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Accurately identifying changes in carbon trading prices can provide reasonable reference indicators for a government's macrocontrol and can also help companies more effectively avoid risks brought by carbon emissions and increase the income of carbon assets. Based on the Prophet model, LSTM neural network model, and backpropagation (BP), this paper proposes a method to predict carbon trading prices using the ensemble learning model and uses the Hubei carbon trading market data to predict carbon trading prices. Results show that in terms of accuracy, the Prophet-LSTM-BP ensemble learning model achieves better predictive ability than existing models; its RMSE, MAE, and MAPE are 1.479, 0.951, and 2.135, respectively, which are markedly smaller than the Prophet model's 5.631, 4.471, and 9.661, and the LSTM model's 3.352, 3.105, and 6.880, respectively. Compared with the traditional time series ARIMA model, the MAPE of ARIMA reaches 12.933, which is nearly 1.5 times that of the Prophet model, nearly 2 times that of the LSTM model, and nearly 7 times that of the ensemble learning model. In terms of applicability, when the model is applied to the national carbon trading market, the difference in MAPE compared with the Hubei carbon trading market is only 0.6%, and the other parameters are not more than 16%. The model improves the relevant research on carbon trading price predicting, and concurrently, this method provides ideas for carbon trading price predicting in other carbon trading markets and promotes the sustainable development of the national carbon trading market. [ABSTRACT FROM AUTHOR]

The tomb of Marquis Yi of Zeng was unearthed in 1978 at Leiguudun in Suizhou City, Hubei Province, China. This is the largest wooden coffin in rock pit and vertical cave in the Warring States period in China. The large wooden coffin components provide reliable material data for the study of the feudal burial system in the pre-Qin period and have high value. In this paper, the wooden coffin excavated from the tomb of Marquis Yi of Zeng was studied. By understanding the composition of wood, studying the weight gain rate, shrinkage rate, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and compression resistance, the wood reinforced by rosin and epoxy resin was compared, and the reinforcement effect and principle of the two reinforcement methods were discussed. The study found that the wood properties of rosin reinforced group were better than those of epoxy reinforced group in the aspects of compressive strength, weight gain rate and volume shrinkage rate, and the mechanical properties of wood were significantly improved. In addition, methanol or ethanol can be used to dissolve the rosin shellac in this strengthening process, which can achieve reversible strengthening treatment. The results provide valuable examples and research ideas for the selection of different conservation techniques of large wooden cultural relics unearthed in the middle and lower reaches of the Yangtze River during the Warring States Period. [ABSTRACT FROM AUTHOR]

It is essential to predict carbon prices precisely in order to reduce CO2 emissions and mitigate global warming. As a solution to the limitations of a single machine learning model that has insufficient forecasting capability in the carbon price prediction problem, a carbon price prediction model (GWO–XGBOOST–CEEMDAN) based on the combination of grey wolf optimizer (GWO), extreme gradient boosting (XGBOOST), and complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is put forward in this paper. First, a random forest (RF) method is employed to screen the primary carbon price indicators and determine the main influencing factors. Second, the GWO–XGBOOST model is established, and the GWO algorithm is utilized to optimize the XGBOOST model parameters. Finally, the residual series of the GWO–XGBOOST model are decomposed and corrected using the CEEMDAN method to produce the GWO–XGBOOST–CEEMDAN model. Three carbon emission trading markets, Guangdong, Hubei, and Fujian, were experimentally predicted to verify the model's validity. Based on the experimental results, it has been demonstrated that the proposed hybrid model has enhanced prediction precision compared to the comparison model, providing an effective experimental method for the prediction of future carbon prices. [ABSTRACT FROM AUTHOR]

Based on the panel data of 16 cities in Hubei Province from 2009 to 2019 (excluding the data during Covid-19 pandemic period), we construct multi-temporal difference-in-differences model (DID) and Spatial Durbin model (SDM) to explore the impact of the High-speed Railway (HSR) construction on the regional economic development in terms of direct economic effect, indirect economic effect and spatial spillover effect. The main conclusions of this paper are as follows: firstly, HSR construction promotes the economic development of cities in Hubei Province in a long time and in a large spatial scope. And the impacts on the economic development are significantly heterogeneous among different types of cities. Also, HSR construction has widened the economic development gap among cities to some extent. Secondly, the upgrading of an industrial structure and the improvement of urbanization level both play mediating roles in the economic impact of HSR construction. Thirdly, the HSR construction has significant spatial spillover effect on economic development, but the spillover effect becomes insignificant with the increase of distance. [ABSTRACT FROM AUTHOR]

The search-engine-based surveillance methods for the early warning and prediction of infectious diseases cannot achieve search engine keywords automatic filtering and real-time updating, lead to powerless for the early warning of emerging infectious diseases. The aim of this study is to develop an artificial intelligence (AI) method for search-engine-based surveillance to improve the early warning ability for emerging infectious diseases. The 32 keywords (444 million search queries) that may be related to the coronavirus disease (COVID-19) outbreak was collected from December 18, 2019 to February 11, 2020 from Baidu's search engine database. The graph convolution network (GCN) model was used to select search engine keywords automatically, and then, multiple linear regression was performed to explore the relationship between the daily query frequencies of keywords and daily new cases. The GCN model was used to automatically select keywords. The prediction trend of the GCN model was highly consistent with the true curve with a mean absolute error of 81.65. Three keywords including "epidemic", "mask" and "coronavirus" were selected. The selection keywords in the search queries were highly correlated with the daily number of confirmed cases (r = 0.96, 0.94, and 0.89; P < 0.01). An abnormal initial peak (3.05 times the normal volume) in queries appeared on December 31, 2019, which could have served as an early warning signal for an outbreak. Of particular concern, 17.5% of query volume originated from the Hubei Province, 51.15% of which was from Wuhan City. The coefficients of determination (R2) of our constructed model were 0.88, 0.88, 0.84, 0.77, 0.77, 0.75, 0.73, and 0.73 for a time lag of 0–7 days, respectively, using selection keywords. The model we constructed was used in the Beijing Xinfadi outbreak as an independent test dataset, which successfully predicted the daily numbers of cases for the following days and detected an early signal during the Beijing Xinfadi outbreak (R2 = 0.79). In this paper search-engine-based surveillance based on the AI method was established for the early detection of the COVID-19 epidemic for the first time. The model achieves automatic filtering and real-time updating of search engine keywords and can effectively detect the early signals of emerging infectious diseases. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) is a novel harmful respiratory disease that has rapidly spread worldwide. At the end of 2019, COVID-19 emerged as a previously unknown respiratory disease in Wuhan, Hubei Province, China. The world health organization (WHO) declared the coronavirus outbreak a pandemic in the second week of March 2020. Simultaneous deep learning detection and classification of COVID-19 based on the full resolution of digital X-ray images is the key to efficiently assisting patients by enabling physicians to reach a fast and accurate diagnosis decision. In this paper, a simultaneous deep learning computer-aided diagnosis (CAD) system based on the YOLO predictor is proposed that can detect and diagnose COVID-19, differentiating it from eight other respiratory diseases: atelectasis, infiltration, pneumothorax, masses, effusion, pneumonia, cardiomegaly, and nodules. The proposed CAD system was assessed via five-fold tests for the multi-class prediction problem using two different databases of chest X-ray images: COVID-19 and ChestX-ray8. The proposed CAD system was trained with an annotated training set of 50,490 chest X-ray images. The regions on the entire X-ray images with lesions suspected of being due to COVID-19 were simultaneously detected and classified end-to-end via the proposed CAD predictor, achieving overall detection and classification accuracies of 96.31% and 97.40%, respectively. Most test images from patients with confirmed COVID-19 and other respiratory diseases were correctly predicted, achieving average intersection over union (IoU) greater than 90%. Applying deep learning regularizers of data balancing and augmentation improved the COVID-19 diagnostic performance by 6.64% and 12.17% in terms of the overall accuracy and the F1-score, respectively. It is feasible to achieve a diagnosis based on individual chest X-ray images with the proposed CAD system within 0.0093 s. Thus, the CAD system presented in this paper can make a prediction at the rate of 108 frames/s (FPS), which is close to real-time. The proposed deep learning CAD system can reliably differentiate COVID-19 from other respiratory diseases. The proposed deep learning model seems to be a reliable tool that can be used to practically assist health care systems, patients, and physicians. [ABSTRACT FROM AUTHOR]

Taking the HD1 well as the research target, through intensive core sampling, experimental test analysis, comprehensive logging interpretation and other methods, the fracability of shale reservoirs was discussed in this paper. The results show that the Gufeng–Dalong Formation in the HD1 well has a high organic carbon content, and the organic-rich shale developed in the lower part of the Gufeng and Dalong Formations with thicknesses of 30 m and 15 m, respectively. For the low-porosity and ultralow-permeability shale reservoir type, the natural fractures are undeveloped in the lower part of the Dalong Formation, with a lower linear density, while they are well developed in the Xiayao and Longtan Formations and the lower part of the Gufeng Formation, and the interlayer bedding fractures are relatively developed. The Gufeng–Dalong Formation shale also has a high mineral brittleness index (average of 44.5%), high static Young's modulus (20–70 GPa), low static Poisson's ratio (0.10–0.31), and high horizontal pressure difference coefficient of two phases (0.17–0.56). It is concluded that the shale reservoir is favorable for fracture development in the lower part of the Dalong Formation, with depths of 1249–1289.5 m, and the lower part of the Xiayao, Longtan and Gufeng Formations, with depths of 1300–1335.3 m. Overall, the fracable shale section with high brittleness and rock strength is beneficial to fracturing. However, the existence of a large number of shale bedding fractures increases the complexity of the fractures, and at the same time, it has a certain negative impact on fracture generation. Double wing fractures are easily formed because of the high two-phase horizontal pressure difference. Therefore, the leakage caused by shale bedding fractures and the influence on the fracture height and extension length should be considered comprehensively in fracturing design. [ABSTRACT FROM AUTHOR]

The genome of the novel coronavirus (COVID-19) disease was first sequenced in January 2020, approximately a month after its emergence in Wuhan, capital of Hubei province, China. COVID-19 genome sequencing is critical to understanding the virus behavior, its origin, how fast it mutates, and for the development of drugs/vaccines and effective preventive strategies. This paper investigates the use of artificial intelligence techniques to learn interesting information from COVID-19 genome sequences. Sequential pattern mining (SPM) is first applied on a computer-understandable corpus of COVID-19 genome sequences to see if interesting hidden patterns can be found, which reveal frequent patterns of nucleotide bases and their relationships with each other. Second, sequence prediction models are applied to the corpus to evaluate if nucleotide base(s) can be predicted from previous ones. Third, for mutation analysis in genome sequences, an algorithm is designed to find the locations in the genome sequences where the nucleotide bases are changed and to calculate the mutation rate. Obtained results suggest that SPM and mutation analysis techniques can reveal interesting information and patterns in COVID-19 genome sequences to examine the evolution and variations in COVID-19 strains respectively. [ABSTRACT FROM AUTHOR]

Red slipped and painted wares (RSW and RPW) were manufactured to cook, serve, or store foods and liquids in the middle Yangtze River valley (MYRV) of China some 8500 and 7800 years ago, respectively. Their primary use narrowed down to serving and drinking in the Upper Qujialing (5300–4500 cal BP) and Shijiahe (4500–4200 cal BP) periods when initial states (bang guo) took shape and developed in the region. The increasing social complexity in MYRV correlated with the formation of community and neighborhood identity through rituals and socio-economic ties involving the widespread use of RSW and RPW. How the two wares were produced and used helps us understand the relationships among productive activities, identity, and social inequality in MYRV. This paper presents the first overview of RSW and RPW in the Neolithic MRYV. It introduces a multi-analytical study of the two wares—mostly dating to the Shijiahe period—unearthed from the site of Fenghuangzui in Xianyang City of Hubei Province, China. Optical microscopic examination revealed that the paint of RPW—50 μm thick on average—was applied using a brush while the slip of RSW is thinner and finer and possibly formed by self-slipping. Handheld X-ray fluorescence (hhXRF) and benchtop micro-XRF analyses ascribed the red paint or slip to iron and iron oxide. Raman and X-ray absorption fine structure analyses confirmed that iron was present in the paint or slip in the form of hematite with a poorly developed crystalline structure. Furthermore, thin-section petrography implied that different pastes were used to produce RPW and RSW, and hhXRF data indicated that the Upper Qujialing and Shijiahe pottery differ in the concentrations of five elements (Zr, Fe, Mn, Ti, and Ba), which might be helpful in future provenance studies of RSW and RPW. Our study discloses the complexity of the manufacture of RSW and RPW at Fenghuangzui. More details of RSW and RPW production and use from our ongoing project shall reveal the role of the two wares in the social dynamics of the Late Neolithic MYRV. [ABSTRACT FROM AUTHOR]

This study is the first systematic assessment of the Lower Ordovician microbial carbonates in Songzi, Hubei Province, China. This paper divides the microbial carbonates into two types according to growth patterns, namely nongranular and granular. The nongranular types include stromatolites, thrombolites, dendrolites, leiolites and laminites; the granular types are mainly oncolites and may include a small amount of microbiogenic oolite. According to their geometric features, the stromatolites can be divided into four types: stratiform, wavy, columnar and domal. Additionally, dipyramidal columnar stromatolites are identified for the first time and represent a new type of columnar stromatolite. The thrombolites are divided into three types: speckled, reticulated and banded. The grazing gastropod Ecculiomphalus and traces of bioturbation are observed in the speckled and reticulated thrombolites. This paper considers these two kinds of thrombolites to represent bioturbated thrombolites. These findings not only fill gaps in the field of domestic Ordovician bioturbated thrombolites but also provide new information for the study of thrombolites. Based on the analysis of the sedimentary characteristics of microbialites, the depositional environments of the various types of microbialites are described, and the distribution patterns of their depositional environments are summarized. The relationship between the development of microbialites and the evolution and radiation of metazoans during the Early to Middle Ordovician is discussed. Consistent with the correspondence between the stepwise and rapid radiation of metazoans and the abrupt reduction in the number of microbialites between the late Early Ordovician and the early Middle Ordovician, fossils of benthonic grazing gastropods (Ecculiomphalus) were found in the stromatolites and thrombolite of the study area. It is believed that the gradual reduction in microbialites was related to the rapid increase in the abundance of metazoans. Grazers not only grazed on the microorganisms that formed stromatolites, resulting in a continuous reduction in the number of stromatolites, but also disrupted the growth state of the stromatolites, resulting in the formation of unique bioturbated thrombolites in the study area. Hydrocarbon potential analysis shows that the microbialites in the Nanjinguan Formation represent better source rocks than those in the other formations. [ABSTRACT FROM AUTHOR]

Carbon pricing is one of the key policy tools in the green recovery of the post-COVID-19 era. As linkages among ETSs worldwide are future trend, the carbon price spillover effects among markets are needed to be explored. This study examines the spillover effects and dynamic linkages of carbon prices using the example of China's pilot carbon markets during 2015–2019, which are seemingly independent carbon markets. A structural vector error correction model and an improved directed acyclic graph approach are applied. The main results are as follows. First, the linkages among the five pilots demonstrate features of "two small-world networks." Specifically, these are the Guangdong and Hubei network and the Beijing, Shenzhen and Shanghai network. Second, Shenzhen, Beijing and Hubei ranked as the top three pilots in terms of external spillover effect, accounting for 36.25%, 29.76%, and 25.59%, respectively. Second, Guangdong pilot has increasing influence on the Hubei, Shenzhen and Beijing pilots. Third, trading activities are positive contributors to the spillover, while the allowance illiquidity ratio and volatility are negative factors. The findings imply that to retain an expectable abatement costs in achieving the climate goals in green recovery, carbon prices in other potentially related markets should be considered by the policy maker in addition to its own policy design. [ABSTRACT FROM AUTHOR]

While increased tourist demand has brought economic benefits to tourist destinations, it has also put tremendous pressure on tourism-related ecological environments. Therefore, the relationship between regional urban tourism's economic benefits and tourism ecologies must be considered by increasing the ecological security levels of regional urban tourism and promoting sustainable tourism development. This paper defines the ecological security of regional urban tourism, constructs an indicator system of said ecological security based on a pressure-state-response social-economic-environment (PSR-SEE) model, uses the entropy method to objectively assign weights to the indicator system, constructs a dynamic multi-scenario simulation model to assess ecological security under continuous, economic priority and various ecological protection scenarios, employs ArcGIS software to analyze the spatial distribution patterns of the ecological security of regional urban tourism, and uses a standard deviation ellipse method to analyze the space–time evolutionary track of the ecological security of regional urban tourism. The index system is applied to 16 cities in Hubei Province to measure the ecological security levels and test the viability of the system. The present research focuses on the space–time evolution of the ecological security of regional urban tourism, addresses the contradictions between socioeconomic development and the ecological security of regional urban tourism, provides an important reference for tourism destination planning, and can assist in improving the levels of the ecological security of regional urban tourism to promote sustainable tourism development. [ABSTRACT FROM AUTHOR]

Developing algorithms for solving high-dimensional uncertain differential equations has been an exceedingly difficult task. This paper presents an α -path-based approach that can handle the proposed high-dimensional uncertain SIR model. We apply the α -path-based approach to calculating the uncertainty distributions and related expected values of the solutions. Furthermore, we employ the method of moments to estimate parameters and design a numerical algorithm to solve them. This model is applied to describing the development trend of COVID-19 using infected and recovered data of Hubei province. The results indicate that lockdown policy achieves almost 100% efficiency after February 13, 2020, which is consistent with the existing literatures. The high-dimensional α -path-based approach opens up new possibilities in solving high-dimensional uncertain differential equations and new applications. [ABSTRACT FROM AUTHOR]

Precipitation is an important climatic parameter, and its variability severely affects regional hydrological processes and water resource management. In order to explore the changing spatial and temporal characteristics of precipitation-related indices, including precipitation amounts indices, extreme precipitation indices, and precipitation concentration indices, in Hubei province, central China during 1961-2010, several precipitation-related indices series were analyzed using the Mann-Kendall test, Pettitt test, and inverse distance weighted interpolation method in this paper. The results are as follows: (1) A clear south-north gradient is apparent in the spatial distribution of the majority of precipitation-related indices, while the distribution of other indices (i.e., CDD, PCD, and PCP) is just the opposite. Most part of Hubei province can experience mixed positive and negative trends in precipitation-related indices. (2) The majority of precipitation-related indices exhibit an increasing trend, but most of which are not significant. July has the largest positive trend, with a regional average of 14.76 mm/decade. As for extreme precipitation indices, there exist large variations in Hubei, especially in R95pTOT. Besides, no obvious trends are shown in precipitation concentration indices. (3) With the exception of consecutive dry days (CDD), most of extreme precipitation indices are strongly correlated with annual mean precipitation (AMP) in Hubei. And the extreme precipitation indices are well correlated with each other except CDD and consecutive wet days (CWD). For six precipitation-related indices (i.e., SDII, RX1day, RX5day, R95pTOT R99pTOT, and PCD), there are significant positive relationships with Northern Hemisphere Subtropical High (NHSH) but significant negative relationships with Northern Hemisphere Polar Vortex (NHPV). Furthermore, the majority of precipitation-related indices have significant negative correlations with East Asian summer monsoon index (EASMI) and South China Sea summer monsoon index (SCSSMI), and the correlation coefficient with EASMI is higher than that with SCSSMI. [ABSTRACT FROM AUTHOR]

The preservation and transmission of traditional villages is crucial to the prosperity and development of ethnic cultures. However, current traditional village surveys usually require a large number of experts and scholars to conduct field research, which is an expensive and time-consuming method, especially for large-scale tasks. Therefore, this study proposes an automatic classification method based on deep learning (DL) for the identification of traditional village heritage value elements (TVHVE). The study evaluates four selected convolutional neural network (CNN) frames using traditional villages in Hubei Province as a sample dataset. The results show that Residual Network152 (ResNet152) is the most suitable CNN frame for identifying TVHVE in Hubei. The stability and consistency of various TVHVE present in the ResNet152 model were evaluated using Area Under Curve (AUC) and Precision Recall Curve (PRC), which indicated satisfactory prediction performance for most elements, except for specific elements such as tombstones and stone carvings, which showed lower accuracy. In addition, the study sheds light on the areas of concern of the model with respect to different TVHVE images and elucidates the reasons behind the confusion between elements through semantic clustering based on image classification and interpretability analysis using the Gradient-Weighted Class Activation Mapping (Grad-CAM) heat map. By using an automated classification method based on DL, this study significantly reduces the cost and effort associated with traditional surveys. At the same time, insight into areas of concern and confusion in the model improves guidance for conservation efforts and provides valuable references for subsequent research. [ABSTRACT FROM AUTHOR]

With the deep integration of the digital economy and agricultural development, agricultural digital transformation promotes agricultural production, and industrial upgrading and broadens sales channels to achieve the strategic goal of rural revitalization in China. To explore whether agricultural digitization can help farmers increase their income and what path can be achieved, this study is based on the theoretical framework of rural revitalization theory and digitization. Through a questionnaire survey of 1500 farmers in Hubei Province, the impact mechanism of agricultural digitization transformation on farmers' income is empirically studied. The empirical results show that the digital transformation of agriculture can promote the increase in farmers' income, and promote the increase in farmers' income by improving production efficiency, broadening sales channels, and promoting the upgrading of agricultural structure. At the same time, both production efficiency and sales channels form a chain double intermediary path with the upgrading of agricultural structure. Under the background of rural revitalization, this study provides theoretical references and guidance for further promoting agricultural digital transformation to increase farmers' income. The marginal contribution of this study is to construct a theoretical model of agricultural digitization to promote farmers' income increase, which has important theoretical reference and guiding significance for guiding the development direction of agricultural digitization and promoting farmers' income increase. [ABSTRACT FROM AUTHOR]

Environmental degradation and climate change are two important issues that threaten human survival and sustainable social development. Based on the characteristics of the same source of sulfur dioxide and greenhouse gases, we analyze the collaborative governance mechanism of carbon emission trading on sulfur dioxide. The results show that carbon emission reduction trading may not necessarily deliver sulfur dioxide reduction co-benefits. Then, we conduct an empirical using data of 30 regions in China from 1998 to 2017. The results show that the co-benefits of carbon emission trading in terms of the reduction of sulfur dioxide are regionally heterogeneous. The carbon emission trading policies implemented in Beijing, Chongqing, Hubei, and Guangdong have realized collaborative governance on sulfur dioxide. However, the carbon emission trading policies implemented in Tianjin and Shanghai have not realized collaborative governance on sulfur dioxide. Moreover, Chongqing has the highest average annual sulfur dioxide reduction co-benefits, followed by Guangdong. The gap of sulfur dioxide reduction co-benefits between Beijing and Hubei's synergistic emission reduction effect is small. Moreover, the sulfur dioxide reduction co-benefits of carbon emission trading are mainly achieved by reducing fossil energy consumption. Meanwhile, the carbon emission trading implemented in Beijing also delivers sulfur dioxide reduction co-benefits by improving pollution reduction technology. [ABSTRACT FROM AUTHOR]

Global warming is caused by rising carbon dioxide concentrations, while ecological and environmental issues are becoming increasingly evident. Many governments' main objective in addressing climate challenges is to achieve carbon balance. Exploring the distribution and changing trends of carbon balance can contribute to carbon balance strategies. This study takes the Yangtze River Economic Belt (YREB) as the study area, analyzing the spatial and temporal dynamic changes in carbon emissions and carbon sequestration in the YREB from 2000 to 2017. The county-level supply–demand ratios and accessibility are calculated based on the enhanced two-step floating catchment method according to the dynamic flow radius. According to our experimental results, carbon emissions can achieve different degrees of carbon balance according to the flow radius. After carbon emissions flow between counties within the province, some provinces can realize the transition from carbon surplus to carbon balance. With the expansion of the flow radius and the increase in internal mobility, more counties can participate in carbon sequestration, and some provinces can achieve carbon neutrality after carbon emissions transfer across provinces. Hubei, Chongqing, Zhejiang, Hunan and other provinces with special carbon sequestration capacity are very sensitive to changes in the radius of carbon emission flow. In 2017, when the carbon emission flow radius is 50 km, the net carbon emission of the flow can be reduced by 3%. If the radius is increased to 200 km, the YREB can achieve carbon balance. This study can provide decision-making information for carbon emission ecosystem management in the YREB. [ABSTRACT FROM AUTHOR]

Liver cancer, a chronic non-communicable disease, represents a serious public health problem. Long-term trends in the burden of liver cancer disease are heterogeneous across regions. Incidence and mortality of liver cancer, based on the Global Burden of Disease, were collected from the Chinese Centre for Disease Control and Prevention. Age–period–cohort model was utilized to reveal the secular trends and estimate the age, period and cohort effects on primary liver cancer due to specific etiologies. Both the age-standardized incidence and mortality rate of liver cancer in Hubei province were on the rise, although there were discrepancies between gender groups. From age–period–cohort analysis, both incidence and mortality of liver cancer due to Hepatitis B virus were the highest in all age groups. The incidence of all liver cancer groups increased with time period in males, while this upward trend was observed in females only in liver cancer due to alcohol use group. Cohort effects indicated the disease burden of liver cancer decreased with birth cohorts. Local drifts showed that the incidence of liver cancer due to specific etiologies was increasing in the age group of males between 40 and 75 years old. The impact of an aging population will continue in Hubei Province. the disease burden of liver cancer will continue to increase, and personalized prevention policies must be adopted to address these changes. [ABSTRACT FROM AUTHOR]

Using untreated livestock manure adversely affects the environment and human health. Although transforming untreated livestock manure into organic fertilizer can alleviate these adverse effects, the adoption rate of this technology is relatively low in rural areas. This study investigates whether information technology adoption, focusing on Internet use, increases farmers' willingness to transform untreated livestock manure into organic fertilizer using data collected from pig breeding farmers in the Hubei province of China. A recursive bivariate probit model addresses the sample selection bias issues. Findings show that Internet use increases the probability of pig breeding farmers' willingness to transform untreated manure by around 35–56 percentage points. Gender, farm size, credit availability, extension contact, environmental protection attitude and governmental regulation are also important factors driving pig breeding farmers' willingness to transform untreated manure. Moreover, the impact of Internet use on farmers' willingness to transform untreated pig manure tends to be higher for pig breeding farmers with a larger breeding size and longer breeding years. Our findings have significant policy implications that help to improve livestock manure management, environmental quality, and the well-being and health of livestock breeding farmers. [ABSTRACT FROM AUTHOR]

The abnormity of climate change poses environmental concerns and indirectly promotes the development of low-carbon economic growth pattern. Climate change mitigation technology innovations contribute significantly to achieve environmental goals in China, and thus, technological cooperation has become a strategic choice for the Chinese provinces to achieve technological breakthroughs. Based on 3, 280, 808 patents, this study uses social network analysis (SNA) method and similarity measurement to discuss the evolution process and structural features of the spatial correlation network to analyze the patent data from 1985 to 2020. The process for the research is as follows. First, a form solution of the correlation problem to calculate the minimum Euclidean distance is available in the field of social network. Second, the European distance is used to measure the similarity between provinces, and then, SNA is used to construct the spatial correlation network. Finally, the similarity of growth trend of provincial patent applications in different years from a nonlinear perspective is described, so as to calculate the potential provincial linkage strength. The results demonstrate that low-carbon technology innovation in China takes the form of a network structure. The network exhibits significant characteristics of regional agglomeration, and several central provinces exert the role of mediation and transmission, such as Tianjin, Hubei, and Henan. The study contributes by illustrating the current situation of green technology innovation and provides a critical theoretical foundation for the government when formulating future low-carbon economy development policies. [ABSTRACT FROM AUTHOR]

Livestock and poultry manure is usually recycled to the nearby cropland as it is rich in elements required for crop growth. It is necessary to measure the pollution risk caused by recycled manure. In this study, a risk assessment model based 2-tuple linguistic is constructed to evaluate the pollution risk. Firstly, based on pollution source factors and migration factors, the index system is established, which includes nominal indexes and quantitative indexes. The 2-tuple linguistic method converts two attribute indexes into one form. Meanwhile, the firework algorithm is adopted to make the weights of indicators objective. Then, livestock and poultry farms in Xinzhou District, Wuhan City, Hubei Province, are taken as the empirical objects to get the spatial distribution of farms' risk. Through the index ablation experiment and sensitivity analysis, it is found that the soil resistance factor, manure removal method and the river channel distance are the main influencing indexes. Targeted improvement of these three influencing indicators can reduce the pollution risk by manure. [ABSTRACT FROM AUTHOR]

Wetlands are one of the most critical components of an ecosystem, supporting many ecological niches and a rich diversity of flora and fauna. The ecological significance of these sites makes it imperative to study the changes in their inundation extent and propose necessary measures for their conservation. This study analyzes all 64 Ramsar sites in China based on their inundation patterns using Landsat imagery from 1991 to 2020. Annual composites were generated using the short-wave infrared thresholding technique from June to September to create inundation maps. The analysis was carried out on each Ramsar site individually to account for its typical behavior due to regional geographical and climatic conditions. The results of the inundation analysis for each site were subjected to the Mann–Kendall test to determine their trends. The analysis showed that 8 sites exhibited a significantly decreasing trend, while 14 sites displayed a significantly increasing trend. The accuracy of the analysis ranged from a minimum of 72.0% for Hubei Wang Lake to a maximum of 98.0% for Zhangye Heihe Wetland National Nature Reserve. The average overall accuracy of the sites was found to be 90.0%. The findings emphasize the necessity for conservation strategies and policies for Ramsar sites. [ABSTRACT FROM AUTHOR]

This study aimed to examine the relationship between the adoption of soil nutrient management practices and climate resilience. We hypothesized that adopting soil testing and formulated fertilization would positively impact farmers' ability to absorb and recover from climate-related shocks and stresses. We employed three economic indicators as proxies for climate resilience: net agricultural income, rice yield, and rice productivity. Data came from a random sample of 753 rice farmers in Hubei, China. We undertook a two-step analysis approach. First, we estimated a Probit model to predict the discrete choice variable of adopting soil testing and formulated fertilization and then used ordinary least squares regression models to examine the effect of soil nutrient management and other control variables on climate resilience indicators. Second, we performed endogenous switching regression to address the potential selection bias in the choice of adopting soil nutrient practices. Our results showed that younger, those with higher levels of education and large-scale farmers were more likely to adopt the soil testing and formulated fertilization. Our study provided evidence that the adoption of soil testing and formulated fertilization significantly enhanced the climate resilience of rice producers in central China. The adoption of soil nutrient management technologies increased net agricultural income, rice yield, and productivity, with estimated causal effects of 5.63% (p < 0.01), 4.44% (p < 0.01), and 2.84% (p < 0.05), respectively. Our findings provide further insight into the role and importance of soil nutrient management in promoting climate resilience in China. [ABSTRACT FROM AUTHOR]

Based on the DEM (digital elevation model) data and land use data from 2000, 2005, 2010, 2015 and 2020, the spatiotemporal differentiation characteristics of habitat quality in Hubei Province were evaluated by the InVEST model (Integrated Valuation of Ecosystem Services and Tradeoffs) and GIS (Geographic Information System) spatial analysis method, and the topographic gradient effect on habitat quality was analyzed by regression analysis and terrain index. The spatial distribution of habitat quality in Hubei Province was quite different, and the overall level was high. High-quality habitats were mainly located in hilly regions and water areas, while low-level habitats are mainly distributed in agricultural areas and urban areas. From 2000 to 2020, the high-level habitats in Hubei Province tended to degenerate to low-level habitats, and the average habitat quality decreased slightly. The habitat quality in most areas of Hubei Province had no obvious change from 2000 to 2020, and the overall spatial features were "rising in the southeast and decreasing in the middle". With the elevation of the terrain grade, the number of county-level units with high-level habitat quality increased gradually, and the average habitat quality increased accordingly. From 2000 to 2020, the habitat quality of the first-class terrain improved, while the habitat quality of the other class terrain showed a downward trend. The results are helpful to discover the spatiotemporal change process of habitat quality in Hubei Province, and provide theoretical support for biodiversity conservation and land use planning in Hubei Province. [ABSTRACT FROM AUTHOR]

With growing regional economic integration, transportation systems have become critical to regional development and economic vitality but vulnerable to disasters. However, the regional economic ripple effect of a disaster is difficult to quantify accurately, especially considering the cumulated influence of traffic disruptions. This study explored integrating transportation system analysis with economic modeling to capture the regional economic ripple effect. A state-of-the-art spatial computable general equilibrium model is leveraged to simulate the operation of the economic system, and the marginal rate of transport cost is introduced to reflect traffic network damage post-disaster. The model is applied to the 50-year return period flood in 2020 in Hubei Province, China. The results show the following. First, when traffic disruption costs are considered, the total output loss of non-affected areas is 1.81 times than before, and non-negligible losses reach relatively remote zones of the country, such as the Northwest Comprehensive Economic Zone (36% of total ripple effects). Second, traffic disruptions have a significant hindering effect on regional trade activities, especially in the regional intermediate input—about three times more than before. The industries most sensitive to traffic disruptions were transportation, storage, and postal service (5 times), and processing and assembly manufacturing (4.4 times). Third, the longer the distance, the stronger traffic disruptions' impact on interregional intermediate inputs. Thus, increasing investment in transportation infrastructure significantly contributes to mitigating disaster ripple effects and accelerating the process of industrial recovery in affected areas. [ABSTRACT FROM AUTHOR]

Existing studies have explored the potential contributions of the Internet and smartphone use to agricultural production and environmental governance. However, there is a lack of in-depth exploration of the internal mechanism of smartphone use influencing farmers' decision-making. Based on survey data of farmers in the Hubei and Henan provinces, this study uses the endogenous switching probit model to empirically test the impact of smartphone use on farmers' willingness to participate in the centralized treatment of rural domestic sewage (WTP-RDSCT). The results of the study showed that 63.71% of the samples used smartphones, and 73.62% of the sampled farmers were willing to participate in rural domestic sewage-centralized treatment. Smartphone use can increase WTP-RDSCT by an average of 29.6%, and there is a positive correlation between use intensity and WTP-RDSCT. In addition to the direct impact, smartphone use also indirectly affects WTP-RDSCT through the intermediary effects of information search costs, environmental awareness and social capital. In addition, the information presentation form (video and text) has a significant positive impact on WTP-RDSCT, and the impact of the presentation form of video is greater than that of text. [ABSTRACT FROM AUTHOR]

Architectural heritage is a testament to human and natural development, and the process of human social development can be glimpsed through the study and exploration of heritage. However, in the long history of human social development, architectural heritage is vanishing, and protecting and restoring such heritage is a pressing issue in contemporary society. This study applies the evidence-based theory of medicine to the virtual restoration practice of architectural heritage, which focuses more on scientific data-driven research and decision-making than does traditional restoration. Combined with the practice of evidence-based medicine, the stages of digital conservation of architectural heritage for virtual restoration based on evidence-based design are investigated, forming a comprehensive knowledge system consisting of clear objectives, evidence-based research, evidence assessment, virtual restoration-guided practice, and post feedback. In addition, it is emphasized that the restoration of architectural heritage should be founded on the outcomes obtained through evidence-based practice that have been translated into evidence, in turn creating a rigorous evidence-based system with high-frequency feedback. The final illustration of the procedure is the Bagong House in Wuhan, Hubei Province, China. The examination of this practice line provides a scientific, humanistic, and practicable theoretical framework for the restoration of architectural heritage and fresh ideas for the restoration of other cultural assets, which have significant practical application value. [ABSTRACT FROM AUTHOR]

Water chestnut is a characteristic aquatic vegetable in China, and its demand for peeling fresh fruit is increasing rapidly. Aiming at the existing problems of high labor intensity and low efficiency of manual peeling, a combined water chestnut peeling machine was designed, which used a rotary knife to remove bud and root, and a differential friction belts to remove side peel. The performance of the peeling machine was tested with water chestnut from Xiaogan, Hubei Provence. Under the conditions of 200 g feeding mass and 10 r/min rotation speed, the single factor test was carried out with cutting speed as the influencing factor and the cutting rate of bud and root as the evaluation index. The results showed that the cutting rate of fresh fruit of water chestnut bud and root were 79.04% and 83.77% respectively when the cutting speed of rotary knife was 1.2 m/s. In the differential friction belts, high and low linear velocities were taken as the influencing factors, and the side peel removal rate was used as the evaluation index. The side peel removal rate was 84.93% at the high-speed linear velocity of 2.1 m/s and the low-speed linear velocity of 1.58 m/s. The performance of the whole machine was evaluated, and the results showed that the working loss of the combined water chestnut peeling machine was 43.03% and the comprehensive peeling rate was 77.43%, which reached the design requirements. This study can provide a reference for the research and development of water chestnut peeling device. [ABSTRACT FROM AUTHOR]

On 11th Jan 2020, the first COVID-19 related death was confirmed in Wuhan, Hubei. The Chinese government responded to the outbreak with a lockdown that impacted most residents of Hubei province and lasted for almost three months. At the time, the lockdown was the strictest both within China and worldwide. Using an interactive web-based experiment conducted half a year after the lockdown with participants from 11 Chinese provinces, we investigate the behavioral effects of this 'shock' event experienced by the population of Hubei. We find that both one's place of residence and the strictness of lockdown measures in their province are robust predictors of individual social distancing behavior. Further, we observe that informational messages are effective at increasing compliance with social distancing throughout China, whereas fines for noncompliance work better within Hubei province relative to the rest of the country. We also report that residents of Hubei increase their propensity to social distance when exposed to social environments characterized by the presence of a superspreader, while the effect is not present outside of the province. Our results appear to be specific to the context of COVID-19 and are not explained by general differences in risk attitudes and social preferences. [ABSTRACT FROM AUTHOR]

Background: Residue-based biogas is considered as a renewable energy that should be used to improve energy security and household livelihoods in rural areas. Observational learning and word-of-mouth learning are critical in the dissemination of knowledge about agricultural technologies. Yet, scholars have little understanding of the impacts of these learning methods on farmers' use of residue-based biogas. Using survey data from rural areas of Hubei China, this study estimates the impacts of observational learning and word-of-mouth learning from different subjects (i.e., relatives, neighbors, cadres, cooperative members, and technical instructors) on the use of residue-based biogas by farmers. Additionally, the moderating role of interpersonal trust in these relationships is explored. Results: Results from logistic regression models show that observational learning from technical instructors significantly increases farmers' use of biogas. Furthermore, interpersonal trust significantly and positively influences the impact of observational learning on farmers' decisions to use biogas. Similarly, interpersonal trust significantly and positively moderates the influence of positive word-of-mouth learning on farmers' decision to use biogas. In contrast, a negative moderating role exists in the relationship between negative word-of-mouth learning and farmers' decision to use biogas. These impacts are further affirmed by robustness checks. Conclusions: The results presented here show that enhancing farmers' interpersonal trust promotes the use of residue-based biogas by farmers. One important implication is that the government might promote the use of residue-based biogas by organizing technology demonstration activities, providing communication platforms, and enhancing mutual trust between farmers and relevant groups. [ABSTRACT FROM AUTHOR]

Anopheles sinensis is the main vector of malaria with a wide distribution in China and its adjacent countries. The smoke from burning dried mugwort leaves has been commonly used to repel and kill mosquito adults especially in southern Chinese provinces. In this study, the essential oils of mugwort leaves collected from seven provinces in China were extracted by steam distillation and their chemical compositions were analyzed. Among a total of 56–87 chemical constituents confirmed by GC–MS analyses, four compounds, eucalyptol, β-caryophyllene, phytol and caryophyllene oxide, were identified with appearances from all seven distilled essential oils. The effectiveness varied in larvicidal, fumigant and repellent activities against An. sinensis from these seven essential oils with different geographic origins. The essential oil from Hubei province showed the highest larvicidal activity against the 4th instar larvae of An. sinensis, with a median lethal concentration at 40.23 µg/mL. For fumigation toxicity, essential oils from 4 provinces (Gansu, Shandong, Sichuan and Henan) were observed with less than 10 min in knockdown time. The essential oil distilled from Gansu province displayed the highest repellent activity against Anopheles mosquitoes and provided similar level of protection as observed from DEET. Eucalyptol was the most toxic fumigant compound and phytol showed the strongest larvicidal activity among all tested mugwort essential oil constituents. [ABSTRACT FROM AUTHOR]

To analyze the spatio-temporal aggregation of COVID-19 in mainland China within 20 days after the closure of Wuhan city, and provide a theoretical basis for formulating scientific prevention measures in similar major public health events in the future. Draw a distribution map of the cumulative number of COVID-19 by inverse distance weighted interpolation; analyze the spatio-temporal characteristics of the daily number of COVID-19 in mainland China by spatio-temporal autocorrelation analysis; use the spatio-temporal scanning statistics to detect the spatio-temporal clustering area of the daily number of new diagnosed cases. The cumulative number of diagnosed cases obeyed the characteristics of geographical proximity and network proximity to Hubei. Hubei and its neighboring provinces were most affected, and the impact in the eastern China was more dramatic than the impact in the western; the global spatio-temporal Moran's I index showed an overall downward trend. Since the 10th day of the closure of Wuhan, the epidemic in China had been under effective control, and more provinces had shifted into low-incidence areas. The number of new diagnosed cases had gradually decreased, showing a random distribution in time and space (P< 0.1), and no clusters were formed. Conclusion: the spread of COVID-19 had obvious spatial-temporal aggregation. China's experience shows that isolation city strategy can greatly contain the spread of the COVID-19 epidemic. [ABSTRACT FROM AUTHOR]

Trends in aquatic food consumption were matched against farm production surveys within Hubei province and compared to official production data and statistics. Surveys showed that consumer tastes were changing to a much broader aquatic food menu as their spending power increased. Traditional aquaculture species were becoming less profitable due to reduced profit margins as input costs increased and consumption preferences changed. Consequently, many producers were diversifying their production to meet local demand. Some farmers were also de-intensifying by reducing commercial aquafeed inputs and reverting to more traditional methods of dyke-crop culture to optimise trade-offs between input costs and labour, and manage their risk more effectively. In addition, analysis of local data showed that wholesale changes were occurring to aquaculture production as environmental protection legislation took effect which reduced the growing area for carps considerably. [ABSTRACT FROM AUTHOR]

Background: Few specific medications have been proven effective for the treatment of patients with severe coronavirus disease 2019 (COVID-19). Here, we tested whether high-dose vitamin C infusion was effective for severe COVID-19. Methods: This randomized, controlled, clinical trial was performed at 3 hospitals in Hubei, China. Patients with confirmed severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection in the ICU were randomly assigned in as 1:1 ratio to either the high-dose intravenous vitamin C (HDIVC) or the placebo. HDIVC group received 12 g of vitamin C/50 ml every 12 h for 7 days at a rate of 12 ml/hour, and the placebo group received bacteriostatic water for injection in the same way within 48 h of arrival to ICU. The primary outcome was invasive mechanical ventilation-free days in 28 days (IMVFD28). Secondary outcomes were 28-day mortality, organ failure (Sequential Organ Failure Assessment (SOFA) score), and inflammation progression (interleukin-6). Results: Only 56 critical COVID-19 patients were ultimately recruited due to the early control of the outbreak. There was no difference in IMVFD28 between two groups (26.0 [9.0–28.0] in HDIVC vs 22.0 [8.50–28.0] in control, p = 0.57). HDIVC failed to reduce 28-day mortality (P = 0.27). During the 7-day treatment period, patients in the HDIVC group had a steady rise in the PaO2/FiO2 (day 7: 229 vs. 151 mmHg, 95% CI 33 to 122, P = 0.01), which was not observed in the control group. IL-6 in the HDIVC group was lower than that in the control group (19.42 vs. 158.00; 95% CI -301.72 to -29.79; P = 0.04) on day 7. Conclusion: This pilot trial showed that HDIVC failed to improve IMVFD28, but might show a potential signal of benefit in oxygenation for critically ill patients with COVID-19 improving PaO2/FiO2 even though. [ABSTRACT FROM AUTHOR]

Aim: Some patients with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) infection rapidly develop to critical condition. Here, we investigated the clinical features of critically ill SARS-CoV-2 patients with and without diabetes and identified risk factors for death of these patients.Methods: The medical records including epidemiological, demographic, clinical, and laboratory data from 49 critically ill SARS-CoV-2 patients were collected and analyzed in Huanggang City and Xiaogan City, Hubei Province, outside Wuhan.Results: Sixty-seven percent (33) of patients survived and 33% (16) of patients died in 49 critically ill patients (32 men, 17 women), with a median age of 63 years (IQR 53-73). Univariate analyses indicated that the deceased patients were more often associated with two or more comorbidities, one or more gastrointestinal symptoms, high neutrophil percentage, low lymphocytes and lymphocyte percentage, high C-reactive protein, high procalcitonin, high fasting blood glucose (FBG), and high lactate dehydrogenase (LDH) compared with the survivors; moreover, the patients with T2DM had the higher neutrophil percentage, the lower lymphocyte percentage, and the higher levels of FBG and LDH compared with the patients without T2DM. Multivariable logistic regression analyses indicated that gastrointestinal symptoms (≥ 1 symptoms), decreased lymphocytes (< 1.1 × 109/L), and increased FBG (≥ 7.0 mmol/L) were the independent risk factors for death of critically ill patients.Conclusions: Critically ill COVID patients with T2DM had more severe damages of the lymphocytes, islet cells, and heart function, and gastrointestinal symptoms, lymphopenia, and increased FBG may be early predictors for poor prognosis.Supplementary Information: The online version contains supplementary material available at 10.1007/s13410-020-00888-3. [ABSTRACT FROM AUTHOR]

In this study, the cluster analysis (CA), probabilistic methods, and artificial neural networks (ANNs) are used to predict landslide susceptibility. The Geographic Information System (GIS) is used as the basic tool for spatial data management. CA is applied to select non-landslide dataset for later analysis. A probabilistic method is suggested to calculate the rating of the relative importance of each class belonging to each conditional factor. ANN is applied to calculate the weight (i.e., relative importance) of each factor. Using the ratings and the weights, it is proposed to calculate the landslide susceptibility index (LSI) for each pixel in the study area. The obtained LSI values can then be used to construct the landslide susceptibility map. The aforementioned proposed method was applied to the Longfeng town, a landslide-prone area in Hubei province, China. The following eight conditional factors were selected: lithology, slope angle, distance to stream/reservoir, distance to road, stream power index (SPI), altitude, curvature, and slope aspect. To assess the conditional factor effects, the weights were calculated for four cases, using 8 factors, 6 factors, 5 factors, and 4 factors, respectively. Then, the results of the landslide susceptibility analysis for these four cases, with and without weighting, were obtained. To validate the process, the receiver operating characteristics (ROC) curve and the area under the curve (AUC) were applied. In addition, the results were compared with the existing landslide locations. The validation results showed good agreement between the existing landslides and the computed susceptibility maps. The results with weighting were found to be better than that without weighting. The best accuracy was obtained for the case with 5 conditional factors with weighting. [ABSTRACT FROM AUTHOR]

In December 2019, some cases of viral pneumonia were epidemiologically related to a new coronavirus in the province of Hubei, China. Subsequently, there has been an increase in infections attributable to this virus throughout China and worldwide. The World Health Organization (WHO) has officially named the infection coronavirus disease 2019 (COVID-19), and the virus has been classified as severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). This appears to be a virus from Rhinolophus bats, but the intermediate host has not yet been identified. The mechanism of infection of SARS-CoV-2 is not yet known; it appears to have affinity for cells located in the lower airways, where it replicates. The interhuman transmission of coronaviruses mainly occurs through saliva droplets and direct and indirect contact via surfaces. As of March 10, 2020, the number of cases worldwide was 113,702. Along with severe acute respiratory syndrome (SARS) and Middle Eastern respiratory syndrome (MERS), COVID-19 appears to cause a severe clinical picture in humans, ranging from mild malaise to death by sepsis/acute respiratory distress syndrome. The prognosis is worse in elderly patients with comorbidities. To date, there is no specific therapy for COVID-19. Prevention of SARS-CoV-2 infection implies strategies that limit the spread of the virus. WHO and other international and national bodies have developed continuously updated strategic objectives and provisions to contain the spread of the virus and infection. [ABSTRACT FROM AUTHOR]

The timing and pattern of biotic recovery from the Permo-Triassic Mass Extinction remains elusive. Here we report new material of the Early Triassic sauropterygian Lariosaurus sanxiaensis and associated fauna from the Jialingjiang Formation in Hubei Province, South China. Phylogenetic analysis based on a novel data matrix of sauropterygians recognizes L. sanxiaensis as a basal nothosaur. Stratigraphic congruence analysis shows that the new phylogenetic consensus tree matches to the stratigraphic distribution of sauropterygians very well. The diversified reptilian fauna and inferred simple food web in the Nanzhang-Yuan'an fauna where L. sanxiaensis was discovered suggest that the Triassic biotic recovery adopted a top-down pattern, in contrast to the prevailing view. Comparison with the Middle Triassic Luoping biota from the same carbonate platform suggests that the Triassic biotic recovery is delayed and healthy ecosystems were not established until the Middle Triassic in South China. Qiang Li & Jun Liu present new fossils from the Early Triassic sauropterygian Lariosaurus sanxiaensis and associated reptile fauna recovered from South China. Their phylogenetic analysis places L. sanxiaensis as a basal nothosaur. [ABSTRACT FROM AUTHOR]

With significant climate warming, droughts over Southwest (SW) China are drawing increasing interest. By using the Mann-Kendall trend test, the continuous wavelet transform (CWT), the rotated empirical orthogonal function (REOF), and the random forest method (RF), this study focused on the trends and spatial variability in wetness/dryness as well as further investigated the potential factors by analyzing the behaviors of precipitation, temperature, and teleconnection indices in SW China. Results show that there are a slight drying trend and a remarkable spatial variation in the patterns of wetness/dryness. Due to the decreasing precipitation and increasing temperature, the drying trend (especially for severe and extreme droughts) significantly increases in the Yunnan-Guizhou Plateau and the Chongqing-Hubei region in the past decades. Additionally, the droughts in this region may also be directly or indirectly affected by the complex karst topographic and hydrological process. The variable importance analysis between climate index and standardized precipitation evapotranspiration index (SPEI) suggests that Atlantic Oscillation (AO) and Sunspots (SS) are the two most important influencing factors for the variations in dryness/wetness over SW China. Particularly, SS (AO) can be the largest influencing factor for the Yunnan-Guizhou Plateau and the Chongqing-Hubei region (Northern and Central Tibet). Besides, El Niño-Southern Oscillation (ENSO), North Atlantic Oscillation (NAO), Indian Ocean Dipole (IOD), and Pacific Decadal Oscillation (PDO) indices also show an impact on the variations in dryness/wetness, but their contributions are different in different regions. The results of this study provide certain references for mitigating the potential widespread impact of drought hazards in SW China. [ABSTRACT FROM AUTHOR]

Reference evapotranspiration (ET0) plays a critical role in irrigation planning and is also important for hydrological cycle, environmental, and other studies. Thus, this research examined the trends in ET0 on seasonal and annual timescales in Hubei Province, China. ET0 was estimated using the Penman Monteith method (P-M) at 16 meteorological stations located in different areas of Hubei Province during the period 1960-2014. The trends in seasonal and annual ET0 were investigated using the Mann-Kendall test and Sen's slope estimator. The periodicities of ET0 in different regions were investigated using wavelet analysis. The major meteorological factors affecting ET0 were investigated using partial correlation analysis and the contribution rate method. The results showed, on a seasonal timescale, that in spring, ET0 increased in all geographic zones. In summer, ET0 decreased in all geographic zones. In autumn and winter, ET0 displayed no significant changes in any of the geographic zones. On an annual timescale, ET0 decreased in all geographic zones, and the magnitudes of the negative trend in annual ET0 were 2.58-10.04 mm 10a−1. In the five geographic zones, the periodic characteristics of ET0 were identical; the significant wavelet power spectra of ET0 had 3-7, 13-17, and 24-32-year modulations in variation. Among the meteorological factors, sunshine hours were the major climate element that influenced the variability in ET0. The results will provide important references for scientific planning for agriculture, water resource allocation, and water-saving irrigation. [ABSTRACT FROM AUTHOR]

The Tonglvshan deposit is the largest Cu-Fe (Au) skarn deposit in the Edong district, which is located in the westernmost part of the Middle and Lower Yangtze River metallogenic belt, China. In this study, we performed a detailed in situ analysis of major and trace elements in amphiboles from the ore-related Tonglvshan quartz monzodiorite porphyry using electron microprobe (EMPA) analysis and laser ablation inductively coupled plasma mass spectrometry (LA-ICP-MS). Two distinct populations of amphiboles, which can be distinguished by their aluminum content, are found in the quartz monzodiorite porphyry. The low-aluminum (Low-Al) amphiboles are subhedral or anhedral and formed at 46.3-73.5 MPa and 713-763 °C. In contrast, the high-aluminum (High-Al) amphiboles are euhedral and formed at 88-165 MPa and 778-854 °C. Some euhedral amphiboles are partially or completely replaced by Low-Al amphibole. The compositions of parental melts in equilibrium with the High-Al amphibole ( Melt 1) and Low-Al amphibole ( Melt 2) were computed by applying solid/liquid partition coefficients. This modeling shows that magma in equilibrium with High-Al amphibole ( Melt 1) underwent 40% fractional crystallization of amphibole, plagioclase and apatite at a depth of ~5 km to evolve to magma in equilibrium with Low-Al amphibole ( Melt 2). Copper enrichment occurred in the magma after undergoing fractional crystallization. The magma had a high oxygen fugacity, increasing from NNO + 1 ( Melt 1) through NNO + 2 to HM ( Melt 2), which could have prevented the loss of Cu (and possibly Au) to sulfide minerals during crystallization. Finally, the evolved magma intruded to shallower depths, where it presumably exsolved aqueous ore-forming fluids. Therefore, the large Cu-Fe-Au reserves of the Tonglvshan deposit can likely be attributed to a combination of controlling factors, including high oxygen fugacity, fractional crystallization, fluid exsolution, and a shallow emplacement depth. [ABSTRACT FROM AUTHOR]

Meteorological conditions during ice accretion on the 500 kV high voltage transmission lines and test cables are presented, together with a calculation of liquid water content (LWC). The data include meteorological observations and real-time ice accretion on the transmission lines of the central China power grid, from 2008 to 2009 in Hubei Province. Also included are observations of ice thickness, microphysics of fog droplets, and other relevant data from a nearby automated weather station at Enshi radar station, from January to March 2009. Results show that temperature at Zhangen tower #307 was correlated with the temperature at Enshi radar station. The temperature on the surface of the high voltage transmission line was 2-4°C higher than ambient air temperature, although the temperatures were positively correlated. Ice formation temperature was about −2°C and ice shedding temperature was about −2 to −1°C on the high voltage transmission line, both of which were lower than the temperature threshold values on the test cable. Ice thickness was significantly affected by temperature variation when the ice was thin. The calculated LWC was correlated with observed LWC, although the calculated value was greater. [ABSTRACT FROM AUTHOR]

Soil samples were collected at an altitude of 500, 1,060, 1,500, 1,950, 2,400 and 3,100 m, respectively, from Shennongjia, a forest reserve in Hubei province (central China). Their corresponding pHs were 5.50, 4.91, 5.64, 5.28, 5.49 and 4.60. By using a plant trap method, a total of 25 soybean rhizobia were isolated from the soil above an altitude of 1,500 m and all identified to be Sinorhizobium fredii. Their genetic biodiversity was characterized by 16S–23S rDNA internally transcribed spacer (ITS) region polymerase chain reaction-restriction fragment length polymorphism (PCR-RFLP) and random amplification DNA (RAPD) analysis. All the tested strains produced a 2.1 kb 16S–23S rDNA ITS fragment. After digestion with three restriction endonucleases ( HaeIII, MspI and CfoI), respectively, great variations in 16S–23S rDNA ITS PCR-RFLP patterns were observed. The tested strains could be differentiated into 11 ITS genotypes. The genotypes of rhizobia were not related to geographical location. Twelve primers were applied to RAPD analysis and a dendrogram was obtained, showing that all the strains (including reference strain S. fredii USDA205) were divided into two diverging groups. Moreover, each group could be further divided into two subgroups. Both RAPD and 16S–23S rDNA ITS PCR-RFLP analysis indicated that a high degree of genetic diversity existed among S. fredii strains isolated from Shennongjia virgin soils. Since Shennongjia is an unexploited forest region in central China and the gene centre of soybean is located in China, the symbiotic genes harboured by these strains may be of great importance and the rich diversity of these strains might contribute to the adaptation of soybean to an alpine environment. [ABSTRACT FROM AUTHOR]

Irrigated agriculture has important implications for achieving the United Nations Sustainable Development Goals. However, there is a lack of systematic and quantitative analyses of its impacts on food–energy–water–CO2 nexus. Here we studied impacts of irrigated agriculture on food–energy–water–CO2 nexus across food sending systems (the North China Plain (NCP)), food receiving systems (the rest of China) and spillover systems (Hubei Province, affected by interactions between sending and receiving systems), using life cycle assessment, model scenarios, and the framework of metacoupling (socioeconomic-environmental interactions within and across borders). Results indicated that food supply from the NCP promoted food sustainability in the rest of China, but the NCP consumed over four times more water than its total annual renewable water, with large variations in food–energy–water–CO2 nexus across counties. Although Hubei Province was seldom directly involved in the food trade, it experienced substantial losses in water and land due to the construction of the South-to-North Water Transfer Project which aims to alleviate water shortages in the NCP. This study suggests the need to understand impacts of agriculture on food–energy–water–CO2 nexus in other parts of the world to achieve global sustainability. Local human activities can lead to cross-border environmental impacts through the food–energy–water–CO2 nexus. Here, the authors report wide variations in environmental impacts of irrigated agriculture across counties within the North China Plain under different environmental and socioeconomic scenarios. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19) was first identified in late 2019 in Wuhan, Hubei Province, China and spread globally in months, sparking worldwide concern. However, it is unclear whether super-spreading events occurred during the early outbreak phase, as has been observed for other emerging viruses. Here, we analyse 208 publicly available SARS-CoV-2 genome sequences collected during the early outbreak phase. We combine phylogenetic analysis with Bayesian inference under an epidemiological model to trace person-to-person transmission. The dispersion parameter of the offspring distribution in the inferred transmission chain was estimated to be 0.23 (95% CI: 0.13–0.38), indicating there are individuals who directly infected a disproportionately large number of people. Our results showed that super-spreading events played an important role in the early stage of the COVID-19 outbreak. Although SARS-CoV-2 has spread rapidly, the contribution of super-spreading events to transmission is unclear. Here, the authors show that the number of secondary infections arising from an individual infection in the early phase of the outbreak was highly skewed, indicating that super-spreading events occurred. [ABSTRACT FROM AUTHOR]