Xiuzhen Li

Bio: Xiuzhen Li is an academic researcher from UCL Institute of Archaeology. The author has contributed to research in topics: Terracotta & Bronze. The author has an hindex of 4, co-authored 6 publications receiving 1164 citations. Previous affiliations of Xiuzhen Li include University College London.

Surface chromium on Terracotta Army bronze weapons is neither an ancient anti-rust treatment nor the reason for their good preservation

Abstract: For forty years, there has been a widely held belief that over 2,000 years ago the Chinese Qin developed an advanced chromate conversion coating technology (CCC) to prevent metal corrosion. This belief was based on the detection of chromium traces on the surface of bronze weapons buried with the Chinese Terracotta Army, and the same weapons’ very good preservation. We analysed weapons, lacquer and soils from the site, and conducted experimental replications of CCC and accelerated ageing. Our results show that surface chromium presence is correlated with artefact typology and uncorrelated with bronze preservation. Furthermore we show that the lacquer used to cover warriors and certain parts of weapons is rich in chromium, and we demonstrate that chromium on the metals is contamination from nearby lacquer after burial. The chromium anti-rust treatment theory should therefore be abandoned. The good metal preservation probably results from the moderately alkaline pH and very small particle size of the burial soil, in addition to bronze composition.

Building the Terracotta Army: ceramic craft technology and organisation of production at Qin Shihuang's mausoleum complex

Abstract: Despite decades of research into the Terracotta Army of the First Emperor of China, many questions remain about how, where and by whom the figures were made. This new study compares the results of microscopic analysis of the life-sized clay statues to other ceramic artefacts recovered from the mausoleum. By focusing on their original raw materials and clay paste recipes, it proves that the terracotta warriors were made near the site. Compositional, technological and spatial links between different artefacts suggest that clay was processed centrally before being distributed to different local workshops in a highly organised system of labour and craft specialisation that laid the foundation for imperial China.

Ink marks, bronze crossbows and their implications for the Qin Terracotta Army

Abstract: At the heart of bureaucratic practice during Warring States and early Imperial China were regular, small acts of accountancy in which objects and people were marked so that their movements could be kept track of, their quality checked and their numbers marshalled. In the mausoleum complex of the Qin Shihuang (259-210 bc, the First Emperor of the Qin Dynasty), the longer texts and shorter inscribed marks found on the bronze weapons of the Terracotta Army are reasonably well known, and such information helps us to understand aspects of Qin craft organisation and logistics at this crucial period of Chinese state formation. This paper’s modest starting point is a study of two further, less well-known ink inscriptions found on crossbow triggers from Terracotta Army Pit 1. Using multispectral photography, digital microscopy and Raman analysis, we uncover evidence of further marks on the same two triggers that suggest a similar pattern of ‘matching’ marks as suggested by the incised evidence. We also identify the black substance used to make the marks as a soot-based ink. Spatial analysis of both the inked and incised trigger marks then provides wider context for how such marking practices amongst Qin bronze-workers may have operated.

Multi-View Stereo: A Tutorial

Abstract: This tutorial presents a hands-on view of the field of multi-view stereo with a focus on practical algorithms. Multi-view stereo algorithms are able to construct highly detailed 3D models from images alone. They take a possibly very large set of images and construct a 3D plausible geometry that explains the images under some reasonable assumptions, the most important being scene rigidity. The tutorial frames the multiview stereo problem as an image/geometry consistency optimization problem. It describes in detail its main two ingredients: robust implementations of photometric consistency measures, and efficient optimization algorithms. It then presents how these main ingredients are used by some of the most successful algorithms, applied into real applications, and deployed as products in the industry. Finally it describes more advanced approaches exploiting domain-specific knowledge such as structural priors, and gives an overview of the remaining challenges and future research directions.

Atmospheric transport is a major pathway of microplastics to remote regions

Abstract: In recent years, marine, freshwater and terrestrial pollution with microplastics has been discussed extensively, whereas atmospheric microplastic transport has been largely overlooked. Here, we present global simulations of atmospheric transport of microplastic particles produced by road traffic (TWPs – tire wear particles and BWPs – brake wear particles), a major source that can be quantified relatively well. We find a high transport efficiencies of these particles to remote regions. About 34% of the emitted coarse TWPs and 30% of the emitted coarse BWPs (100 kt yr−1 and 40 kt yr−1 respectively) were deposited in the World Ocean. These amounts are of similar magnitude as the total estimated direct and riverine transport of TWPs and fibres to the ocean (64 kt yr−1). We suggest that the Arctic may be a particularly sensitive receptor region, where the light-absorbing properties of TWPs and BWPs may also cause accelerated warming and melting of the cryosphere. Plastic pollution is a critical concern across diverse ecosystems, yet most research has focused on terrestrial and aquatic transport, neglecting other mechanisms. Here the authors show that atmospheric transport is a major pathway for road plastic pollution over remote regions.

Short-term water quality variable prediction using a hybrid CNN–LSTM deep learning model

Abstract: Water quality monitoring is an important component of water resources management. In order to predict two water quality variables, namely dissolved oxygen (DO; mg/L) and chlorophyll-a (Chl-a; µg/L) in the Small Prespa Lake in Greece, two standalone deep learning (DL) models, the long short-term memory (LSTM) and convolutional neural network (CNN) models, along with their hybrid, the CNN–LSTM model, were developed. The main novelty of this study was to build a coupled CNN–LSTM model to predict water quality variables. Two traditional machine learning models, support-vector regression (SVR) and decision tree (DT), were also developed to compare with the DL models. Time series of the physicochemical water quality variables, specifically pH, oxidation–reduction potential (ORP; mV), water temperature (°C), electrical conductivity (EC; µS/cm), DO and Chl-a, were obtained using a sensor at 15-min intervals from June 1, 2012 to May 31, 2013 for model development. Lag times of up to one (t − 1) and two (t − 2) for input variables pH, ORP, water temperature, and EC were used to predict DO and Chl-a concentrations, respectively. Each model’s performance in both training and testing phases was assessed using statistical metrics including the correlation coefficient (r), root mean square error (RMSE), mean absolute error (MAE), their normalized equivalents (RRMSE, RMAE; %), percentage of bias (PBIAS), Nash–Sutcliffe coefficient ($$E_{NS}$$), Willmott’s Index, and graphical plots (Taylor diagram, box plot and spider diagram). Results showed that LSTM outperformed the CNN model for DO prediction, but the standalone DL models yielded similar performances for Chl-a prediction. Generally, the hybrid CNN–LSTM models outperformed the standalone models (LSTM, CNN, SVR and DT models) in predicting both DO and Chl-a. By integrating the LSTM and CNN models, the hybrid model successfully captured both the low and high levels of the water quality variables, particularly for the DO concentrations.

Microbial diversity drives carbon use efficiency in a model soil

Abstract: Empirical evidence for the response of soil carbon cycling to the combined effects of warming, drought and diversity loss is scarce. Microbial carbon use efficiency (CUE) plays a central role in regulating the flow of carbon through soil, yet how biotic and abiotic factors interact to drive it remains unclear. Here, we combine distinct community inocula (a biotic factor) with different temperature and moisture conditions (abiotic factors) to manipulate microbial diversity and community structure within a model soil. While community composition and diversity are the strongest predictors of CUE, abiotic factors modulated the relationship between diversity and CUE, with CUE being positively correlated with bacterial diversity only under high moisture. Altogether these results indicate that the diversity × ecosystem-function relationship can be impaired under non-favorable conditions in soils, and that to understand changes in soil C cycling we need to account for the multiple facets of global changes.