Chinese Clinical Trial Registry 13-year data collection and analysis: geographic distribution, financial support, research phase, duration, and disease categories.

Objective: To evaluate the current status of trial registration on the Chinese Clinical Trial Registry (ChiCTR). Design: In this descriptive study, a multi-dimensional grouping analysis was conducted to estimate trends in the annual trial registration, geographical distribution, sources of funding, targeted diseases, and trial subtypes. Setting: We have analyzed all clinical trial records (over 30,000) registered on the Chinese Clinical Trial Registry (ChiCTR) from 2007 to 2020 executed in China. Main outcomes and measures: The main outcome was the baseline characteristics of registered trials. These trials were categorized and analyzed based on geographical distribution, year of implementation, disease type, resource and funding type, trial duration, trial phase, and the type of experimental approach. Results: From 2008 to 2017, a consistent upward trend in clinical trial registrations was observed, showing an average annual growth rate of 29.2%. The most significant year-on-year (yoy%) growth in registrations occurred in 2014 (62%) and 2018 (68.5%). Public funding represented the predominant source of funding in the Chinese healthcare system. The top five ChiCTR registration sites for all disease types were highly populated urban regions of China, including Shanghai (5,658 trials, 18%), Beijing (5,127 trials, 16%), Guangdong (3,612 trials, 11%), Sichuan (2,448 trials, 8%), and Jiangsu (2,196 trials, 7%). Trials targeting neoplastic diseases accounted for the largest portion of registrations, followed by cardio/cerebrovascular disease (CCVD) and orthopedic diseases-related trials. The largest proportions of registration trial duration were 1-2 years, less than 1 year, and 2-3 years (at 27.36, 26.71, and 22.46%). In the case of the research phase, the top three types of all the registered trials are exploratory research, post-marketing drugs, and clinical trials of new therapeutic technology. Conclusion and relevance: Oncological and cardiovascular diseases receive the highest share of national public funding for medical clinical trial-based research in China. Publicly funded trials represent a major segment of the ChiCTR registry, indicating the dominating role of public governance in this health research sector. Furthermore, the growing number of analyzed records reflect the escalation of clinical research activities in China. The tendency to distribute funding resources toward exceedingly populated areas with the highest incidence of oncological and cardiovascular diseases reveals an aim to reduce the dominating disease burden in the urban conglomerates in China.

Residents' Behavioral Intention of Environmental Governance and Its Influencing Factors: Based on a Multidimensional Willingness Measure Perspective.

The rapid development of industrialization has brought about a huge demand for mineral resources, and the mining industry has posed a threat to sustainable land use while promoting economic development. In the context of collaborative governance, residents are an important aspect of land pollution abatement. Therefore, understanding residents' willingness to participate in environmental governance and exploring its influencing factors have important implications for improving the motivation of residents to participate in environmental governance and improve the local habitat. Using the multidimensional willingness measurement data of rural households' perception of mining environment governance around the Dabaoshan mining area in Shaoguan in 2020, based on the extended theory of planned behavior, this study established a multidimensional measurement of willingness, including willingness to participate, willingness to pay, and willingness to mobilize, and used structural equation modeling to explore the factors influencing residents' behavioral intention of environmental governance. The results suggest that behavioral attitudes had a positive effect on willingness to participate and willingness to pay. In addition, subjective norms had no significant effect on willingness to participate but were negatively related to willingness to pay. Although perceived behavioral control had no significant effect on willingness to participate, it had a positive effect on willingness to pay. In addition, the results also show that the willingness to pay and willingness to participate of farmers were positively related to their willingness to mobilize. Based on the above findings, this study proposes some policy implications to improve residents' behavioral intention of land pollution abatement, including strengthening value perception, improving subjective awareness, building communication platforms, and improving personal capacity.

Construction of Au and C60 quantum dots modified materials of Institute Lavoisier-125(Ti) architectures for antibiotic degradation: Performance, toxicity assessment, and mechanistic insight.

High-performance and stabilized photocatalytic degradation of antibiotic contaminants still remains a challenge in environmental photocatalysis and has been studied worldwide. In this work, hybrid Au and C60 quantum dots decorated Materials of Institute Lavoisier-125(Ti) (MIL-125(Ti)) composites were successfully fabricated for visible-light photocatalytic tetracycline degradation with pristine MIL-125(Ti) as a comparison. The experimental results revealed that the introduction of C60 quantum dots and Au nanoparticles resulted in highly enhanced visible-light harvesting and charge separation for efficient tetracycline degradation. The optimal Au/C60-MIL-125(Ti)-1.0% sample exhibited the highest visible-light photocatalytic performance, and the corresponding rate constant was approximately 9.19 times of MIL-125(Ti), indicating the significant roles of Au and C60 quantum dots in boosting visible-light absorption and charge separation. Furthermore, the radical species, possible degradation pathways and toxicity assessment, and photocatalytic mechanism were also investigated. Current work indicates a synergistic strategy for enhancing visible-light harvesting and charge separation to fabricate high-performance composite photocatalysts.

Subjective and objective quality assessment of gastrointestinal endoscopy images: From manual operation to artificial intelligence.

Gastrointestinal endoscopy has been identified as an important tool for cancer diagnosis and therapy, particularly for treating patients with early gastric cancer (EGC). It is well known that the quality of gastroscope images is a prerequisite for achieving a high detection rate of gastrointestinal lesions. Owing to manual operation of gastroscope detection, in practice, it possibly introduces motion blur and produces low-quality gastroscope images during the imaging process. Hence, the quality assessment of gastroscope images is the key process in the detection of gastrointestinal endoscopy. In this study, we first present a novel gastroscope image motion blur (GIMB) database that includes 1,050 images generated by imposing 15 distortion levels of motion blur on 70 lossless images and the associated subjective scores produced with the manual operation of 15 viewers. Then, we design a new artificial intelligence (AI)-based gastroscope image quality evaluator (GIQE) that leverages the newly proposed semi-full combination subspace to learn multiple kinds of human visual system (HVS) inspired features for providing objective quality scores. The results of experiments conducted on the GIMB database confirm that the proposed GIQE showed more effective performance compared with its state-of-the-art peers.

Does Industrial Agglomeration or Foreign Direct Investment Matter for Environment Pollution of Public Health? Evidence From China.

This article focuses on the interaction between China's industrial agglomeration, foreign direct investment (FDI) and environmental pollution of public health in the past 15 years. By conducting theoretical and empirical research, we try to reveal the relationship and mechanism between the economic growth and public health from the perspective of environmental pollution. By constructing an embedded theoretical model of industrial agglomeration and FDI, this article combines other environmental pollution influencing factors, expounds the impact mechanism of industrial agglomeration on environmental pollution. Based on the provincial-level panel data of China on environmental pollution and industrial agglomeration, the empirical test is carried out through the threshold panel regression model. According to the results, industrial agglomeration can significantly rectify the regional environmental pollution, thereby benefiting public health. FDI has a phased impact on the relationship between industrial agglomeration and environmental pollution. Specifically, when the level of FDI is low, the positive improvement effect of industrial agglomeration on environmental pollution is relatively strong. This is mainly because industrial agglomeration can promote economic growth, technological progress, and enhance environmental awareness. When the level of FDI exceeds the first threshold and continues to rise, the positive improvement effect of industrial agglomeration is maximized. Before the level of FDI exceeds the second threshold, this effect gradually weakens. The population concentration and excessive expansion of city scale brought about by industrial agglomeration will lead to the increase of regional resource and energy consumption, thus aggravating environmental pollution. The policy implication is that while the government and enterprises are vigorously increasing the level of foreign investment, they must pay equal attention to economic growth and public health, and the level of industrial agglomeration should match the level of foreign investment so as to give full play to the positive improvement effect of industrial agglomeration on environmental pollution, and realize the coordinated development of the regional economy, environment and population health.

The impact of economic complexity on embodied carbon emission in trade: new empirical evidence from cross-country panel data.

Establishing a fair platform for allocating carbon emission responsibility worldwide determines the sustainability and efficiency of the world's climate policy and framework. In the context of global environmental load displacement and CO2 transfer, this paper endeavors to examine the relationship between economic complexity and embodied carbon emissions based on cross-country panel data. Our study utilizes the generalized method of moments (GMM) approach to estimate our dynamic models covering 34 OECD countries and 24 non-OECD countries from 1995 to 2015. The empirical results show a heterogeneous impact of economic complexity on embodied carbon emissions in exports (EEE) and imports (EEI). Besides, the scale effect, composition effect, and technology effect are also significant drivers of embodied carbon emissions. The improvement of economic complexity can decrease the marginal effects of export scale and export structure on foreign EEE (but not domestic EEE) significantly, while the marginal positive impacts of technology on EEE can be further enhanced by economic complexity growth. Moreover, there is no strong evidence to prove the significant indirect impacts of economic complexity on foreign carbon emission embodied in imports, while economic complexity has significantly positive indirect impacts on domestic carbon emission embodied in imports only through import scale. In the subsample regressions, we found asymmetric impacts of economic complexity between high-income countries and low- and middle-income countries.

An approach for water-inrush risk assessment of deep coal seam mining: a case study in Xinlongzhuang coal mine.

Most coal mines in China are opting for deep mining due to the rapid reduction of shallow coal reserves, which increases the risk of water-inrush accidents. Given the limitation of water-inrush coefficient method in evaluating the risk of water inrush from the coal seam floor, we analyzed the permeability resistance of the floor under different lithology combinations, and structural conditions of the lower group coal in Yanzhou mining area, based on the in situ pressure permeability test data. Our comprehensive analysis of the influencing factors of water inrush from the coal seam floor reveals key indices for evaluating the water inrush from the coal seam floor and also recommend values for average water-resistance strength of the different lithology, and structure of the lower coal seam floor of Xinglongzhuang coal mine. Besides, we establish a model based on the water-resistance conditions, and two adjacent lower coal working faces minefields of Xinglongzhuang coal mine in Yanzhou are used for the evaluation. Comparative analysis of water-inrush coefficient method and impermeability resistance condition to evaluate the applicability of safety conditions of coal mining under pressure are also performed. Our results show that the impermeability strength is a better measurement for the water-resistance capacity of the floor. These findings may guide the prevention and control of water disasters in coal mining under pressure in the lower formation of the minefield.

Examining the effects of socioeconomic development on China's carbon productivity: A panel data analysis.

China, which is the largest carbon emitter and the largest developing country in the world, faces the challenge of achieving energy conservation and emission reduction without sacrificing economic development. Improving carbon productivity consists a possible way to seek a coordination between economic development and carbon emission reduction. Therefore, it is of great significance to examine the effects of socioeconomic development on China's carbon productivity and accordingly provide policy suggestions for China's low-carbon economic development. However, this topic has not been adequately addressed in previous studies. In order to fill this gap, this study detailed an empirical investigation into the impacts of socioeconomic development on China's carbon productivity. First, aided by spatial analysis methods, a detailed analysis of the spatiotemporal patterns and dynamics of China's province-level carbon productivity was conducted. Moreover, using an extended STIRPAT model and panel data modeling technique, the effects of a range of socioeconomic factors on China's carbon productivity were quantitatively examined. The results indicated that China's carbon productivity increased gradually between 1997 and 2016, and carbon productivity in East China was much higher than that of their counterparts in Central China and West China. Provincial administrative units with highly developed economies witnessed spectacular increases in carbon productivity. Panel data analysis demonstrated that GDP per capita, technology level, trade openness, and foreign direct investment exerted positive effects, while energy consumption structure, industrial proportion, and urbanization level exerted negative effects, on China's carbon productivity. Based on the findings of this study, a series of policy suggestions with respect to improving China's carbon productivity were proposed.

Examining the spatially varying effects of factors on PM2.5 concentrations in Chinese cities using geographically weighted regression modeling.

Whilst numerous studies have explored the spatial patterns and underlying causes of PM2.5, little attention has been paid to the spatial heterogeneity of the factors affecting PM2.5. In this study, a geographically weighted regression (GWR) model was used to explore the strength and direction of nexus between various factors and PM2.5 in Chinese cities. A comprehensive interpretive framework was established, composed of 18 determinants spanning the three categories of natural conditions, socioeconomic factors, and city features. Our results indicate that PM2.5 concentration levels were spatially heterogeneous and markedly higher in cities in eastern China than in cities in the west of the country. Based on the results of GWR, significant spatial heterogeneity was identified in both the direction and strength of the determinants at the local scale. Among all of the natural variables, elevation was found to be statistically significant with its effects on PM2.5 in 95.60% of the cities and it correlated negatively with PM2.5 in 99.63% cities, with its effect gradually weakening from the eastern to the western parts of China. The variable of built-up areas emerged as the strongest variable amongst the socioeconomic variables studied; it maintained a positive significant relationship in cities located in the Pearl River Delta and surrounding areas, while in other cities it exhibited a negative relationship to PM2.5. The highest coefficients were located in cities in northeast China. As the strongest variable amongst the six landscape factors, patch density maintained a positive relationship in part of cities. While in cities in the northeast regions, patch density exhibited a negative relationship with PM2.5, revealing that increasing urban fragmentation was conducive to PM2.5 reductions in those regions. These empirical results provide a basis for the formulation of targeted and differentiated air quality improvement measures in the task of regional PM2.5 governances.

Evaluating the energy-environment efficiency and its determinants in Guangdong using a slack-based measure with environmental undesirable outputs and panel data model.

Environmental sustainability has become a significant goal for policymakers and practitioners since increasing environmental degradation owing to anthropogenic activities. Energy-environment efficiency, linked to a progressive reduction in the environmental impacts that may occur throughout their life cycle to levels that should be below or equal the Earth's estimated carrying capacity, is a crucial point for constructing an environment friendly society while maintaining rapid economic growth. Thus, this study combined a slack-based measure (SBM) with environmental impacts as undesirable outputs with spatial analysis techniques to measure energy-environment efficiency of 21 cities in Guangdong and its changing patterns during the period 2006-2016. What and how socioeconomic factors affecting energy-environment efficiency over time and space was further examined using heterogeneous panel data model. Here are the main findings: during the study period, energy-environment efficiency showed apparent spatiotemporal diversity with high values predominantly concentrated in coastal areas, especially in the center area of the Pearl River Delta. Energy-environment efficiency increased continuously in the western Guangdong and the Pearl River Delta, while it of eastern Guangdong showed a decreasing trend and of northern Guangdong remained stable at a low level. The results of the heterogeneous panel data model revealed that technological progress exerted the greatest positive effects on energy-environment efficiency, followed by population density, economic growth. Conversely, Openness was evaluated as an inhibiting factor. Interestingly, this study found that industrial structure demonstrated significant negative correlations with respect to energy-environment efficiency in the Pearl River Delta while it exerted significant positive influence in the peripheral areas of Guangdong. And foreign trade and energy-environment efficiency had a significant positive correlation in the Pearl River Delta, unlike the negative correlation in the peripheral areas of Guangdong. This study's findings hold a helpful reference for both policymakers and practitioners to coordinate the economy, energy and environment and established environment-friendly society in the fast-developed areas like Guangdong.