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Evaluation of the Deterioration State of Historical Palm Leaf Manuscripts from Burma

Palm leaf manuscripts were a prevalent literary medium from South Asia and Southeast Asia prior to the widespread use of paper. This study focuses on the analysis of historical palm leaf manuscripts from South and Southeast Asia. Sample palm leaf manuscripts from Burma were used as a case study; simulated palm leaf manuscripts were also created as a reference for comparison. The anatomy, chemical composition, and mechanical properties of the manuscripts were analyzed to find various forms of deterioration, including damage, fractures, pollution, acidification, and microbial deterioration. Specifically, the S1–S3 layers of the cell walls exhibited complete cracking, and the S2 layer showed numerous circular or nearly circular cavities caused by microbial erosion, while the middle lamella remained intact. The severe degradation of polysaccharides and pectin, accompanied by an increase in the relative content of lignin, caused the historical manuscripts to become more brittle. Additionally, the tensile strengths of historical palm leaf manuscripts were markedly reduced; their longitudinal tensile strength was significantly greater than their transverse tensile strength. This study can contribute to a better understanding of the deterioration process of historical palm leaf manuscripts and provide valuable insights for their restoration and preservation.

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An end-to-end method for palm-leaf manuscript segmentation based on U-Net

As a virtual restoration tool, digital imaging is widely used to share the patrimony and preserve the original material of ancient documents. However, the captured raw images usually consist of multiple backgrounds, influencing subsequent image processing, especially the damage investigation process. In this study, an end-to-end method named PLM-SegNet was proposed for the palm-leaf manuscript (PLM) segmentation based on U-Net. Two cameras (Nikon and Sony) were used to capture 83 palm-leaf manuscript images. The images were labeled by the software Labelme and were then cropped into patches with a specific size to train, validate, and test the PLM-SegNet model. The patch was fed into PLM-SegNet, and the foreground distribution map of this patch was obtained. The foreground distribution map of each patch in an image was predicted and stitched together into one global foreground distribution map. With assistance from the distribution map, the PLM can be segmented from the image easily. The results on two independent test sets showed pixel accuracy of 99.73% and 98.36%, intersection over union (IoU) of 99.42% and 98.31%, Recall of 99.68% and 99.95%, and F1-Score of 99.70% and 99.15% could be achieved, respectively. Additionally, damage detection was adopted as a case study to show the significance of PLM-SegNet. Compared with the raw PLM images, the performance of damage detection on segmented PLM images was improved by 15.00% and 19.33% on F1-Score and IoU, respectively. The results show that PLM-SegNet is a precise and automated method for palm-leaf manuscript segmentation when the labeled training data is limited. The source code is available at https://github.com/Ryan21wy/PLM-SegNet.

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Integrative analysis of chloroplast genome, chemicals, and illustrations in Bencao literature provides insights into the medicinal value of Peucedanum huangshanense.

The genus Peucedanum L. (Apiaceae) is a large group comprising more than 120 species distributed worldwide. Many plants of the genus Peucedanum have been studied and used in traditional Chinese medicine. In 2020, a new species, Peucedanum huangshanense Lu Q. Huang, H. S. Peng & S. S. Chu, was found in the Huangshan Mountains of Anhui Province, China. However, little is known about its medicinal properties. Thus, the objective of this study is to explore the potential medicinal value of P. huangshanense and its relationship with other Peucedanum species. Through textual research on illustrations of Qianhu in Bencao literature, it can be inferred that at least five species of genus Peucedanum have been used in Chinese medicine. Therefore, we chose these five species of Peucedanum and P. huangshanense together for subsequent research. We conducted morphological, chloroplast genome, and chemical analyses of six Peucedanum species, including the newly discovered P. huangshanense. The chloroplast genomes of Peucedanum showed a typical tetrad structure, and the gene structure and content were similar and conservative. There were significant differences in genome size and the expansion of the inverted repeat boundary. Through nucleotide polymorphism analysis, we screened 14 hotspot mutation regions that have the potential to be used as specific molecular markers for the taxonomy of Peucedanum. Our results showed an inversion of the trnD-trnY-trnE gene in the P. huangshanense chloroplast genome, which can be developed as a specific molecular marker for species identification. Phylogenetic analysis showed that the phylogenetic trees had high support and resolution, which strongly supports the view that Peucedanum is not a monophyletic group. P. huangshanense had the closest genetic relationship to P. ampliatum K. T. Fu, followed by P. harry-smithii Fedde ex Wolff. Furthermore, the main coumarins of P. huangshanense were most similar to those of P. japonicum Thunb. and P. harry-smithii. In summary, our research lays a foundation for the systematic classification of Peucedanum and sheds light on the medicinal value of P. huangshanense.

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Clustering analysis of acoustic emission signals in the monitoring of stone monuments: case of the freeze‒thaw deterioration of tuffs

Acoustic emission (AE) technology is a promising technique for monitoring cultural monuments due to its characteristic ability to reflect status changes and perceive the development process of deterioration and damage even before their visual appearance. This study was established on the motivation of providing basic data and a methodology that can improve the signal processing, characteristics analysis and classification for the AE technique in the long-term in-situ monitoring of deterioration processes, starting from the freeze‒thaw deterioration of tuff monuments at the Chengde site. AE monitoring was carried out with an indoor freeze–thaw deterioration experiment. As a result, a set of procedures and related methodology is proposed based on the hit-based AE waveform parameters for denoising and classification of monitored AE signals by applying hierarchical cluster analysis, k-means clustering, distribution statistics, etc. The clustering results show that some signals may indicate deterioration and signals with certain characteristics are more likely to occur at a particular deterioration phase. Signals characterized by the significant absolute energy (ABE) are presumed to be related to the propagation of cracks to the outer layer. Signals characterized by a higher indirect parameter RA (Rise time divided by peak amplitude) value may connect with the opening/closing of microcracks in the earlier phase of deterioration prior to the exposure of visible surface cracks. The peak frequency (PF) is likely to decrease as the deterioration proceeds.

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