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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]

Fragile ecosystems that are affected by erosion from high pollution environmental loads pose a serious threat to human health and well-being. The evaluation of regional environmental loads has become a major issue for eco-environmental conservation and management. As a key region in central China, Hubei Province relies on ecosystems and the environment, which offer an important foundation for sustainable development and continuous improvements in social productivity. For this study, seven influencing factors were selected, and a correlation degree model was applied to assess Hubei Province's environmental loads during the period from 1995-2019. The results show that the overall environmental loads exhibit a fluctuating decreasing trend in response to economic growth and development actions. Moreover, as eco-environmental pollution problems have been addressed and improved over time, the ecosystem operating status has been gradually optimized. Finally, the paper concludes with a proposal of specific measures designed to mitigate Hubei Province's ecological loading from the perspectives of industrial structure, public awareness and technological innovation. [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]

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]

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]

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]

Timely and accurate crop yield information can ensure regional food security. In the field of predicting crop yields, deep learning techniques such as long short-term memory (LSTM) and convolutional neural networks (CNN) are frequently employed. Many studies have shown that the predictions of models combining the two are better than those of single models. Crop growth can be reflected by the vegetation index calculated using data from remote sensing. However, the use of pure remote sensing data alone ignores the spatial heterogeneity of different regions. In this paper, we tested a total of three models, CNN-LSTM, CNN and convolutional LSTM (ConvLSTM), for predicting the annual rice yield at the county level in Hubei Province, China. The model was trained by ERA5 temperature (AT) data, MODIS remote sensing data including the Enhanced Vegetation Index (EVI), Gross Primary Productivity (GPP) and Soil-Adapted Vegetation Index (SAVI), and a dummy variable representing spatial heterogeneity; rice yield data from 2000–2019 were employed as labels. Data download and processing were based on Google Earth Engine (GEE). The downloaded remote sensing images were processed into normalized histograms for the training and prediction of deep learning models. According to the experimental findings, the model that included a dummy variable to represent spatial heterogeneity had a stronger predictive ability than the model trained using just remote sensing data. The prediction performance of the CNN-LSTM model outperformed the CNN or ConvLSTM model. [ABSTRACT FROM AUTHOR]

Total phosphorus (TP) concentration is high in countless small inland waterbodies in Hubei province, middle China, which is threating the water environment. However, there are almost no ground-based water quality monitoring points in small inland waterbodies, because the cost of time, labor, and money is high and it does not meet the needs of spatiotemporal dynamic monitoring. Remote sensing provides an effective tool for TP concentration monitoring spatiotemporally. However, monitoring the TP concentration of small inland waterbodies is challenging for satellite remote sensing due to the inadequate spatial resolution. Recently, unmanned aerial vehicles (UAV) have been applied to quantitatively retrieve the spatiotemporal distribution of TP concentration without the challenges of cloud cover and atmospheric effects. Although state-of-the-art algorithms to retrieve TP concentration have been improved, specific models are only used for specific water quality parameters or regions, and there are no robust and reliable TP retrieval models for small inland waterbodies at this time. To address this issue, six machine learning methods optimized by intelligent optimization algorithms (IOA-ML models) have been developed to quantitatively retrieve TP concentration combined with the reflectance of original bands and selected band combinations of UAV multispectral images. We evaluated the performances of models in terms of coefficient of determination (R2), root mean squared error (RMSE), and residual prediction deviation (RPD). The results showed that the R2 of the six IOA-ML models for training, validation, and test sets were 0.8856–0.984, 0.8054–0.8929, and 0.7462–0.9045, respectively, indicating the methods had high precision and transferability. The extreme gradient boosting optimized by genetic algorithm (GA-XGB) performed best, with the highest precision for the validation and test sets. The spatial distribution of TP concentration of each flight derived from different models had similar distribution characteristics. This paper provides a reference for promoting the intelligent and automatic level of water environment monitoring in small inland waterbodies. [ABSTRACT FROM AUTHOR]

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

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]

Forest logging detection is important for sustainable forest management. The traditional optical satellite images with visible and near-infrared bands showed the ability to identify intensive timber logging. However, less intensive logging is still difficult to detect with coarse spatial resolution such as Landsat or high spatial resolution in fewer spectral bands. Although more high-resolution remote sensing images containing richer spectral bands can be easily obtained nowadays, the questions of whether they facilitate the detection of logging patterns and which spectral bands are more effective in detecting logging patterns, especially in selective logging, remain unresolved. Therefore, this paper aims to evaluate the combinations of visible, near-infrared, red-edge, and short-wave infrared bands in detecting three different logging intensity patterns, including unlogged (control check, CK), selective logging (SL), and clear-cutting (CC), in north subtropical plantation forests with the random forest algorithm using Sentinel-2 multispectral imagery. This study aims to explore the recognition performance of different combinations of spectral bands (visual (VIS) and near-infrared bands (NIR), VIS, NIR combined with red-edge, VIS, NIR combined with short-wave infrared bands (SWIR), and full-spectrum bands combined with VIS, NIR, red edge and SWIR) and to determine the best spectral variables to be used for identifying logging patterns, especially in SL. The study was conducted in Taizishan in Hubei province, China. A total of 213 subcompartments of different logging patterns were collected and the random forest algorithm was used to classify logging patterns. The results showed that full-spectrum bands which contain the red-edge and short-wave infrared bands improve the ability of conventional optical satellites to monitor forest logging patterns and can achieve an overall accuracy of 85%, especially for SL which can achieve 79% and 64% for precision and recall accuracy, respectively. The red-edge band (698–713 nm, B5 in Sentinel-2), short-wave infrared band (2100–2280 nm, B12 in Sentinel-2), and associated vegetation indices (NBR, NDre2, and NDre1) enhance the sensitivity of the spectral information to logging patterns, especially for the SL pattern, and the precision and recall accuracy can improve by 10% and 6%, respectively. Meanwhile, both clear-cutting and unlogged patterns could be well-classified whether adding a red-edge or SWIR band or both in VIS and NIR bands; the best precision and recall accuracies for clear-cutting were enhanced to 97%, 95% and 81%, 91% for unlogged, respectively. Our results demonstrate that the optical images have the potential ability to detect logging patterns especially for the clear-cutting and unlogged patterns, and the selective logging detection accuracy can be improved by adding red-edge and short-wave infrared spectral bands. [ABSTRACT FROM AUTHOR]

With the change of social environment and the influence of multiple values, the concept of love of postgraduates in universities in the new era has changed greatly compared with that of postgraduates in the middle and late 20th century. Post-graduates’ concepts of marriage and love have an important impact on the implementation effect of China's new population policy (three child policy). Through the questionnaire survey on postgraduates in Hubei province, the concept of love of male and female postgraduates in colleges is comprehensively displayed from the five aspects of love status, love attitude, love motivation, mate selection standard and marriage concept. It is found that there are great differences in the love views of male and female postgraduates. This paper mainly analyzes the reasons for the differences of love views between male and female postgraduates in universities from the four aspects: the empowerment of women by social development, the pressure of love consumption, the differences between male and female gender culture and the influence of family and marriage ideas. It suggests to optimize the cultivation environment of social love concept, strengthen the education of love concept in universities, and enhance the self-education of postgraduates, so as to guide postgraduates to form a correct love concept, actively pursue love, improve their ability to love, and achieve a happy marriage. [ABSTRACT FROM AUTHOR]

This paper establishes a quantitative model based on the system dynamics (SD) theory to reflect the 'socioeconomic–water–environment' coupling system and proposes three future development scenarios. Hubei province, China, is selected to verify this model. The simulation results from 2020 to 2030 demonstrate that the pressures of water supply and demand and pollution under the sustainable development scenario are less than those under two other scenarios. It means that adjustment of the industrial structure has a relatively weaker effect on alleviating the pressure of water supply and demand. Saving water and controlling pollution should be the emphasis of current efforts. [ABSTRACT FROM AUTHOR]

Fluvial deposits are commonly abundant in groundwater resources, indicating very high potential for the application of groundwater heat pump (GWHP) systems. To better understand the geospatial variation in geothermal potential, this paper presents a multidisciplinary 3D approach to assess aquifer-based geothermal resources in the Han River area in Hubei Province, China. The geological setting was determined by collecting data from 1286 drilling holes. The thermophysical-hydraulic properties were investigated, and the geospatial variation was characterized by 3D geological modeling. A GIS-based procedure was deployed for the assessment of the geothermal potential by extracting parameters from these 3D models as input. The results show that the middle reaches have the highest potential due to the great thickness of the aquifer layers, followed by the upper reaches, and the lower reaches have the lowest potential. The validation of 3 specific cases, located in the upper, middle, and lower reaches, shows that the model has uncertainties of 6.1%, 14.7% and 5.5%, respectively. This implies that the accuracy of the estimated potential depends on both the available drilling data and geological heterogeneity. This study shows that integrated 3D modeling and the GIS-based approach could be useful in geothermal planning in fluvial deposit areas. • The geological setting and material property of a fluvial area has been investigated. • The geospatial variation of ground conditions was characterized by 3D modeling. • A GIS-based approach was deployed to evaluate the geothermal potential. • The uncertainty of the estimated geothermal potential was discussed. [ABSTRACT FROM AUTHOR]

Since the Ming Dynasty, the flood disaster has been one of the important natural disasters affecting our country. However, relatively few studies on the evolution law of flood disaster at large spatial–temporal scales have been reported. Based on the datasets of flood stations from 1470 to 2000, this paper quantitatively analyzes and discusses the spatial and temporal evolution pattrens of floods in China in the past 500 years by using regional gravity center model, wavelet analysis, Daniel index, and M–K test, and predicts the trend of flood disasters in the future. The results show that: (1) Since 1470, there has been an increasing trend of flood intensity index in China;(2) From 1470 to 2000, there were two high centers of flood disaster in 1560 and 1925, respectively, and one low center appeared in 1745; (3) Flood risk changes from easy occurrence in the eastern coastal area to uniform spatial distribution over the whole study region; (4) In the past 500 years, the graver center of flood disaster is located in Baokang County, Hubei Province, and the gravity center of flood disaster shows a trend of moving to northwest both at time scales of 50-year and 100 year; (5) The flood disaster in the studied area will still show an overall upward trend in the future.The research can provide decision support for the precise prevention and control of flood disasters in China. [ABSTRACT FROM AUTHOR]

High spatial resolution (1 km or finer) precipitation data fields are crucial for understanding the Earth's water and energy cycles at the regional scale for applications. The spatial resolution of the Global Precipitation Measurement (GPM) mission (IMERG) satellite precipitation products is 0.1° (latitude) × 0.1° (longitude), which is too coarse for regional-scale analysis. This study combined the Geographically Weighted Regression (GWR) and the Multifractal Random Cascade (MFRC) model to downscale monthly GPM/IMERG precipitation products from 0.1° × 0.1° (approximately 11 km × 11 km) to 1 km in Hubei Province, China. This work's results indicate the following: (1) The original GPM product can accurately express the precipitation in the study area, which highly correlates with the site data from 2015 to 2017 (R2 = 0.79) and overall presents the phenomenon of overestimation. (2) The GWR model maintains the precipitation field's overall accuracy and smoothness, with even improvements in accuracy for specific months. In contrast, the MFRC model causes a slight decrease in the overall accuracy of the precipitation field but performs better in reducing the bias. (3) The GWR-MF combined with the GWR and MFRC model improves the observation accuracy of the downscaling results and reduces the bias value by introducing the MFRC to correct the deviation of GWR. The conclusion and analysis of this paper can provide a meaningful experience for 1 km high-resolution data to support related applications. [ABSTRACT FROM AUTHOR]

In response to the COVID-19 pandemic, the emergency policy for online learning at home has been launched in China. Live video, prerecorded video, and their combination are the three main video learning modes. Parental involvement emerges as a unique factor that may influence online learning effects. Students' behaviors such as interaction, engagement may vary from traditional online learning context due to multiple reasons. Hence, the previous acceptance models are not applicable. In this paper, by modifying and extending the Expectation Confirmation Model, a unified acceptance and continuance intention model (UACIM) is proposed to explore the factors affecting students online learning continuance intention. Confirmation is omitted for the lack of a priori knowledge. Perceived ease of use is included as the factor assessing technology performance. Parental involvement, interaction, and students' engagement are considered as external variables. The hypotheses are validated by the data collected from 306,139 students from Grade 4 to Grade 9 in Hubei Province, China. The results of structural equation modeling reveal that all variables are significant in explaining continuance intention, which indicates the vital roles of the external factors. Some negative correlations are found with suggestions that practitioners should be prudent when implementing interactions as well as offering individualized instructional designs. Furthermore, three video learning modes are found to be successful. The most preferable way is the hybrid mode with the most interactions, engagement, strongest satisfaction, perceived usefulness, and continuance intention. There are significant differences among three groups for most paths. Therefore, appropriate interventions can be devised to enhance learning effects and continuance intention when utilizing different video learning. [ABSTRACT FROM AUTHOR]

This paper critically engages with dynamics of the Chinese food safety governance infrastructure following the melamine crisis. It presents a qualitative analysis of 16 in-depth stakeholder interviews in Hunan, Hubei, and Henan. We reveal tensions between a segmented model of governance and a centralized model, between a centralized top-down model and stakeholder participation, and between a public model and a private, decentral market where corporate reputation must result in transparency and trust. Stakeholders also see gaps between ideals of inclusive, transparent, and participatory governance, and effective problem-solving. Governance of food safety in China, even when targeting inclusion and public accountability, remains heavily dependent on the central state as a pivotal actor. [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]

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

In recent years, sustainable digital economy development goals have received increasing attention because of the serious influence environment with the development of the rapid economy. Meanwhile, Multiple Criteria Decision-Making (MCDM) methods are efficient ways to measure sustainable developments, which can controls environmental pollution and protects sustainability improvement. However, assessment results are unconvincing by using the common MCDM methods because various areas that caused pollution conditions are inconsistent. Therefore, the purpose of this paper is to design an interpretable MCDM method with uncertain weights for various areas evaluation. In particular, MCDM can be used to evaluate environmental sustainability development efficiently, and deep neural networks can supply credible weights to MCDM. Interpretability is used to understand key factors in the decision-making process. Referring to the national policy and urgent areas from central areas in China, six areas are conducted to assess the sustainable digital economy problem, which could be interpreted as demonstrating the effectiveness and feasibility of the proposed approach. Besides, the experimental results demonstrate that the sustainable developmental level of Hubei is the best in the latest year. [ABSTRACT FROM AUTHOR]

Little has been done to examine socioeconomic differences in the trajectory of psychological well-being during a disaster. This paper investigates educational differences in the changes in psychological well-being during the COVID-19 pandemic and explores the mechanisms of the differences. The data were obtained from a unique two-wave panel survey conducted during the peak and waning phases of the pandemic in Hubei province, the epicenter of the pandemic in China. At the peak of the pandemic, individuals with different levels of education reported high and similar levels of psychological distress. As the pandemic subsided, the psychological well-being of people of all educational levels rebounded, but the recovery was greater and faster for those with tertiary education. Mediation analysis shows that neighborhood social support and evaluation of the performance of grassroots government partially explained the advantage of the more highly educated. This study reveals how social differentiation is translated into health disparities when a disaster strikes. It may also inform public policy by identifying segments of the population at heightened risk of psychological distress during the COVID-19 pandemic. [ABSTRACT FROM AUTHOR]

Social interaction has become one of the key factors affecting the spatial reconstruction of rural settlements (SRRS). However, most studies ignored the multi-scale impact of social networks on the identification of restructuring types of rural settlements. This paper, taking Ezhou City of Hubei Province, China as the case study area, developed a potential inter-settlement network through considering settlements as nodes, and inter-settlement interactions induced by the spatial disparity of public facilities as edges, divided towns in Ezhou City into three zones based on community structure at the town level, and then identified four types of rural settlements in light of the characteristics of cluster patterns and centrality at the patch level. The results show that the inter-settlement network in Ezhou City presents apparent disparities in terms of community structure, cluster patterns and centrality. In community analysis, high inter-community and intra-community interactions are concentrated in well-developed areas in the north and east, while weak interactions between communities occur in the southern areas dominated by traditional agricultural production. Accordingly, three zones are divided such as the urban-leading zone, urban-rural integration zone and rural-leading zone. For the network centrality and cluster patterns, high-level rural settlements are mainly distributed in the urban-leading zone, followed by the urban-rural integration zone and the rural-leading zone. Moreover, the lump cluster pattern is observed in each zone, but the chain pattern and dispersed pattern largely occur in the rural-leading zone. At same time, four types of rural settlements are identified, namely urbanized settlements, central settlements, grassroots settlements and relocated settlements. The corresponding plans are discussed in different zones regarding urbanization, integration and characteristics to provide meaningful insights for policymakers to guide SRRS. This study would contribute to our understanding of the impact of social network involved in daily life on rural settlement reconstruction, and expect to provide theoretical and methodological support for rural sustainable development in practice. [ABSTRACT FROM AUTHOR]

Purpose: The provision of high-quality e-Government services requires efficient and collaborative sharing of data across varied types of government agencies. However, interagency government data sharing (IDS) is not always spontaneous, active and unconditional. Adopting a stickiness theory, this paper reports on a research study, which explores the causes of data stickiness in IDS. Design/methodology/approach: This study employed an inductive case study approach. Twenty-three officials from the government of City M in Hubei Province, Central China, were approached and interviewed using a semi-structured question script. Findings: The analysis of the interview data pointed to 27 causes of data stickiness in five main themes: data sharing willingness; data sharing ability; data articulatability; data residence; and data absorptive capacity. The analysis revealed that interagency tensions and lack of preparedness of individual agencies are the main causes of data stickiness in IDS. Originality/value: The case setting is based on China's Government, but the findings offer useful insights and indications that can be shared across international borders. [ABSTRACT FROM AUTHOR]

Fagineura was established by Vikberg & Zinovjev in Shinohara et al. (2000). In this paper, two new species of Fagineura are described and illustrated, F. brevicornis sp. nov. collected in Hubei Province and F. longitangia sp. nov. collected in Hunan Province from China. A key to all species of Fagineura worldwide is provided, now including six species. [ABSTRACT FROM AUTHOR]

Swine manure has become rural China's primary source of agricultural non-point pollution. Various sustainable manure management practices (SMMPs) have been promoted in rural areas. Although SMMPs are becoming more popular, little evidence exists regarding their ex-post impacts. Using survey data from 710 swine breeding households in rural Hubei, China, this paper estimates the adoption and impacts of SMMPs on household income and happiness. This paper discusses the use of an endogenous switching regression model to solve the endogeneity problem that arises from both observable and unobservable factors. The results showed that 48.5% of farmers sampled adopted SMMPs. Furthermore, on average, the adoption of SMMPs was associated with a 28.7% increase in household income per capita and a 22% improvement in happiness. Then robustness tests were conducted to verify these results. Several factors influence the adoption of SMMPs, including health, swine breeding scale, swine breeding experience, technical training, internet, waste management facility and previous adoption experience. These findings highlight the importance of promoting SMMPs in rural areas to increase farmer income and enhancing their perceived happiness, and measures should be taken to scale up their adoption of SMMPs. • Adoption of SMMPs positively influences swine breeding farmers' household income. • Adoption of SMMPs significantly enhances swine breeding farmers' happiness. • Less than half of the sampled swine breeding farmers adopted SMMPs. • Endogenous switching regression model is employed. [ABSTRACT FROM AUTHOR]

As global warming intensifies, the reduction of carbon emissions is imminent. Carbon price is directly related to whether carbon can be effectively reduced. Therefore, accurately forecasting carbon price has important practical significance. Aiming at the nonstationary and nonlinear characteristics of carbon price, this paper proposes a novel hybrid model for forecasting carbon price, which is based on improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), multiscale fuzzy entropy (MFE), complete ensemble empirical mode decomposition (CEEMD), improved random forest by salp swarm algorithm (SSARF), improved back propagation by cuckoo search (CSBP), improved extreme learning machine by whale optimization algorithm (WOAELM) and error correction (EC), named ICEEMDAN-MFE-CEEMD-SSARF-CSBP-WOAELM-EC. Firstly, carbon price is decomposed by ICEEMDAN, divided into high-, medium-, and low-complexity components by MFE. Secondly, high-complexity components are merged and secondarily decomposed by CEEMD, which are still recorded as high-complexity components. Then, SSARF, CSBP and WOAELM are used to forecast high-, medium-, and low-complexity components, respectively, and forecasting results are reconstructed. Finally, EC is carried out using an extreme learning machine to obtain the final forecasting results, and the Diebold-Mariano test is introduced for a comprehensive evaluation of the model. Taking carbon price in the pilot cities of Shenzhen and Hubei as examples, after 6 aspects and 20 comparative experiments, the results show that the proposed model has higher forecast accuracy, with MAPE, MAE and RMSE up to 0.03131, 0.00089 and 4.02e-06 in Hubei, and its forecasting ability is better than other commonly used international carbon financial price forecasting models, providing a theoretical and data basis for carbon pricing and formulating carbon reduction policies in China. The main contributions of this paper are the improved primary decomposition, the use of secondary decomposition, and the innovative combination of three optimal models to forecast carbon price, but it still needs to be optimized for practice. [Display omitted] • Propose improve CEEMDAN by using Hermite interpolation and calculating residuals first. • High complexity components are secondarily decomposed to reduce its complexity. • Improved RF by SSA, named SSARF, is proposed to solve the dilemmas of RF. • Propose a high-accuracy hybrid model for carbon price forecasting. • Compare multi-faceted experiments to prove the validity of the proposed model. [ABSTRACT FROM AUTHOR]

Wushi Powder initially was used as medicine for typhoid fever in ancient China. In the Wei, Jin and Northern and Southern dynasties, Wushi Powder became fashionable among the upper classes. However, because of the limitation of documentary evidence, it is difficult to find the true identity of Wushi Powder. Little archaeological evidence has been discovered to date, and no scientific analysis have been performed for Wushi Powder. This paper focuses on the suspected items of Wushi Powder from the Xiangyang Institute of Cultural Relics and Archaeology. X‐ray diffraction (XRD), X‐ray fluorescence (XRF), polarized optical microscopy, Raman spectroscopy and atomic fluorescence spectrophotometry (AFS) were employed to characterize the compositions. The results show that the granular suspected items consist of quartz (SiO2), magnesium calcite [(Mg0.03Ca0.97)CO3], muscovite [KAl (Si3AlO10)(OH,F)2], alum [KAl (SO4)2(OH)6], sulfur (S8), chlorite or clinochlore [(MgAl)6(Si,Al)4O10(OH)8], kaolinite [Al2Si2O5(OH)4] and albite (NaAlSi3O8). According to the mineral paragenetic relationship and modern knowledge of mineral medicine, the granules contain quartz, alum, kaolinite, sulfur, stalactite and lapis chloriti. They are probably the famous Wushi Powder from history. These findings provide further new clues for the study of Chinese medical history. [ABSTRACT FROM AUTHOR]

In the Southwest of Hubei Province, there are four national nature reserves, namely Houhe, Mulinzi, Qizimeishan and Xingdoushan. Together they form a interconnecting protected area in which rare animals and plants are similar and complementing each other. At the same time, its rich landform types and water resources created an excellent living environment for bryophytes. This paper used the method of combining field investigations and published records to analyze the species richness and composition of bryophytes in these national nature reserves of Southwest Hubei, and to compare the diversity of bryophytes with those in Southeast Chongqing and Northwest Hunan. The results were as follows:(1)There were 601 species of bryophytes of 197 genera and 77 families in the national nature reserve group of Southwest Hubei, accounting for 19.89% and 71.46% of the total number of bryophytes in China and Hubei, respectively. Among them, 27 species are endemic to China, 15 families and 91 genera have only one species each.(2)There are some similarities and complementarities among bryophytes in the four national nature reserves within the national nature reserve group, it conforms to the principle of biodiversity conservation in China.(3)The bryophytes flora compositions in the national nature reserve group in Southwest Hubei are diverse, and the number of bryophytes species is significantly higher than that in Southeast Chongqing and Northwest Hunan. Therefore, the national nature reserve group in Southwest Hubei not only effectively protects large-sized rare and endangered plants and animals, but also nurtures a rich and diverse bryophytes flora, thus an important area for of the conservation bryophytes diversity. [ABSTRACT FROM AUTHOR]

The targets of limiting global warming levels below 2°C or even 1.5°C set by Paris Agreement heavily rely on bioenergy with carbon capture and storage (BECCS), which can remove carbon dioxide in the atmosphere and achieve net zero greenhouse gas (GHG) emission. Biomass and coal co‐firing with CCS is one of BECCS technologies, as well as a pathway to achieve low carbon transformation and upgrading through retrofitting coal power plants. However, few studies have considered co‐firing ratio of biomass to coal based on each specific coal power plant's characteristic information such as location, installed capacity, resources allocation, and logistic transportation. Therefore, there is a need to understand whether it is worth retrofitting any individual coal power plant for the benefit of GHG emission reduction. It is also important to understand which power plant is suitable for retrofit and the associated co‐firing ratio. In order to fulfill this gap, this paper develops a framework to solve these questions, which mainly include three steps. First, biomass resources are assessed at 1 km spatial resolution with the help of the Geography Information Science method. Second, by setting biomass collection points and linear program model, resource allocation and supply chain for each power plants are complete. Third, is by assessing the life‐cycle emission for each power plant. In this study, Hubei Province in China is taken as the research area and study case. The main conclusions are as follows: (a) biomass co‐firing ratio for each CCS coal power plant to achieve carbon neutral is between 40% and 50%; (b) lower co‐firing ratio sometimes may obtain better carbon emission reduction benefits; (c) even the same installed capacity power plants should consider differentiated retrofit strategy according to their own characteristic. Based on the results and analysis above, retrofit suggestions for each power plant are made in the discussion. [ABSTRACT FROM AUTHOR]

This paper proposes an eigenvector spatial filtering-based (ESF-based) regression model for land cover pattern simulation in China's Hubei province. The significance and influence of biophysical, climatic, and socio-economic factors have been detected and analyzed in the study region. The ESF-based multinomial logistic regression (spatial model) is constructed for discrete choices to take spatial autocorrelation into consideration. For the massive raster pixels, a segmentation processing (grid-based partition) approach is employed to resolve the large datasets to smaller ones to improve calculation efficiency. Both 32 × 32 and 64 × 64 cell sizes are used to compare the differences and influence of these approaches. For the 32 × 32 cell size, the hitting ratio increased from 0.70 to 0.89 and the deviance decreased 65.6%. For the 64 × 64 cell size, the hitting ratio increased from 0.68 to 0.77 and the deviance decreased 33.2%. The fitted results and maps show that spatial autocorrelation (SA) plays an important role in land cover patterns. Besides, the ESF-based spatial model can isolate SA in land cover pattern simulation, and therefore can improve the fitting accuracy and decrease the model uncertainty. The experiment shows that ESF-based multinomial logistic regression method provides a promising approach for discrete choice regression for massive raster datasets. [ABSTRACT FROM AUTHOR]

With the continuous emission of greenhouse gases, the carbon trading market has become a powerful weapon to contain it. It is indispensable to analyze the carbon price of China that acts as the largest emitter of carbon dioxide worldwide. Therefore, this paper proposes an innovative hybrid carbon price forecasting model that incorporates fast ensemble empirical mode decomposition (FEEMD) and extreme learning machine optimized by particle swarm optimization (PSO-ELM) with external and internal influencing factors considered. The original carbon price series are disassembled into several intrinsic mode functions (IMFs) and one residual via FEEMD. The PSO-ELM is then employed to forecast the sub-series. It's remarkable that the inputs of the PSO-ELM model are divided into external and internal influencing factors. Factor analysis is used to extract potential factors from energy prices, macroeconomics and other influencing factors associated with the original carbon price as external influencing factors, and the partial autocorrelation function (PACF) is exploited to select internal influencing factors. A case study in Hubei Province, China shows that the proposed carbon price forecasting model is superior to the contrast models in terms of the smallest prediction error (MAE = 0.1274 yuan, MAPE = 0.8368%) and the strongest stability (RMSE = 0.0116 yuan). And the forecasting results demonstrate that the developed model with external and internal influencing factors considered can highly improve carbon price prediction performance and have potential in a wider range of carbon price forecasting. In addition, accurate carbon price forecasting can help the government realize macro control and the investors fulfill risk minimization. [ABSTRACT FROM AUTHOR]

Precipitation prediction is central in hydrology and water resources planning and management. This paper introduces a semi-empirical predictive model to predict monthly precipitation and compares its predictive skill with those of machine learning (ML) methods. The stochastic method presented herein estimates monthly precipitation with one-step-ahead prediction properties. The ML predictive skill of the algorithms is evaluated by predicting monthly precipitation relying on the statistical association between precipitation and environmental and topographic factors. The semi-empirical predictive model features non-negative matrix factorization (NMF) for investigating the influence of multiple predictor variables on precipitation. The semi-empirical predictive model's parameters are optimized with the hybrid genetic algorithm (GA) and Levenberg-Marquardt algorithm (LM), or GALMA, yielding a validated model with high predictive skill. The methodologies are illustrated with data from Hubei Province, China, which comprise 27 meteorological station datasets from 1988–2017. The empirical results provide valuable insights for developing semi-empirical rainfall prediction models. [ABSTRACT FROM AUTHOR]

Emission trading scheme (ETS), the most popular market-based instrument, is widely used to solve carbon emission problems in the world. With the development of carbon market, carbon asset has been a popular financial product to invest and the risk management becomes important for government, regulated enterprises and other investors. As carbon prices have tail characteristics, this paper explores the extremal risks between carbon markets in US, Europe and China using tail dependence correlation coefficients. The empirical analysis demonstrates the tail dependence structure between carbon markets in US. and Europe is the same sign, which indicates that it is unwise to hold these two carbon assets as a portfolio. Moreover, the co-movements between European Union Emission Trade Scheme (EU ETS) and China's carbon markets are partly significant, and the operation mechanisms in China should be improved. In addition, the tail dependence test among the carbon pilots in China shows diversity. Hubei carbon trading pilot, located in central China, has extremal dependence with all other selected pilots for its regulatory program operation. The findings give insight to the carbon market regulars to improve the operation mechanism and are also useful for the investors to manage their portfolios, policymakers to make practically applicable regulations, and relevant organizations to develop procedures. [ABSTRACT FROM AUTHOR]

Covid-19 pandemic started in Wuhan in December 2019 and then spread to the rest of China and the world. This paper examines the relationship between inter-provincial migration and the diffusion of covid-19 outbreak in Mainland China from 26 January to 6 March 2020. Statistical analysis shows that the migrants who moved into Hubei in 2010–2015 are a good indicator of the population flow from Hubei to other provinces that caused the spread of covid-19 in early 2020. Many migrants traveled back to hometown before the traffic ban of Wuhan city for the spring holiday. Thus, the migration flow from a province to Hubei in the period 2010–2015 had the highest correlation coefficient with the number of covid-19 cases in four selected dates. The population flow based on the big data from the Baidu map on 20 January 2020 was also highly correlated with the number of cases, but not as good as the above migration data. The finding demonstrates the role of established migrant networks in the spread of covid-19. [ABSTRACT FROM AUTHOR]

Urban construction in China has changed from "extensional sprawl" to "connotative development". With the accelerating urbanization and improvement of living standards, citizens have had higher and more requirements on urban waterfront spaces. Starting from the history and characteristics of comprehensive river regulation, this paper explored its multiple meanings against the new appeals of urban development. On this basis, design and planning approaches were discussed from 3 perspectives of waterfront space planning control, waterfront space design optimization, and improvement of waterfront landscape, then the planning and design strategies of urban waterfront spaces were explored by studying the comprehensive waterfront space regulation project of the Old Huan River in Hubei, so as to provide references for the comprehensive water system regulation and space planning and construction of urban waterfront spaces. [ABSTRACT FROM AUTHOR]

We propose a varying coefficient Susceptible-Infected-Removal (vSIR) model that allows changing infection and removal rates for the latest corona virus (COVID-19) outbreak in China. The vSIR model together with proposed estimation procedures allow one to track the reproductivity of the COVID-19 through time and to assess the effectiveness of the control measures implemented since Jan 23 2020 when the city of Wuhan was lockdown followed by an extremely high level of self-isolation in the population. Our study finds that the reproductivity of COVID-19 had been significantly slowed down in the three weeks from January 27th to February 17th with 96.3% and 95.1% reductions in the effective reproduction numbers R among the 30 provinces and 15 Hubei cities, respectively. Predictions to the ending times and the total numbers of infected are made under three scenarios of the removal rates. The paper provides a timely model and associated estimation and prediction methods which may be applied in other countries to track, assess and predict the epidemic of the COVID-19 or other infectious diseases. [ABSTRACT FROM AUTHOR]

Benggang as a fragmented landform type, can be characterized by a deep-cut slope with various shapes and depressions on the vast weathered crust slopes in southern China. Normally, the gully heads have been continuously collapsed to form a typical landform, such as chair-like erosion. Benggang usually develops rapidly due to large amount of erosion, and thereby to threaten land resources and ecological environment. The identification of Benggang development become necessary to control erosion, and then clarify the behind mechanism. However, conventional methods have low levels of automation for a large-scale work, particularly on local inquiry, in-situ search, and manual interpretation for high-resolution images from satellite remote sensing. This paper proposes a novel Bag of Visual-Topographic Words (BoV-TW) model combining high resolution Digital Orthophoto Map (DOM) and Digital Surface Model (DSM) local features to represent Benggang areas, according to the classification and recognition methods in remote sensing images. The local features of DOM can be set in Harris-Affine and Maximally Stable Extremal Regions (MSER), with Scale-Invariant Feature Transform (SIFT) descriptors. The local features of DSM were extracted by a 3D Douglas-Peucker algorithm, representing by gradient direction-invariant descriptors developed in this study. A Latent Dirichlet Allocation (LDA) was used to balance latent semantic analysis, thereby to construct low-dimensional high-level semantic representations. Support Vector Machine (SVM) was used as a supervised learning training classifier to achieve high-precision and fast automatic Benggang recognition. Three Benggang areas in Tongcheng County, Hubei Province were selected as the experimental objects. The original data were collected by DJI Phantom 3 Pro micro UAV in September 2016. Photoscan processing was used to obtain the DOM and DSM of three Benggang areas. Uniform grid division can be used to achieve DOM and DSM tiles in 0.15 m resolution of 256 × 256 pixels. A high-resolution 3D Benggang model was used to mark the areas with/without Benggang. Two Benggang areas were taken as the training set, and the rest were taken as the test set, in three comparative experiments. The results show that: 1) With changing numbers of LDA topics, the proposed method can maintain a total accuracy of 95%, while the recall rate and precision of Benggang 80%, indicating the maximums were 97.22% and 94.44%, respectively. The total accuracy, recall and precision have increased by 12%, 11% and 32%, respectively, compared with that of only DOM features. The recognition performance was significantly improved after combining with DSM features. 2) With changing sizes of the BoV-TW vocabulary, the total accuracy was 90%, and the maximum was 96.10%, while the recall rate reached 90%, the maximum recall rate of 100%. Precision gradually increased as the increase of vocabulary size, with the maximum of 85.00%. Compared with that of only BoV-TW, three performance indicators increased by 13%, 12% and 30%, respectively, indicating that LDA for latent semantic analysis can greatly improve the recognition detection performance. 3) The proposed method was better than that on the ResNet50 network using DOM and DOM + DSM as the data source. It infers that Benggang as a landform type cannot be well recognized using only DOM, or the simple combination of DOM and DSM. Meanwhile, it needs to combine feature extraction and fusion strategy for efficient application. This finding can deliver a useful tool for the quantitative analysis on the identification, control and erosion mechanism for Benggang. [ABSTRACT FROM AUTHOR]

With the widespread adoption of market-oriented policies and the decentralization of political power, localities have become important agencies of growth. However, the literature on innovation and growth concentrates on national and regional innovation systems and has not fully explored the characteristics and dynamics of local innovation systems (LISs). In this paper, concepts of networks, collectivity, coevolution and autonomy are used to define an LIS and identify four LIS typologies. A case study of Optics Valley in China's Hubei province draws on these four concepts and reveals the significance of local features and their articulation with regional and national factors in explaining system dynamics and of LIS as a category in its own right. [ABSTRACT FROM AUTHOR]

Evolution of cultivated land needs to be clarified in the driving mechanism for the sustainable land use in China. Most studies generally focused on the relationship between the evolutionary data and the driving mechanism in the evolution of cultivated land use. However, research on the drivers of marginal impact is rare, such as the spatial factor density that represented by the POI points. There can be a tipping point of marginal impact, when the quantity reaches a specific region, in the distance to the main road represented by the human factors. In this paper, the marginal effect analysis was used to make a preliminary evaluation, taking Southern Hubei as an example. Landsat remote sensing images were selected for the interpretation data of three periods from the year of 2000-2005, 2005-2010 and 2010-2015, including 15 types of POI spatial data. Firstly, the evaluation system of spatial factors was constructed from three aspects, including Euclidean distance indices, POI kernel density indices and locational indices. The Spatio-temporal evolution of cultivated land was systematically analyzed, together with dynamic change in the area from 2000 to 2015. Secondly, the principal component analysis was used to integrate the driving factors for the change of cultivated land. The extracted principal components were selected to construct the multiple Logistic regression models, including interaction terms. Lastly, an emphasis was put on the bidirectional marginal effect of spatial elements in the driving mechanism, and the change rule that illustrated by the graph of marginal effect. The results indicated that: (1) The evolution types of cultivated land showed the agglomeration of the same type, while the specific conversion type strongly depended on the spillover effect of neighboring land use. There was obvious evolution trend from cultivated land to construction, but others were not, possibly due to the influence from the macro-control policy of land use. From 2000 to 2015, the proportion of cultivated land converted to construction land increased from 28% to 44%, and then jumped to 96%, mainly concentrated in areas around towns and along major roads. (2) Marginal effects of spatial factors depend on types of cultivated land and periods. From 2000 to 2015, there was a gradual decrease in the variation range of marginal effect for different conversion types of cultivated land, while the homogeneous intersection point moved in the same direction for marginal effect curves of different transformation types. The dominated marginal effect of natural location factors was on the conversion of cultivated land to construction land, and cultivated land to water area, when the spatial elements of POI were different values. (3) The curve of spatial factors implied that there was a critical value of marginal effect. In the range of critical values, marginal effect changed significantly with the spatial factors, but outside the range, marginal effect tended to be stable. The qualitative and quantitative analysis on the marginal effect of spatial factors can offer a theoretical reference for the optimization of cultivated land layout, and the formulation of regional coordinated development strategy. [ABSTRACT FROM AUTHOR]

In this paper, we investigate a time-delayed differential model of the transmission dynamics of schistosomiasis with seasonality. In order to study the influence of water temperature on egg hatching into miracidia and the development from miracidia to cercariae, we incorporate time-dependent delays into the model to describe the maturation period and the extrinsic incubation period. We first introduce the basic reproduction number R 0 for this model and establish a threshold-type result on its global dynamics in terms of R 0. More precisely, we show that the disease is uniformly persistent when R 0 > 1, while the disease-free periodic solution is globally attractive when R 0 < 1. Then we choose parameters to fit the schistosomiasis epidemic data in Hubei province of China. Our numerical simulations indicate that the schistosomiasis will continue to prevail in the near future unless more effective control measures are taken. A further sensitive analysis demonstrates that the parameters with a strong impact on the outcome are baseline transmission rate, recovery rate, schistosome egg output rate, contact rate between miracidia and snails, and cercariae output rate. [ABSTRACT FROM AUTHOR]

In December 2019, a new virus (initially called 'Novel Coronavirus 2019-nCoV' and later renamed to SARS-CoV-2) causing severe acute respiratory syndrome (coronavirus disease COVID-19) emerged in Wuhan, Hubei Province, China, and rapidly spread to other parts of China and other countries around the world, despite China's massive efforts to contain the disease within Hubei. As with the original SARS-CoV epidemic of 2002/2003 and with seasonal influenza, geographic information systems and methods, including, among other application possibilities, online real-or near-real-time mapping of disease cases and of social media reactions to disease spread, predictive risk mapping using population travel data, and tracing and mapping super-spreader trajectories and contacts across space and time, are proving indispensable for timely and effective epidemic monitoring and response. This paper offers pointers to, and describes, a range of practical online/mobile GIS and mapping dashboards and applications for tracking the 2019/2020 coronavirus epidemic and associated events as they unfold around the world. Some of these dashboards and applications are receiving data updates in near-real-time (at the time of writing), and one of them is meant for individual users (in China) to check if the app user has had any close contact with a person confirmed or suspected to have been infected with SARS-CoV-2 in the recent past. We also discuss additional ways GIS can support the fight against infectious disease outbreaks and epidemics. [ABSTRACT FROM AUTHOR]

Accurate carbon price projections can serve as valuable investment guides and risk warnings for carbon trading participants. However, the escalation of uncertain factors has brought numerous new hurdles to existing carbon price forecast methods. In this paper, we develop a novel probabilistic forecast model called quantile temporal convolutional network (QTCN) that can precisely describe the uncertain fluctuation of carbon prices. We also investigate the impact of external factors on carbon market prices, including energy prices, economic status, international carbon markets, environmental conditions, public concerns, and especially uncertain factors. Taking China's Hubei carbon emissions exchange as a study case, we verify that our QTCN outperforms other classical benchmark models in terms of prediction errors and actual trading returns. Our findings suggest that coal prices and EU carbon prices have the most significant effect on Hubei carbon price forecasting, while air quality index appears to be the least important. Besides, we demonstrate the great contribution of geopolitical risk and economic policy uncertainty to carbon price projections. The effect of these uncertainties is more pronounced when the carbon price is at a high quantile level. This research can offer valuable guidelines for carbon market risk management and provide new insight into carbon price formation mechanisms in the era of global conflict. • We propose a novel probability forecast model QTCN for carbon prices. • The impact of economic policy uncertainty and geopolitical risks are considered. • The prediction accuracy of QTCN outperforms other competing models. • QTCN-based trading strategies yield higher profits than a point forecast-based one. [ABSTRACT FROM AUTHOR]

The utilization of renewable energy sources to produce electricity is capable of relieving the pressure of limited natural resources and achieving the sustainability of future energy. This paper addresses a multi-period multi-feedstock multi-technology biomass-based power generation supply chain planning problem with parameter uncertainty, balancing economic, environmental, and social objectives simultaneously. However, the main challenges in optimizing this problem are correlated with multiple conflicting objectives and uncertain parameters. This study proposes a novel globalized robust goal programming model with goal constraints to balance three conflicting objectives using priority levels and characterize the uncertainty of unit emissions and social scores by inner–outer uncertainty sets. After transforming globalized robust environmental and social goal constraints into their equivalent forms, the tractable counterpart of the proposed model is obtained, which is mixed-integer linear programming (MILP). Finally, the effectiveness of the proposed model is demonstrated through a case study about the design of a sustainable biomass-based power generation supply chain (SBPSC) network in Hubei Province, China. Computational results reveal that by adjusting several parameters, environmental and social aspired goals consistently can achieve, whereas the economic objective is vulnerable to being influenced. Comparative studies with nominal goal programming model and robust goal programming model indicate that the proposed model is uncertainty-immunized and less conservative. Under investigated circumstances, the realized economic profit by the proposed model is approximately 42.5% higher than that of the robust goal programming model on average. • A pair of uncertainty sets is presented to characterize the parameter uncertainty. • Globalized robust environmental and social goal constraints are established. • Established goal constraints are transformed into their computationally tractable forms. • A novel globalized robust goal optimization method is proposed to balance three conflicting objectives using priority structure. • A case study shows the feasibility and effectiveness of the proposed method. [ABSTRACT FROM AUTHOR]

This paper analyzes the overall and time-varying efficiency of China's carbon market, which is crucial for carbon risk management and policymaking. Due to the severe illiquidity problems other pilots face, we first select Shenzhen, Guangdong, and Hubei pilots out of the eight carbon pilots for our study based on the statistical analysis. We then test the overall efficiency of the markets using the LM, CD, and Wild-Bootstrap variance ratio test. The sliding window method is combined with the above three variance ratio test methods to validate the time-varying efficiency. The results prove that the overall efficiency of the three carbon pilots has not yet reached a weak form. And we observe that the short-term efficiency is time-varying and may be weak-form. The study further found Hubei to be the most efficient carbon pilot, followed by Guangdong and Shenzhen. Finally, this paper conducts a qualitative analysis on the low efficiency from five aspects namely carbon trading rules, market liquidity, price volatility, trading volume, and types of trading products. Several conclusions and recommendations are proposed for policymakers and investors. • Sliding window method is combined with a series of variance ratio test methods. • The overall and time-varying efficiency of China's carbon market is tested. • China's carbon market has not reached weak-form efficiency. • Hubei is the most efficient carbon pilot. • Reasons for low efficiency are analyzed. [ABSTRACT FROM AUTHOR]

Flood Detection and Service (FD&S) is of great significance to flood management and decision-making. But most of the state-of-art FD&S methods are weak in timeliness and extensibility, and could not reflect flood processes. To resolve the abovementioned problems, this paper proposed the process-based FD&S (PFD&S) method based on Sensor Web. PFD&S is a four-layer architecture the core components of which are the access adapter and process-based detection rules and it is capable of heterogeneous sensor access, flood detection, and flood phase adaptive services. A prototype was developed based on the PFD&S method. Two floods occurring in the Huanghan basin (Hubei, China, with the area of 5.04 × 104 km2) during July 2016, was selected as the case studies to validate the PFD&S method and prototype. The results demonstrated that the proposed PFD&S method and prototype could achieve the instant flood process detection and services in 2.7 min. • The PFD&S method was proposed to achieve the rapid flood process detection and flood phase adaptive services. • The PFD&S prototype was designed and implemented based on the PFD&S method. • The method and prototype were sensor web - based, with universality and extensibility. [ABSTRACT FROM AUTHOR]

Coronavirus disease 2019 (COVID-19), first reported in December 2019 in Wuhan, China, has progressed to a pandemic associated with substantial morbidity and mortality. Little is known about the healthcare workers who died fighting the disease in China. This paper analyzed the data of 78 Chinese healthcare workers who died in the fight against COVID-19 between 23 January and 2 June, 2020, and revealed the following characteristics. First, compared to the number of deaths directly attributable to COVID-19, more healthcare workers died from pre-existing disease attack induced by excessive fatigue or died from accidents. Second, the median age of the healthcare workers who died directly from COVID-19 was younger than that of the Wuhan non- healthcare workers who died of COVID 19. Third, although more women than men were involved in fighting the pandemic, more men died. Fourth, more healthcare workers died in Hubei than in other provinces. Fifth, most of the healthcare workers who died directly from COVID-19 were non-professionals. [ABSTRACT FROM AUTHOR]

Exploring trade-offs of land use functions (LUFs) affected by urbanization and ecological construction in hilly-mountainous cities is vital to balancing urban development and ecological protection. Previous studies biased toward the single impact of urbanization or ecological construction on LUFs trade-offs. However, the interaction of urbanization and ecological construction affecting the trade-offs among LUFs remains unclear. This paper used Xiangyang City, Hubei Province, one of the typical hilly-mountainous cities in central China, as an empirical case study to explore the underlying mechanisms of the interactive effects of policies on ecological construction and urbanization-induced forces. The results showed that land-use production function (LPF), living function (LLF), and ecology function (LEF) maintained different growth trends and spatial patterns during 2000 and 2019. Compared with 2000, the synergy between LPF and LLF went stronger at the city-wide level, while the trade-off between LEF and LLF became stronger and the trade-off between LEF and LPF became weaker in 2019, respectively. Spatially, the trade-off of LLF and LEF was generally stronger than that of LPF and the other two LUFs. We also found that urbanization, environmental governance, and ecological restoration had differential impacts on the trade-offs among LUFs. As interaction with urbanization occurred, ecological restoration aggravated the trade-off between LPF and LEF while mitigating the trade-off between LLF and LEF; meanwhile, environmental governance aggravated the trade-off between LPF and LEF while mitigating the trade-off between LLF and the other two functions. Territorial space optimization in hilly-mountainous cities should focus on the collaborative promotion of urbanization and ecological construction for reconciling the trade-offs among LUFs. •We explored the interactive impacts of urbanization and ecological construction on trade-offs in land use functions (LUFs). •Magnitudes of trade-offs between paired LUFs changed with temporal and regional variation. •Urbanization and ecological construction factors showed different importance in various LUFs trade-offs. •Provide a case reference for coordinating LUFs in mountainous cities in China. [ABSTRACT FROM AUTHOR]