Heritage Science最新文献

Ancient Chinese architecture drawing is a unique form of ancient Chinese art. Porcine blood hydrogels are distinctive ingredients used to prepare the composite material of the substrate layer for architecture drawing. This investigation was focused on the mechanism for porcine blood hydrogel formation. Based on the traditional Chinese recipe for the preparation of porcine blood-lime mortar, samples with different ratios of porcine blood and lime water were prepared, and the molecular-scale interactions between the lime water and proteins in the porcine blood were examined with FTIR spectroscopy, Raman spectroscopy, XRD, XPS, fluorescence spectroscopy, contact angle and rheology studies. The mechanical properties and morphological features of the samples were detected with a rheometer, universal material testing machine and SEM. The results indicated that an appropriate amount of lime water was required for gelation of the porcine blood. With the appropriate alkalinity, sufficient carboxyl groups on the fibrin chains were deprotonated, and coordination with Ca²⁺ ions formed enough cross-links for support networks within the structure of the porcine blood hydrogel. Complexation of deprotonated carboxyl groups on the fibrin chains with Ca²⁺ ions led to the formation of a hydrophobic surface due alterations of the fibrin conformation and increased the adhesive properties of the hydrogel. With the hydrogel used as an ingredient in the preparation of a substrate layer composite, it is suggested that the hydrophobic surface of the hydrogel facilitated mixing with hydrophobic tung oil during preparation of the composite material, and strong adhesion of the hydrogel increased the mechanical strength and crack resistance of the substrate layer.

The study aims at investigating the use of reflectance Hyperspectral Imaging (HSI) in the Visible (Vis) and Near Infrared (NIR) range in combination with Deep Convolutional Neural Networks (CNN) to address the tasks related to ancient Egyptian hieroglyphs recognition. Recently, well-established CNN architectures trained to address segmentation of objects within images have been successfully tested also for trial sets of hieroglyphs. In real conditions, however, the surfaces of the artefacts can be highly degraded, featuring corrupted and scarcely readable inscriptions which highly reduce the CNNs capabilities in automated recognition of symbols. In this study, the use of HSI technique in the extended Vis-NIR range is proposed to retrieve readability of degraded symbols by exploiting spectral images. Using different algorithmic chains, HSI data are processed to obtain enhanced images to be fed to the CNN architectures. In this pilot study, an ancient Egyptian coffin (XXV Dynasty), featuring a degraded hieroglyphic inscription, was used as a benchmark to test, in real conditions, the proposed methodological approaches. A set of Vis-NIR HSI data acquired on-site, in the framework of a non-invasive diagnostic campaign, was used in combination with CNN architectures to perform hieroglyphs segmentation. The outcomes of the different methodological approaches are presented and compared to each other and to the results obtained using standard RGB images.

In computer artificial intelligence, there is great potential in research on the protection of Suzhou's traditional gardens, a world cultural heritage site. As a special material in Suzhou's traditional garden architecture, shedthin tile is widely used in roof base laying and is one of the important materials for building roofs. However, professionals need to reach the roof and spend much time and effort assessing the damage before repairing it. Therefore, the main goals of this study are to investigate a machine learning-based method for finding targets and determining the type of surface damage on a shedthin tile using the YOLOv4 model trained in this study. Using 500 shedthin tile on-site photos as training samples, the model was trained for 750 epochs. The main results of this study are as follows: (1) An object detection method based on machine learning can efficiently and accurately identify damage content, overcoming the manpower and time–cost limitations of traditional assessment methods. (2) The detection model in this study has an accuracy of 85.89% for water stain recognition of shedthin tiles, 93.29% for surface scaling, 87.37% for color aberration, and 96.15% for too wide a gap. The comprehensive accuracy is 90.20%, which meets the basic testing requirements. (3) The model demonstrated its robustness and reliability in complex environments in application tests in actual scenarios, providing a methodological reference for computer vision and target detection technology in cultural heritage protection.

Abstract

A refutation of the claims by Scott Stripling et al. regarding the epigraphic analysis and paleographic contents of the folded lead object discovered on Mt. Ebal in 2019 during the wet-sifting of dump piles from a previous archaeological excavation by Adam Zertal. This piece of lead is often referred to as the “Mt. Ebal Curse Tablet”. An article regarding this find, by Stripling et al., titled “You are Cursed by the God YHW:” an early Hebrew inscription from Mt. Ebal”, was published on May 23, 2023, in the Heritage Science journal.

Western cities have been confronted with the challenges of 'urban shrinkage' as changes in economic and production models have caused population migrations. China, in its recent history, has experienced rapid and dynamic growth in its cities. However, as its economy is evolving, it too is being confronted with 'urban shrinkage' challenges. Populations have been shifting inside small and medium sized cities in response to problems created during this rapid growth period. Most heritage areas have suffered from depopulation, neglect and disinvestment. The historic Puhuiquan neighborhood in Yulin City is a classic example of the current Chinese version of 'urban shrinkage'. Yulin City, founded as a military city five centuries ago, has shifted its focus away from its historic purpose into a resource-based city. Yulin’s planning developed new areas, drawing population out of the historic Puhuiquan neighborhood. This area is the home to a unique urban cultural landscape where land and architecture have merged to create the development of various types of 'cave' houses. Suffering from the effects of 'urban shrinkage', this research explores the causes and potential solutions for the area employing both 'smart shrinkage' and 'urban renewal' strategies, informed by local resident participation, to develop a sustainable renewal strategy for this heritage area.

Abstract

This study focuses on the application of deep learning for transforming semantic point clouds into semantic Building Information Models (BIM) to create a Heritage Digital Twin, centering on Taoping Village, a site of historical and cultural significance in Sichuan, China. Utilizing advanced technologies such as unmanned aerial vehicles and terrestrial laser scanning, we capture detailed point cloud data of the village. A pivotal element of our methodology is the KP-SG neural network, which exhibits outstanding overall performance, particularly excelling in accurately identifying 11 categories. Among those categories, buildings and vegetation, achieves recognition rates of 81% and 83% respectively, and a 2.53% improvement in mIoU compared to KP-FCNN. This accuracy is critical for constructing detailed and accurate semantic BIM models of Taoping Village, facilitating comprehensive architecture and landscape analysis. Additionally, the KP-SG’s superior segmentation capability contributes to the creation of high-fidelity 3D models, enriching virtual reality experiences. We also introduce a digital twin platform that integrates diverse datasets, their semantic information, and visualization tools. This platform is designed to support process automation and decision-making and provide immersive experiences for tourists. Our approach, integrating semantic BIM models and a digital twin platform, marks a significant advancement in preserving and understanding traditional villages like Taoping and demonstrates the transformative potential of deep learning in cultural heritage conservation.

The Wujiafentou site is located at the heart of the Youziling (5900–5500 cal BP) and Shijiahe (4500–4200 cal BP) cultures in the Handong region, a core area to understand the relationship among economic networks, sociopolitical integration, and regional identity in the middle Yangtze River valley (MYRV) of central China during the Late Neolithic times. Its pottery assemblages are important clues to understanding the pottery production and use before and after the extensive walled town construction activities in the Neolithic MYRV. Wujiafentou is less than 5 km from the Shijiahe walled town, the largest city of its time in the MYRV. This paper applies microscopic examination and chemical and mineralogical analyses to a sample of 152 sherds of the Youziling-period and Shijiahe-period utilitarian vessels unearthed from the Wujiafentou site, revealing the changes and continuity in pottery production and use over time. We also probe into the socio-economic ties between Wujiafentou and the Shijiahe walled town mainly through hong tao bei (red clay cups), which were widely distributed and presumably highly symbolic items of the Shijiahe culture related to drinking, feasts, and rituals. We propose that the Wujiafentou inhabitants produced their red clay cups by mimicking those made within the Shijiahe walled town. Our study highlights an alternative interpretation of the formation of a regional identity during the Shijiahe period.

The “Eight Scenic Views Paintings” represent crucial visual materials for investigating the history of cultural landscapes. However, traditional methods of interpreting materials struggle to discern the inherent connections between different landscape elements. This study proposes an approach for deconstructing historical images, taking the example of the Forty Scenic Views in the Late Ming Dynasty in Nanjing, China. To explore the co-occurrence structure, hierarchical clustering, and correlation features among various elements, various digital humanities quantification methods were applied, including spatial analysis of ArcGIS, co-occurrence and clustering of KH Coder, and correlation analysis of SPSS. This study reveals the characteristics of the landscape construction of Nanjing in the Late Ming: natural landscape as the foundation, artificial landscape as the core, and advocating tradition as the fashion. It also uncovers the landscape order: mountains, waters, and scenic views interweaved and coexisted, as well as nature and culture intertwined and clustered. In addition, multiple information graphs of the subordinate and co-occurrence relationships of 20 landscape elements were constructed, 5 landscape paradigms were extracted, and 36 pairs of related relationships were discovered, deepening the historical understanding of the urban landscape construction of Nanjing in the Late Ming. This paper puts forward the idea of analyzing historical images by digital method, which provides some essential and detailed historical basis for explaining the value of cultural landscape heritage and shaping contemporary urban landscape.

In recent times, there has been a proactive effort by various institutions and organizations to preserve historic manuscripts as repositories of traditional knowledge and cultural heritage. Leveraging digital media and emerging technologies has proven to be an efficient way to safeguard these invaluable documents. Such technologies not only facilitate the extraction of knowledge from historic manuscripts but also hold promise for global applications. However, transforming inscribed stone artifacts into binary formats presents significant challenges due to angle distortion, subtle differences between foreground and background, background noise, variations in text size, and related issues. A pivotal aspect of effective image processing in preserving the rich information and wisdom encoded in stone inscriptions lies in employing appropriate pre-processing methods and techniques. This research paper places a special focus on elucidating various preprocessing techniques, encompassing resizing, grayscale conversion, enhancement of brightness and contrast, smoothening, noise removal, morphological operations, and thresholding. To comprehensively assess these techniques, we undertake a study involving stone inscription images extracted from the Tanjore Brihadeeswar Temple, dating back to the eleventh century during the reign of Raja Raja Chola. This choice is informed by the manifold challenges associated with image correction, such as distortion and blurring. We undertake an evaluation encompassing a diverse array of stone background structures, including types like flawless-bright-moderately legible, dark-illegible, flawless-bright-illegible, flawless-dull, flawless-irregular-moderate, highly impaired-dark-legible, highly impaired-irregular-illegible, impaired-dark-moderate, impaired-dull-moderately legible, impaired-dusky dark-moderate, and very impaired-dusky dark-legible. Subsequently, the processed outputs are subjected to character recognition and information extraction, with a focus on comparing the outcomes of various pre-processing methods, including binarization and grayscale conversion. This study seeks to contribute insights into the most effective pre-processing strategies for enhancing the legibility and preservation of ancient Indian script images etched onto diverse stone background structures.

Abstract

Plain pottery excavated from the Tang Dynasty tomb of Liu Jing was taken as the research object. The color, chemical composition, microstructure, and phase were tested to investigate the influencing factors of color for plain pottery fragments. The results indicated that the contents of Fe₂O₃ and TiO₂ in all fragments varied little, and the influence of humic acids in clay as well as the firing atmosphere on the appearance color of plain pottery was excluded. Therefore, the main factor affecting color saturation (C*) was identified as the firing temperature (T). More importantly, the correlation between C* and firing temperature was established by replicas fired at different temperatures. Before the appearance of the glass phase, iron-containing minerals played a major role in coloring, and after that, iron ions in the glass phase and iron crystallization rose the important function of coloring. Consequently, with the increase of firing temperature, C* value increased firstly and then decreased. The inflection point of the fitted C* − T curve corresponded to the glass phase formation temperature. By comparing the estimated firing temperatures obtained by the fitted C* − T correlation curve with the known firing temperature of replicas, it was demonstrated that the color measurement is an ideal method for deducing the firing temperatures of ancient plain pottery.