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This research scrutinizes the trends and dynamics of Intellectual Property Protection (IPP) of Intangible Cultural Heritage (ICH) in China, utilizing a dataset of 91 papers from the CNKI database spanning 2011 to 2020. The study uses CiteSpace software to visualise and analyse the literature across multiple dimensions, including article count, authorship, institutional affiliations, and keyword co-occurrence. Findings indicate a lack of robust collaboration among authors and institutions in IPP and ICH, with a scarcity of active cooperative groups. Critical research hotspots identified encompass intangible cultural heritage, intellectual property protection, inheritors, legal protection, copyright, intellectual property law, and geographical indications, with the legal safeguarding of ICH's intellectual property, digital conservation, traditional cultural expressions, and original authentication emerging as the leading research frontiers. This investigation provides a holistic view of China's IPP and ICH landscape, offering essential scientific insights for ongoing scholarly discourse. This study mainly benefits policymakers and stakeholders in the cultural heritage sector, underscoring the necessity of enhanced authorial and institutional collaboration and the prioritization of legal and digital protection mechanisms to safeguard China's intangible cultural legacy for posterity. The analysis is critical, informing policy formulation and strategic planning to bolster ICH's protection and sustainable management in China. [ABSTRACT FROM AUTHOR]

This paper examines the impact of environmental decentralization on the export domestic value-added rate of enterprises using combined data from 2000–2014 from China Industrial Enterprise Database, China Customs Database, WIOD, China Environment Yearbook and China Enterprise Patent Database. The research findings show that the overall environmental decentralization has an inverted U-shaped impact on enterprises' export DVAR, with 94.4% of the sample in the promotion interval. 73.2% of ordinary trade enterprises and 85.7% of processing trade enterprises are in the suppressive interval of the U-shaped impact of administrative decentralization; 69.2% of ordinary trade enterprises are in the suppressive interval of the U-shaped impact of monitoring decentralization, and 85.7% of processing trade enterprises are in the promotion range of the inverted U-shaped impact; 66.0% of ordinary trade enterprises and 86.7% of processing trade enterprises are in the suppression range of the U-shaped impact of monitoring decentralization. In addition, cost markup and R&D innovation as mediating variables are important transmission channels for environmental decentralization to influence enterprises' export DVAR. [ABSTRACT FROM AUTHOR]

Global regional trade agreements (RTAs) are experiencing unprecedented rapid development in terms of number and content of provisions, and China's RTAs are no exception. With the increase in the depth of RTA provisions, as an important form of economic and trade relations between countries, investment is bound to be affected. Taking the RTAs signed by China as the research object and using the bilateral foreign direct investment (FDI) data provided by the International Monetary Fund CDIS database and the relevant data from the World Bank RTA text depth database, this paper constructs various indexes to measure the depth of RTA provisions and analyzes the effects of changes in the depth of RTA provisions on inward FDI stocks to China. The study finds that the improvement of all four depth indexes, namely, total depth, core depth, depth of WTO+ provisions, and depth of WTO-X provisions, significantly promotes China's inward FDI stocks. This conclusion still holds after a series of endogenous problem analysis and robustness tests. Further analysis shows that the improvement of the depth of RTA provisions has a relatively greater effect on FDI stocks from developing countries and countries along the "Belt and Road". At the same time, there exists significant heterogeneity in the effects of different RTA provisions on the FDI stocks. The insights that we obtain in this paper enable us to provide policy recommendations for the construction of China's global high-standard free trade agreement (FTA) network. [ABSTRACT FROM AUTHOR]

In the era of Internet big data, the extensive use of cloud data has promoted the development of various fields. In the field of sports, western contemporary sports are gradually influencing China's existing traditional sports and folk sports culture, thus the dominant position of China's traditional folk sports culture is threatened. In order to effectively carry out folk sports and better resist foreign sports culture, the public should be made aware of the importance of developing and protecting sports cultural heritage, and the government and other administrative departments can establish and designate corresponding management methods. In order to make people better understood cultural heritage, this paper created a data database with efficient retrieval function according to the characteristics of folk sports culture, sports form, historical data arrangement, figures and graphics. This paper designed a special website for the digital protection of folk sports culture, which can initially met the functions of network storage, display and academic exchange of folk sports cultural heritage. Under the concept of integrating data cloud, built a digital platform for folk sports cultural heritage, realized the flexible deployment and sharing of folk sports culture, and further promoted the integration of traditional sports cultural heritage protection and national fitness. The frequency of national fitness integration is 3–10 times. The development and protection of the digital platform for folk sports cultural heritage can enable people to better understand and understand cultural heritage. It effectively cultivated the cultural consciousness and initiative of the public to participate in the protection of cultural heritage for the society. [ABSTRACT FROM AUTHOR]

A carbon emission factor (CEF) database is required for the basis of carbon emission calculation in construction projects. However, the default values for existing CEF databases cannot cover the complex resources involved in a construction project. Therefore, this paper proposes a three-step method to guide the establishment of an extensible CEF database for the construction industry, including (1) data collection and parser, (2) data extension, and (3) data encoding and storage. The data extension mechanisms provide the supply chain perspective considering temporal issues and the accounting perspective to streamline the process. Aiming to address the lack of a comprehensive CEF database for the construction industry in China, this paper uses this method to establish a carbon emission factor database for the Chinese construction industry (CEFD for CCI). This database is open and free with 646 CEFs, including five parts: energy, human, material, machinery, and greenspace. This paper provides a way for developing and less developed countries to establish an expandable CEF database, which benefits the parser, extension, encoding, and storage of new resources, as well as computer access. [ABSTRACT FROM AUTHOR]

Introduction: This study presents a global research scenario in the broad domain of osteoarthritis (OA) research, using quantitative and qualitative publication and citation indicators. Methods: The study is based on 45,368 global publications, sourced from the Scopus bibliographical database, covering three decades (1994–2023). We studied the performance of the top 12 developed and top 12 developing countries. The key countries, organizations and authors at national and international levels were identified. The broad subject areas and key journals contributing to global OA research were delineated, besides identifying the broad characteristics of highly cited papers in the field. Results: The United States and China were the most productive countries, while the Netherlands and Canada made the largest citation impact. Harvard Medical School and the University of Sydney made the most contribution, while Boston University and Pfizer Inc., USA registered the highest citation impact. Hunter DJ and Guermazi A were the most productive authors, while Lohmander LS, and Hochberg MC registered the highest citation impact. Osteoarthritis and Cartilage (n = 4879) and Annals of the Rheumatic Diseases (n = 786) published the maximum papers, while Arthritis and Rheumatism and Nature Reviews Rheumatology registered the largest citation impact. The highly cited papers with 100 or more citations constituted 6.25% of the total publications. Conclusions: There has been a systematic growth of publications on OA. The research on OA was mainly done in developed countries, with the maximum publications coming from the United States of America, China and Canada. The most impactful publications on OA were from the Netherlands, Canada and the United States of America. [ABSTRACT FROM AUTHOR]

Performance degradation with intraspeaker variability is a hot topic in speaker recognition. Accuracy dropping over time has become a common and accepted phenomenon in the field of speaker recognition. In China, many people travel between their birthplace and workplaces. Different cultural atmospheres and customs have an effect on the pronunciation of speech. The ongoing work focuses on time-varying and region-changed factors that are caused by population migration. This paper introduces a time-varying and region-changed speech database (TRSD) collected from 55 university students over 3 years. In total, it contains 3795 utterances. To study the impact of the time-varying and region-changed factors on speaker identification and explore hidden factors that may lead to performance degradation, there are also many experimental studies for the database. In the experiments, the changes in characteristic parameters (pitch, intensity, formant and spectrogram) are analyzed and grouped by gender and birthplace. The Gaussian mixture model-universal background model, deep neural network model, i-vector/PLDA and x-vector/PLDA are evaluated on TRSD to provide a reference performance. For the time-varying and region-changed factors, this paper also provided three kinds of corresponding solutions: speaker model adaption, cepstral mean normalization and mel-frequency cepstrum coefficient normalization. [ABSTRACT FROM AUTHOR]

Background: Bibliometric analysis is a mature method for quantitative evaluation of academic productivity. In view of the rapid development of research in the field of systemic lupus erythematosus (SLE) in the past decade, we used bibliometric methods to comprehensively analyze the literature in the field of SLE from 2013 to 2022. Methods: The relevant literature in the field of SLE from 2013 to 2022 was screened in the Web of Science Core Collection database. After obtaining and sorting out the data, CiteSpace and VOSviewer software were used to visualize the relevant data, and SPSS software was used for scientific statistics. Results: A total of 18,450 publications were included in this study. The number of articles published over the past 10 years has generally shown an upward trend, while Altmetric attention scores have also shown a clear upward trend in general and in most countries. Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers. The USA, China, Japan, Italy, and the UK are the most productive countries, but China and Japan are significantly inferior to other countries in terms of research influence. Four of the top ten authors are at the center of the collaboration network. LUPUS is the most contributing journal. The theme of systemic lupus erythematosus research mainly focuses on the pathogenesis, treatment, and management of SLE, and the emerging trend is related research on machine learning and immune cells. Conclusion: This study shows the research status of SLE, clarifies the main contributors in this field, discusses and analyzes the research hotspots and trends in this field, and provides reference for further research in this field to promote the development of SLE research. Key Points • Through bibliometric analysis, Altmetric analysis, and visual analysis, we reveal the global productivity characteristics of SLE-related papers in the past 10 years. • The number of global SLE-related studies has shown a significant increase, indicating that SLE is still a hot topic and deserves further study. • Citation analysis and Altmetric analysis can mutually prove and supplement the influence of papers, and the attention of related literature among non-professional researchers is increasing. • The theme of SLE research mainly focuses on the pathogenesis, treatment, and management of SLE. The emerging trend is machine learning and immune cells, which may provide new strategies for the diagnosis and treatment of SLE in the future. [ABSTRACT FROM AUTHOR]

Background: On September 5, 2022, an Ms 6.8 earthquake occurred in Luding County, Ganzi Tibetan Autonomous Prefecture, Sichuan, China. The casualties and economic losses caused by the earthquake are huge. Most of the landslides triggered by this earthquake are small and medium-sized rock collapses and destructive debris flows, with a small number of large-scale landslides. Results: There are 5007 coseismic landslides in the Luding earthquake VII-degree area and above, with a total area of 17.36 km2. The landslides are generally distributed along the NW-SE direction. The highest landslide area density is 13.8%, and the highest point density of the landslide is 35.73 km2. High-density area is mainly concentrated in the IX-degree area and the northeast side of the seismogenic fault. Coseismic landslides are more likely to develop in the area with the slope more than 40°, the slope direction of 67.5-112.5 degree, and vegetation coverage of 40–50%. More landslides are developed in granite areas and forest areas. The closer distance to active faults, the more the coseismic landslides develop. The correlation between coseismic landslide development and the distance from the active faults is better than that between coseismic landslide development and the distance from the seismogenic faults. Conclusion: The landslide database used in this paper is the most perfect at present, and the research results have also been verified by fieldwork. This is also the first applied study of this landslide inventory, which indirectly confirmed the accuracy of the inventory. The results obtained in the manuscript will provide an important guideline for future disaster prevention and mitigation in southwest China. [ABSTRACT FROM AUTHOR]

Some scholars have shown that the machine learning methods based on a single-source data can successfully monitor the risks of formal financial activities, but not those of informal financial activities. This is because the data generated by formal financial activities, whether it is the structured or unstructured data, are of high quality and quantity, while the data generated by informal financial activities are not. Therefore, multi-source data are the key to monitor the risks of informal financial activities through machine learning. Although a few studies attempted to use multi-source data for financial risk prediction, they simply stack the obtained multi-source data, but ignore the original sources, heterogeneity, mutual redundancy and other characteristics of the data, so that the improvement of the prediction effect is not obvious. Therefore, TSAIB_RS method based on the two-stage adaptive integration of multi-source heterogeneous data was constructed in the paper, in which the data with different sources and different distributions were adaptively integrated. In order to test the reliability of TSAIB_RS method, the paper takes the default risk of microcredit in China as the test target and compares the prediction results of various test methods. It concludes that TSAIB_RS method can significantly improve the prediction effects. [ABSTRACT FROM AUTHOR]

This paper proposes a comprehensive coordination and cross-efficiency evaluation model, aimed at tackling the problems evident in self-evaluation systems based on data envelopment assessment models, such as "developing strengths and avoiding weaknesses," "ranking strengths and weaknesses completely," extreme weights, and so on. Based on environmental sustainability, economic development sustainability, social well-being sustainability, and scientific and technological innovation sustainability these four dimensions, this paper proposed model contains 19 evaluation indicators, of which 11 are inputs and 8 are outputs. This model can overcome the problems of multiple solutions and the instabilities of the average cross-efficiency model by relaxing the average hypothesis of the general cross-efficiency model, thereby reducing the distance between the weighted input and the weighted output of the regional sustainable development system assessment. Taking the evaluation of the sustainable development efficiency of 31 provinces and regions in China as an example, the results showed that the evaluation function of the comprehensive coordination and cross-efficiency evaluation model constructed in this paper has potential in measuring regional sustainable development system. [ABSTRACT FROM AUTHOR]

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In recent years, there has been a rapid increase in scientific research into hydrogeochemical research on glacier meltwater. Nevertheless, systematic and quantitative analyses are lacking to investigate how this research field has developed over the years. As a result, this study is aimed at examining and evaluating recent research trends and frontiers in hydrogeochemical research on glacier meltwater throughout the previous 20 years (2002–2022) and at locating collaboration networks. This is the first global-scale study, and visualization of the key hotspots and trends in hydrogeochemical research has been presented here. The Web of Science Core Collection (WoSCC) database aided in the retrieval of research publications related to hydrogeochemical research of glacier meltwater published between 2002 and 2022. From the beginning of 2002 till July 2022, 6035 publications on the hydrogeochemical study of glacier meltwater were compiled. The result revealed that the number of published papers on the hydrogeochemical study of glacier meltwater at higher altitudes had grown exponentially, with USA and China being the main research countries. The number of publications produced from the USA and China accounts for about half (50%) of all publications from the top 10 countries. Kang SC, Schwikowski M, and Tranter M are highly influential authors in hydrogeochemical research of glacier meltwater. However, the research from developed nations, particularly the United States, emphasizes hydrogeochemical research more than those from developing countries. In addition, the research on glacier meltwater's role in streamflow components is limited, particularly in the high-altitude regions and needs to be enhanced. [ABSTRACT FROM AUTHOR]

It is controversial that high-fluoride and high-iodine combined exposure affects the prevalence of dental fluorosis and goiter. The aim of this study was to explore the potential association between high-fluoride and high-iodine combined exposure with dental fluorosis and goiter. We retrieved relevant articles from PubMed, Cochrane Library, China National Knowledge Infrastructure, Wanfang Database and China Science and Technology Journal Database (VIP). The query format was 1 # "Fluorosis" OR "Fluoride," 2 # "Iodine" OR "Iodide," and 3 # 1 AND 2. A total of 20 papers were included in this study after independent review by two investigators. Our analysis showed that high-fluoride and high-iodine biphasic exposure was significantly associated with the prevalence of goiter (OR = 4.69, 95% CI 2.82–7.80, P < 0.001). The prevalence of dental fluorosis was also significantly raised (OR = 11.71, 95% CI 7.57–18.14, P < 0.001). Sensitivity analysis suggested that combined statistics of multiple studies were reliable. For goiter, subgroup analysis revealed study province, sample size and published year as sources of heterogeneity (P < 0.001). For dental fluorosis, only sample size was the impact factor of heterogeneity. As well, funnel plot, Begg's test and Egger's test suggested there was no publication bias (P > 0.05). Overall, our study demonstrates that high-fluoride and high-iodine combined exposure is a risk factor for occurrence of dental fluorosis and goiter. The chronic of high-fluoride and high-iodine combined exposure is a significant higher risk of disease than normal. [ABSTRACT FROM AUTHOR]

Side issues of economy development break out in China during recent decades, like environmental pollution or the widely ignored one, shadow economy. Using annual data for the three provinces at northeast China over the period 2000 to 2016, this paper examines the size of the shadow economy by MIMIC model first and then adopts the dynamic panel analysis to study the direct relationship between the shadow economy and pollution level. The major innovation point of this paper is the pioneering study of the impact from the pollution level on the size of shadow economy. We also employ various pollution descriptions from terrestrial, aquatic, and atmospheric ecosystems as the robustness check to make our following conclusions more comprehensive and credible: (1) shadow economy is a direct quality factor to the increase of the pollution level. (2) A positive effect from pollution to shadow economy also exists: the higher the pollution level is, the larger the size of shadow economy will be. In the end, this paper proposes several valuable information and suggestions to the government in economy development and pollution abatement. [ABSTRACT FROM AUTHOR]

The seismic sedimentology is an emerging inter-discipline originating from the seismic stratigraphy and sequence stratigraphy. However, implementation of the seismic sedimentological research is found with high difficulties, due to influences imposed by structural and depositional background data (including strong reflections). In this paper, seismic records are regarded as a combination of the reflection from the depositional background and lithological data volumes, and moreover, the seismogram of the depositional background data is characterized by the low frequency and stable phase. Subsequently, the Kernel Hebbian Algorithm (KHA) has been modified to remove the influence of the depositional background data. The seismic trace data are used as the training set, and an innovative attempt has been made to incorporate the Ricker wavelet kernel function. Finally, a depositional background data volume extraction methodology with respect to input of higher-dimension seismic data has been developed, on the basis of the Modified KHA (MKHA), so as to obtain the lithological data volume. Utilizing the unsupervised online learning capabilities of the MKHA, iterative calculation of Kernel PCA can greatly reduce the computational complexity and can be adapted to big data problems. This paper introduces the Ricker wavelet kernel function to transform the original seismic data into the feature space through the inner-product operation, extract the non-linear features, and solve the problem that the seismic data of the original sample space is linearly inseparable. The seismic sedimentological analysis based on the lithological data volume that is able to reflect hidden sand bodies can achieve elaborate carving of the reservoir. The proposed method has been tested in Working Block A of the East-1 district in the Sulige gas field, the Ordos Basin, China. The case study demonstrates that the presented method is capable of efficiently removing the depositional background data, and making great contributions to improving accuracy of the seismic sedimentological analysis of the effective reservoir, with the help of higher-dimension seismic data. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of foreign bank entry on the export quality of firms. For this purpose, we mainly use the transaction data from the Chinese Customs Database and the production data from the Annual Survey of Industrial Firms of China during the years 2000–2006. The obtained data consists of 62,483 observations gathered from 19,888 firms. The results show that foreign bank entry enhanced the export quality of firms that are more externally financially dependent. This influence is stronger for non-state-owned firms and ordinary-trade firms than for the other types of firms. We further demonstrate that foreign bank entry is mainly through promoting innovation endeavors and improving the quality of intermediate inputs yielding to enhance the export quality of firms that have more external financial requirements. [ABSTRACT FROM AUTHOR]

Input-Output (IO) data describing supply-demand relationships between buyers and sellers for goods and services within an economy have been used not only in economics but also in scientific, environmental, and interdisciplinary research. However, most conventional IO data are highly aggregated, resulting in challenges for researchers and practitioners who face complex issues in large countries such as China, where firms within the same IO sector may have significant differences in technologies across subnational regions and different ownerships. This paper is the first attempt to compile China's interprovincial IO (IPIO) tables with separate information for mainland China-, Hong Kong, Macau, Taiwan-, and foreign-owned firms inside each province/industry pair. To do this, we collect relevant Chinese economic census data, firm surveys, product level Custom trade statistics, and firm value-added tax invoices and consistently integrate them into a 42-sector, 31-province IO account covering 5 benchmark years between 1997–2017. This work provides a solid foundation for a diverse range of innovative IO-based research in which firm heterogeneity information about location and ownership matters. [ABSTRACT FROM AUTHOR]

Balancing water environmental protection and economic growth is a priority for the local governments. Many literature analyses found that the reform of the River Chief System (RCS) had a significant effect on water quality improvement in China; however, it is important and necessary to comprehensively assess the impact of the RCS on the total factor productivity (TFP) of industrial enterprises, which provides a realistic basis for the decisions of local governments. This paper uses the quasi-natural experiment of the RCS in China and data from Chinese industrial enterprises to analyze the impact of the policy reform on the total factor productivity (TFP) of enterprises and the influence mechanism. The empirical analysis finds that the TFP of industrial enterprises in the reformed areas of the RCS is significantly lower than that in the non-reformed areas. This indicates that the implementation of the RCS is not conducive to the improvement of TFP of industrial enterprises. And this effect is mainly generated by reducing the output level and capital allocation efficiency of industrial enterprises. Meanwhile, the impact of the RCS on the TFP of industrial enterprises exists heterogeneity. [ABSTRACT FROM AUTHOR]

Increasing research attention is being paid to the factors influencing the comprehensive and healthy development of adolescents. However, few studies have specifically considered cultural contexts, including that of China. Based on public database—The China Education Panel Survey (CEPS) data, this paper takes fixed-effect model to examine the relationship between family structure and physical exercise/academic achievement of middle-school students, plus their intermediary mechanisms. The results were as follows: (1) Middle-school students in intact families displayed higher levels of physical exercise and academic achievement than those in families with one or both parents absent; (2) Family structure influenced middle-school students' development through the two mechanisms of family socioeconomic status and parental input; and (3) Fathers and mothers fulfill different roles in middle-school students' development: fathers contribute more to their involvement in physical exercise; mothers contribute more to their academic achievement. The results carry theoretical and practical implications for the development of adolescents, both in China and elsewhere. [ABSTRACT FROM AUTHOR]

The validity of performance evaluation is determined by, and therefore greatly influenced by, the accuracy of data set. To address such imprecise and negative data problems widely spread in the real world, this paper proposes a second-order cone based robust data envelopment analysis (SOCPR-DEA) model, which is more robust to data variety. Further, this new computational tractable model is applied to analyze 13 new-energy vehicle (NEV) manufacturers from China. The findings support that the SOCPR-DEA model could well mitigate the deficiency caused by data variety, and the evidence from Chinese NEV industry shows that a focus strategy is more likely to enhance a firm's efficiency especially at its emerging stage, and the efficiency is more sensitive with production cost than other factors such as research and development, sales income, earnings per share, and predicted income. In addition, this paper also gives some industrial implications and policy suggestions based on these interesting findings. [ABSTRACT FROM AUTHOR]

In recent years, the rapid development of fresh food e-commerce in China has brought about more development opportunities for the cold chain logistics industry but has also presented new challenges. With the development of cloud computing and big data technology, it is increasingly important to study the application of big data and cloud computing technology in cold chain logistics. The purpose of this paper is to study the intelligent algorithm of cold chain logistics distribution optimization based on big data cloud computing analysis. Based on the constituent elements of the cold chain distribution problem and using cloud computing technology to obtain real-time traffic information in the transportation system through a unified access interface, this article analyses the distribution time and cost of refrigerated vehicles, thereby establishing a cold chain distribution vehicle path optimization model. By analysing the parallel programming mode of cloud computing, the parallel design and analysis of a coarse-grained genetic algorithm are used to solve the simulation model of the established optimization model. The experimental results show that the method of optimizing cold chain logistics vehicle routing using cloud computing is effective. When comparing 1, 2, 4, and 8 processors, the execution times are 19.89, 14.52, 8.12, and 6.41, respectively. It can be seen that the more processors there are, the shorter the calculation time. [ABSTRACT FROM AUTHOR]

Since its official establishment in 2010, Google Earth Engine (GEE) has developed rapidly and has played a significant role in the global remote sensing community. A bibliometric analysis was conducted on 1995 peer-reviewed articles related to GEE, indexed in the Scopus database up to December 2022 to investigate its trends and main applications. Our main findings are as follows: (1) The number of GEE-related articles has increased rapidly, with nearly 85% of them published in the last three years; (2) The top three domains where GEE has been extensively applied are earth and planetary sciences, environmental science, and agricultural and biological sciences. The majority of GEE-related articles were authored by scholars from China and the US, accounting for 58% of the total, with US scholars having the largest impact on the community, contributing to over 50% of the total citations; (3) Remote Sensing published the highest number of articles (26.82%), whereas Remote Sensing of Environment received the highest number of citations (30.40%); (4) The applications of GEE covered a broad range of topics, with a focus on land applications, water resource applications, climate change, and crop mapping; (5) Landsat imagery were the most popular and widely used dataset; and (6) Random forest, decision trees, support vector machines were the most commonly used machine learning algorithms in GEE. Although having a few limitations, this type of analysis should be conducted regularly to observe the development of this field on a regular basis, as the number of publications related to GEE is expected to continue to increase strongly in the coming years. [ABSTRACT FROM AUTHOR]

Previous study found out that Chinese household have a higher awareness of environmental protection but less positive initiative to protect the environment. With the increasing Chinese income, higher education, and changing environment state, public awareness and behavior on environment protection are changing. This paper tries to find out the current public willingness to participate in environment protection, by using the latest data from China Household Finance Survey (CHFS) which is investigated in 2017 that covered 31 provinces with a sample of more than 40 thousand households. Besides, logit regression model is adopted to find out the impact of pollution and household demographic on environment protection behavior. The results show that around 71.6% of surveyed households have willingness to pay for the environment protection. Rural residents and higher educated individuals are more likely to participate in environment protection. However, things are different if they face different degrees of pollution. Results can be explained with China's current economic situation and would have some implications for future study on China's public willingness to pay for the environment. [ABSTRACT FROM AUTHOR]

Based on a homogenized data set of monthly mean temperature, minimum temperature, and maximum temperature at Shijiazhuang City Meteorological Station (Shijiazhuang station) and four rural meteorological stations selected applying a more sophisticated methodology, we reanalyzed the urbanization effects on annual, seasonal, and monthly mean surface air temperature (SAT) trends for updated time period 1960–2012 at the typical urban station in North China. The results showed that (1) urbanization effects on the long-term trends of annual mean SAT, minimum SAT, and diurnal temperature range (DTR) in the last 53 years reached 0.25, 0.47, and − 0.50 °C/decade, respectively, all statistically significant at the 0.001 confidence level, with the contributions from urbanization effects to the overall long-term trends reaching 67.8, 78.6, and 100%, respectively; (2) the urbanization effects on the trends of seasonal mean SAT, minimum SAT, and DTR were also large and statistically highly significant. Except for November and December, the urbanization effects on monthly mean SAT, minimum SAT, and DTR were also all statistically significant at the 0.05 confidence level; and (3) the annual, seasonal, and monthly mean maximum SAT series at the urban station registered a generally weaker and non-significant urbanization effect. The updated analysis evidenced that our previous work for this same urban station had underestimated the urbanization effect and its contribution to the overall changes in the SAT series. Many similar urban stations were being included in the current national and regional SAT data sets, and the results of this paper further indicated the importance and urgency for paying more attention to the urbanization bias in the monitoring and detection of global and regional SAT change based on the data sets. [ABSTRACT FROM AUTHOR]

Using the Chinese corpus of Google Books Ngram in line with other macro-level socioeconomic data, this paper examines and analyzes the trend of change in public discourse about social structure in China from 1949 to 2008, as well as the mechanism that influences this trend. We find that since the reform and opening-up, the official discourse on "class," as constructed by the official ideology, has gradually declined, while the importance of a "stratum" discourse oriented toward the mass population has increased. Using principal component analysis, we generate an index for public attention on social strata and run it through a Granger causality test along with time-series data such as macro-level economic and political indicators. The results show that since the reform and opening-up, public attention to stratum has been influenced by the general trend of the economy, income disparity, and level of political participation. Income disparity influences public attention on stratum-related topics more than macro-level economic indicators do. Official intervention on public opinions does not affect public attention on the stratum, but the former is affected by the latter. [ABSTRACT FROM AUTHOR]

With the rapid development of China's national economy, the effects of traditional public security management methods have been greatly weakened, and various new types of criminal activities have continued to occur. Social development has gradually separated from the previous model, and new social contradictions have become prominent. These social contradictions not only impact the lifestyle of ordinary individuals, but also affect the development of society. The data contains the laws of social development and crime governance. How criminal governance adapts to the big data wave has become the key to carrying out the number management in the three-dimensional security prevention and control. The data culture has promoted the transformation of the social governance model and has also led to the transformation of the social science research paradigm. At present, most of the research on big data is only from the perspective of informatics, and there is not much discussion about big data from the legal system level. With the advent of the era of big data, a series of problems have come along. In addition to strengthening the security of the big data operation process at the technical level, it is necessary to strengthen research from a technical level. In this paper, we propose a smart crime prevention and control big data analysis system based on machine Internet of Things and industrial object system. The experimental results show that the proposed method has higher data collection rate and crime prevention and control efficiency. [ABSTRACT FROM AUTHOR]

In recent years, the entrepreneurial development of taobao village based on e-business has led to a new round growth of rural economy in China. This paper constructs a evolution analysis framework of taobao village entrepreneurial network based on the big data of taobao intelligent information system from the year 2007–2016. The social network analysis software Gephi was applied to analyze the data. The result is that: Firstly, the early emerged online shops with high betweenness centrality degree, which mastered important resources and power within the network, formed a strong entrepreneurial demonstration effect to the other shops. Secondly, in taobao village, the number of strong relationship shops increased, entrepreneurial network gradually dispersed characteristics of clustering, entrepreneurial network community structure strength enhanced, division quality optimization, and the transmission performance and efficiency of entrepreneurial network improved. The entrepreneurial network has a typical characteristic of "acquaintance" network business conduction. Thirdly, the entrepreneurial network density of Lin'an taobao village showed a significant downward trend, which may be more conducive to the homogeneous agricultural products sales shops to access to heterogeneous resources, so as to obtain more long-term future development. [ABSTRACT FROM AUTHOR]

This paper investigates whether and how the systematic forecast errors of the quarterly GDP announcements in China depend on the state of the economy. Our contribution is both theoretical and empirical. On the theoretical side, we extend the predictive threshold regression of Gonzalo and Pitarakis (J Bus Econ Stat 35:202–217, 2017) by incorporating a time-varying and state-dependent threshold, which is a function of macroeconomic variables that affect the separation of regimes. On the empirical side, we apply our model to assess the quality of China's preliminary GDP data. Our empirical results show that there exist forecast biases in the preliminary GDP data conditional on the state of the economy. Our results also lean toward supporting that there exist behavioral biases of underestimation and over-reaction to new information during periods of relatively good state. These results suggest some scope to improve the accuracy of the preliminary GDP data based purely on econometric models. [ABSTRACT FROM AUTHOR]

Understanding the spatiotemporal (ST) characterization of precipitation is one key component in evaluating the climatic environment, which provides executable and effective suggestions for the implementers of climatic strategy. In this paper, the ST ordinary kriging method was used to obtain the ST distribution of annual precipitation (AP) based on the data collected from the 961 meteorological monitoring stations of the Huanghuaihai basin in China during 1956–2016. Then, stochastic site indicators were applied to quantitatively assess the ST uncertainties, and the results indicated the probability that higher precipitation (beyond 1000 mm) occurred simultaneously at any two regions was extremely low. In terms of the AP intensity analysis, the annual rainfall in 50% of the regions exceeded 500 mm, and the largest fragmentation of AP was calculated from this threshold. Furthermore, the ST characterization and the trend of AP were analyzed in different climatic environments, indicating that (1) the AP increased from west to east, exhibiting a significant gradient distribution; (2) overall, the temperature of the study area increased by approximately 0.027 °C per year, while the AP decreased by 0.2 mm per year; (3) the temperature in three subregions [high-altitude region (HAR), mid-altitude region (MAR), low-altitude region (LAR)] exhibited an increasing trend, and the significant increasing trend of temperature occurred primarily in the LAR; (4) the precipitation in the MAR and LAR exhibited a nonsignificant decreasing trend, while the precipitation in the HAR showed a significant increasing trend. [ABSTRACT FROM AUTHOR]

This paper investigates whether the economic growth of China and India matters for the long-term economic prosperity of African nations. Annual World Bank data were used to determine the growth of African countries for the period from 2002 to 2016. The analytical framework includes the standard Barro growth regression model. The Arellano and Bond generalised method of moments estimation procedure was used to analyse the data, overcoming the issues of autocorrelation, heteroscedasticity and endogeneity. The findings based on the generalised method of moments estimation indicate that the economic growth of China, but not of India, had a statistically significant (p = 0.10) positive correlation with the economic growth of Africa. It is concluded that the economic growth of China assumed a significant decisive role in supporting African economic prosperity. Hence, Africa is likely to realise long-term beneficial growth and development effects by actively integrating with large economies, such as China. [ABSTRACT FROM AUTHOR]

As an environmentally friendly means of transport, the high-speed rail (HSR) is conducive to promoting corporate performance. An innovative approach extends the impact of HSR networks on pollution emissions from the regional level to the micro-enterprise level. Based on the quasi-natural experiment of the opening of HSR, a difference-in-difference model is used to investigate the impact of HSR on enterprise pollution emission levels and its action mechanism by using the matched data from the Chinese Enterprise Pollution Emission Database, the Chinese Industrial Enterprise Database, and the Chinese City Statistical Yearbook from 2000 to 2010. The results show that opening HSR significantly reduces the enterprises' pollution emission level, while reducing the number of polluting enterprises and transportation costs as well as improving the innovation capacity of enterprises are the corresponding action mechanisms. The impact of HSR on the enterprises' pollution emission varies with industry intensity, population size, and regional economic development level. The conclusions not only provide important insights to increase the ecological quality of China's environment through transportation infrastructure upgrades but also bring some guidance to more developing countries to improve their air environment. [ABSTRACT FROM AUTHOR]

Financial distress prediction models are much challenged in identifying a distressed company two or more years prior to the occurrence of its actual distress, on the grounds that the distress signal is too weak to be captured at an early stage. The paper innovatively proposes to predict the distressed companies by a factorial discriminant model based on interval data. The main idea is that we use a new data representation, i.e., interval data, to summarize four-quarter financial data, and then build a interval-data-based discriminant model, namely i-score model. Interval data makes both average and volatility information comprehensively included in the proposed prediction model, which is expected to improve prediction performance on the distressed companies. A comparison based on a real data case from China's stock market is conducted. The i-score model is compared with five commonly used models that are based on numerical data. The empirical study shows that i-score model is more accurate and more reliable in identification of companies in high risk of financial distress in advance of 2 years. [ABSTRACT FROM AUTHOR]

Haze pollution is a key obstacle for environmental management faced by China and many other developing countries. The survey on residential families' economic losses and willingness to pay (WTP) are regarded as an essential reference for the implementation of environmental policies for haze treatment. For Jiangsu province of China, the authors of this paper first conducted three qualitative interviews with respectively meteorologists, meteorological administrators, and residents, a questionnaire was then elaborately designed, and subsequent surveys of 1123 families were administered in Jiangsu province. Further, the authors investigated measurements of direct economic losses by using the contingent valuation method (CVM) and explored influential factors of WTP by utilizing the binary logistic regression. From this survey, the estimated total economic loss incurred by haze disasters and total treatment cost for haze-related diseases were respectively 22.38 billion (in RMB) and 8.4 billion for Jiangsu province. 55.9% of residential families were willing to pay 11.6 billion RMB annually (51.97% of total loss) for haze treatment, leaving a shortage of 11.05 billion RMB, which the government is responsible to pay. These findings provide empirical information reflecting the opinions of communities and residential families, useful for the governments and industrial sectors to design environmental policies to meet the requirements of the public and control environmental pollution in an effective way to achieve sustainable development. [ABSTRACT FROM AUTHOR]

Spatiotemporal data mining has many important applications in environmental modelling. This paper introduces a modified algorithm for spatiotemporal data mining based on density-based spatial clustering of applications with noise (DBSCAN). Based on the modified algorithm, a geographic information system (GIS) add-in was developed using ArcObjects and C#, and is freely available for download. Compared with some existing methods, the new method can perform automated detection of spatiotemporal clusters using limited user input parameters. The application of the add-in was demonstrated using summer temperature data collected from 104 weather stations in southwest China from 1961 to 2011. The results suggest that the modified algorithm can provide more detailed partition of temperature zones compared with some existing methods. [ABSTRACT FROM AUTHOR]

With the development of social economy, sports industry has shown its economic advantages. Sports industry has shown great potential in improving national economy, guiding social consumption, and adjusting industrial structure. However, compared with the rapid development of sports industry, the research on the growth law of sports culture industry is relatively backward, which largely restricts the further development of China's sports industry. For this problem, this paper analyzes the feasibility of sports culture industry growth prediction based on big data theory from the perspective of data mining, proposes a sports culture industry growth prediction model based on genetic neural network, and realizes the prediction of sports culture industry growth law under the background of big data. The simulation result shows that the optimized neural network can effectively improve the efficiency and accuracy of prediction and show strong robust in predicting the laws of sports culture industry. [ABSTRACT FROM AUTHOR]

China, is characterized by its remarkable ethnical diversity, which necessitates whole genome variation data from multiple populations as crucial tools for advancing population genetics and precision medical research. However, there has been a scarcity of research concentrating on the whole genome of ethnic minority groups. To fill this gap, we developed the Guizhou Multi-ethnic Genome Database (GMGD). It comprises whole genome sequencing data from 476 healthy unrelated individuals spanning 11 ethnic minorities groups in Guizhou Province, Southwest China, including Bouyei, Dong, Miao, Yi, Bai, Gelo, Zhuang, Tujia, Yao, Hui, and Sui. The GMGD database comprises more than 16.33 million variants in GRCh38 and 16.20 million variants in GRCh37. Among these, approximately 11.9% (1,956,322) of the variants in GRCh38 and 18.5% (3,009,431) of the variants in GRCh37 are entirely new and do not exist in the dbSNP database. These novel variants shed light on the genetic diversity landscape across these populations, providing valuable insights with an average coverage of 5.5 ×. This makes GMGD the largest genome-wide database encompassing the most diverse ethnic groups to date. The GMGD interactive interface facilitates researchers with multi-dimensional mutation search methods and displays population frequency differences among global populations. Furthermore, GMGD is equipped with a genotype-imputation function, enabling enhanced capabilities for low-depth genomic research or targeted region capture studies. GMGD offers unique insights into the genomic variation landscape of different ethnic groups, which are freely accessible at https://db.cngb.org/pop/gmgd/. [ABSTRACT FROM AUTHOR]

The era of big data bring influence to the world within the scope of all walks of life, ideological and political work is also facing a huge impact, including the impact of education object and education carrier, these effects also in the ideological and political education work has brought opportunities and challenges. This paper puts forward the innovation in the way of thinking, mode of supervision, education mode of thought, emphasizing the ideological and political education workers to enhance data awareness, improve the way of thinking, to fully grasp the big data analysis techniques, to prepare for the big data era, groping in the ideological and political education of law, improve the quality of Ideological and political education the promote education level, help the majority of college students in the era of big data context consciously set up the correct world outlook, outlook on life and values, and constantly improve their own comprehensive quality and professional skill level, become the main force of the social and economic development China. [ABSTRACT FROM AUTHOR]

Guangdong is the most economically developed province in China, which is a large CO emitter and hence is faced with severe carbon reduction pressures. In this paper, a cost assessment methodology based on scenario analysis is presented. A CO source and sink database was built at Guangdong after detailed investigations on the point sources and sedimentary basins. Fifteen transport and five storage scenarios were defined and studied, respectively. Cost estimates based on these scenarios show that during its lifetime, the costs of both transport and storage depend on the amount of CO processed. More CO being processed will bring down the unit costs of both transport and storage. However, it was observed that there is a cost inflection point between the storage amount of 35.2 and 52.8 Mt/year, which means that as the storage amount increases, the storage cost will first decrease and then increase. Source region S1 in Guangdong has been recommended for an early chance of CO storage. Preliminary cost comparisons have shown that the results presented in this study are reasonable, but to improve the cost assessment accuracy of offshore CO storage, a methodology based on a CO storage design that can integrate local prices needs to be further developed. [ABSTRACT FROM AUTHOR]

Transshipment is one of the most common activities occurring between carriers and fishing vessels to exchange fish, as well as fuel, crew, and gear at sea or at port. While transshipment reduces the need for the fishing vessels to visit ports to offload their catches, thus increasing their efficiency, research has shown that this activity is also one of the major facilitators of IUU fishing. Research on transshipments is limited, and the few studies that are available on the subject focus on identifying hotspots of transshipment, and networks of actors involved. No study to date has examined the role ports play in facilitating transshipment activities, nor are there any studies that examine whether ports that are affiliated with China (the country with the highest IUU Fishing Index Score and 38% of the global share of distant-water fleets) are more likely to experience disproportionately higher volumes of FOC-flagged carrier vessel visits. Therefore, using the carrier vessel portal database from Global Fishing Watch, which contains information on the origin and destination ports of the carrier vessels involved in transshipment activities between 2015 and 2022, this research aims to (a) understand hot spots of FOC-flagged carrier vessel activity in the high seas and where such vessels offload their catches around the world; and (b) empirically test the characteristics of the ports (and the countries where these ports are located) used by these vessels to offload their catches. Findings suggest that ports that experience higher volumes of fishing vessel traffic, are in close proximity to high-seas transshipment activities involving FOC-flagged carriers, are designated ports of entry for foreign vessels, are Chinese-affiliated, as well as have low monitoring, control, and surveillance capacity are significantly more likely to be visited by FOC-flagged high-risk carrier vessels. This research proposes policy recommendations deriving from the findings of this research. [ABSTRACT FROM AUTHOR]

Environmental taxation has captured significant attention to tackle climate change, and a plethora of studies have been done within the vicennial on this new tax regime. The present study analyses the scientific studies through bibliometric analysis to find out the research gap and research variables for modelling of environmental tax. The 590 literature is retrieved from the Scopus database (2001–2022 March) with the keywords Environmental* Tax* OR Carbon Tax*, after limiting the subject area to economics, business, finance, and social science. The analysis is confined to the most impactful journals, organisations, countries, keywords, international collaboration, and the direction of recent research areas. It is found that the "Journal of cleaner production" has the highest growth rate due to the most productive (43 publications) and highly cited (1115 total citations) journal. The 'North China Power University' has published the highest 26 documents, and China is the most productive (148 publications) and highly cited (2598 total citation) country. The USA has the highest international collaboration in 118 documents with 17 countries. The study found that over half of the top 30 keywords are about policymaking, reform, regulation, and environmental tax impact. Analysis of keywords and network analysis directs that the Impact of environmental and carbon tax, Double dividend, Effect on macroeconomic variables, Emission trading system and Public acceptability are the research hotspots in the published literature. It is suggested from the analytical results that research collaboration among social science and environmental science subjects and developed and developing nations will bridge the knowledge gap and bring new insights to the environmental tax area. [ABSTRACT FROM AUTHOR]

The environmental issue has attracted ever-increasing attention from governments and academics in recent years. Environmental performance analysis is widely considered a crucial and useful tool for effectively protecting the environment and developing a sustainable society. Many analytical techniques have been used to assess carbon emission performance, among which data envelopment analysis (DEA) is prominent. However, few previous DEA-related carbon emission performance studies recognize that the total amount of carbon dioxide emissions is limited to a specific level by authorities; ignoring this fixed-sum requirement may lead to distortions and deviations in empirical results. This paper proposes an alternative Malmquist DEA approach for evaluating the carbon emission performance while considering fixed-sum undesirable outputs. For this purpose, we develop a generalized equilibrium efficient frontier DEA model with fixed-sum undesirable outputs and combine the model with the Malmquist productivity index (MPI). The proposed approach is applied to assess the carbon emission performance of 30 provincial regions in China from 2009 to 2015. Then we provide analytical results and policy suggestions regarding the provincial carbon emission performance in China. [ABSTRACT FROM AUTHOR]

Lung cancer is still the most concerned disease around the world. Lung nodule generates in the pulmonary parenchyma which indicates the latent risk of lung cancer. Computer-aided pulmonary nodules detection system is necessary, which can reduce diagnosis time and decrease mortality of patients. In this study, we have proposed a new computer aided diagnosis (CAD) system for detection of early pulmonary nodule, which can help radiologists quickly locate suspected nodules and make judgments. This system consists of four main sections: pulmonary parenchyma segmentation, nodule candidate detection, features extraction (total 22 features) and nodule classification. The publicly available data set created by the Lung Image Database Consortium (LIDC) is used for training and testing. This study selects 6400 slices from 80 CT scans containing totally 978 nodules, which is labeled by four radiologists. Through a fast segmentation method proposed in this paper, pulmonary nodules including 888 true nodules and 11,379 false positive nodules are segmented. By means of an ensemble classifier, Random Forest (RF), this study acquires 93.2, 92.4, 94.8, 97.6% of accuracy, sensitivity, specificity, area under the curve (AUC), respectively. Compared with support vector machine (SVM) classifier, RF can reduce more false positive nodules and acquire larger AUC. With the help of this CAD system, radiologist can be provided with a great reference for pulmonary nodule diagnosis timely. [ABSTRACT FROM AUTHOR]

China and ASEAN have strong ties and have become each other's largest trading partners. This article uses the gravity model and a novel method of analysis, based on massive event data provided by the Global Database of Events, Language, and Tone, to empirically analyse the impact of fluctuations in bilateral relations between China and ASEAN on bilateral trade from January 2001 to December 2020. An analysis of 1,204,126 event records concluded that the improvement of bilateral relations was beneficial for trade between China and ASEAN countries. China's export trade was more easily influenced by bilateral relations than its import trade. Compared to the one-way behavioural attitude of ASEAN countries towards China, China's one-way reaction to ASEAN countries had a greater impact on bilateral trade. China's trade dependence level with most ASEAN countries maintains a trend of steady growth, and trade relations show a positive trend. Therefore, China and ASEAN should maintain stable political ties, enhance mutual understanding, strengthen economic and political connectivity, take adequate measures to promote mutual trust, continue optimizing cooperation models, and promote high-quality and high-level sustainable development across the region. [ABSTRACT FROM AUTHOR]

Empirical methods are commonly employed to predict the PGA distribution of an earthquake and are widely used. However, current empirical methods assume the seismic source to be a point source, a line source, or a plane source, where the energy is concentrated and released uniformly. An empirical attenuation model of the near-field peak ground acceleration (PGA) was proposed that considers a nonuniform spatial distribution of seismic fault energy and its 3D scale. Then, this model was used to reconstruct the PGA distribution of the 2008 Wenchuan, China, Mw7.9 earthquake based on the data of a seismic fault model and ground acceleration records of the mainshock and aftershocks collected by seismic stations. The predicted PGA values show similar attenuation characteristics to the interpolated map of the PGA recorded by seismic stations. A comparison with the results of a finite-fault model developed by the USGS indicates that the proposed model can provide more details and give a more precise result in the near field. The analysis of landslides triggered by the Wenchuan earthquake demonstrates that the PGA distribution estimated by this model can be used to validate the findings of other researchers. [ABSTRACT FROM AUTHOR]

The incidence of plague has rebounded in the Americas, Asia, and Africa alongside rapid globalization and climate change. Previous studies have shown local climate to have significant nonlinear effects on plague dynamics among rodent communities. We analyzed an 18-year database of plague, spanning 1998 to 2015, in the foci of Mongolia and China to trace the associations between marmot plague and climate factors. Our results suggested a density-dependent effect of precipitation and a geographic location-dependent effect of temperature on marmot plague. That is, a significantly positive relationship was evident between risk of plague and precipitation only when the marmot density exceeded a certain threshold. The geographical heterogeneity of the temperature effect and the contrasting slopes of influence for the Qinghai-Tibet Plateau (QTP) and other regions in the study (nQTP) were primarily related to diversity of climate and landscape types. [ABSTRACT FROM AUTHOR]

Nanjing Yunjin, one of China's traditional silk weaving techniques, is renowned for its unique local characteristics and exquisite craftsmanship, and was included in the Representative List of the Intangible Cultural Heritage of Humanity by UNESCO in 2009. However, with rapid development in weaving technology, ever-changing market demands, and shifting public aesthetics, Nanjing Yunjin, as an intangible cultural heritage, faces the challenge of survival and inheritance. Addressing this issue requires efficient storage, management, and utilization of Yunjin knowledge to enhance public understanding and recognition of Yunjin culture. In this study, we have constructed an intelligent question-answering system for Nanjing Yunjin digital resources based on knowledge graph, utilizing the Neo4j graph database for efficient organization, storage, and protection of Nanjing Yunjin knowledge, thereby revealing its profound cultural connotations. Furthermore, we adopted deep learning algorithms for natural language parsing. Specifically, we adopted BERT-based intent recognition technology to categorize user queries by intent, and we employed the BERT + BiGRU + CRF model for entity recognition. By comparing with BERT + BILSTM + CRF, BERT + CRF and BILSTM + CRF models, our model demonstrated superior performance in terms of precision, recall, and F1 score, substantiating the superiority and effectiveness of this model. Finally, based on the parsed results of the question, we constructed knowledge graph query statements, executed by the Cypher language, and the processed query results were fed back to the users in natural language. Through system implementation and testing, multiple indices including system response time, stability, load condition, accuracy, and scalability were evaluated. The experimental results indicated that the Nanjing Yunjin intelligent question-answering system, built on the knowledge graph, is able to efficiently and accurately generate answers to user's natural language queries, greatly facilitating the retrieval and utilization of Yunjin knowledge. This not only reinforces the transmission, promotion, and application of Yunjin culture but also provides a paradigm for constructing other intangible cultural heritage question-answering systems based on knowledge graphs. This has substantial theoretical and practical significance for deeply exploring and uncovering the knowledge structure of human intangible heritage, promoting cultural inheritance and protection. [ABSTRACT FROM AUTHOR]

Over the past century, atmospheric inorganic nitrogen (IN) deposition to terrestrial ecosystems has significantly increased and caused various environmental issues. China has been one of the hotspot regions for IN deposition, yet limited data exist regarding IN deposition fluxes in China at the regional scale. In this study, based on NO2 and NH3 columns acquired by satellite sensors, coupled with atmospheric chemical transport model (CTM), mixed-effects model and site observations, we constructed regional-scale IN dry and wet deposition models respectively, and finally proposed a spatially explicit database of IN deposition fluxes in China. The database includes the dry, wet and total deposition fluxes in China during 2011–2020, and the data are presented in raster form with a resolution of 0.25° × 0.25°. Overall, the database is of great importance for monitoring and simulating the trends of IN deposition over a long time series in China. [ABSTRACT FROM AUTHOR]

Medulloblastoma (MB) is one of the most common malignant childhood brain tumors (WHO grade IV). Its high degree of malignancy leads to an unsatisfactory prognosis, requiring more precise and personalized treatment in the near future. Multi-omics and artificial intelligence have been playing a significant role in precise medical research, but their implementation needs a large amount of clinical information and biomaterials. For these reasons, it is urgent for current MB researchers to establish a large sample-size database of MB that contains complete clinical data and sufficient biomaterials such as blood, cerebrospinal fluid (CSF), cancer tissue, and urine. Unfortunately, there are few biobanks of pediatric central nervous system (CNS) tumors throughout the world for limited specimens, scarce funds, different standards collecting methods and et cl. Even though, China falls behind western countries in this area. The present research set up a standard workflow to construct the Beijing Children's Hospital Medulloblastoma (BCH-MB) biobank. Clinical data from children with MB and for collecting and storing biomaterials, along with regular follow-up has been collected and recorded in this database. In the future, the BCH-MB biobank could make it possible to validate the promising biomarkers already identified, discover unrevealed MB biomarkers, develop novel therapies, and establish personalized prognostic models for children with MB upon the support of its sufficient data and biomaterials, laying the foundation for individualized therapies of children with MB. [ABSTRACT FROM AUTHOR]

Economic historians have used GDP and its backwards projections to quantify economic growth and the process of early globalisation from year 1 CE to the present day. This has generated a lively debate concerning which methodologies are the most accurate for quantitative history and which data are most reliable. In addition, whilst an overwhelming amount of scholarship has emerged on the supply side, the demand side and family economic changes have been less popular in economic history. In this article, I present a concrete case study to analyse consumer behaviour: the circulation of Chinese goods in western Mediterranean markets during the eighteenth century. In so doing, I test the "trickle-down" theory with new archival data using GECEM Project Database, and apply the OLS and SNA to measure the social distribution of these goods through trade networks' intermediation. The main result is that the agency of middle social groups—mainly merchants—was changing consumers' behaviour in western Mediterranean markets, and not local oligarchies and nobility as the "trickle-down" theory has conventionally assessed. [ABSTRACT FROM AUTHOR]