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This paper aims to explore the multidimensional poverty of the migrant workers. This paper also analyzed the migrant worker's household registration differences and multi-period analysis of the migrant workers. China Family Panel Studies (CFPS), a household and social tracking survey data conducted by the Institute of Social Science Survey, was used in this article. Income, health, education, social security, living standards, and urban intergration were considered Multidimensional Poverty. This paper combines the A-F multidimensional poverty method and Foster's duration analysis method to construct a long-term multidimensional poverty index. Our results show that the long-term multidimensional poverty situation of the migrant workers is not severe but is mainly concentrated in certain dimensions and specific indicators. However, the long-term multidimensional poverty of the migrant workers is a persistent problem. Although the situation is gradually improving, the long-term negative impact of multidimensional poverty on individuals is increasingly prominent. [ABSTRACT FROM AUTHOR]

According to the existing literature on public management, many factors affect popular satisfaction with the government (referred to as "government satisfaction"), one of which is the use of e-government. Particularly, many e-government proponents regard increasing government transparency as an important way to improve government satisfaction. To test the validity of this assertion, this paper uses an Ordinary Least Squares (OLS) regression model to analyze survey data on individuals' e-government usage frequencies in China and their satisfaction with the local government during the COVID-19 pandemic. We find that people can rationally and consciously evaluate governments and make decisions by using the government performance information that they know about. This conscious and rational process is also mixed with the impact of some irrational and emotional factors, which is consistent with the concept of the "framing effect." This paper identifies local government transparency as the mediating variable in the relationship between the use of e-government and government satisfaction and public knowledge of local government performance in COVID-19 outbreak control as the moderating variable in the relationship between local government transparency and government satisfaction. Since the mediating effect of local government transparency is significantly different from those of overall and central government transparency, government transparency at different levels plays a role of the above-mentioned "frame." [ABSTRACT FROM AUTHOR]

The coronavirus first appeared in China in 2019, and the World Health Organization (WHO) named it COVID-19. Then WHO announced this illness as a worldwide pandemic in March 2020. The number of cases, infections, and fatalities varied considerably worldwide. Because the main characteristic of COVID-19 is its rapid spread, doctors and specialists generally use PCR tests to detect the COVID-19 virus. As an alternative to PCR, X-ray images can help diagnose illness using artificial intelligence (AI). In medicine, AI is commonly employed. Convolutional neural networks (CNN) and deep learning models make it simple to extract information from images. Several options exist when creating a deep CNN. The possibilities include network depth, layer count, layer type, and parameters. In this paper, a novel Xception-based neural network is discovered using the genetic algorithm (GA). GA finds better alternative networks and parameters during iterations. The best network discovered with GA is tested on a COVID-19 X-ray image dataset. The results are compared with other networks and the results of papers in the literature. The novel network of this paper gives more successful results. The accuracy results are 0.996, 0.989, and 0.924 for two-class, three-class, and four-class datasets, respectively. [ABSTRACT FROM AUTHOR]

Using deep learning in remote sensing interpretation could reduce a lot of human and material costs. Semantic segmentation is the main method for this task. It can automatically outline the objects and it has recently achieved great success in remote sensing images. However, in the appliance of remote sensing interpretation, the accuracy of contour largely determines the evaluation of remote sensing interpretation. Though the current loss functions reflect the segmentation performance, they could not guide the model to optimize itself toward a more precise contour. This paper proposed an exactly defined contour loss (CL) for remote sensing interpretation with Residual Attention U-Net (RA U-Net) as the main framework. The RA U-Net uses the residual attention module as the skip connection layer. It enhances the judgment of U-Net. In CL, image processing methods are used to extract the contours of the foreground. And elements-sum and elements-subtract operations are used to transfer the contour information to a matrix of the same size as label images. Then, these matrices would be the weights for CE. By assigning different weights for different elements in different regions, this function will guide the model to reach a balance between accurate segmentation results and precise contours. The experiment on open datasets shows a good performance. The proposed model was also trained on the Construction Disturbance Dataset collected from Jiang Xi Province, China. The dataset was labeled manually. The evaluation enhanced a lot on the Construction Disturbance Dataset and the IoU on two datasets increased 1 % to 2 % when using CL as the loss function. This paper also compared the proposed method with other state-of-the-art methods and the results showed extensive effectiveness. [ABSTRACT FROM AUTHOR]

These days, digital art has substantially improved painting art in China's new media period through modern technology and specialized software, which has generated various digital artworks with painting effects, demonstrating their aesthetic, commercial, and cultural worth. As a result, designers must actively investigate intelligently linked and content-diverse techniques to innovate and improve digital media art. This investigation will allow the extraction of pivotal role of digital media communication and drives visual art to new heights in the modern digitized era. Based on this motivation, this paper tries to analyze the application of new media digital technology in painting creation using big data. The major goal is to obtain insights into artists' behavior using behavioral habits and a morphological algorithm in digital painting. The study investigates the design process of digital painting by analyzing the habitual forms displayed by diverse painting user groups and integrating them with user experience design concepts. Two experimental methodologies are used to investigate painting user behavior: the interview method and the observation method. The observation approach collects comprehensive user behavior data to develop a habit form model that includes a both virtual and real activity. This model employs a hierarchical scheme emphasizing various behavior aspects and concepts throughout user interactions and painting system operations. The interview method, on the other hand, captures the psychological activities involved in user behavior decision-making. The study discovers and decomposes the traits specific to these characters by defining painted characters, developing their behavior sets, and making interactive decisions. The learned information and experience are then applied in interactive decision-making processes. Based on these findings, prototype development includes explicit and implicit interaction prototypes, with usability tests determining whether specific roles are included in the design. Throughout the study process, fuzzy mapping associations are built, and behavior mappings are labeled with varying relationship proportions, providing critical data for the habit form model's construction. The paper uses new digital media technology to create paintings and experiments with 50 randomly selected persons. Forty-four participants are well-known authors on major painting websites, with the remaining six being interviewed as professional painters. [ABSTRACT FROM AUTHOR]

In recent years, there have been several sports-related injuries and even unexpected fatalities due to the rapid growth of China's sports industry and increasing fitness issues. The prevalence of sports injuries is rapidly rising because of the intense competition among athletes and the increased physical load caused by players' long-term training. The difficulty in the sports' world is the identification and evaluation of risk factors associated with athletes. To address these issues, this paper develops a cognitive evaluation model for risk assessment of injuries in sports using big data to understand and determine the likelihood of injuries. The model is used to prevent the issues faced by running athlete's injury and thereby lowers the risk of overall risk. This paper also analyzes the injury factors while running by establishing an estimation model based on the radial basis function (RBF) neural network and explains how to compare RBF neural networks and risk level evaluation using certain criteria. The estimation model helps to identify and judge the risk factors of athletes. It designs an algorithm for Gaussian distribution, which predicts the output of the model. It discusses how the error may be calculated, which measures the neural network model's overall performance in forecasting sports' injury outcomes using a particular dataset. Finally, it designs the RBF Neural Network Learning algorithm, which generates the leaned network parameters to evaluate the reliability of risk cognitive model for athletes based on big data. The Lagrange and Bayesian models are selected for comparison. The results indicate that this model has a higher evaluation effect and accuracy as compared to other models. For this purpose, the comparison is made using 25 athletes. The proposed model's evaluation time is the lowest in comparison to the existing approaches. After examining the impact of cognitive evaluation, it has been reflected that the proposed model can be utilized to assess the different injury types for different parts of athletes. The highest single injury degree is 38.76%, while the highest composite injury degree is 40.05%. [ABSTRACT FROM AUTHOR]

The excavation of large-scale lacustrine soft soil in natural state in the Chengmenshan Copper mine, Jiujiang, China, is a challenging work and is illustrated in this paper. A geometrical model of the lacustrine soft soil was built based on data from more than 150 boreholes. A series of tests were conducted on the natural soft soil including compaction, consolidation and common test, and also remolded samples containing water contents of 10%, 13%, 16%, 21%, 25%, 30% and 37% were tested. Results showed that the cohesion and friction angle decrease rapidly with the increase in water content, but the consolidation pressure played a positive role to the increase in shear strength. To properly manage the soil properties and optimize the surcharge program, settlement and uplift during the infill construction were numerically analyzed by means of SIGMA/W. Based on the test and numerical simulation results, a long term in situ improvement method was proposed and performed. Finally, drilling sampling tests, settlement monitoring and slope stability analysis were conducted to evaluate the overall effects of the surcharge. The properties of the soft soil were improved greatly under the infill surcharge, especially the cohesion and frictional increased by 279.3% and 152.8% and almost reached the value of remolded soil after consolidation under pressure 300 kPa. The settlement rate decreased with the surcharge duration. The safety factor of slope after improvement also met the design requirement. Furthermore, the method proposed in this paper which have given consideration to economic conservation and environmental protection is significant for the soft soil excavation projects. [ABSTRACT FROM AUTHOR]

This paper examines the roles of digital finance development in household income, consumption, and financial asset holding from an extreme value theory perspective. Three types of extreme pairs (Min to Min, Max to Max, and Max to Min) are constructed, corresponding to the three aspects of the economic welfare of digital finance: fairness, efficiency, and their trade-off. Using panel data from the Peking University Digital Financial Inclusion Index of China (PKU-DFIIC) and China Family Panel Studies (CFPS) over time span 2014–2018, this paper models the block maxima and minima of variables by fitting them with generalized extreme value (GEV) distribution. The binary expansion testing (BET) is used to detect the nonlinear dependence between digital finance and household economic variables. The tail quotient correlation coefficient (TQCC) is used to quantify the tail dependencies. The results show that: (1) digital finance has significant fairness effects in reducing poverty, increasing consumption, and promoting financial asset holding; (2) digital finance shows effects of promoting incentives and efficiency in household income and financial asset holding, but this effect is relatively limited in household consumption; (3) digital finance generally increases efficiency without harming fairness in terms of all cases of household income and consumption, and most of the cases regarding household financial asset holding; (4) the positive spatial externality of digital finance exists for all household economic variables; and, for pairs regarding household income and consumption, the wider the scope, the greater the spatial spillover effect. The result of this paper implies many novel policy implications. [ABSTRACT FROM AUTHOR]

Atmospheric chemistry research has been growing rapidly in China in the last 25 years since the concept of the "air pollution complex" was first proposed by Professor Xiaoyan TANG in 1997. For papers published in 2021 on air pollution (only papers included in the Web of Science Core Collection database were considered), more than 24 000 papers were authored or co-authored by scientists working in China. In this paper, we review a limited number of representative and significant studies on atmospheric chemistry in China in the last few years, including studies on (1) sources and emission inventories, (2) atmospheric chemical processes, (3) interactions of air pollution with meteorology, weather and climate, (4) interactions between the biosphere and atmosphere, and (5) data assimilation. The intention was not to provide a complete review of all progress made in the last few years, but rather to serve as a starting point for learning more about atmospheric chemistry research in China. The advances reviewed in this paper have enabled a theoretical framework for the air pollution complex to be established, provided robust scientific support to highly successful air pollution control policies in China, and created great opportunities in education, training, and career development for many graduate students and young scientists. This paper further highlights that developing and low-income countries that are heavily affected by air pollution can benefit from these research advances, whilst at the same time acknowledging that many challenges and opportunities still remain in atmospheric chemistry research in China, to hopefully be addressed over the next few decades. [ABSTRACT FROM AUTHOR]

Improving total factor carbon emission efficiency (TCE) is the key to achieving carbon emission reduction targets while ensuring economic growth. In this paper, the global Malmquist index based on the SBM model is used to measure TCE of 283 cities in China from 2011 to 2019. On this basis, this paper uses the spatial econometric model and intermediary effect model to empirically analyze the impact of industrial co-agglomeration on TCE and its transmission mechanism. Furthermore, considering the differences in geographical location and resource endowment among regions, this paper analyzes the heterogeneous effect of industrial collaboration agglomeration on TCE in different regions and cities. The results show that: (1) Industrial co-agglomeration can improve TCE, and its main transmission channel is technological innovation. (2) Industrial co-agglomeration has a positive spatial spillover effect. Industrial co-agglomeration in one region can improve the TCE in the surrounding regions. (3) Industrial co-agglomeration of cities with different geographic locations and resource endowments has a heterogeneous effect on TCE. Regarding geographical heterogeneity, the industrial co-agglomeration in the eastern region has the greatest promoting effect on TCE, followed by the central region. However, the impact of industrial co-agglomeration in the western region on TCE is not significant. Regarding resource endowment heterogeneity, the industrial co-agglomeration in non-resource-based cities has a greater promoting effect on TCE than that in resource-based cities. [ABSTRACT FROM AUTHOR]

The paper presents a novel statistical approach for analyzing the daily coronavirus case and fatality statistics. The survival discretization method was used to generate a two-parameter discrete distribution. The resulting distribution is referred to as the "Discrete Marshall–Olkin Length Biased Exponential (DMOLBE) distribution". Because of the varied forms of its probability mass and failure rate functions, the DMOLBE distribution is adaptable. We calculated the mean and variance, skewness, kurtosis, dispersion index, hazard and survival functions, and second failure rate function for the suggested distribution. The DI index demonstrates that the proposed model can represent both over-dispersed and under-dispersed data sets. We estimated the parameters of the DMOLBE distribution. The behavior of ML estimates is checked via a comprehensive simulation study. The behavior of Bayesian estimates is checked by generating 10,000 iterations of Markov chain Monte Carlo techniques, plotting the trace, and checking the proposed distribution. From simulation studies, it was observed that the bias and mean square error decreased with an increase in sample size. To show the importance and flexibility of DMOLBE distribution using two data sets about deaths due to coronavirus in China and Pakistan are analyzed. The DMOLBE distribution provides a better fit than some important discrete models namely the discrete Burr-XII, discrete Bilal, discrete Burr-Hatke, discrete Rayleigh distribution, and Poisson distributions. We conclude that the new proposed distribution works well in analyzing these data sets. The data sets used in the paper was collected from 2020 year. [ABSTRACT FROM AUTHOR]

The mining of open pit mines is widespread in China, and there are many cases of landslide accidents. Therefore, the problem of slope stability is highlighted. The stability of the slope is a factor that directly affects the mining efficiency and the safety of the entire mining process. According to the statistics, there is a 15 percent chance of finding landslide risk in China's large-scale mines. And due to the expansion of the mining scale of the enterprise, the problem of slope stability has become increasingly obvious, which has become a major subject in the study of open-pit mine engineering. In order to better predict the slope stability coefficient, this study takes a mine in China as a case to deeply discuss the accuracy of different algorithms in the stability calculation, and then uses a deep learning algorithm to study the stability under rainfall conditions. The change of the coefficient and the change of the stability coefficient before and after the slope treatment are experimentally studied with the displacement of the monitoring point. The result shows that the safety coefficient calculated by the algorithm in this paper is about 7% lower than that of the traditional algorithm. In the slope stability analysis before treatment, the safety factor calculated by the algorithm in this paper is 1.086, and the algorithm in this paper is closer to reality. In the stability analysis of the slope after treatment, the safety factor calculated by the algorithm in this paper is 1.227, and the stability factor meets the requirements of the specification. It also shows that the deep learning algorithm effectively improves the efficiency of the slope stability factor prediction and improves security during project development. [ABSTRACT FROM AUTHOR]

Tourism is the pillar industry of many cities, and it is also an important key point to promote urban development and maintain urban vitality. At present, the analysis of urban tourism activity in China can better assist the research of regional economic development and promote the orderly development of regional economy. Many scholars have carried out the analysis in this respect. As a new and growing field, artificial intelligence also plays an important role in urban tourism. With the continuous development of science and technology, and the human intelligence field of human research is also developing. New artificial intelligence products continue to emerge. The workload of most artificial intelligence may exceed the manual workload. In order to continuously update artificial intelligence, individuals effectively combine data mining and artificial intelligence, and combine many knowledge disseminated by the network with artificial intelligence technology to create an advanced knowledge network model. This paper uses the OPTICS-based clustering algorithm to analyze the clustering of photographs on the Flickr website and obtain information about tourism activities in Chinese cities. With the help of visualization software to visualize the experimental data and verify the experimental results introduced in this article, city tourism activities can be recommended to the destination. At present, many scholars have studied the application of improved density clustering algorithm in the field of biology and image analysis, but there are still some gaps in the development of tourism. This paper can make some contributions to the related fields. [ABSTRACT FROM AUTHOR]

This paper uses patterned structure to study the efficiency improvement of the practical application of BIM technology, and innovates the patterned structure method based on this. The research focus of this paper is to build a new assembly component library based on BIM model and algorithm optimization, improve its use and management efficiency, and use the core technology to design the simulation analysis model of BIM in construction, and give timely feedback of experimental information. Analyze the existing assembly structure design process, design standards and design methods, propose a BIM-based assembly structure design method, change the design idea from the whole to the component elements, and use the design method for prefabricated components. Taking the W prefabricated component library as a starting point, select the prefabricated components of the component library for assembly design to form a BIM model with a complete structure. Adjust and optimize the BIM model, and finally form a reasonable design plan. This BIM type of product includes production, transportation and assembly construction. Prefabricated buildings have obvious advantages, its comprehensive benefits are higher than other buildings, and the technical content is relatively high, through innovative prefabricated buildings, the modernization level of the building can be improved. Therefore, on the basis of in-depth investigation and study of the development status of China's construction industry, this paper studies the impact of different factors on engineering costs, including engineering schemes, engineering construction modes, etc., based on a large number of actual engineering operations and application cases, so as to achieve the purpose of reducing cost costs, improving project income, and improving project quality. [ABSTRACT FROM AUTHOR]

On June 17, 2020, Aniangzhai landslide, an ancient landslide located in Danba County, southwest China, was reactivated by Meilonggou debris flow. The front edge of the slope collapsed, mobilizing a soil mass of about 2.35 × 106 m3. Evaluating the stability of the whole slope is of great importance to avoid further landslides and mitigate the damage for Aniangzhai villagers living on this slope. This paper focuses on the inaccessible upper slope of Aniangzhai landslide (no attention paid before) that exhibits a relative elevation difference of more than 1000 m. Multi-source remote sensing, including unmanned aerial vehicle (UAV) photogrammetry, light detection and ranging (LiDAR), and satellite-based interferometric synthetic aperture radar (InSAR) techniques, was used in this research to identify and evaluate this high mountain upper slope potential hazard in Aniangzhai landslide. Considering the huge height difference and the steep slope of Aniangzhai landslide, an iterative route planning method was proposed and adopted to obtain a 3D model with 0.02 m resolution and a DEM with 0.25 m resolution by using UAV and LiDAR close-in flight method, respectively. Meter-level huge cracks were clearly identified by the high-resolution UAV 3D model and LiDAR data, which confirm that the location of these cracks is related to the morphological structure of this ancient landslide. Time series InSAR analysis reveals the activity of this high-altitude area, with a maximum LOS displacement rate of 15 cm/a. The combination of the above remote sensing technologies confirms and reveals the high potential risk and the reactivated condition of the upper slope of Aniangzhai landslide. Through this finding, we show that the evolution of Aniangzhai landslide happened through four stages with a cascading effect. This paper proves the usefulness of an integrated method to successfully identify and evaluate the high-altitude upper slope potential hazard and compares the technical features of them, providing a reference for future works that aimed at mitigating the potential damage of the upper slope. [ABSTRACT FROM AUTHOR]

This paper considers how 'knowledge' of China was presented for Victorian and Edwardian children in The Boy's Own Paper ( BOP) between 1879 and 1914. It considers how genre affects the representation of China in the BOP by comparing travel narratives and adventure stories. First, it focuses on non-fiction about China, examining the rhetorical strategies employed by the authors of travel narratives. The travellers express confidence in their ability to survey the land and present readers with the idea that China is abundant in resources awaiting British discovery. Second, the article discusses adventure stories about opium and piracy written during a period when the tension between the British and the Chinese was particularly fraught over these issues. While the authors of the adventure stories created a strong contrast between the Chinese as villains and the British as heroes, the BOP travel writers provide contradictory statements within their narratives, reflecting an ambivalence about China and the Chinese. However, because the BOP contributors sought to instil a sense of patriotism, to inspire their readers to serve the British Empire, both the non-fiction travel narratives and the fictional adventure stories related to China were published not only to entertain readers with exotic facts and thrilling stories, but more importantly, to persuade them of the necessity of British intervention. [ABSTRACT FROM AUTHOR]

Collecting feedback from students through course, program and other evaluation questionnaires has become a costly and time consuming process for most colleges. Converting to data collection through the internet, rather than completion on paper, can result in a cheaper and more efficient process. This article examines several research questions which need to be answered to establish that results collected by the two modes of administration are equivalent. Data were gathered for a program evaluation questionnaire from undergraduate students at a university in Hong Kong. Students were able to choose between completion on paper or through the internet. In six of the seven Faculties the number of responses through each mode was roughly the same. Students in the Engineering Faculty favored the internet. Scores on the 14 out of 18 scales in the instrument showed small differences by mode of response, which became smaller still with controls for pertinent demographic variables. The main response question addressed in the study was whether there was any difference in the way respondents to the two modes interpreted the questions. The study demonstrated the equivalence of the two data sets by showing that both could be fitted to a common model with structural equation modeling (SEM). Five levels of tests of invariance further confirmed the comparability of data by mode of administration. This study, therefore suggests that changing to internet collection for course and program evaluations will not affect the comparability of ratings. [ABSTRACT FROM AUTHOR]

Microbiota from herbivore rumen is of great interest for mining glycoside hydrolases for lignocellulosic biomass biorefinement. We previously isolated a highly active but poorly thermostable xylanase (LXY) from a rumen fluid fosmid library of Hu sheep, a local high-reproductive species in China. In this study, we used a universal enzyme-engineering strategy called SpyTag/SpyCatcher molecular cyclization to improve LXY stability via isopeptide-bond-mediated ligation. Both linear and cyclized LXY (L- and C-LXY, respectively) shared similar patterns of optimal pH and temperature, pH stability, and kinetic constants (km and Vmax). However, the C-LXY showed enhanced thermostability, ion stability, and resilience to aggregation and freeze–thaw treatment than L-LXY, without compromise of its catalytic efficiency. Circular dichroism and intrinsic and 8-anilino-1-naphthalenesulfonic acid-binding fluorescence analysis indicated that the cyclized enzyme was more capable of maintaining its secondary and tertiary structures than the linear enzyme. Taken together, these results promote the cyclized enzyme for potential applications in the feed, food, paper pulp, and bioenergy industries. [ABSTRACT FROM AUTHOR]

The motivation for analyzing the impact of deep neural networks on enterprise performance is mainly due to the continuous deepening of enterprise information construction, shifting from traditional paper-based data acquisition methods to electronic data management. The data generated by the sales, production, logistics and other links of enterprises is also becoming increasingly large. How to scientifically and effectively process these massive amounts of data and extract valuable information has become an important issue that enterprises need to solve. The continuous and stable growth of China's economy has promoted the development and growth of enterprises, however, it has also made enterprises face a more complex competitive environment. The question of how to improve the performance of enterprises to enhance their competitiveness in the market has become a major issue to be addressed in the face of fierce competition and to ensure the long-term development of enterprises. In this paper, based on the research of firm performance evaluation, deep neural network is introduced to analyse the influence of ambidextrous innovation and social network on firm performance, and the theories of social network, ambidextrous innovation and deep neural network are sorted out and analysed, and a deep neural network-based firm performance evaluation model is established, and finally the sample data is obtained using crawler technology, and then the response values are analysed. Innovation and the improvement of the mean value of social networks are helpful to firm performance. [ABSTRACT FROM AUTHOR]

This paper redefines the connotation of China's human development in the context of the new development concept and high-quality development, and constructs the China Human Development Index (CHDI) indicator system accordingly. Then, based on the inequality adjustment model and DFA model, the human development level of each region in China from 1990 to 2018 is measured, and the spatial and temporal evolution characteristics of China's CHDI and the current situation of regional imbalance are analyzed accordingly. Finally, LMDI decomposition technique and spatial econometric model were used to study the influencing factors of China's human development index. The results show that: (1) The weights of the CHDI sub-index estimated by the DFA model have good stability, and it is a relatively good objective weighting method. (2) Compared with the HDI, the CHDI in this paper can better reflect the level of human development in China. (3) China's human development has made great achievements and has basically achieved the leap from the low human development level group to the high human development level group. However, there are still significant gaps between regions. (4) From the results of LMDI decomposition, the livelihood index is the most important driving index of CHDI growth in each region. From the results of spatial econometric regressions, there is a strong spatial autocorrelation of China's CHDI among the 31 provinces. GDP per capita, financial education expenditure per capita, urbanization rate, and financial health expenditure per capita are the main influencing factors of CHDI. Based on the above research findings, this paper proposes a scientific and effective macroeconomic policy with important reference value for promoting the high-quality development of China's economy and society. [ABSTRACT FROM AUTHOR]

This paper measures the human development indices of 31 inland provinces (municipalities) in China in a continuous time series during 2000–2017 according to the 2010 HDI compilation method. It uses a geographically and temporally weighted regression model for conducting an empirical study on the effects of R&D investment and network penetration on human development in each province (municipality) of China. There is significant spatial and temporal heterogeneity in the impact of R&D investment and network penetration on human development across provinces (municipalities) in China due to differences in resource endowments and economic and social development. For R&D investment, eastern provinces (municipalities) have mostly positive effects on human development, and central regions have mostly weak positive or negative effects. In contrast, western provinces (municipalities) show different development paths, with weak positive effects in the early stage and significant positive effects after 2010. Most provinces (municipalities) show a continuous and increasing positive effect for network penetration. The marginal contributions of this paper are mainly in improving the shortcomings in research perspectives, empirical methods, and research data in the study of human development influencing factors in China relative to the study of HDI itself in terms of measurement or application dimensions. This paper constructs a human development index for China, analyzes its spatial and temporal distribution, and explores the impact of R&D investment and network penetration on human development in China, with the hope of providing lessons for China and developing countries to promote the level of human development and cope with the pandemic. [ABSTRACT FROM AUTHOR]

To solve various extraction-related problems existing in the deep Jurassic mines in the Inner Mongolia-Shaanxi (IM-S) mining area, this paper puts forward a method to control roof water hazards based on the sedimentary facies pattern of the aquifers. The problems we addressed are: high volumes of goaf water, mixing of coal-based water in the stope, the proximity of the upper coal seam to water-enriched aquifers, and the inability for safe excavation and stoping. We first investigated the spatial distribution patterns of the aquifers and their related storage properties. A zone model of water yield compatible with the hydrogeological characteristics of a deep Jurassic mine was evaluated for the aquifers in the roof of the 3–1 coal mine, and a "low-position and lateral interception" control method was proposed based on the water yield zonation. The results demonstrated that the depositional environment for the sandstone aquifer in the first member of the Zhiluo Formation had a channel bar paleoenvironment that was characterized by a varying water table. This proved to be the key layer for preventing and controlling the water hazards. The method described in this paper effectively mitigated the water hazards associated with that aquifer in the study area and thus provide a theoretical and practical basis for controlling mine water hazards in the IM-S mining area. [ABSTRACT FROM AUTHOR]

'Environment, Social and Governance' (ESG) considerations have received increasing attention as components that inform responsible investments in mining and global material supply. In ESG, governance variables have been mostly regarded as internal firm structures and broad national institutional strength, neglecting closer observations on economic regulation and national raw material politics. Improved governance data to support ESG evaluations should include historical and current relations between public governance bodies and private-sector firms. This paper examines the role of competition regulation agencies in weighing out and mediating relations between the State and multinational private-sector mining firms to unpack components that affect governance in mining. It does so by commenting on empirical data of the purchase held in 2018 of SQM shares—the world's second largest lithium raw materials supplier—by Tianqi Lithium—one of the largest lithium materials producers in China. The paper discusses the place of equity markets in politics of nation-making and economic regulation in mining. It argues that beyond internal corporate structures, the governance criteria in the ESG profile of mining companies are largely affected by domestic economic regulation maturity, domestic political geography histories of private equity and imageries of corporate shares and natural resources. Furthermore, this paper argues that the effective governance of lithium supply in Chile is affected by political tensions between economic regulation and contractual regimes in mining. [ABSTRACT FROM AUTHOR]

Henan Province is the birthplace of Chinese civilization and one of the earliest human settlements, which means that the area has an important national cultural heritage. Traditional villages are an important facet of this cultural heritage, and studying their spatiotemporal characteristics in different periods has important theoretical and practical significance for the sustainable development and protection of cultural heritage in the region. This paper takes the traditional villages of the Yellow River basin in Henan Province that were formed before 1919 as the research object. Information on the ancient river was obtained through a literature search as well as via field research, and the important tributaries of the Yellow River in different periods were mapped using ArcGIS 10.0 software. The nearest neighbor index, kernel density estimation, standard deviation ellipse and other methods were adopted to analyze the spatiotemporal characteristics of the traditional villages. The factors that influenced the evolution of traditional villages were explored in depth by combining changes in the course of the river and water conservancy projects. The results show that the formation of traditional villages along the Yellow River in Henan Province has experienced a historical track of growth, contraction, growth and prosperity and stability. The traditional villages along the Yellow River in Henan Province generally show a clustered pattern, forming a dense concentration of traditional villages in the middle reaches of the Yellow River. The center of gravity shows a migration trend from southeast to northwest. In terms of influencing factors, the spatiotemporal evolution of the relationship between villages and their distance to water is closely related to climatic fluctuations, changes in channel, water conservancy projects and social and cultural factors. This paper deepens our understanding of the relationship between traditional village evolution and watersheds by improving the consistency between village spatial distribution and historical geography and provides a useful theoretical reference for the sustainable development of China's traditional villages. [ABSTRACT FROM AUTHOR]

With the gradual increase of international willingness to reach the carbon peak and carbon neutrality, this paper decomposes the influencing factors of China's carbon emission changes from 2008 to 2019 using the Logarithmic Mean Divisia Index model (LMDI), and analyzes the contribution amount and rate of each influencing factor. The results found that: for the whole country, the cumulative growth of carbon emissions during the study period is about 416,484.47 (104 tons), among which the economic growth effect plays a major role in promoting, with a cumulative contribution rate of 284.16%; The increase in regulation intensity and the optimization of industrial structure, however, suppress carbon emissions well, with a cumulative contribution rate of about – 199.21% and − 64.75%, respectively, during the study period. For economic regions, the cumulative influence direction of each driver is the same as that of the whole country, while the population size effect in the northeast economic region and the regulation input effect in the eastern coastal economic region act in the opposite direction from other economic regions, and the carbon emission reduction direction of the energy intensity effect varies from one economic region to another. Accordingly, this paper proposes policy recommendations to enhance regulatory intensity, optimize industrial and energy consumption structure, implement localized emission reduction strategies, and promote synergistic emission reduction in economic zones. [ABSTRACT FROM AUTHOR]

Background: Gout is the most common inflammatory arthritis with an increasing prevalence and incidence across the globe. We aimed to provide a comprehensive and systematic knowledge map of gout research to determine its current status and trends over the past decade. Methods: Publications on gout research were obtained from the Web of Science Core Collection (WOSCC) database. Bibliometric R, VOSviewer, and Citespace were employed to analyze the eligible literature. Results: A total of 5535 publications concerning gout research between 2012 and 2021 were included. Most publications and citations both numerically came from China. The strongest international cooperation belonged to the USA. The University of Auckland was the most productive institution with a leading place in research collaboration. The prime funding agency was the National Natural Science Foundation of China. Most papers were published in Clinical Rheumatology. Annals of the Rheumatic Diseases achieved the highest number of citations, H-index and IF, which showed the most excellent comprehensive strength. The individual author with the most paper authorship was Dalbeth Nicola with 241 publications and 46 H-index. Keywords and co-citation analysis discovered that pathological mechanism remains the future hotspot in gout research. It may involve gout connection with gut microbiota, NLRP3 inflammasome, xanthine oxidase, and urate-transporter ABCG2. In addition, besides metabolic diseases, the relationship between gout and heart failure may need more attention. Conclusion: This study clarified the current status and research frontier in gout over the past decade, which would provide valuable research references for later researchers. Key Points •We disclosed the current status and frontier directions of gout over the past 10 years worldwide. •We identified future hotspots of gout research, including gout connection with gut microbiota, NLRP3 inflammasome, xanthine oxidase, and urate-transporter ABCG2. •We discovered that the relationship between gout and heart status would be the research frontier. [ABSTRACT FROM AUTHOR]

Constructing salt cavern gas storage in the ultra-deep strata more than 2000 m in depth is an important strategic technologic development in China. A primary issue is that higher temperature affects the creep behaviors of the surrounding rock. In this paper, triaxial creep tests of rock salt under multi-stage temperatures were carried out to study the influence of temperature on the creep behaviors of rock salt. Synchronous acoustic emission monitoring was performed throughout the creep test to study the microcracks characteristics of rock salt during creep at different temperatures. The results show that the duration of transient creep decreases with the increase of temperature. The steady-state creep rate increases with the increase of temperature. Temperature rise prompts the creep of rock salt to enter the volume dilatation stage. By analyzing the acoustic emission energy/counts, it is found that the microcracks generated during the creep at high temperature are few, but there are more at low temperature. Microcracks are generated intensively during rising temperatures. By analyzing the characteristics of the acoustic emission spectrum, it is found that when the temperature is low, the cracks generated are mainly intergranular cracks with a larger size, and they are mainly intragranular cracks with a smaller size when the temperature is high. The research in this paper is helpful to further understand the creep properties of ultra-deep rock salt and provides an important basis for the design, construction, and operation of gas storage salt caverns. Highlights: The influence of temperature on the creep of rock salt was studied by performing multistage temperature creep tests. The microcracks characteristics of rock salt during creep at different temperatures were studied using acoustic emission technology. The duration of transient creep phase decreases with the increase of temperature. The steady-state creep rate increases exponentially with the increase of temperature. [ABSTRACT FROM AUTHOR]

As one of the common physical materials in Chinese archaeological excavations, ancient bronzes are an essential basis for studying the development of Chinese bronze culture, which is of great significance for exploring the development law of ancient human civilization and reconstructing ancient human society. As China's infrastructure advances, the number of bronzes unearthed by archaeological excavations continues to increase. However, environmental damage to artefacts is very complex, whether the buried environment of the artefacts or the above-ground environment when the artefacts are unearthed, leading to different health problems for the excavated bronzes. A scientific assessment of these bronzes needs to be carried out prior to extraction to inform staff how they should be extracted, moved, and transported and how they should be restored afterwards. In response to the above problems, this paper takes excavated bronzes from archaeological sites as the research object and, by analysing and studying the relevant industry standards and the disease characteristics of bronzes, establishes a three-tier indicator framework for assessing the health of bronzes in a layer-by-layer refinement and proposes quantitative indicators with typical correlations. Through extensive research and testing, we screened out efficient, non-destructive, convenient and reliable assessment and testing methods and assessment models that combine subjective and objective aspects suitable for archaeological sites. On this basis, the paper achieves a scientific and practical assessment of the health status of bronzes excavated from archaeological sites. After repeated experiments, a set of comprehensive methods for quickly and conveniently assessing the health status of excavated bronzes was proposed for the first time and successfully applied to the archaeological excavation site of Sanxingdui site in Guanghan City, Sichuan Province, China. [ABSTRACT FROM AUTHOR]

Chinese calligraphy is an important part of Chinese national culture and art and part of the essence of Chinese national culture. Stone calligraphy is one of the important elements of Chinese calligraphy art. Stone carved calligraphy characters have high cultural and artistic value. Therefore, accurately recognizing stone carved calligraphy characters are of great importance. Stone carved calligraphy can identify hard-to-preserve stone calligraphy paper materials in electronic data that can be preserved for a long time, thereby offering important reference materials for the study of the historical development of Chinese calligraphy art. Moreover, with the development of science and technology and the investment of China in cultural and artistic undertakings, computer-aided calligraphy character recognition technology is also constantly improving, and its application in calligraphy recognition is becoming increasingly extensive. This article aims to study a method of stone inscription calligraphy recognition based on convolutional neural networks. In this paper, we use an image recognition and optimization method consisting of a convolutional neural network to carry out an experiment with stone inscription calligraphy characters. It was concluded that the recognition accuracy of stone calligraphy characters by the convolutional neural network reached 99.2%, indicating that this stone calligraphy character recognition method based on a convolutional neural network has a good ability to recognize stone calligraphy characters. [ABSTRACT FROM AUTHOR]

An urban subway network system is a complex public transportation system. To compare rail transit braking digital command control strategies based on neural network, this article analyzes and studies the characteristics of subway vehicle driver controllers and their design methods from three aspects: mechanical, electrical and software-assisted design. The learning rule of the BP neural network is called the mentor system learning rule, which is a kind of error-correcting algorithm. In the learning and training process, the expected output value needs to be given. The weights and thresholds of the BP neural network are optimized by selecting the parameters of the SA algorithm. The search method of SA is heuristic, and it has the following advantages: The selection of the initial solution does not affect the optimal solution. The simplified model extracts the core data processing individual analysis. In this paper, the physical data are extracted from the physical entity operation process for analysis, and the twin model is established to extract the twin data for analysis. This paper uses the characteristics of physical data to test the modeling effect and utilizes the twin data to carry out algorithm experiments on physical data. The ultimate goal is to use twin data to predict the state information of physical entities. The network error in the scheme designed by the article is 6%. The smooth implementation of this research constitutes an important reference for the design of subway train network control systems in other cities in China. Therefore, this research has great application value. [ABSTRACT FROM AUTHOR]

As a UNESCO World Heritage Site, the Grand Canal of China has a rich intangible cultural heritage along its route. The intangible cultural heritage of folk songs is an important representative. The study of its distribution characteristics and influencing factors is of practical and social significance for promoting the protection and development of intangible cultural heritage in relevant areas and the construction of the Grand Canal Cultural Belt. This paper uses the theories of musicology, geography, sociology, data statistical analysis and spatial analysis to analyze the distribution pattern of intangible cultural heritage items in the Grand Canal basin from the dimensions of geographical space and folk song genre, and from the perspective of natural and social conditions. The results show that the spatial distribution of folk songs in the Grand Canal basin shows a trend of "distribution along the river, two cores and two belts". Water system, landform and regional culture are the main influencing factors for the formation and dissemination of intangible cultural heritage of folk songs. At the same time, compared with the mountainous areas in western China, the economic and social development and frequent population flow in the Grand Canal basin lead to the diversified and unified musical culture characteristics of folk songs. Finally, this paper proposes the suggestions that the folk songs should be integrated into the local social and economic activities. [ABSTRACT FROM AUTHOR]

With the aging of the population, China has a large population base. The number of people suffering from mild cognitive impairment has gradually increased and gradually turned to senile dementia. The probability of mild cognitive impairment in China is about 5–7%, and about 15 million people are ill. To better distinguish mild cognitive impairment from Alzheimer's, this paper adopted the machine learning (ML) method to model. The evaluation table was established by considering the reaction time, education, background, memory, and other aspects of the patient. Machine learning has been applied in the fields of the Internet, finance, medicine, automation, biological science, etc. Through the machine learning support vector machine (SVM) and linear regression, the classification accuracy was compared. The results showed that the classification accuracy of SVM was 87.92% under the ML algorithm. It also showed that ML was more conducive to classification and recognition, which has played an important role in the identification of mild cognitive impairment in the current aging population. [ABSTRACT FROM AUTHOR]

The integration of 3D geometric models with semantic information significantly improves the applicability and comprehensibility of cultural heritage. The semantic 3D modeling of Chinese grottoes poses challenges for individuals without expertise in cultural heritage due to gaps in domain knowledge and discrepancies in understanding. However, the existing domain ontology and knowledge graph provide an insufficient representation of the knowledge of Chinese grottoes. To overcome these obstacles, we propose a knowledge graph representation method to provide explicit knowledge for participants at different stages of semantic 3D modeling of Chinese grottoes, which includes schema layer construction and data layer construction. On the schema layer, we design a domain ontology named ChgOnto (Chinese Grottoes Ontology) that consists of four high-level concept classes: spatial object, informational object, digital device, and temporal object. Among the classes in the ChgOnto, the components (e.g., cliff wall, cave roof, cliff wall footing), elements (e.g., primary Buddha statue, pedestal, decoration), the properties (e.g., length, width, depth) of caves and niches in Chinese grottoes as well as the spatial relationships between them are all precisely defined. ChgOnto also reuse the classes from the renowned CIDOC CRM ontology in the cultural heritage field and GeoSPARQL in the geospatial domain, facilitating integration between the two subjects. Considering the schema layer as the conceptual data model, the data layer extracts knowledge from unstructured text through natural language processing tools to instantiate the abstract classes and fill the properties of the schema layer. Finally, the knowledge required for semantic 3D modeling of Chinese grottoes is expressed in the data layer by a knowledge graph in a fixed expression form. Dazu Rock Carvings, a World Heritage site in China, is selected as a case study to validate the practicality and effectiveness of the proposed method. The results reveal that our method offers a robust knowledge-sharing platform for the semantic 3D modeling of Chinese grottoes and demonstrates excellent scalability. The method proposed in this paper can also serve as an informative reference for other types of cultural heritage. [ABSTRACT FROM AUTHOR]

The Ordovician–Silurian bentonite is an ideal target for probing complex geological events such as biological extinction, glaciations, and volcanic events at that time due to its global distribution and multiple intercalary strata. In this paper, Late Ordovician–Early Silurian K–bentonites from the western segment of the South Dabashan are sampled for zircon U–Pb dating and geochemical analyses. A mean 441.4 ± 4.2 Ma zircon U–Pb age is obtained from the K–bentonite and is regarded as volcanic eruptive time. Geochemical data show characteristics of intermediate volcanic rocks and typical K–bentonites elsewhere. Chondrite–normalized REE+Y distribution patterns display LREE (light REE) enrichment and HREE (heavy REE) depletion with distinct negative Eu anomalies. Primitive mantle–normalized spider diagram shows similar concave-up patterns with relatively enriched LILE (Ba, U, and Pb), HFSE (Nb, Ta, Zr, and Hf) and distinctly depleted elements such as P, Sr and Ti, indicating these K–bentonites derive from high–K and alkaline primary magma and represent the product of within–plate volcanic eruption. High-frequent and periodic volcanic eruptions from the Yangtze block, its peripheral areas, and the southern margin of the North China block began with Early Ordovician and lasted to Late Silurian. In the Dabashan region, ca. 440–448 Ma K–bentonites may primarily originate from the within–plate volcanic setting, but volcanic ashes of ca. 450–460 Ma K–bentonites are perhaps island arc products connected with north–dipping continuous subduction of the Paleo–Qinling Ocean during Early Paleozoic. [ABSTRACT FROM AUTHOR]

The carbonate rock formations have obvious dual media characteristics, fracture development and good physical conditions, which are the main seepage channels and storage spaces for gas after the reconstruction of underground gas storage. The carbonate strata of the Ordovician system are important natural gas reservoirs in the eastern area of Sulige Gas Field in the Ordos Basin, and the identification and characterization of their fractures are of great significance for the modeling of fractures in the later stage and the improvement of the operation scheme of the gas storage. At present, there is little research on fractures, which restricts exploration and development. Therefore, taking the 39–61 gas storage reservoir in the eastern area of Sulige Gas Field in the Ordos Basin as the research object, this paper identifies and studies the characteristics of the fractures by core, microscopic, conventional logging curves, and imaging logging identification. The results show that the fracture length ranges from 5 to 15 cm and the width ranges from 0.1 to 3 mm. The fracture angles are mostly between 75° and 90° and the main direction is NW–SE. In conventional logging curves, porosity logging has a good response to fractures, while resistivity logging has a general response to fractures; In layers with more developed fractures, natural gamma values are mostly higher than 40API, rock volume density is less than 2.8 g/cm3, neutron porosity is greater than 12.5%, and acoustic time difference is greater than 160 μ s/m. This study is of great significance for improving the identification of carbonate fractures, enriching the relevant theories, and providing guidance for the construction of carbonate gas storage. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of the Internet has promoted the continuous expansion of the scale of China's tourism industry, and the amount of tourism data has surged. However, tourists need help bringing personal interest and high-value data from the plethora of tourism information. The rise of artificial intelligence has transformed traditional tourism into an intelligent, data-driven industry. This shift has generated vast tourism data, offering both opportunities and challenges. The paper discusses an AI and IoT-based Intelligent Tourism Recommendation System (ITRS) that offers travelers predefined itineraries, personalized suggestions, and tourism insights. This system simplifies attraction discovery, unveiling hidden value within tourism data at the intersection of AI and IoT. The present study thoroughly investigates AI-based recommendation algorithms before delving into the system's architecture. It categorizes user-based, project-based, and article-based collaborative filtering methodologies tailored to specific goals. First, thoroughly examine AI-based recommendation algorithms before delving into the system architecture. Second, categorize collaborative filtering methods as user-based, project-based, and article-based, each tailored to specific objectives. Third, delve into the Apriori algorithm's complexity within the context of weighted association rules and introduce an enhanced iteration for improved efficiency. The proposed scheme encompasses an elaborate ITRS plan featuring a user interest model and a client module, crucial for the computation and analysis of users' long-term and short-term interests. Rigorous performance testing confirms the ITRS's superiority across varying support levels, with experimental results demonstrating the Apriori algorithm's exceptional accuracy, achieving a 94.3% improvement over other methods. The Apriori algorithm is better than traditional recommendation algorithms such as Linear Regression, Logistic Regression, Decision Trees, Random Forest, Support Vector Machines, K-nearest neighbor, Naive Bayes, and XGBoost. [ABSTRACT FROM AUTHOR]

Forests play an essential role in ensuring ecological balance and providing valuable resources. Evaluating their ecological effectiveness and economic value is vital for sustainable forest management. Due to the ecological benefits of the forest ecosystem, China has introduced reforms in its ecological system and incorporated them into its national economic accounting system. This research study selects different types of forests based on their locality and species. It formulates a classifier using Convolution Neural networks (CNN) and Artificial Neural Networks (ANN) data mining techniques. First, this paper briefly discusses data mining methods used to determine the impact of different factors on forest health, quality, and its importance in terrestrial ecology. Second, this research study designs a classifier for labeling forests according to their ecological and economic worth. A confusion matrix was used to ascertain the performance of the classifier. Third, it illustrates an index system for evaluating forest ecosystem service function value and explores dynamic characteristics of forest resource variation and the change process of the forest ecosystem. Finally, a statistical analysis is performed among the proposed study and related studies. The proposed study outperforms its predecessors and depicts that the proposed technique is more accurate than traditional systems. The study showed that the amount of nutrients accumulated by trees and the amount of polluted gas absorbed have doubled, and the growth rate of other functions has also increased by more than 70%. The average growth of forest ecological functions is 97.93%, significantly improving people's awareness of forest resource protection. [ABSTRACT FROM AUTHOR]

In recent years, lung cancer has become one of the most common malignant tumors in the world, which seriously threatens the life and health of mankind. Lung cancer is also the most common cancer in China. According to the latest report released by the National Cancer Center, the incidence and fatality rate of lung cancer in China rank first among malignant tumors, which has brought great disease burden to the Chinese. In this paper, using ethyl acetoacetate as the starting material, eight benzo[d]imidazolium[2,1-b]thiazole target compounds containing chromone were synthesized in five steps through alpha halogenation, cyclization, hydrolysis, condensation and cyclization reaction. MTT assay was used to evaluate the in vitro inhibitory activity of the target compounds on A549 and H2228 cancer cells. The IC50 values of the target compounds to A549 and H2228 cell lines ranged from 17.63 to 369.09 μmol/L. Some target compounds have moderate anti-tumor activity in vitro, which could be used to prepare related anti-tumor drugs for lung cancer. [ABSTRACT FROM AUTHOR]

Topographic variables derived from digital elevation models (DEM) are the most commonly used predictor in soil organic carbon (SOC) prediction. The predictability of these variates for the spatial heterogenity of SOC is influenced by the size of DEM resolution. This paper aimed to investigate the resolution effect of the topography at multiple DEM resolutions and select the most important predictors and the optimal resolution for SOC prediction in a large-scale and morphologically complex area. The study area, covering 15 600 km2, was located in a mountainous province in China. A total of 3901 soil samples and three machine learning algorithms including random forest, gradient boosted regression tree, and artificial neural network were used to construct the SOC predictive models at resolutions of 30, 90, 150, 210, 270, 330, and 390 m. Topographic factors were derived from the DEM with an original resolution of 30 m and were subsequently resampled to varying resolutions by using the bilinear interpolation algorithm. The importance of each variate in each predictive model was computed for the most important predictor selection, and the determination coefficient (R2) and Root Mean Square Error (RMSE) of each predictive model were computed for the accuracy evaluation. Comparative analysis on point-based topographic representation, the accuracy of predictive models, and the variable importance was conducted among different resolutions to investigate the resolution effect on the SOC prediction and to select the optimal resolution for this study area. The results showed that topography was the key factor influencing the SOC distribution in this large-scale area due to the region's mountainous nature, and the relative contribution of the topography to predict SOC distribution varied with DEM resolutions. Elevation was the most important topographic factor at all DEM resolutions. Accuracies of predictive models built on the three machine learning algorithms were all dependent on DEM resolution, indicating that DEM resolution had an important effect on SOC prediction. Overall, the random forest predictive models outperformed the other two algorithms, and its most accurate result was obtained at the 210-m resolution with an R2 of 0.25 and an RMSE of 4.77 g kg–1. In conclusion, we found that finer resolutions did not necessarily produce more accurate SOC predictions even in a large-scale and morphologically complex area. Therefore, investigating the resolution effect is suggested to select an optimal resolution for the SOC prediction in other regions characterized by similar geomorphological conditions as this study area. [ABSTRACT FROM AUTHOR]

This study investigates the gender disparities and cohort differences in the health trajectories of older Chinese adults. Drawn on a nationally representative dataset from the 2008–2018 Chinese Longitudinal Healthy Longevity Survey, this paper uses a growth-curve model to identify the health trajectories of older adults in functional limitations, chronic diseases, depressive symptoms and cognitive skills. Older women are found to have slightly fewer functional limitations than older men at an earlier age; however, this gap is reversed later because of women's higher deterioration rate in physical function. Compared to men, older women have more chronic diseases at younger ages, but this trend is changed after age 90 years due to a faster decline rate in chronic diseases for women. The gender gap in the depressive symptom trajectories narrows with age, whereas the gap in the trajectories of cognitive skills widens. The cohort differences favouring later-born cohorts decrease with age in the trajectory of functional limitations. The latest cohort born in 1940 or after has the fastest increase in chronic diseases and depressive symptoms among all the cohorts. These results are further verified using the random-effect model and pooled ordinary least squares. Policymakers should promote gender equality and reduce cohort differences to achieve the national goal of "Healthy China". [ABSTRACT FROM AUTHOR]

In many societies, parents prefer sons over daughters, but the well-being effects of child gender, especially in later life, are less studied. Using the latest two waves of the China Health and Retirement Longitudinal Study, this paper evaluates the impacts of having daughters on older parents' subjective well-being (SWB) in urban China, which has a rapidly aging population and the traditional preference for sons. Studying the cohort of parents whose child gender is as good as random, we find that having more daughters promotes older parents' SWB, especially overall life satisfaction, satisfaction with health, and satisfaction with children. Our results suggest that the increase in SWB is achieved through better health, more financial support from daughters and more spending on leisure. The positive SWB effects of daughters are found to be more salient among more vulnerable groups, including those who are older, less educated, and with fewer children. [ABSTRACT FROM AUTHOR]

The residual oil in the fluvial reservoir of Gudong Oilfield is still widely developed even after decades of development. In order to explore the remaining oil in the subsurface fluvial reservoir, we need to have a better understanding on fluvial sandbody characteristics. This paper aims to demonstrate how to quantify anatomy on meandering fluvial architecture. Sedimentologic data from core descriptions of Guantao Formation in Gudong Oilfield were used to identify architectural elements. The upper Guantao Formation consists of ten lithofacies composing five different types of architectural elements. By integrating the available core, well logs, and seismic data, the fluvial sandbody distribution was analyzed. To characterize the fluvial channel geomorphology, we used seismic inversion to explore the stratigraphic correlation and stacking patterns of different channels, and the sandbody isopach map was obtained by the sandbody thickness in each layer. The architectural elements distribution in the plane was mapped by the sandbody isopach map and its profile characteristics. According to the investigation of meandering fluvial outcrops, subsurface meandering fluvial seismic geomorphology, and modern river morphology, the upstream-bar portions are always eroded because of the downstream channel migration. Statistics on the morphometric parameters (bar width/looplength, upstream deflection angle, downstream deflection angle, etc.) of modern meandering river (Ob River) were obtained from the Google Earth. It is found that the bar width/looplength and upstream deflection angle, upstream deflection angle and downstream deflection angle in modern meandering river have a good relationship, and we can have a quantitative architectural characterization on the point bar. This study quantified lithofacies thickness and proportion in different architectural elements of Guantao Formation, which ensures a high resolution anatomy on the small scale architecture. By using this method, the lithofacies proportion in each architectural element was computed, and through the statistics of architectural elements proportion in the Gudong Oilfield, the different lithofacies proportions in the block 6 can be computed. [ABSTRACT FROM AUTHOR]

Rockburst is a frequent challenge during tunnel and other underground construction and is an extreme rock damage phenomenon. Therefore, it is very crucial to accurately estimate the damage potential of rockburst events. Microseismic (MS) monitoring can be used to obtain the relevant MS parameters for short-term rockburst prediction in real time that reflect the evolution of short-term rockburst. In this study, short-term rockburst potential data containing 7 MS parameters (cumulative number of events, cumulative released energy, cumulative apparent volume, event rate, energy rate, apparent volume rate, and incubation time) and 91 rockburst events (none rockburst, low rockburst, moderate rockburst, and high rockburst) were collected from the Jinping Hydropower Station diversion tunnel project in China. The objective of this paper is to propose an ensemble learning (EML) model based on the LévyFlight-Jaya optimization (LFJaya) and fivefold cross-validation (CV) method to achieve an accurate prediction of short-term rockburst damage potential using MS information. The EML consists of light gradient boosting machine (LGBM), extreme gradient boosting (XGBoost), random forest (RF), support vector machine (SVM), and logistic regression (LR), with seven MS parameters as the EML inputs and four rockburst levels as target variables. 70% and 30% of the cases were randomly selected for training and testing, respectively. Five metrics (accuracy, kappa, precision, recall, and F1-score) and nonparametric statistical tests were used to evaluate the performance of the model. It can be observed from the results of this study that the proposed EML has a higher test accuracy (89.29%) than the multiple base classifiers used in the study. With the use of the ensemble model, the decision boundary becomes more precise and overfitting is significantly improved. Additionally, the internal decision-making process of EML was elucidated through an analysis of the model parameters using SHapley Additive exPlanations (SHAP) and Local Interpretable Model-agnostic Explanations (LIME). It was discovered that the cumulative released energy, the number of MS events, and the cumulative apparent volume (which reflects the number and strength of rock fractures) exert a significant influence on the prediction of short-term rockburst potential. Finally, developed graphical user interface (GUI) accurately predicted six instances of rockburst in the deeply buried tunnel of Jinping. Verification results indicated that the proposed EML exhibits strong generalization and can effectively utilize MS information to achieve precise short-term rockburst potential predictions. [ABSTRACT FROM AUTHOR]

With the rapid rise of infrastructure construction in southwestern mountainous areas of China, tunnel engineering has become increasingly frequent in crossing faults with strong activity. However, there has been a long-term lack of an effective theoretical method for identifying the stability and controlling the deformation of extremely weak surrounding rocks inside active faults. Therefore, this paper researched stability analysis and deformation control of a tunnel with extremely weak surrounding rocks caused by strike-slip fault activity. Firstly, uniaxial and triaxial compression tests were conducted on completely decomposed granite within active faults under drained and undrained conditions, and the mechanical properties of granite after desertification and argillization were analyzed. Then, based on the upper-bound limit theory and according to the relationship between the safety factor, instability trace, and geo-stress, a theoretical method to evaluate the stability of the unsupported surrounding rock under different geo-stress levels was proposed. Furthermore, a comprehensive deformation control method, including advanced support, excavation face reinforcement, and radial grouting, was proposed, and the applicability of this method was analyzed and verified through field mechanics and deformation measurements. The results showed that under the triaxial stress state, the post-peak stress drop characteristics of completely decomposed granite basically disappear, and the plastic deformation characteristics of rock become prominent. The comprehensive strengthening scheme has a significant deformation control effect, and the surrounding rock deformation can be reduced by over 65%. In addition, the pressure on the supporting structure will be more than 3.2 times higher with no reinforcement scheme. Highlights A comprehensive deformation control scheme has been proposed to prevent and control deformation disasters in extremely weak surrounding rocks caused by strike-slip fault activity. An upper-bound limit theory considering the influence of geo-stress is established to judge the stability of the unsupported surrounding rock. The mechanical properties of completely decomposed granite caused by strike-slip fault activity under drained and undrained conditions are analyzed. [ABSTRACT FROM AUTHOR]

This paper explores the understudied topic of international student sojourners' emotional intercultural competence in higher education by analysing Chinese postgraduate students' experiences of managing their emotions evoked during intercultural communication at a British university. Through ethnographic interviews, this study examines how students construct their emotion-management experiences as well as the effects of social interaction on the construction processes of these experiences. The findings shed light on the motives and pathways for deploying specific emotional intercultural competencies in practice. Furthermore, the findings manifest the central role of the interplay between individual agency and societal forces in shaping the substances, facilitators, constraints, two-way interactivity and dialectical tensions of emotional intercultural competencies that unfold in specific interpersonal, moral and structural contexts. By representing competence as a repertoire of action strategies performed by individuals to address their important concerns and by illuminating the meanings of competence in particular environments at particular moments, this study moves beyond the traditional 'list' approach which largely conceptualises competencies as fixed, individual-based and universal entities. The study emphasises the importance of taking a holistic, process-oriented, context-based and dialectical perspective in developing a deeper level of competence for empowering individuals engaged in intercultural communication. In addition, this study discusses practical implications regarding the development of emotion-management strategies towards competence for intercultural education in higher education. [ABSTRACT FROM AUTHOR]

Numerous Neoproterozoic granitic intrusions occur over a north-south distance of more than 700 km in the western Yangtze Block. In this paper, we use zircon U-Pb age, Lu-Hf isotopic, and whole-rock geochemical data to show that the Shimian granitic pluton, which was previously thought to be S-type granite in the area, is actually A-type granite. The selected fresh samples for this study are mainly porphyritic biotite monzogranites. They are weak peraluminous, A2-subtype granites, displaying high contents of SiO2 and K2O, low CaO, and Na2O, with the characteristics of high silica, rich potassium, and low sodium. They are enriched in incompatible trace elements, but low in trace elements compatible in mafic silicates and feldspars, and they are chemically characterized by having high REE and Ga abundances and low Al, Ni, Ba, Sr, and Eu abundances. The major and trace element behavior suggests that the granites underwent the process of advanced fractional crystallization and were likely generated by crustal partial melting. Zircon U-Pb age shows the Shimian granitic pluton was emplaced at 728 ± 20 Ma. The studied granites yield an average 176Hf/177Hf value of 0.282363 (εHf = 0.8) with a 94% confidence interval of 0.282334 to 0.282384 (εHf = −0.1 to 1.7), indicating that they are derived from the remelting of juvenile continental crust. We suggest that this newly identified A-type granite at Shimian, southwestern China, was more plausibly generated in an extensional setting as a result of slab window caused by ridge subduction, which can account not only for the formation of voluminous granitoids, but also for the basalts, boninitic pillow lavas, and SSZ-type ophiolites in western Yangtze Block. [ABSTRACT FROM AUTHOR]

Purpose: Tuberculosis is one of the oldest diseases in human history, and spinal tuberculosis (STB) is the most common form of extrapulmonary tuberculosis. A large number of research has been conducted in this field. However, there has been no bibliometric analysis performed in recent years in STB. The aim of this study was to analyze trends and hotspots in research on STB. Methods: Publications regarding STB between 1980 and 2022 were extracted from the Web of Science database. CiteSpace (V5.7.R2) and VOSviewer (1.6.10) were used to perform global analyses of the number of publications, countries, institutions, authors, journals, keywords, and cited references. Results: A total of 1262 articles were published between 1980 and 2022. We observed rapid growth in the number of publications since 2010. Spine had the highest number of publications (47, 3.7%). Zhang HQ and Wang XY were key researchers. The Central South University published the most papers (90, 7.1%). China was the leading contributor in this field with 459 publications and 29 H-index. National partnerships are dominated by the USA, and there is a lack of active cooperation between other countries and authors. Conclusion: research on STB has achieved great progress, with an increasing number of publications since 2010. Surgical treatment and debridement are current research hots pots, and diagnosis, drug resistance, and kyphosis are likely research frontiers. Cooperation between countries and authors needs to be further strengthened. [ABSTRACT FROM AUTHOR]

Eco-civilization, as a civilizational discourse led by China, implies the next stage of civilization after industrial civilization, the essence of which is to respect, conform to, and protect nature. Although the international community is paying more attention to eco-civilization, the existing literature still lacks a systematic discussion of which theories and practices underpin the construction of eco-civilization. Due to the ambiguity of eco-civilization, some even criticize it as being a form of purely partisan politics in China. By systematically reviewing its theoretical pillars, practical actions, and major achievements, this perspective paper argues that China's eco-civilization is not a partisan politics, but rather a legitimate, imperative pathway to global sustainable development rooted in complementary theory and practice, i.e., theories guide practices, and practices enrich theories. We highlight that the theoretical basis and practical actions of eco-civilization constitute a continual improvement process that permits diverse viewpoints and understanding, and any action exploring how to achieve a harmonious coexistence between humans and nature is consistent with the values of eco-civilization. [ABSTRACT FROM AUTHOR]

China prioritizes ecological civilization construction and embraces the concept of "lucid waters and lush mountains are invaluable assets." Great achievements have been made in ecological protection and restoration through implementing a series of policies and projects. This paper reviews the history of ecological restoration in China and the current development of the "integrated protection and restoration project of mountains, rivers, forests, farmlands, lakes, grasslands, and deserts (IPRP)." Furthermore, the characteristics of IPRP were systematically elaborated from the perspectives of the ecological civilization thought, the policy management, and the key scientific issues. Also, the current achievements were summarized in the fields of national ecological space management, biodiversity conservation, and ecological protection and restoration. Existing challenges in management policy, scientific issues, and engineering practices were highlighted. Future perspectives include ecological space control, nature-based Solutions, biodiversity big data platform, modern techniques, and value realization mechanisms of ecological products. [ABSTRACT FROM AUTHOR]

China's concept of "ecological civilization" can be understood as a new system of development and governance based on the perspective of political decision-making. Environmental management, ecological restoration, and green development are its primary principles—distinctly different from industrial and agricultural-oriented civilizations. In this paper, we evaluate the evolution of political connotations of the ecological civilization concept in China over the past 15 years through a textual analysis approach. Additionally, we systematically outline an ecological civilization indicator system and analyze its evolutionary process, applicable scales, and role in guiding the implementation of the ecological civilization concept. Eco-civilization demonstration sites and experiences are also discussed, followed by a review of academic research and policy-making responses. Finally, we propose different perspectives on the outlook for the future of ecological civilization development in China. [ABSTRACT FROM AUTHOR]