methods, focusing on direct inundation, often fail to consider the crucial impact of socio-economic factors, which are significantly vulnerable to sea level rise. To bridge this gap, this study introduces an innovative Sea Level Rise Cultural Heritage Impact Assessment Model (SLR-CHIA Model), a novel approach that integrates both land inundation and socio-economic aspects. This comprehensive model evaluates potential risks to various types of cultural heritage in coastal China, including intangible cultural heritage, relics, and traditional villages. The study's findings are striking: (1) About 7.79% of coastal villages, 53.94% of relics, and 2.53% of intangible cultural heritage are potentially at high risk in a 100-year sea level rise event; (2) Relics in the Eastern coast and villages in the Southern coast are most vulnerable; (3) Different types of cultural heritage rely on diverse principal factors for protection, resulting in varied risk levels under sea level rise conditions. The SLR-CHIA Model provides a vital methodological framework for evaluating cultural heritage risks in other global regions. [ABSTRACT FROM AUTHOR]

Different cultural backgrounds give birth to distinct styles of cultural heritage. In order to explore the relationship between the cultural relics' materials and the specific cultural attributes, a new method for the recognition of cultural relics' image materials based on computer vision and attention mechanism is proposed. Moreover, this paper analyzes the relationship between Chinese traditional cultural relics and Chinese dynasties as an example. The methodology of this work consists first of using Python to collect and manually screen cultural relic images of common material types. Then, the nine datasets with different materials are uploaded to the EfficientNet-B0 network with the attention mechanism for iterative training. The best weight model is stored and put to the test. Finally, the improved EfficientNet-B0 network is applied to recognize the cultural relics image datasets of each dynasty, and the relationship between the materials and the cultural attributes of each dynasty is analyzed. As for the outcomes, the experimental results show that the EfficientNet-B0 model, with the attention mechanism, can effectively enhance the extraction of image material information, and the accuracy of the recognized cultural relics image materials reaches up to 88.15%, with an average precision of 83.3%. The comparison experiment on the material dataset shows that the proposed method has excellent ability in the recognition of material image compared with other common image recognition methods. [ABSTRACT FROM AUTHOR]