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The 2019 outbreak of coronavirus disease (COVID‐19) in Wuhan (Hubei province of China) has given rise to a pandemic spread of virus, more than 240 million incidences and a death toll larger than 5 million people. COVID‐19 has set off large efforts in research, therapy and patient care, as well as public and private debates in every imaginable form. A number of scientists used the publication platforms provided by the Federation of the European Biochemical Societies (FEBS) to present their research data, reviews, opinions and other contributions relating to COVID‐19 and severe acute respiratory syndrome coronavirus 2 (SARS‐CoV‐2). Here, I highlight the recent COVID‐19 papers which have been published and collected in a Virtual Issue in FEBS Letters, and discuss their implications towards understanding the molecular, biochemical and cellular mechanisms of SARS‐CoV‐2 infections, vaccine development and antiviral discovery strategies. [ABSTRACT FROM AUTHOR]

The paper industry is a traditional industry with extensive consumption of resources and energy and more pollutant discharge. This industry is closely related to production and life activities. Under the general requirement of "to step up conservation of the Yangtze River and stop its over development", the paper industry in the Yangtze River Economic Belt should urgently undergo green transformation and development. Based on the undesirable slack-based measure model, spatial gravity center, Kernel density function, Theil index, and panel model, this study examines the green development efficiency and its influencing factors of the paper industry in the Yangtze River Economic Belt from 2001 to 2016. Results showed the following: (1) from 2001 to 2016, the green development efficiency of the paper industry in the Yangtze River Economic Belt had been steadily improved, but the overall efficiency value is not high, which has a great potential for improvement. The green development efficiency of the paper industry in the Yangtze River Economic Belt presents the regional differentiation law of gradient increasing in the upper, middle, and lower reaches. (2) According to the gravity center trajectory, the efficiency center of the green development of the paper industry in the Yangtze River Economic Belt is located in the middle reaches of Hunan Province and Hubei Province and moves to the southwest. This case indicates that the green development efficiency of the paper industry in provinces and cities in the southwest has been improved. (3) The regional differences in the green development efficiency of the paper industry in the Yangtze River Economic Belt show the characteristics of narrowing fluctuations. The regional differences are the primary sources of the difference in the green development efficiency of the paper industry. (4) The enterprise scale, science and technology investment, and industrial structure have significant positive effects on the green development efficiency of the paper industry in the Yangtze River Economic Belt. On the contrary, environmental regulation and foreign investment intensity have significant negative effects. However, the above factors have different effects on the green development efficiency of the paper industry in the upper, middle, and lower reaches of the Yangtze River Economic Belt. [ABSTRACT FROM AUTHOR]

The existence of structural change features can lead to the failure of traditional econometric methods. This research uses the Bai-Perron test to diagnose the structural change point of the price series in China's pilot carbon market. It uses an impulse response function to analyze the interaction between carbon prices and energy prices, stock price indices, power industry index, and similar asset prices. The results show structural change points in all five carbon markets during the operation period, and the timing of these structural change points relates to an economic situation and compliance period. In addition, the impact mechanisms of the stock price index, similar asset price, and power industry index on the price of China's pilot carbon market change significantly before and after the structural change points, but the impact of energy price does not change except for the Hubei carbon market. [ABSTRACT FROM AUTHOR]

The study of a Late Devonian arborescent lycopsid, Sublepidodendron songziense Chen 1977, from the Hsiehchingssu (Xiejingsi) Formation of Hubei Province and the Wutung (Wutong) Formation of Anhui Province, PR China, provides new insight into its holistic architecture and taxonomic affinity. The study allows several interrelated organ species to be integrated into a conceptual whole-plant species, Sublepidodendron songziense Chen emend. Wang Q., Hao, Wang D.-M., Wang Y. et Thomas Denk. The plant consists of a monocaulous trunk growing from a stigmarian rhizomorph, the trunk bearing biseriate, sub-opposite to possibly alternate lateral branches that expand by means of isotomous to slightly anisotomous dichotomies, forming an excurrent canopy. Lateral branches bear terminal Lepidostrobus-like strobili. These strobili are dimorphic, implying separate mega- and microsporangiate strobili considered with the associated organ assemblage, including Triletes-type megaspores, Lepidocarpopsis-like sporophylls, and Lepidophloios-like cauline anatomy. Moreover, lateral branches are caducous, and ulodendroid scars mark the attachment sites of former branches onto the trunk. The plant diagnostically possesses spirally arranged, small, vertically elongated leaf cushions, which have false leaf scars. The presence of ligule cannot be excluded definitely. The characteristics of leaf cushions show considerable variation corresponding to different taphonomic processes, preservational modes and ontogenetic stages so that this plant megafossil has been classified in various ways previously. Based on organic connection and cauline anatomy, a reconstruction of the whole-plant species is tentatively given. We compare this whole-plant species to the type species S. mirabile (Nathorst) Hirmer, and revise Sublepidodendron (Nathorst) Hirmer 1927, suggesting that the genus and its own family Sublepidodendraceae sensu Kra¨usel et Weyland 1949 should be separated from the order Protolepidodendrales sensu Pichi-Sermolli 1958 and referred to the order Isoe¨tales sensu lato and the suborder Dichostrobiles sensu DiMichele et Bateman 1996. Present Ulodendron specimens are the earliest record in the Late Palaeozoic strata, demonstrating that the pseudomonopodial branching architectural form in the arboresent lycopsids had evolved by the Late Devonian (Famennian). Such a woody trunk together with lateral branches (or cauline peduncles), separate mega- and microsporangiate strobili, and rootlet-bearing rhizomorph epitomize the major structural units (or architectural modules) of a true arborescent lycopsid, and S. songziense appears to be the oldest arborescent lycopsid showing an apparently centralized rhizomorph. We suggest that phylogenetically more advanced arborescent lycopsids did not precipitately occur in the Carboniferous, rather they had already evolved by the Late Devonian (Famennian). The widespread genus Sublepidodendron (Nathorst) Hirmer 1927 emend., especially abundant in China, among the Late Devonian–Early Carboniferous floras may represent one of the progenitors of the phylogenetically advanced arborescent lycopsids. [Copyright &y& Elsevier]

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Global climate change, driven by increasing carbon emissions, has posed a significant challenge to both human survival and development, becoming a major issue for the sustainable progress of human society. This paper examines the characteristics and differences in the spatio-temporal distribution of carbon emissions and sinks across Wuhan Region that is stratified into three spatial levels: Hubei Province, the Wuhan Metropolitan Area, and Wuhan City. On a finer spatial scale within Wuhan, it uncovers the spatial correlations and disparities between carbon emissions and sinks. Accordingly, the paper proposes strategies and suggestions for reducing carbon emissions and enhancing carbon sinks at the provincial, metropolitan, and municipal levels. These findings are valuable for sustainable regional development and construction of low-carbon eco-cities. [ABSTRACT FROM AUTHOR]

Accurately predicting the price of carbon is an effective way of ensuring the stability of the carbon trading market and reducing carbon emissions. Aiming at the non-smooth and non-linear characteristics of carbon price, this paper proposes a novel hybrid prediction model based on improved feature extraction and non-linear integration, which is built on complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), fuzzy entropy (FuzzyEn), improved random forest using particle swarm optimisation (PSORF), extreme learning machine (ELM), long short-term memory (LSTM), non-linear integration based on multiple linear regression (MLR) and random forest (MLRRF), and error correction with the autoregressive integrated moving average model (ARIMA), named CEEMDAN-FuzzyEn-PSORF-ELM-LSTM-MLRRF-ARIMA. Firstly, CEEMDAN is combined with FuzzyEn in the feature selection process to improve extraction efficiency and reliability. Secondly, at the critical prediction stage, PSORF, ELM, and LSTM are selected to predict high, medium, and low complexity sequences, respectively. Thirdly, the reconstructed sequences are assembled by applying MLRRF, which can effectively improve the prediction accuracy and generalisation ability. Finally, error correction is conducted using ARIMA to obtain the final forecasting results, and the Diebold–Mariano test (DM test) is introduced for a comprehensive evaluation of the models. With respect to carbon prices in the pilot regions of Shenzhen and Hubei, the results indicate that the proposed model has higher prediction accuracy and robustness. The main contributions of this paper are the improved feature extraction and the innovative combination of multiple linear regression and random forests into a non-linear integrated framework for carbon price forecasting. However, further optimisation is still a work in progress. [ABSTRACT FROM AUTHOR]

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 Chinese government is making vigorous efforts to control agricultural pollution. The promotion of an ecological planting–breeding mix pattern is one of them. Farmers' mode of production will affect their subjective well-being. Thus, this paper aims to analyze the impact of adopting the ecological planting–breeding mix pattern on farmers' subjective well-being based on 895 pieces of survey data from the provinces of Hubei, Hunan, and Jiangxi in the middle and lower reaches of the Yangtze River. Using the endogenous switching regression model, we find that the adoption of the ecological planting–breeding mix pattern has a significant positive effect on farmers' subjective well-being. Based on this counterfactual hypothesis, if the farmers who actually adopted the ecological planting–breeding mix pattern did not adopt it, their subjective well-being would decrease from 4.006 to 3.669. Further examination indicates that self-worth identification, income increase, and neighborhood communication could be potential mechanisms. Additionally, the effect of the ecological planting–breeding mix pattern on subjective well-being is stronger in the group with low financial support. The technical support provided by the government does not have a significant regulatory effect on the adoption of the ecological planting–breeding mix pattern on subjective well-being. Our results suggest that farmers' ecological production behavior can affect their subjective well-being. This may be relevant to many developing countries today that are attempting to adopt ecological agriculture patterns. [ABSTRACT FROM AUTHOR]

Predicting carbon prices is crucial for the growth of China's carbon trading industry. This paper proposes a residual correction model that considers multiple influencing factors. First, the best historical data and main external factors input by the model are determined by using the partial autocorrelation function and Spearman correlation analysis, and the carbon price forecasting index system is constructed. Second, the whale optimization algorithm (WOA) is utilized to determine the optimal parameters of the extreme gradient boosting (XGBoost), and the WOA‐XGBoost model is built to perform preliminary carbon price forecasts and obtain the residual series. Finally, the carbon price residual series undergoes decomposition into multiple components utilizing the complete ensemble empirical mode decomposition for subsequent forecasting and the aggregation of outcomes. Experiments are conducted to predict two carbon trading markets in Hubei and Guangzhou, and a feature importance analysis is performed. The results indicate that the proposed hybrid model consistently outperforms the comparative models in terms of prediction accuracy. Furthermore, it is revealed that historical carbon prices and European Union carbon prices are the key factors influencing the prediction of carbon market prices. [ABSTRACT FROM AUTHOR]

Using the policy package pilot implemented in Hubei Province, China, in April 2020 as a natural experiment, we use the synthetic control (SC) and synthetic difference in differences (SDID) methods to estimate the impact of the Chinese government's support policy on the economic resilience and to analyze the mechanisms by which it impacts. This study finds that the policy package has contributed to the growth of economic resilience in the pilot provinces, with the policy package increasing the average economic resilience of the pilot provinces by 0.062 compared to their potential resilience. The validity and robustness of the above conclusions are objectively confirmed by multidimensional quantitative outcomes such as placebo tests, ranking tests, and replacements in calculating resilience. The mechanism analysis shows that the investment in real estate development, the stimulus for consumption, and the core industry development are virtual channels for the policy package to promote economic resilience growth in the pilot provinces. Moreover, traditional investment in transportation fixed assets plays a minor role. This paper quantitatively corroborates the academic idea that government governance capacity affects regional economic resilience (RER), and research can provide empirical support for regional economic recovery and policy support under a major crisis. [ABSTRACT FROM AUTHOR]

Aiming at the poor prediction performance of the traditional GM(1,N) model, this paper proposes the GM(1,N) model with expression optimization: firstly, unknown parameters are introduced into the coefficient matrix of the traditional GM(1,N) model to obtain the model expression with unknown parameters: Then the average relative error function with unknown parameters is constructed: Finally, the particle swarm optimization algorithm is used to obtain the parameter column with the smallest average relative error. Taking the carbon emission of the logistics industry in Hubei Province as an example, this paper forecasts the carbon emission by using the traditional GM(1,N) model, GM(1, N) model with background value optimization, and GM(1, N) model with expression optimization. By comparing and analyzing the prediction results of the three models, it is concluded that GM(1,N) model with expression optimization has better prediction performance. [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]

Carbon emission trading scheme (ETS) policy is a quota-based market-motivated environmental regulation policy that by transforming environmental responsibility of enterprises into self-consciousness behavior induces green innovation behavior in enterprises and influences the level of regional green innovation. Based on panel data on green patents in 30 Chinese provinces from 2010 to 2019, the paper uses six Chinese pilot provinces for ETS as the treatment group to investigate the induced effects of Chinese ETS policy on the quantity and quality of green technology innovation using difference in difference model and analyzes the effects of Chinese ETS policy on green technology innovation in different pilot regions using a synthetic control method. The findings indicate that (1) compared with non-pilot areas, the carbon emission trading policies in pilot areas can induce green technology innovation activities. (2) In all pilot regions, ETS policies significantly promoted the quantity and quality of green technology innovation, but the quantity promotion effect was significantly better than the quality promotion effect, and the low-quality promotion effect was better than the high-quality promotion effect. (3) In terms of individual pilot region, ETS policies in Guangdong, Hubei and Tianjin significantly promoted the quantity of green technology innovation and low-quality green technology innovation, but significantly inhibited or suppressed high-quality green technology innovation in a short term. This paper not only enriches the research on environmental equity trading policies and green technology innovation theoretically, but also provides empirical evidence for the effectiveness of carbon emission trading schemes pilots program in China. [ABSTRACT FROM AUTHOR]

The complex interaction between emissions, meteorology, and atmospheric chemistry makes accurate predictions of particulate pollution difficult. Advanced data mining techniques can reveal potential laws, providing new possibilities for understanding the evolution and causes of air pollution. Based on the Granger method and block modeling analysis, this paper explored the intercity spillover effects of hourly PM2.5 in Hubei Province, China, to determine the specific role (i.e., overflow, limited overflow, bilateral, inflow, and limited inflow) of each city on regional pollution formation. Furthermore, a dynamic Apriori algorithm considering time-lag effects was used to mine the spatio-temporal associations of extreme PM2.5 pollution events among different cities. Results suggest that the northern and central cities with high-level PM2.5 concentration in Hubei have a significant spillover effect, whereas the eastern and southern cities generally play a role as the sink of pollutants. Based on the association rules of extreme PM2.5 pollution, four main pollutant transport channels were excavated and well matched with the trajectories extracted by the atmospheric model. This paper provides new insights for exploring the interaction of intercity particulate pollution, which is a supplement and cross-validation of the model results. [ABSTRACT FROM AUTHOR]

Based on Geoscience Laser Altimeter System onboard the Ice, Cloud, and land Elevation Satellite (ICESat/GLAS) data, this paper analyzes the vertical accuracy of four kinds of digital elevation model (DEM)dataset in Hubei Province, and discusses the influence of the geographical factors (slope, aspect, geomorphic and land-use types) on DEM data accuracy. The results show that: (1) Among the four kinds of DEM data in Hubei Province, the ALOS World 3D -30 m (AW3D30) offers the highest data accuracy; (2) High slope and vegetation coverage will affect DEM data accuracy, and AW3D30 data accuracy is the least affected by this; (3) No obvious relationship has been found between data accuracy and aspect. In addition, using ICESat/GLAS data as reference, this paper also attempts to improve the vertical accuracy of DEM by employing five machine learning based methods, the results show that each DEM dataset obviously outperform themselves by using the models selected in this paper. [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]

The growth of rural residential land (RRL) areas has led to the encroachment of cultivated land, which has seriously reduced cultivated land use efficiency (CLUE). This paper takes 42 cities in the middle reaches of the Yangtze River (MRYR) as an example, using the kernel density estimation method, the Super-SBM model, and mediating effect test methods to explore the impact of RRL change on CLUE during 2000–2020. Specifically, based on the analysis of the spatiotemporal distribution characteristics of RRL and CLUE, this paper attempts to further explore the influence path of RRL change on CLUE and test whether there is a mediating effect. The results show that (1) the overall RRL area increased by 30,386.34 hm2, except for the decrease in RRL area in a few regions of Hunan Province, and the RRL area in other regions increased. (2) The hot-spot and sub-hot-spot regions of CLUE in the MRYR were mainly concentrated in northwestern Hubei Province and eastern Hunan Province, and the hot-spot and sub-hot-spot regions in Hunan Province are the highest among the three provinces. (3) Under the control of socioeconomic variables, the change in RRL has a significant negative impact on CLUE. (4) The area of cultivated land occupied by rural residential land (CLRRL) has a mediating role during 2000–2020, while the per capita cultivated land area (PCLA) and the rural permanent population (RPP) only have a mediating role during 2000–2010. In the future, the government should strictly prohibit the occupation of cultivated land by RRL and to improve the CLUE. [ABSTRACT FROM AUTHOR]

The Global Investment Report 2023 revealed that after a sharp decline in 2020 and a strong rebound in 2021, global foreign direct investment (FDI) declined by 12 percent to $1.3 trillion in 2022. However, in developing countries, FDI increased by 4% to $916 billion, a record share of more than 70% of global flows. The number of greenfield investment projects in developing countries increased by 37 percent and international project finance transactions by 5 percent. Foreign investment from China, the second largest recipient of foreign investment globally, increased by 5 percent. The service industry has become the mainstream industry in the global FDI structure. The global industry is accelerating its transformation to a "service-based economy," international FDI in productive service industries has become an essential means of industrial transfer in developed countries and a meaningful way to upgrade the industrial structure and high-quality development in emerging economies. As a representative province in central China, Hubei Province has unique advantages in human capital, factor cost, and market potential, which provide preferential conditions to attract foreign investment. This paper first introduced the concept of the productive service industry, based on the relevant statistical data from 2011 to 2022, focused on the current situation of foreign investment utilization in five major sub-sectors of the productive service industry in Hubei Province in the past ten years, and empirically investigated the impact of foreign investment utilization in five major sub-sectors of the productive service industry on the economic growth of Hubei Province, and obtained that the level of foreign investment attraction varied significantly among the regions in Hubei Province. The three productive service industries, namely transportation, storage and postal services, information transmission, software and information technology services, and financial services, played a significant role in the active attraction and optimal utilization of foreign capital and the economic development of Hubei Province. Based on this, it was proposed to build a market-oriented rule of law and internationalized business environment, improve the infrastructure construction in different regions of the province, focus on the training of professional talents for the development of productive service industries, and pay attention to the improvement of independent innovation capacity. [ABSTRACT FROM AUTHOR]

Artificial corner reflectors are widely used in the vegetated landslide for time series InSAR monitoring due to their permanent scattering features. This paper investigated the RCS features of a novel dihedral CR under multi-resource SAR datasets. An RCS reduction model for the novel dihedral corner reflector has been proposed to evaluate the energy loss caused by the deviation between the SAR incident angle and the CR's axis. On the Huangtupo slope, Badong county, Hubei province, tens of dihedral CRs had been installed and the TSX–spotlight and Sentinel-TOPS data had been collected. Based on the observation results of CRs with more than ten deviation angles, the proposed reduction model was tested with preferable consistency under a real dataset, while 2 dBsm of systematic bias was verified in those datasets. The maximum incident angle deviation in the Sentinel data overlapping area is over 12°, which leads to a 2.4 dBsm RCS decrease for horizontally placed dihedral CRs estimated by the proposed model, which has also been testified by the observed results. The testing results from the Sentinel data show that in high, vegetation-covered mountain areas like the Huangtupo slope, the dihedral CRs with a 0.4 m slide length can be achieve 1 mm precision accuracy, while a side length of 0.2 m can achieve the same accuracy under TSX–spotlight data. [ABSTRACT FROM AUTHOR]

The Autonomous Underwater Vehicle (AUV) is capable of autonomously conducting underwater cruising tasks. When combined with docking operations, the AUV can replenish its electric power after long-distance travel, enabling it to achieve long-range autonomous monitoring. This paper proposes a positioning method for the cruising and docking stages of AUVs. Firstly, a vision guidance algorithm based on monocular vision and threshold segmentation is studied to address the issue of regional noise that commonly occurs during underwater docking. A solution for regional noise based on threshold segmentation and proportional circle selection is proposed. Secondly, in order to enhance the positioning accuracy during the cruising stage, a fusion positioning algorithm based on particle filtering is presented, incorporating the Doppler Velocity Log (DVL) and GPS carried by the AUV. In simulation, this algorithm improves positioning accuracy by over 56.0% compared to using individual sensors alone. Finally, experiments for cruising and docking were conducted in Qingjiang, Hubei, China. The effectiveness of both methods is demonstrated, with successful docking achieved in four out of five attempts. [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]

Quantifying the spatiotemporal characteristics of government support for rural development is crucial for accurately optimizing or formulating policies for rural development, but research identifying government support for rural development at the geospatial scale has not yet emerged. This paper used Hubei Province, China, as the study area and constructed indicators of government support for rural development based on the intensity of support, the spatial direction of support, and the spatial agglomeration of support based on the characteristics of legal rural construction land allocation. Panel data regression was used to quantify the direction of rural development that requires the allocation of rural construction land based on government support. The results showed that government support for rural development through legal rural construction land allocation has strong spatiotemporal characteristics: From 2009 to 2018, the intensity of support grew, the spatial direction of support was regular, and government support was increasingly manifested as local agglomeration. The orientations of government support through legal rural construction land allocation for rural development include farmers' production, farmers' livelihoods and social security. This research provides a reference for quantifying government support through legal rural construction land allocation for rural development and the direction of government support. [ABSTRACT FROM AUTHOR]

This paper presents the research and implementation of a China Digital Radio (CDR) experimental network in Hubei, China with two FM-CDR transmitting stations and four FM-band channels. To study the coverage discontinuity problem in a large service area, the experimental network employs an intelligent networking solution that incorporates multiple service channels carrying Broadcast and Data Services (BADS) and a Common-Regional-Information Channel (CRIC) carrying both service data and system information. The electronic service guide (ESG) carried by CRIC can inform the receivers about the available contents from current and adjacent stations, which can significantly improve the service quality in the overlapping areas, especially under mobile reception environment. Two RF tuners are used in the receiving terminals to access the CRIC, and the BADS channel, respectively. This paper first presents the frequency analysis and coverage tests of the experimental network, and then summarizes the coverage performances with different transmission parameter combinations. The goal is to gain experience in frequency planning, coverage prediction, as well as supportable services of CDR digital audio broadcasting. [ABSTRACT FROM AUTHOR]

This paper explores the short-run impact of work resumption, extensively launched on February 10, 2020 in China, on air quality after the subsiding of COVID-19. Utilizing the data of 1012 air-quality monitoring sites in 233 cities derived from the Real-time Release Air Quality Platform and the difference-in-differences method, we find that alternative measures of air quality index in non-Hubei provinces increase significantly, compared with those in Hubei province which was temporarily not allowed work resumption due to the severity of epidemic. Specifically, our results reveal a rise in AQI of 11.28 per cent, in PM2.5 of 12.47 per cent, in PM10 of 10.49 per cent, and in NO2 of 23.64 per cent, relative to the baseline mean. Moreover, the deterioration of air quality is found to be caused by intracity rather than intercity migration. [ABSTRACT FROM AUTHOR]

Based on deep learning, precipitation prediction has been a hot topic in hydrological research in recent years. Monthly precipitation data is a typical small sample data that cannot meet the demands of deep learning for large datasets. Therefore, this paper proposes an improved method for predicting monthly precipitation using a Long Short-term Memory Network (LSTM) by integrating the ideas of signal decomposition and boundary correction. First, to address the " end effect" of sequence decomposition, this paper uses the waveform feature matching extension method to expand the boundaries of the original sequence. Then, the original sequence is decomposed twice by using Extreme-point Symmetric Mode Decomposition (ESMD) and Variational Mode Decomposition (VMD). ESMD is used to extract different sorts of scale information of the monthly precipitation sequence, obtaining several model components with decreasing frequencies and one residual component. After cutting off the most severe extension parts that "pollute" the internal data of each sub-sequence, VMD further smooths the high-frequency components. Finally, LSTM is applied to each sub-sequence for prediction, and the predicted results are reconstructed to obtain the final prediction. The monthly precipitation in Badong County, Hubei Province is selected as an example for verification. Through model comparison analysis, the results show that compared to traditional single SVM and LSTM models, the prediction model combining signal decomposition algorithm has more advantages in monthly precipitation prediction; The method of boundary correction is incorporated into the combined model by using the ESMD algorithm improves the overall prediction accuracy of the model. The prediction effect of high-frequency components is a key factor determining the accuracy of combined model prediction. The method proposed in this paper not only performs the best on the selected evaluation indicators but also significantly improves the fitting effect of extreme data points. In particular, even when facing a small sample dataset, the Nash-Sutcliffe Efficiency coefficient (NSE) of monthly precipitation prediction can reach 0.960 0 by using this model. [ABSTRACT FROM AUTHOR]

The rapid advancement of renewable energy technologies necessitates innovative solutions for the efficient deployment and management of microgrid systems. This paper presents a detailed study on the implementation of edge–cloud collaboration-based plug and play (PnP) and topology identification for microgrids, focusing on the Jingshan AC/DC Microgrid Cluster System (JS-MCS) in Hubei, China. Firstly, the paper elucidates the structure of JS-MCS's physical system and integral information system, comprising the cloud platform, communication network, edge unit, and terminal unit. The following sections introduce the methods of PnP, describing the overall procedure and specific implementations between cloud and edge and cloud and terminal. Then, a novel approach for topology identification is further explored based on correlation analysis. An exemplary analysis of the studied system, JS-MCS, accentuates practical insights into the application of PnP and topology identification methods. [ABSTRACT FROM AUTHOR]

The Chinese government encourages rural economic entities to use farmland management rights as collateral for loans, which helps to alleviate multi-level financing needs in rural areas. Based on the panel data of counties in Hubei Province, this paper adopts the Difference-in-Differences (DID) and the intermediary effect model to evaluate the impact of farmland management rights mortgage loans (FMRML) on the agri-food industrial agglomeration (AIA) in China. The study found that the pilot policy has significantly promoted the AIA. Moreover, the regression results remain robust after conducting the placebo test and the Propensity Score Matching Difference-in-Differences (PSM-DID) model, which demonstrates that the improvement effect is stable and long-lasting. From the heterogeneity analysis, it can be seen that the policy of FMRML has a more significant effect on the AIA in mountainous and hilly areas. By further analysis of the mechanism of action, it can be concluded that the pilot policy promotes the AIA by enhancing agricultural specialized production. The main findings can provide information for policymakers in China. The recommendations we have summarized encompass gradually expanding the scope of the pilot policy of FMRML, advancing the institutionalization and legalization of the policy, and promoting agricultural production specialization. [ABSTRACT FROM AUTHOR]

The fluctuation of the carbon price and its related components can effectively reflect the overall economy. This paper explores the fluctuation of the carbon price and its influencing factors. First, the ensemble empirical mode decomposition (EEMD) method is used to decompose the carbon price series of eight pilot projects at multiple timescales. Second, according to the historical trading records in the eight pilot projects, this paper constructs a national carbon price under a variety of scenarios. Finally, based on the average of the eight pilot market daily trading datasets, the national carbon price is constructed, and a short-term prediction is made. The results show that: (1) the pilot projects in Beijing and Hubei are susceptible to short-term external factors, and Beijing's pilot internal market mechanism has a large impact on the carbon price; (2) in most scenarios, the national price fluctuates, with the highest carbon price approaching 70 CNY/tCO2 and the lowest falling below 10 CNY/tCO2; and (3) China's carbon price is still has ample room to rise in the future. This paper provides a theoretical basis and practical guidance for the prediction of carbon prices in China. [ABSTRACT FROM AUTHOR]

The security of water resources is of great importance to long-term sustainability. In order to better ensure the security of water resources, a significant link is to conduct water resources security evaluation, which should be considered from many perspectives as it involves natural reserves, social production, the efficiency of use, and environmental protection. In this paper, a fuzzy analytic hierarchy process sort (AHPSort) II-entropy weight (EW) method for regional water resources security evaluation is proposed based on the security of visible water and virtual water. Firstly, this paper takes into account the criterion of efficiency of water use in addition to two other criteria of quantity of water resources, pressure on water resources to establish a comprehensive water resources security evaluation system. Secondly, a combination method of hesitant fuzzy language judgment and entropy weight is employed to obtain the weight of each indicator. Thirdly, AHPSort II is used to classify the security levels of the evaluated regions, in which the security levels of regional water resources are divided into five levels. Furthermore, a case study on the cities of Hubei province, China, is conducted to show the applicability of the proposed method, the effectiveness, and reliability of the method are then verified by being compared with a subjective method and an objective method as well as sensitivity analysis. Finally, according to the comprehensive evaluation results, specific management suggestions for improving the water resources security in the case are put forward. [ABSTRACT FROM AUTHOR]

Objective: In 2021, an outbreak of an infectious disease characterized by torticollis, cataracts, and neurological disorders caused massive mortality in farmed American bullfrogs Rana catesbeiana in Hubei province, China. We identified the causal agent in this outbreak, characterized its pathogenicity, and screened candidate antimicrobial agents for future disease control. Methods: Bacterium was isolated from the diseased American bullfrogs and identified based on biochemical tests, sequence analyses (16S ribosomal RNA; DNA gyrase subunit B), and experimental challenge. Furthermore, antibiotic sensitivity of the isolated strain was detected with Kirby–Bauer paper diffusion method, and the antibacterial activity of 60 traditional Chinese herbal extracts against the isolated strain was evaluated by agar disc diffusion and broth dilution assays. Result: We identified Elizabathkingia miricola strain FB210601 as the causative agent of this disease. The isolated E. miricola strain FB210601 exhibited extensive antibiotic resistance to all tested quinolones, β‐lactam antibiotics, and aminoglycosides. Eight herbal extracts exhibited excellent antimicrobial activity against E. miricola FB210601, especially Caesalpinia sappan and Rhus chinensis, with minimal inhibitory concentrations less than 0.2 mg/mL. Additionally, the combined effects of two‐component herbal mixtures containing C. sappan or R. chinensis were greater than those of the individual extracts. Conclusion: Our results provide a reference for understanding the pathogenesis of Elizabethkingia infection in frogs. Furthermore, this study will aid in the application of herbal extracts for protection against infections caused by multidrug‐resistant Elizabathkingia in the future. Impact statementThis work studies the Elizabathkingia miricola isolated from diseased American bullfrogs in China, including bacterial identification, its sensitivity to different antibiotics and Chinese herbal extracts, which provides a scientific reference for understanding the pathogenesis of Elizabethkingia in frogs as well as for disease control and prevention. [ABSTRACT FROM AUTHOR]

In order to analyze the impact of policy effect on carbon efficiency in the Yangtze River Economic Belt, an experimental group, including 11 regions possessing the features of the Yangtze River Economic Belt, was constructed using the synthetic control method for project evaluation with the single-factor carbon efficiency value as the core study variable in this paper. Several regions were selected from the control group as synthetic objects through a data-driven approach to construct synthetic regions that meet basic requirements before the implementation of the policy by weight ratio and evaluate the policy's impact on the implementation of carbon efficiency in the Yangtze River Economic Belt. The study shows that the single-factor carbon efficiency is relatively robust. But since 2013, the tail of the Yangtze River Economic Belt has seen an improvement. Sichuan, Hunan, Chongqing and Hubei have exceeded other provinces and cities in the growth of carbon efficiency and Shanghai has also witnessed a significant increase. Upon the inception of the synthetic Yangtze River Economic Belt, there's a discernible divergence between the curves of the real and the synthetic Yangtze River Economic Belt. Specifically, policy interventions in the Yangtze River Economic Belt didn't work well in earlier years and even restrained the economic growth of the whole region. [ABSTRACT FROM AUTHOR]

Developing renewable energy has become a major strategy for China to accelerate the energy transition and combat climate change. Accordingly, a guarantee mechanism for renewable energy consumption with renewable portfolio standards (RPS) has been set in China. However, currently, the top-down allocation of regional renewable energy consumption targets often has issues of unfairness and inefficiency. It is necessary to investigate the issue of how to stimulate the renewable energy consumption potential on the demand side and reasonably formulate the consumption responsibility weights of various market entities. This paper aimed to develop a new methodology for the weight allocation of renewable energy consumption responsibilities. In doing so, an integrated model of an evolutionary game and stochastic optimization was constructed between market entities and governments. Then, the equilibrium strategies of market entities and governments were obtained through the evolutionary game. Furthermore, based on the equilibrium strategies, this paper optimized the renewable energy consumption weight of each market entity, which constitutes the optimal allocation scheme of renewable energy consumption responsibility weights. Finally, using the data of 7069 market entities in Hubei Province in 2021, this study simulated the model to verify its effectiveness and practicability. The results indicate that the willingness of market entities to assume more consumption responsibility is positively correlated with the government's incentives and the maturity of the green electricity trading market. This study provides important implications for optimizing government regulations and promoting renewable energy consumption. [ABSTRACT FROM AUTHOR]

This paper addresses the reactions of domestic helpers to the Wuhan (Hubei Province) lockdown that began on January 23, 2020. We use a novel dataset containing the information of over 40,000 Chinese domestic helpers registered on a leading professional website from November 2019 to June 2020. The results indicate a declining pattern of short-term labor supply of domestic helpers across 11 major Chinese cities, which shows an increase in the expected monthly wage of domestic helpers in these cities. More importantly, using a difference-in-difference (DID) model, this paper provides some evidence on the existence of labor market discrimination against domestic helpers born in Hubei Province due to employers' fear of infection. [ABSTRACT FROM AUTHOR]

The relationship between labor migration and the medical benefits of migrant children (MC) has long been a focus of empirical research. Based on the dynamic monitoring data of the migrant population in Hubei Province by the National Health and Family Planning Commission of China in 2014 and the characteristic data of prefecture‐level cities, this paper studies the impact of labor migration on the medical benefits of MC. The major findings of this paper are that in China, due to the huge spatial differences in medical services, the migration of labor to urban areas with better public services helps to improve the medical benefits of women during pregnancy and lying‐in period, and at the same time significantly enhances the medical benefits of the MC. In contrast, due to the relative lag of the reform in social security system such as household registration and medical care, in the long run, labor migration is not conducive to the improvement of the medical benefits of MC. Based on the above findings, this paper suggests that in the short‐term, more public resources should be allocated to the urban medical supply, especially at the primary level, and in the long‐term, the adjustment of public policies should be aimed at eliminating urban‐rural differences. [ABSTRACT FROM AUTHOR]

Agricultural modernisation is a longstanding goal of China's Party-state. Since the early 2000s, it has pursued this goal through policies designed to facilitate land consolidation and support the expansion of large agricultural enterprises – 'New Agricultural Operators' (NAOs). In this paper we explore the effect of these policies on the livelihoods of a cohort of smallholder orange growers in the mountainous regions of Hubei province and the local political economy. An analysis of data from a 2019 survey of 266 households and interviews with villagers, agribusiness executives, cooperative leaders, and government officials, we find smallholder farmers are earning good incomes as independent commodity producers, withstanding attempts by local officials at land consolidation, and bypassing NAOs to self-determine their own modes of production and exchange. Our results speak to the ongoing debate about the future of smallholder farming in China, identify the strengths and limitations of recent state-centric analyses of agrarian transition, and re-iterate the pitfalls of the central government's agricultural modernisation agenda. [ABSTRACT FROM AUTHOR]

Rapeseed is an essential oil crop in China. Hubei, Hunan and Jiangxi provinces in the middle reaches of the Yangtze River are the main planting areas, which account for more than 42% of the total planted area of rapeseed in China. The area has more than 3467 ha of winter fallow field, with massive potential for utilization. The total mechanization rate of rapeseed tillage, seeding and harvesting in the 3 provinces is higher than national average. Still, the uneven development, insufficient utilization of idle fields in winter, low levels of mechanization and intelligence, are technical bottlenecks for improving whole mechanization. This paper analyzes the basic situation and existing problems of the rapeseed industry in the middle reaches of the Yangtze River, combing tillage, seeding, harvesting key aspects of mechanization and intelligent technology, and gives the technical routes of the whole process of rapeseed production that could be promoted. Meanwhile, a comparative analysis of the technical characteristics and difficulties in promoting the application of the "tillage, seeding and harvesting" aspects of rapeseed production was carried out. Development trend of rapeseed production technology mode is semi-mechanized, mechanized to intelligent in the winter fallow fields. The technical development paths of each link are discribed as "anti-blocking, anti-sticky, anti-tangle, and high-speed, efficient, low consumption" mechanized tillage. Integrated tillage and seeding technology to complete multiple working procedures simultaneously. High efficiency, low consumption and low loss of rapeseed mechanized harvesting technology should be combined with harvesting and two-stage harvesting synergistic development. The mechanization technology of feeding, vegetable and fertilizer and the critical technology of intelligent rapeseed production should also be developed and applied simultaneously. To achieve high yield, several suggestions for promoting full mechanization technology model for rapeseed in the mid-winter fields of the Yangtze River are proposed. [ABSTRACT FROM AUTHOR]

To test the relationships of stochastic dominance, this paper firstly proposes two new consistent K-S-type statistics based on a quantile rule. Then, we consider their asymptotic properties. We introduce the bootstrap method to calculate the p-values of our proposed tests and use the Monte Carlo method to compare the power of our proposed test with the DD test and the BD test. The simulations showed that our test performed better than these two tests. Finally, we applied our proposed tests to compare the visibility of four provinces in China and compared the effects with the DD test and BD test. The empirical results showed that the conclusions of our proposed method are more-consistent with the dominance relationships of the four provinces. For a given significance level, the results of our proposed test demonstrate that the visibility of Jiangxi province is the best. The next one is the visibility of Hubei province, which outperforms the visibility of Anhui province. The visibility of Hunan province is the poorest. [ABSTRACT FROM AUTHOR]

This paper investigates the multi-dimensional risk spillover effects of carbon markets with energy, non-energy commodity and stock markets in China before and after the COVID-19 outbreak, through the DY network with GARCHSK-VaR method. The empirical results denote that the total bidirectional risk spillovers of carbon markets become larger after the COVID-19 outbreak than before the COVID-19 outbreak. Interestingly, in both periods, energy is the largest risk contributor to carbon markets, while carbon markets become the net risk contributors to stock markets under COVID-19. Furthermore, compared with Guangdong pilot, the impacts of Hubei pilot on other markets are stronger in both periods. Finally, the dynamic risk spillover effects have obvious differences between before and after the COVID-19 outbreak. [ABSTRACT FROM AUTHOR]

The rapid urbanization in China in the past four decades has significantly affected traditional villages, resulting in a series of problems such as limited space, damaged style, weak sense of place, and loss of skills. This paper aims to establish a new paradigm for the sustainable development of village architecture in the Tujia ethnic area of southwestern Hubei province in order to address the issues of development and renovation of village architecture in this region. To achieve this goal, first, through field investigations and records of typical villages in the Tujia ethnic area of southwestern Hubei province, the characteristics of Tujia ethnic villages and the real challenges faced by village architecture are summarized. Secondly, the Delphi method is used to conduct expert interviews, construct a multi-level evaluation index system for sustainable development, and use the analytic hierarchy process to determine the weights of each index. Based on this, four design paradigms for sustainable development are proposed, including flexible layout, integrated interface composition, localized housing construction, and modular unit construction. These four paradigms cover the entire design stage from function to form, and from overall scene to structure. At the same time, multiple modular options are provided for each stage. Compared to the traditional experience-based construction model, the comprehensive benefits and promotability are greatly improved. Therefore, the research findings provide a reference for the sustainable development of traditional village architecture in other ethnic areas of China. [ABSTRACT FROM AUTHOR]

The transformation of the government into a digital entity is imperative, serving not only as a catalyst for the modernization of China's governance system and capacity but also as a cornerstone for advancing the digital economy and the establishment of a digital China. This paper presents a multi-level analytical framework designed to assess the digital transformation performance of local governments. Utilizing a dataset comprising macro-regional and micro-individual data from Hubei province, we conduct an extensive analysis to examine the underlying mechanisms that influence the digital transformation performance of local governments and employ the hierarchical linear model (HLM) as the primary analytical instrument. The results of our analysis show that individual-level government–citizen interactions, government image, and district-level department collaborative capacities exert substantial and positive influences on the digital transformation performance of local governments. Furthermore, it is worth noting that department collaborative capacity plays a significant and positive moderating role in the relationship between government image and the digital transformation performance of local governments. These findings not only offer valuable insights for optimizing policy formulation but also contribute to a more comprehensive understanding of the mechanisms underlying the digital transformation performance of local governments. [ABSTRACT FROM AUTHOR]

Carbon trading as a vital tool to reduce carbon dioxide emissions has developed rapidly in recent years. Reasonable prediction of the carbon price can improve the risk management in the carbon trading market and make healthy development of the carbon trading market. This paper aims to enhance the predictive performance of carbon price in the China's carbon markets, especially the China's national carbon market, by adding the online news sentiment index which is a kind of unconstructed data, to a deep learning model using traditionally constructed predictors innovatively. Long short–term memory (LSTM) network was applied as the primary model to predict carbon price and random forest as the additional experiment to validate the effectiveness of online news sentiment. The results in the China's national carbon market and Hubei pilot carbon market both proved that the model including the sentiment index performed better than the model does not, and the improvement was significant. [ABSTRACT FROM AUTHOR]

Aiming at the problems such as slow traceability efficiency, poor sharing, and the difficulty of matching the throughput of a blockchain single chain structure due to the complexity of the grain food supply chain links, the large number of participants, and the large amount of data information, this paper proposes a grain food blockchain traceability information management model based on the master-slave multichain structure by analyzing the processes and data characteristics of each link in the grain food supply chain; on this basis, the PLEW consensus algorithm based on Raft + improved PoW algorithm is designed for the master chain, and the CI-PBFT consensus algorithm based on trusted information degree is designed for the slave chain. The master chain and slave chain are anchored to each other through hash locking, and the data is uploaded and queried through smart contracts. In order to verify the effectiveness of the model, the blockchain traceability system is designed and implemented based on Hyperledger Fabric2.2. At the same time, it is compared with the transaction throughput and traceability efficiency of the blockchain single chain structure. Through the safety analysis of the data information of a company in Hubei, the results show that the grain traceability system designed and implemented in this study has certain advantages over the blockchain single chain structure in all aspects. It can also solve the grain food security problems that consumers worry about, and provide reference for the research of grain blockchain traceability information management. [ABSTRACT FROM AUTHOR]

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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]

The Mahuagou gold deposit is among the most important gold deposits in the core of the Huangling Anticline. However, the geochemical exploration on the surface of the mining area presents challenges due to the thin overburden. This paper focuses on the overburden soil of the Fengxiangshugou (FXS)-Mahuanggou (MHG) section as the research object. It utilizes chemical form analysis of gold, soil halogen survey, and heat-released mercury survey to determine the key deep-penetrating geochemical methods for the mining area. The results indicated that Si and Al components of samples exhibit minimal variation, suggesting that drift loads did not influence the overburden soil. Based on the systematic clustering, As, Sb, Mo, Bi, W, and Hg emerge as ore-body or ore-belt front elements of hydrothermal gold deposits. In the study area, the predominant chemical form of gold in soil is the strong organic bond. Compared to the total amount, strong organic bound gold and heat-released mercury show higher anomaly contrasts, making them crucial indicators of faults, intrusions, and hidden ore bodies. Consequently, chemical form analysis of gold and heat-released mercury surveys can enhance the anomaly contrast, proving beneficial for geochemical prospecting for weak anomalies in this area. [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]

WeChat is a social media widely used in rural China because of its operability and simplicity. This paper contributes to the role of WeChat in rural e-commerce. Based on 406 orange farmers' survey data from Hubei Province, China, we used the Heckman model to analyze the factors that influence farmers' participation in WeChat e-commerce and estimate the impacts of WeChat e-commerce on farmers' sales performance. We found that information literacy and social connections are important factors affecting farmers' participation in WeChat e-commerce. WeChat e-commerce has a positive effect on farmers' sales performance, and the social connections between farmers and consumers can reinforce this effect. Particularly, the beneficial effects are more prominent for farmers with a higher level of Internet development, who operate on a smaller scale, and who are less educated. The results show that WeChat e-commerce improves sales performance mainly by increasing the quantity of sales and the profit rate. These findings will help countries like China to use daily social media to develop rural e-commerce. [ABSTRACT FROM AUTHOR]

With the widespread recognition of underground salt cavern compressed air storage at home and abroad, how to choose and evaluate salt cavern resources has become a key issue in the construction of gas storage. This paper discussed the condition of building power plants, the collection of regional data and salt plant data, and the analysis of stability and tightness. Comprehensive analysis and evaluation methods were put forward from four aspects, including ground comprehensive conditions, regional geological conditions and formation lithology, salt mine characteristics, stability, and tightness of salt caverns. The limit equilibrium theory was applied to establish the limit equilibrium failure mode of salt caverns under operating pressure, and the stability coefficient calculation method of the target salt cavern was determined by combining the mechanical characteristics. Based on the physical and mechanical properties of salt rocks, it was found that salt rocks with enough thickness around the salt cavity could be used as sealing rings to ensure the tightness of the salt cavern. Combined with the field water sealing test, the tightness of the target salt cavern is verified. This method has been applied to the salt cavern screening and evaluation of a 300 MW compressed air energy storage power plant project in Yingcheng, Hubei Province, and remarkable results have been obtained, indicating the rationality of the method. [ABSTRACT FROM AUTHOR]