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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]

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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]

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]

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]

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]

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 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]

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]

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]

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]

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]

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]

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]

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]

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 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]

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]

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]

Straw burning is one of the important causes of environmental pollution in rural China. As an important green production technology, straw returning is beneficial to the improvement of rural environment and the sustainable development of agriculture. Based on the improved planned behavior theory, taking the survey data of 788 farmers in Shandong, Henan, Hubei, and Hunan provinces as samples, this paper uses a multi-group structural equation model to explore the driving mechanism of subjective cognition on the adoption behavior of farmers’ straw returning technology. The results show that behavioral attitude, subjective norm, and perceived behavioral control, which represent subjective cognition, all have significant driving effects on farmers’ intention to adopt straw returning technology. Behavioral intention plays a mediating role in the process of subjective cognition driving farmers’ adoption behavior of straw returning technology. Government support has a moderating role in the path from farmers’ behavioral intention to behavioral response. The subjective cognition of different types of farmers has a significant driving effect on the adoption intention of straw returning technology, but the driving strength weakens with the increase of the degree of farmers’ concurrent occupation. This study provides guidance for improving the government’s straw returning policy and regulating straw returning behavior. [ABSTRACT FROM AUTHOR]

For the requirement of refinement prediction of transmission line icing thickness, this paper presents an icing thickness correction method which combines the prediction using low spatial resolution with the correction of micro-topography features. By analyzing the regional characteristics of micro-topography, the areas can be divided into three categories: plains, high-altitude mountains, and low-altitude mountains. For each type of terrain, the quantitative correction model for the icing thickness is established, which is dependent on altitude, relief amplitude, slope angle, slope aspect, water system, valley line, ridge line, and saddle point. In this paper, according to the numerical prediction on the 3 × 3 km2 grids, the icing thickness of the transmission line on the 300 × 300 m2 grids with micro-topography is obtained using the correction method in Hubei Power Grid. The results show that the correction method is efficient and convenient to improve the accuracy of icing thickness prediction of the transmission line. [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]

Background/objective: The outbreak of COVID-19 in China since 2019 has had a significant impact on the mental health of people in Hubei Province during the three-year pandemic period. Therefore, studying the prevalence of depression among the population of Hubei Province since the pandemic is of great significance. Methods: Based on opportunity and stress theory, we collected provincial-level data from Hubei (N = 3,285) to examine the impact of declining economic status on depressive symptoms and to investigate the moderating effect of psychological resilience during the period of economic adjustment. Results: We used propensity score matching to estimate the treatment effect of economic status decline on depression severity and confirmed the moderating effect of psychological resilience. We found that the more that an individual's economic status declines, the more severe that his or her depressive symptoms become. Specifically, each unit decrease in economic status is associated with an increase of approximately 0.117 units in depression level. In addition, our results indicated that psychological resilience significantly moderated the relationship between economic decline and depression (-0.184*). Conclusions and implications: Our study confirms the role of economic status in depressive symptoms. Compared with traditional research on the relationship between economic status and mental illness, this paper expands the research regarding the two in the context of a major public health emergency. Furthermore, we suggest ways to improve people's mental health following the pandemic. [ABSTRACT FROM AUTHOR]

Land transfer and its socio-economic impact are key areas of research interest. Such an examination can help to enhance the sustainability of farming livelihoods, maximise livelihood strategies, and achieve sustainable development. This paper establishes a sustainable livelihood evaluation index for rural households based on sustainable livelihood theory. It measures the degree of sustainability in the livelihoods of farmers based on field research data from 650 rural households in Hubei Province, China, and analyses the impact of land transfer using a multiple linear regression model. A number of control variables were identified and introduced into the analysis. It also uses the regression decomposition approach to investigate the impact of each factor on the sustainable livelihoods of rural households. The findings revealed that (1) land transfer can significantly increase the sustainability of rural households' livelihoods; (2) livelihood sustainability increases with the size of the land transfer area; and (3) the primary elements determining the ability of rural households to maintain sustainable living are land transfers, the amount of land transferred, and the size of the family. Based on these findings, this study argues for the need to improve land transfer management, accelerate agricultural industrialisation and promote the transfer of land contract management rights to improve the livelihoods of rural households. [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]

In the context of anomalous global climate change and the frequent occurrence of droughts and floods, studying trends in the conversion rate between precipitable water vapor (PWV) and actual precipitation in a certain region can help in analyzing the causes of these natural disasters. This paper examines the variation trend in the conversion rate between PWV and actual precipitation on a monthly scale in Hubei from 1960 to 2020. To estimate historical PWV data, we propose a new method for estimating PWV using water vapor pressure based on the RF algorithm. The new method was evaluated by radiosonde data and improved the accuracy by 1 mm over the traditional method in Hubei. Based on this method, we extrapolate the monthly average PWV in Hubei from 1960 to 2020 and analyze the conversion rate between PWV and precipitation during this period. Our results showed that there was no obvious cyclical pattern in the conversion rate in either the longitude or latitude directions. In Hubei, where the topography varies significantly in the longitude direction, the conversion rate is influenced by topography, with the smallest conversion rate being in the transition zone between the mountainous region of western Hubei and the Jianghan Plain. In the latitudinal direction, the conversion rate decreases with increasing latitude. [ABSTRACT FROM AUTHOR]

The Outbreak of Coronavirus (COVID-19) came to the world in early December 2019. The early cases of coronavirus were reported in Wuhan City, Hubei Province, China. Till May 18, 2020, 198 countries have been affected by this life-threatening disease. The most common and known traits of COVID-19 are tiredness, fever, and dry cough. In this paper, we have discussed the Predictive data mining approach for COVID-19 predictions. In Predictive data mining, a model is developed and trained using supervised learning and then it predicts the behavior of provided data. Predictive data mining is a renowned technique known to many health organizations for the classification and prediction of diseases such as Heart disease and various types of cancers etc. There are several factors for comparing the model's accuracy, scalability, and interpretability. This predictive model is compared to the basics of its accuracy. In this proposed approach, we have used WEKA as it provides a vast collection of many machine learning algorithms. The main objective of this paper is to forecast the possible future incidence of corona cases in Pakistan. This study concludes that the number of corona cases will increase swiftly. If the government take proactive steps and strictly implement precautionary measures, then Pakistan may be able to overcome this pandemic. [ABSTRACT FROM AUTHOR]

The world is urgently looking for ways to flatten the coronavirus disease 2019 (COVID-19) curve, and many governments have resorted to implementing strict lockdowns, as researchers show the effectiveness of China's approaches in containing the virus. However, this paper argues that the draconian lockdowns instituted in Wuhan, Hubei, China, may have actually contributed to intensifying patient surges and incapacitating local health systems. Medical aids were rushed to Hubei and new hospitals were rapidly built, however, the healthcare system was still unable to match the staggering increase of patients in the early stages of the lockdowns. The paper proposes using patient evacuation to enhance sustainable COVID-19 mitigation during lockdowns. It demonstrates that patients in Hubei could have been transported to other Chinese provinces where hospitals were under-utilized. This could have theoretically saved thousands of lives by reducing inequities between Hubei and the rest of China in healthcare capacity for treating COVID-19 patients. [ABSTRACT FROM AUTHOR]

The paper combines the testing work of ground source heat pump systems, discusses the installation, use methods and test errors of various testing instruments, combines the field test experience, gives the main points of ground source heat pump energy efficiency testing, and according to the problems encountered during the test. The faults with high frequency are analyzed, and the methods of fault diagnosis and troubleshooting are given. This paper uses the ground-source heat pump experimental platform to set up multiple sets of experimental schemes to study the impact of factors such as pipe diameter, single and double U, and flow rate on system energy efficiency. At the same time, it combines engineering cases to "large flow small temperature difference", "imbalance" phenomenon is analyzed, and feasible solutions and suggestions are given. By analyzing the energy efficiency and benefits of actual engineering of multiple ground source heat pump systems, the paper concludes that ground source heat pump systems have a wider application prospect in a certain area of Hubei. [ABSTRACT FROM AUTHOR]

As a kind of concealed flawed geology, karst geology easily forms soil caves, which may have a negative effect on adjacent transmission tower foundations. Since soil caves are continuously developed when surface water flows through soil, or groundwater levels fluctuate, the effect of soil caves on transmission lines in power transmission engineering needs to be investigated. In this paper, combined with an engineering case in western Hubei, first, FLAC3D finite element software is used to analyze the influence of a single underground soil cave during continuous development and expansion on 500 kV transmission towers. Considering the maximum allowable tilt value of the tower foundation, the stress state and deformation of the soil around the foundation are monitored during the calculation and analysis, and the maximum allowable radius of the soil cave in different positions is obtained. For example, the development radii of soil holes are 1.5 m, 2 m, and 2.5 m for burial depths of 7 m and 8 m, 9 m, and 10 m and 11 m, respectively, and the development radius of soil holes with a burial depth of 12 m and below can reach the maximum value of 3 m under actual working conditions. Then, the results of two simplified models of columnar and spherical development of the soil cave are compared, and it is found that columnar development is more in line with the actual situation. Finally, under the condition of a close-range double soil cave distribution, the influence of the size, depth, and gap between the two caves on the tower foundation is simulated. This study can provide references for the safe operation of transmission towers and the assessment of possible disasters in karst areas. [ABSTRACT FROM AUTHOR]

Online reviews published on the e-commerce platform provide a new source of information for designers to develop new products. Past research on new product development (NPD) using user-generated textual data commonly focused solely on extracting and identifying product features to be improved. However, the competitive analysis of product features and more specific improvement strategies have not been explored deeply. This study fully uses the rich semantic attributes of online review texts and proposes a novel online review–driven modeling framework. This new approach can extract fine-grained product features; calculate their importance, performance, and competitiveness; and build a competitiveness network for each feature. As a result, decision making is assisted, and specific product improvement strategies are developed for NPD beyond existing modeling approaches in this domain. Specifically, online reviews are first classified into redesign- and innovation-related themes using a multiple embedding model, and the redesign and innovation product features can be extracted accordingly using a mutual information multilevel feature extraction method. Moreover, the importance and performance of features are calculated, and the competitiveness and competitiveness network of features are obtained through a personalized unidirectional bipartite graph algorithm. Finally, the importance performance competitiveness analysis plot is constructed, and the product improvement strategy is developed via a multistage combined search algorithm. Case studies and comparative experiments show the effectiveness of the proposed method and provide novel business insights for stakeholders, such as product providers, managers, and designers. History: Accepted by Ram Ramesh, Area Editor for Data Science and Machine Learning. Funding: This work was supported by the National Natural Science Foundation of China [Project 72071151] and the Natural Science Foundation of Hubei Province, China [Grant 2023CFB712]. Supplemental Material: The software that supports the findings of this study is available within the paper and its Supplemental Information (https://pubsonline.informs.org/doi/suppl/10.1287/ijoc.2022.0333) as well as from the IJOC GitHub software repository (https://github.com/INFORMSJoC/2022.0333). The complete IJOC Software and Data Repository is available at https://informsjoc.github.io/. [ABSTRACT FROM AUTHOR]

Mortality is one of the most important epidemiological measures and a key indicator of the effectiveness of potential treatments or interventions. In this paper, a permutation test method of variance analysis is proposed to test the null hypothesis that the real-time fatality rates of multiple groups were equal during the epidemic period. In light of large-scale simulation studies, the proposed test method can accurately identify the differences between different groups and display satisfactory performance. We apply the proposed method to the real dataset of the COVID-19 epidemic in mainland China (excluding Hubei), Hubei Province (excluding Wuhan), and Wuhan from 31 January 2020 to 30 March 2020. By comparing the differences in the disease severity for differential cities, we show that the severity of the early disease of COVID-19 may be related to the effectiveness of interventions and the improvement in medical resources. [ABSTRACT FROM AUTHOR]

Rural land consolidation projects (RLCPs) have become one of the largest organized human activities to change land use patterns and impact terrestrial ecosystems, and it may also be an important precondition to improving ecosystem service value (ESV). Evaluating the change in ecosystem service value (ESV) is an important basis for measuring the effectiveness of RLCPs. Therefore, this paper, taking RLCPs implemented at County Level in Hubei Province, China, as an example, uses the improved ESV evaluation model to analyze the spatial differentiation of ESV change in RLCPs and then adopts geographic detectors and a geographically weighted regression model to identify the dominant factors affecting the ESV change in RLCPs. The results showed that (1) although RLCPs make the unevenness of land use obvious, they reduce the complexity of land use evidently and improve the dominance of land use significantly; (2) The ESV of RLCPs in 71 counties of Hubei Province increased, with an average increase of USD 2.37 × 107 a−1. The ESV increase is large in central Hubei, while small in eastern and western Hubei. However, the increase rate of ESV is high in eastern and central-north Hubei, while low in western and central-south Hubei. This indicates that RLCPs can effectively promote ESV, but there are significant regional differences, and (3) the ESV increase is positively correlated with GDP and construction scale, but negatively linked with investment and per capita income of rural residents. The ESV increase rate is negatively associated with cultivated land proportion and land use diversification index change, but it is positively related to the change in the land use evenness index. However, their driving effects have significant spatial heterogeneity. [ABSTRACT FROM AUTHOR]

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Since 2020, COVID-19 has repeatedly arisen around the world, which has had a significant impact on the global economy and culture. The prediction of the COVID-19 epidemic will help to deal with the current epidemic and similar risks that may arise in the future. So, this paper proposes a hybrid prediction model based on particle swarm optimization variational mode decomposition (PSO-VMD), Long Short-Term Memory Network (LSTM) and AdaBoost algorithm. To address the issue of determining the optimal number of modes K and the penalty factor (α) in the variational mode decomposition (VMD), an adaptive value for particle swarm optimization (PSO) is proposed. Specifically, the weighted average sample entropy of the relevant coefficients is utilized to determine the adaptive value. First, the epidemic data are decomposed into multiple modal components, known as intrinsic mode functions (IMFs), using PSO-VMD. These components, along with policy-based factors, are integrated to form a multivariate forecast dataset. Next, each IMF is predicted using AdaBoost-LSTM. Finally, the prediction results of all the IMF components are reconstructed to obtain the final prediction result. Our proposed method is validated by the cumulative confirmed data of Hubei and Hebei provinces. Specifically, in the case of cumulative confirmation data, the coefficient of determination ( R 2 ) of the mixed model is increased compared to the control model, and the average mean absolute error (MAE) and root mean square error (RMSE) decreased. The experimental results demonstrate that the VMD–AdaBoost–LSTM model achieves the highest prediction accuracy, thereby offering a new approach to COVID-19 epidemic prediction. [ABSTRACT FROM AUTHOR]

To overcome the difficulty that existing hydrological models cannot accurately simulate hydrological processes with limited information in irrigated paddy areas in southern China, this paper presents a prediction model combining the Ensemble Empirical Mode Decomposition (EEMD) method and the Long Short-Term Memory (LSTM) network. Meteorological factors were set as the multivariate input to the model. Rainfall, regarded as the main variable affecting runoff, was decomposed and reconstructed into a combination of new series with stronger regularity by using the EEMD and K-means algorithm. The LSTM was used to explore the data laws and then to simulate and predict the runoff of the irrigated paddy areas. The Yangshudang (YSD) watershed of the Zhanghe Irrigation System (ZIS) in Hubei Province, China was taken as the study area. Compared with the other models, the results show that the EEMD-LSTM multivariate model had better simulation performance, with an N S E above 0.85. Among them, the R 2 , N S E , R M S E and R A E of the EEMD-LSTM (3) model were the best, and they were 0.85, 0.86, 1.106 and 0.35, respectively. The prediction accuracy of peak flows was better than other models, as well as the performance of runoff prediction in rainfall and nonrainfall events, while improving the N S E by 0.05, 0.24 and 0.24, respectively, compared with the EEMD-LSTM (1) model. Overall, the EEMD-LSTM multivariations model is suited for simulating and predicting the daily-scale rainfall–runoff process of irrigated paddy areas in southern China. It can provide technical support and help decision making for efficient utilization and management of water resources. [ABSTRACT FROM AUTHOR]

The aim of this paper is to understand the current situation of the care burden of patients with mental illness in remission and the factors affecting it in order to provide a scientific basis for targeted interventions. This paper reviews the concept of telemedicine, the application of telemedicine in home hospice care, and the remaining problems and improvement strategies of telemedicine in home hospice care, with the aim of providing a reference for the application of telemedicine in home hospice care in China. The Zarit Burden Scale, Family Care Scale, and Social Functioning Scale were used to conduct one-to-one interviews with 201 schizophrenic patients in remission and their primary caregivers in Hubei Province. Among them, 66, 72, and 25 cases (32.8%, 35.9%, and 12.4%) had mild, moderate, and severe burdens, respectively. Caregivers' family care and patients' social functioning were generally poor. The results of multiple linear regression analysis showed that caregiver age, caregiver education, caregiver family care, patient medical costs, and patient social functioning were factors influencing the burden of care for patients with schizophrenia in remission (P < 0.05). The government, mental health centers, and families should understand the level of caregiving burden of patients with schizophrenia in remission and the factors influencing it and provide targeted measures to reduce the caregiving burden. [ABSTRACT FROM AUTHOR]

Packet transport network (PTN) has problems such as waste of resources and low network stability due to the excessive complexity of the existing network or improper network architecture design. The optimization of the transport networks can not only make the network structure more reasonable but also reduce all kinds of unexpected scenarios in the network operation, improving the network efficiency and reducing the failure rate. This research will be optimized from three aspects. (1) In order to solve the problem of the same active and standby routing in the existing network, an optimization algorithm for the same active and standby routing of LSP is proposed. The essence of the optimization algorithm is to search the existing routing using the K -shortest path (KSP) between two network nodes as protection routing for LSP protection. (2) Aiming at the link with a high CIR bandwidth occupancy rate, a method is completed without adding optical fibers and other physical resources; an optimization method for the committed information rate bandwidth occupancy rate based on the KSP algorithm is proposed. (3) When the PTN ring formation rate is low, the security of the PTN is seriously reduced. In order to solve the problem of low ring formation rate in the network, this paper proposes a ring formation rate optimization scheme for PTN access layer equipment based on network elements accounting income. Through the experimental verification on the mobile PTN in one city, Hubei Province, the combination optimization method can improve the network LSP protection rate by 24%, the CIR bandwidth occupancy rate is reduced by 13.82%, and the nonring forming rate was reduced by 17.9%. This method improved network stability, reducing the risk of failure in service transportation effectively. [ABSTRACT FROM AUTHOR]

Based on social embeddedness theory, this paper aims to explore the influence mechanism of network embeddedness and environmental awareness on farmers’ participation in improving rural human settlements (IRHS). This research applies the Logit model and the Bootstrap method, using survey data from 495 farmers in Hubei Province, China. The results show that: (1) relational embeddedness has a significant negative impact on the centralized treatment of farmers’ domestic sewage, implying that strengthening the relationship between farmers and households helps to provide them with centralized treatment for domestic sewage; (2) environmental awareness has a significant positive impact on the centralized treatment of farmers’ domestic sewage, implying that the enhancement of farmers’ environmental awareness increases the promotion centralized treatment for domestic sewage; and (3) structural embeddedness can further affects farmers’ environmental awareness and then affects their participation in the centralized treatment of domestic sewage, implying that environmental awareness has a mediating effect between structural embeddedness and the centralized treatment of farmers’ domestic sewage. Overall, it is necessary not only to encourage the establishment of extension and discussion networks for farmers (relational embeddedness) to participate in IRHS but also to improve environmental education for farmers, especially by increasing their access to environmental knowledge and information (environmental awareness in mountainous areas, and, finally to support farmers. The relationship between the members and the village cadres (structural embeddedness) can further improve farmers’ awareness of participation in IRHS to better guide them in the centralized treatment of domestic waste and domestic sewage. [ABSTRACT FROM AUTHOR]

The impacts of COVID-19 on travel demand, traffic congestion, and traffic safety are attracting heated attention. However, the influence of the pandemic on electric bike (e-bike) safety has not been investigated. This paper fills the research gap by analyzing how COVID-19 affects China's e-bike safety based on a province-level dataset containing e-bike safety metrics, socioeconomic information, and COVID-19 cases from 2017 to 2020. Multi-output regression models are adopted to investigate the overall impact of COVID-19 on e-bike safety in China. Clustering-based regression models are used to examine the heterogeneous effects of COVID-19 and the other explanatory variables in different provinces/municipalities. This paper confirms the high relevance between COVID-19 and the e-bike safety condition in China. The number of COVID-19 cases has a significant negative effect on the number of e-bike fatalities/injuries at the country level. Moreover, two clusters of provinces/municipalities are identified: one (cluster 1) with lower and the other (cluster 2 that includes Hubei province) higher number of e-bike fatalities/injuries. In the clustering-based regressions, the absolute coefficients of the COVID-19 feature for cluster 2 are much larger than those for cluster 1, indicating that the pandemic could significantly reduce e-bike safety issues in provinces with more e-bike fatalities/injuries. [ABSTRACT FROM AUTHOR]