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This paper analyzes changes in sacrifice by burning and the space inhabited by ghosts in ancient China from philological and linguistic perspectives. During the Shang and Zhou dynasties, rulers believed that they could convey their offerings and reverence to their ancestors in heaven by burning firewood and sacrifices (尞). From the Spring and Autumn Period to the Han dynasty, the ancient Chinese metaphors for naming the underground space inhabited by ghosts experienced a transformation from a natural space (Yellow Spring (黄泉)) to human settlements (li (里), big cities (都)) and then to government institutions for criminal penalty (government (府), prisons (狱)), which symbolized the gradual establishment of a living order in the space inhabited by ghosts based on the human society. When the new living order of the space inhabited by ghosts was established, the ancient Chinese began to reconstruct sacrifice by burning during the Wei and Jin dynasties, and the objects burnt were represented by joss paper. The use of the term "transforming (化)" to refer to sacrifice by burning suggests that people believed that burning with fire was a way to transfer objects from the real world to the world of ghosts. The act of burning joss paper not only embodied the Chinese concept of ancestor worship to "treat the dead as if they were alive" but also gave "fire (火)" rich religious connotations while greatly simplifying the process and cost of sacrificial rituals, thus gradually becoming popular. [ABSTRACT FROM AUTHOR]

The Sino–U.S. Treaty of Tientsin (1858), for the first time in history, included an article to ensure the toleration of Protestantism in China, i.e., the Toleration Article. The man behind the article was S. Wells Williams (1812–1884), an interpreter of the U.S. Legation and a former missionary, who negotiated with Chinese officials directly. Williams produced the Chinese version of the clause and its official English translation which led to controversies, for it was deemed unfaithful and humiliating to the Christian communities. This paper revisits this important episode in the history of Protestantism in China. Using first-hand archival materials, it reveals how Williams was forced to "mistranslate" the article in order to satisfy both parties. It further discusses the reasons behind the missionaries' criticism, which placed Williams in the center of clashes between religious and political interests. Through Williams' case, the paper foregrounds the crucial roles and difficult situations of missionary translators in diplomatic incidents that shaped the history of Christianity in China. More importantly, it highlights the conflicting interests and ideas in early Sino–U.S. diplomacy and shows how missionary translators worked to mediate the dilemma between evangelism and politics in 19th-century China. [ABSTRACT FROM AUTHOR]

The energy content of ground motion records obtained during the 2019 M6.4, M7.1 Ridgecrest, California earthquakes which occurred closely in space is analyzed in this paper. Wavelet transform as a powerful tool, that is used to evaluate the energy content characteristics including cumulative energy profiles and the total energy for different components of records, was obtained at three stations named China Lake Christmas Canyon, China Lake and Tower 2. Due to the fact that the energy-based parameters could be well correlated with damage distribution, this path has been taken into consideration in this research work with the light of the powerful wavelet tool. For this purpose, the discrete wavelet transform is efficiently used for multi-resolution decomposition of acceleration time history of ground motions and a new framework is presented to estimate the energy level of earthquake sequences. Finally, the pseudo-period corresponding to each corresponding level is introduced and the distribution of energy vs. the pseudo-period is discussed. The results of this paper provide a platform to compare the energy levels of different earthquake sequences in different periods. Analysis showed that wide-band energy distribution could be identified at China Lake Christmas Canyon and Tower 2 stations. [ABSTRACT FROM AUTHOR]

The low-carbon economy represents a global transformation that encompasses production methods, lifestyles, values, national interests, and the destiny of humanity. As a significant contributor to carbon emissions, China has made a momentous strategic decision on carbon peaking and neutralization, infusing momentum into the global effort to address climate change. The rapid growth of the digital economy offers a fresh approach to achieving the "double carbon" objective and advancing the development of low-carbon transformation. Based on the panel data of 30 provinces in China, this paper uses the least square method to investigate the impact of digital economy development on regional low-carbon inclusive development. It is found that there is a significant inverted U shape in the impact of the digital economy on low-carbon inclusive development and the mechanism is resource allocation and ecological inequality. The threshold test found that the role of the digital economy in promoting low-carbon inclusive development shows a marginal decreasing trend. The inverted U-shaped impact of the digital economy on low-carbon inclusive development in the eastern and coastal areas and areas with a low level of factor productivity is more significant. Based on the knowledge factor spillover perspective, we found that the impact of the digital economy on low-carbon inclusive development has a spatial spillover effect, and this effect is more obvious under the role of R&D personnel mobility. [ABSTRACT FROM AUTHOR]

Intelligent forecasting of economic indexes has been an important demand for sustainable management of smart cities. Existing methods for this purpose were mostly established upon the basis of economic mechanism. Econometric models are the most general technical means in this area. However, in era of digital economy, increasing amount of big data has brought great change to traditional production. It is becoming more difficult for conventional technological forecasting methods to deal with multi-dimensional economic indexes. To deal with such challenge, this paper introduces the artificial intelligence algorithms to implement automatic information processing, and proposes a deep neural network-based intelligent forecasting method for multi-dimensional economic indexes in smart cities. Specifically, a deep neural network with three-layer structure is developed as the backbone methodology. For empirical analysis, the real-world data from "Chengdu–Chongqing Economic Circle" in China from 2012 to 2022 are selected as the main simulation scenario. Four major indexes are selected as the main research object: gross product (GDP), per capita GDP, GDP growth rate and the proportion of tertiary industry in GDP. The experimental results show that the proposal can well deal with such forecasting problem from a data-driven perspective, with a proper forecasting effect on historical data. [ABSTRACT FROM AUTHOR]

Maintaining and improving ecological resilience is of great practical significance for the Yellow River Basin to reduce potential ecological risks and deliver sustainable development. Based on the essential characteristics of evolutionary resilience, this paper developed an ecological resilience index system of "resistance-recovery-reconstruction-renewal" and calculated the ecological resilience of 75 prefecture-level cities in the Yellow River Basin from 2007 to 2021 with the improved TOPSIS method. Then the spatiotemporal evolution characteristics of ecological resilience were analyzed using the gravity center-standard deviation ellipse, Dagum Gini coefficient decomposition, and spatial autocorrelation analysis. Furthermore, the dynamic spatial Durbin model (DSDM) was used to investigate the influencing factors of ecological resilience. The main results are as follows: (1) The ecological resilience of the Yellow River Basin showed an overall fluctuating upward trend, and the average annual growth rate in the downstream region was larger than in the upstream and midstream regions. (2) Cities with similar levels of ecological resilience were distributed in a "large settlement, small scattered" pattern. The center of gravity shifted to the southeast, and the spatial distribution exhibited a "northwest-southeast" pattern and a trend towards an "east–west" pattern. The primary source of spatial differences was the intensity of transvariation. (3) The ecological resilience in the Yellow River Basin showed significant spatial clustering, with the H–H clustering area shifting from the Hubao-Eyu urban agglomeration to the Shandong Peninsula urban agglomeration, and the L–L clustering area mainly distributed around the Central Plains city cluster. (4) The ecological resilience of the Yellow River Basin exhibited significant snowball, spillover, and siphon effects in time, space, and space-time dimensions, respectively. In the short and long term, population density and openness significantly positively affected the ecological resilience of local and surrounding cities. Urbanization had a long-term effect on ecological resilience without a short-term effect. GDP per capita and industrial structure only imparted a significant positive influence on local ecological resilience. The negative spatial spillover of the intensity of financial investment in technological innovation gradually turned into a positive effect. [ABSTRACT FROM AUTHOR]

This paper examines the impact of the digital economy on sustainable development, using panel data from cities at the prefecture level and above in China from 2011 to 2019. The results indicate: (1) The digital economy is conducive to boosting growth, increasing employment, reducing energy consumption, and cutting emissions, thereby promoting sustainable development. These findings prove robust. (2) Mechanism test outcomes reveal that, from the perspective of technological innovation, the digital economy can promote sustainable development through increasing R&D input and enhancing innovation output. (3) An extended analysis of the risk of a digital "divide" demonstrates that "dividend" of the digital economy is primarily manifests in spurring economic growth, enhancing energy efficiency, and strengthening environmental protection in lagging regions, while the digital "divide" effect is manifested in the stronger employment stimulating effect of developed regions versus backward areas. The results of this study not only enrich the relevant research system, but also provide empirical evidence to support accelerating digital transformation, strengthening technological innovation governance, and advancing sustainable development. [ABSTRACT FROM AUTHOR]

Accurate prediction of regional terrestrial water storage change (TWSA) is of great significance for water resources planning and management, and early warning of extreme climate disasters. Aiming at the problem that the conventional methods on prediction of TWSA time series are difficult to be accurate, the six typical regions are selected in China as examples, including the upper reaches of the Yangtze River (UYR), the southwest region (SWR), the Liaohe River Basin (LRB), the North China Plain (NCP), the Qinghai-Tibet Plateau (QTP), and the Pearl River Basin (PRB). The mascon product from GRACE/GRACE-FO provided by CSR is used to extract TWSA time series in six typical areas. The improved Back Propagation (BP) neural network, Long Short-Term Memory (LSTM) neural network and the latest Bidirectional LSTM (BiLSTM-attention) neural network model based on attention mechanism are proposed to predict and analyze the regional TWSA. In the experiment, the selection of the optimal model parameters such as the number of hidden layer nodes and the number of hidden units of the neural network model is tested and analyzed in detail. Meanwhile, the model prediction results are compared with the traditional least squares method and random forest (RF) prediction method. The root mean square error (RMSE), determination coefficient (R2), Nash–Sutcliffe efficiency coefficient (NSE) and mean absolute percentage error (MAPE) were used to evaluate the accuracy of the predicted results. The results show that the improved BP, LSTM and Bi-LSTM-attention neural network models all achieve higher prediction accuracy in UYR and SWR areas. RMSE is less than 2.641 cm, R2 is as high as 0.8 or more, NSE is above 0.6, and MAPE is within 0.1. Compared with the least square method, the RMSE of the predicted results from three neural network decreased by 0.998 cm, 0.700 cm and 0.7563 on average, and the R2 increased by 81.75%, 69.89% and 72% on average. Compared with RFML method, the RMSE from three neural network is reduced by 0.601 cm, 0.316 cm and 0.360, and R2 is increased by 38.20%, 24.60% and 27.06% on average. NSE and RMSE are improved to varying degrees in the above regions. It shows that the improved BP, LSTM and BiLSTM-attention model used can effectively predict TWSA. The research methods and results in this paper can provide important reference for the rational utilization of regional water resources and disaster risk assessment. [ABSTRACT FROM AUTHOR]

The tomb of Marquis Yi of Zeng was unearthed in 1978 at Leiguudun in Suizhou City, Hubei Province, China. This is the largest wooden coffin in rock pit and vertical cave in the Warring States period in China. The large wooden coffin components provide reliable material data for the study of the feudal burial system in the pre-Qin period and have high value. In this paper, the wooden coffin excavated from the tomb of Marquis Yi of Zeng was studied. By understanding the composition of wood, studying the weight gain rate, shrinkage rate, scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR) and compression resistance, the wood reinforced by rosin and epoxy resin was compared, and the reinforcement effect and principle of the two reinforcement methods were discussed. The study found that the wood properties of rosin reinforced group were better than those of epoxy reinforced group in the aspects of compressive strength, weight gain rate and volume shrinkage rate, and the mechanical properties of wood were significantly improved. In addition, methanol or ethanol can be used to dissolve the rosin shellac in this strengthening process, which can achieve reversible strengthening treatment. The results provide valuable examples and research ideas for the selection of different conservation techniques of large wooden cultural relics unearthed in the middle and lower reaches of the Yangtze River during the Warring States Period. [ABSTRACT FROM AUTHOR]

As a crucial component of hierarchical diagnosis and treatment systems, medical alliances in China are responsible for promoting the downward allocation of high-quality medical resources. Remote consultation, as an essential means to achieve this goal, is of practical importance in the realization of resource sharing between hospitals within medical alliances in China. The existing research on the construction of remote consultations within medical alliances has achieved fruitful results in both theory and practice. However, the establishment of remote consultation involves many factors, and the current research mainly focuses on the influence of traditional economic profit and loss on the construction of remote consultation. In view of the practical problems existing in the operation of medical and health services in China, such as the need to improve the capacity of primary medical and health services and the poor sinking effect of high-quality medical resources, it is of great importance to systematically study the promotion strategy of the construction of remote consultation within the medical alliance to build a reasonable order of medical treatment. Therefore, by determining the logical path formed by the remote consultation channel and on the basis of traditional profit and loss parameters, this paper fully considers the relevant influence of the resource sinking utility caused by the remote consultation channel. The stability of the evolutionary system is analyzed, and a numerical simulation is used to explore the impact of key parameters on system evolution. The research results indicate that the establishment of a remote consultation system between hospitals at different levels is primarily influenced by factors such as the initial proportion of the establishment strategy chosen by both parties, the establishment cost, the distribution proportion of the government subsidy, the distribution proportion of the economic benefit, and the effectiveness proportion in the utility derived from the downward allocation of resources and reputational damage. The findings suggest that moderate to high levels of reputation loss do not significantly influence the final decision-making process for either party. Government subsidies can have an impact on hospital decision-making in the early stages, and in the long term, the resource sinking utility is more appealing than the economic benefits. To a certain extent, this study enriches the related research on remote consultation and the sinking of high-quality medical resources, provides reliable theoretical and method support for the sinking of high-quality medical resources, promotes the construction of remote consultation in medical alliances in China, and provides a decision-making reference and basis for the government and health administrative departments to formulate relevant policies. [ABSTRACT FROM AUTHOR]

Background: Continuing care is needed for the growing number of chronically ill patients who struggle with health problems after discharge. This study aims to elucidate the development process, functionalities, service protocols, and utilization of an Internet Plus Care (IPC) platform devised by our hospital to offer healthcare services to discharged patients. Methods: This was a mixed study. After describing the development process, function and usage of IPC platform, we retrospectively collect data such as the general information of service recipients and service providers, service items, and service prices through the IPC platform from January 2021 to September 2023 to characterize these services. Results: 69 nurses delivered a total of 788 services to 211 patients through the IPC platform. The majority of services (N = 652, 82.7%) were delivered offline, with almost half of the recipients (N = 384, 48.7%) being elderly individuals. 46.4% of nurses provided services ≥ 3 times. Furthermore, 26.5% of patients received services three or more times. Notably, patients' care requirements exhibited variations across age groups, with wound care (n = 243, 63.3%), pressure injuries care (n = 50, 13.0%), and replacement of indwelling nasogastric tubes (n = 20, 5.2%) emerging as the top three services favored by the elderly. Conclusions: The IPC platform demonstrates potential in delivering diverse health services to patients; however, the involvement of nurses and patients needs to be enhanced. It is necessary to implement relevant safeguard policies to promote the effective use of IPC platform for health management of discharged patients in the future. What does this paper contribute to the wider global clinical community?: The prevalence of chronic diseases is on the rise, and patients with chronic diseases continue to struggle with health problems after discharge and require extended treatment and rehabilitation. Our study proves that IPC platform presents a promising avenue for addressing these challenges. It is anticipated that governmental authorities will undertake comprehensive policy, legislative, and medical insurance reforms to facilitate the extensive adoption of IPC platform-based services. [ABSTRACT FROM AUTHOR]

In this study, a bibliometric analysis technique is used for performance analysis and science mapping of artificial intelligence (AI) applications in streamflow research. This paper examines the current trends in the literature using the Scopus database over the last 37 years. RStudio Bibliometrix software was used to analyse the titles, keywords, abstracts, and full texts of 3000 publications to identify trends in AI models, publication types, journals, citations, authors, countries, and regions. The highest frequency AI-related keyword is "artificial neural networks," which was used in a total of 25587 times. The most common publication type, at 82.1%, is journal articles, and the highest rate of country production is 25% for China. In recent years, streamflow research studies have significantly increased their use of AI applications. [ABSTRACT FROM AUTHOR]

This paper investigates the effect of Chinese outward foreign direct investment (FDI) on youth unemployment in sub-Saharan Africa (SSA) by using a modified labour demand model to identify the investment sources that are helpful for reducing youth unemployment in the region. The model is applied to a panel of 42 countries for the period 2003–2021 using random-effect, and generalized method of moment (GMM) estimators for robustness check. Our results suggest that Chinese FDI has direct short-term reducing effect on youth unemployment in SSA. The direction of China's capital investment to infrastructure development and other labour-intensive activities leads to immediate reduction in youth unemployment. However, overtime, due to absence of linkages with domestic firms, and thus lack of demand effects, Chinese FDI becomes insignificant for employment creation. Our results also indicate that Other FDI does not lead to significant reduction in youth unemployment both currently and overtime. Our analysis gives partial support to the argument that the impact of Chinese FDI may differ from those of developed countries. Finally, we could not find evidence that the effect of FDI on employment depends on host country human capital and institutional quality. Several specifications of the empirical model are tested, and explanations are provided for the results. Policy implications are highlighted, especially the need to attract more job absorbing FDI and improve domestic absorptive capacity. [ABSTRACT FROM AUTHOR]

This study explores the peer and economic effects of corporate poverty alleviation behavior. Using the data of A-share non-financial listed corporates in Shanghai and Shenzhen of China from 2016 to 2020, the empirical analysis of this study finds that: corporate poverty alleviation behavior has significant peer effects; the guidance of local poverty alleviation policies weakens the peer effects of corporate poverty alleviation behavior; compared to private enterprises, the poverty alleviation behavior of the peer firms has a more significant impact on state-owned enterprises; and corporate poverty alleviation behavior can result in the backflow of economic benefits and achieve the organic unity of economic and social benefits. The purpose of this paper is to explore the peer effects of corporate poverty alleviation behaviors through empirical analysis using available public data. The results of the study not only increase the motivation of corporate to participate in poverty alleviation from a peer effects perspective, but also reveal key factors for sustaining corporate poverty alleviation behaviors. [ABSTRACT FROM AUTHOR]

In the study of urban development, it is very important to evaluate the influence of production factors reasonably and efficiently for the region to achieve efficient development. The principal aim of this investigation is to amalgamate the conventional measurement model characterized by robust interpretability with the non-parametric model characterized by limited interpretability, thereby enhancing the precision of research outcomes. Towards this objective, the study employs an optimized directional distance function integrated with a global Malmquist–Luenberger index to formulate a comprehensive total factor productivity measurement framework. In elucidating the homogeneous attributes of regions, departing from prior methodologies reliant on manual or direct algorithmic partitioning, this paper employs the K-means clustering algorithm for index discernment, abstracting the concept of K-means clustering centroids to encapsulate regional homogeneity, thereby delineating results through the visualization of regional development potential maps and the evolution of centroid-based clustering trend maps. The findings of the investigation illuminate common patterns of change across disparate regions, proposing a strategy for leveraging regional resource endowments towards a cohesive framework, thereby transcending constraints imposed by production efficiency limitations. Amidst the backdrop of the COVID-19 pandemic, this study draws upon provincial-level data spanning from 2000 to 2018 in China. The conclusive analytical outcomes underscore the pivotal role of energy factors in regional development efficiency, particularly within high-potential development regions, followed by the capital and labor factors. Concurrently, the study discerns a discernible hierarchical pattern among areas of development potential, which exhibits correlation with factor mobility dynamics. [ABSTRACT FROM AUTHOR]

In the process of hikers' choosing a destination, searching for information is one of the important elements, playing a decisive role in decision-making. Based on the Baidu Index for "hiking," this paper analyzes the spatial and temporal characteristics of and factors that influenced network attention to hiking in China from 2016 to 2021. The study found that (1) Network attention to hiking in China was generally relatively stable across the period studied, with highly uneven distribution between different months. The search volume was higher on weekends, and mobile searches increased year by year, far exceeding computer searches. (2) Different regions in China experienced different levels of network attention, with the highest levels in the east, followed by the center, and the lowest in the west. Except for East China, network attention to hiking was highly unevenly distributed within each region. (3) The COVID-19 pandemic increased the geographical concentration index and coefficient of variation but reduced the primacy index. A region's level of economic development, degree of network development, population size, and population age structure are proposed as factors that affect network attention to hiking. [ABSTRACT FROM AUTHOR]

Under economic globalization, countries are linked through trade and investments. This economic interdependence creates vulnerabilities. The indirect vulnerability induced by interdependent networks of trade and investments can put a country's economy at risk, but this risk has yet to be systematically quantified and investigated. In this paper, we developed the novel Potential Indirect Vulnerability Index (PIVI) to capture how interdependencies between networks of trade and foreign direct investment (FDI) may induce economic vulnerabilities. The model consisted of three main components: a target country (the importer of goods), an investing country (the exporter of FDI), and the intermediary countries that export commodities to the target country and receive FDI from the investing country, serving as conduits of the vulnerabilities caused indirectly by the investing country. The PIVI quantifies the indirect vulnerabilities based on the product of two fractions: 1) the dependency of the target country on commodities from each intermediary country; and 2) the dependency of each intermediary country on FDI from the investing country. We demonstrated the utility of PIVI by examining the US economy's vulnerability to China using 2019 trade and FDI data. Several Asian countries and a mix of agricultural products and raw materials were identified as conduits through which China could potentially influence the US economy. Vietnam was a sizeable risk because, while it has been a primary source of many US imports, it also received about 30% of its FDI from China. The US policy makers might opt to increase diversity in trade partners or to promote investment in countries such as Vietnam. We also applied the PIVI analysis to critical minerals, identifying cobalt, tungsten, and copper as the most vulnerability-inducing among them. PIVI is a flexible metric than can be aggregated and modified to provide a more nuanced and focused assessment of an economy's vulnerability. [ABSTRACT FROM AUTHOR]

Urban last-mile express delivery in China encounters several challenges. This paper presents the establishment of a sharing logistics center aimed at enhancing the overall efficiency of urban last-mile express delivery while optimizing the utilization of essential resources. The successful implementation of shared delivery within sharing logistics center necessitates the creation of a robust collaborative mechanism. Recognizing that cooperative benefit allocation is dynamically influenced by factors such as resource input, operational efficiency, risk management, and other cost-related considerations, this study introduces a multi-weight H-Shapley value method for benefit allocation. By conducting empirical analyses of urban last-mile express delivery in Beijing within a sharing logistics service framework, our findings reveal that the revised benefit allocation model better aligns with the interests of participating entities and positively correlates with their contributions. Analyzing the impact of delivery volume and express operational costs changes, it is found that when the delivery volume and express operational costs of the sharing logistics center change, the benefits of participating enterprises move in the same direction. The benefit allocation model established in this study enriches the existing body of research in the field of shared delivery and offers valuable insights for benefit allocation issues that necessitate consideration of the dynamic effects of multiple parameter variations. [ABSTRACT FROM AUTHOR]

This paper focuses on firms in which insiders pledge their shares as collateral for loans. By investigating a natural experiment—China's enactment of provisions on share reductions that restrict pledge creditors' cashing-out behavior—we find that pledging firms exhibited more conservative financial reporting after the implementation than non-pledging firms. This effect was pronounced in firms with a higher ratio of pledged shares, a longer maturation period of the pledged shares, and more concentrated pledge creditors. Additionally, we show that pledging firms increased their accounting conservatism after the shock, leading to a lower risk of margin calls and stock price crashes. The effect on accounting conservatism was stronger in firms with controlling pledgers or when the pledge creditors were banks. Our results remained consistent after we performed several robustness tests. These behaviors are economically logical because the provisions heighten creditors' liquidity risk and the potential losses of loan default. Pledging shareholders embrace more accounting conservatism to mitigate creditors' concerns about agency costs and avoid triggering margin calls. Our findings provide direct support that creditors have a real demand for accounting conservatism and highlight the impact of shareholder-creditor conflicts on the financial reporting policies of pledging firms. [ABSTRACT FROM AUTHOR]

With the development of earthquake disaster reduction efforts in China, the content of earthquake disaster reduction policies has become increasingly enriched and improved. Particularly, multiple provincial governments have proposed earthquake disaster reduction planning policies. It is important to explore whether these policies can affect disaster mitigation. Therefore, this paper summarizes the earthquake disaster reduction plans and factors influencing seismic resilience. Panel data from 24 provinces between 2012 and 2021 were collected, and a difference-in-differences approach was used to construct an econometric model to evaluate the policy effects and analyze the enhancement of seismic resilience. The results show that the implementation of earthquake disaster reduction policies has a positive impact on earthquake monitoring, evacuation, and emergency relief capabilities, and the estimated policy effects are statistically significant. Moreover, a series of tests were conducted. The conclusions are as follows: (1) Earthquake disaster reduction policies have a positive impact on the improvement of seismic resilience in provinces. (2) Provinces with a higher number of earthquakes experience more significant effects from earthquake disaster reduction policies. (3) Provinces with higher seismic peak ground acceleration values exhibit more pronounced improvements in seismic resilience. [ABSTRACT FROM AUTHOR]

Amid the increasing global internet penetration, understanding the impact of internet use on residents' health is crucial. This aids in formulating more effective health policies and provides empirical evidence for promoting health equity and improving overall public health. Drawing on the China Health and Retirement Longitudinal Study (CHARLS), this paper employs the Propensity Score Matching-Difference in Differences (PSM-DID) method to examine the impact of the internet on individual health and further explores the pathways through which the internet affects health. We introduce the research background and significance in the introduction. Then, in the theoretical analysis, it incorporates internet variables into the Becker health demand model to analyze changes in health demand and impact pathways. The empirical analysis tests the theoretical findings, leading to empirical results. Finally, the study discusses the results and provides relevant recommendations. The findings indicate significant positive effects of the internet on both physical and psychological health. These effects are realized through reducing health information asymmetry, lowering health costs, and increasing exposure to health-promoting environments. In the heterogeneity analysis, economic-related internet content shows a significant positive impact on resident health. Intensive internet use adversely affects psychological health. The beneficial effects of the internet on health are more pronounced among older individuals, those covered by medical insurance, and regions with higher levels of digital economy. Based on these findings, the study offers policy recommendations concerning individuals' internet use patterns, the digital evolution of the healthcare industry, and government infrastructure development. [ABSTRACT FROM AUTHOR]

Traditional Chinese settlements (TCSs) embody millennia of human and cultural history, with the core culture serving as their essence. However, any damage incurred by these TCSs can result in the loss of core culture. In the era of China's digital transformation, there has recently been a significant push toward digital reforms within TCSs. Thus, this paper introduces a traditional settlement digitalization (TSD) framework with a specific focus on enhancing the preservation of TCS landscape gene through the perspective of landscape gene information chain theory. This research uses the Nanxun traditional settlement (TS), Huzhou, Zhejiang, China, the first TS listed as a World Cultural Heritage Site in China, as a case study, extracting and identifying the area landscape gene and digital visualization status. This article identifies the landscape gene information chain of the Nanxun TS and redesigns its digital visualization and application scheme under the TSD framework. This study reveals the notion of a digital landscape gene characterized by four key attributes: identical, interactive, immersive, and intelligent. The results of this study will help guide the sustainable protection, application and planning of the Nanxun TS and promote the study of other sections of landscape gene theory. This research not only contributes to the development of landscape gene information chain theory but also provides guidance for the digital transformation of other TCSs and cultural heritage sites. [ABSTRACT FROM AUTHOR]

Lobopodians represent a key step in the early history of ecdysozoans since they were the first animals to evolve legs within this clade. Their Cambrian representatives share a similar body plan with a typically cylindrical annulated trunk and a series of non-jointed legs. However, they do not form a monophyletic group and likely include ancestors of the three extant panarthropod lineages (Tardigrada, Onychophora, Euarthropoda). Some species display astonishing protective devices such as cuticular plates and spines. We describe here the armor and molting process of Microdictyon from the early Cambrian of China. Microdictyon secreted ovoid paired cuticular sclerites that were duplicated in a non-synchronous way along the animal's body. The reticulated pattern and cuticular architecture of these sclerites have similarities to extant armored tardigrades that recently served in hypothesizing that tardigrades are possibly miniaturized lobopodians. Ecdysis and hard cuticular protection are now well documented in the whole spectrum of early Cambrian ecdysozoans such as soft-bodied scalidophorans, lobopodians and fully articulated euarthropods. We hypothesize that the secretion of sclerotized cuticular elements periodically renewed via ecdysis was a key innovation that opened large-scale evolutionary opportunities to invertebrate animal life, specifically ecdysozoans, both in terms of anatomical functionalities and ecological success. This paper presents exceptionally preserved fossils from the early Cambrian that show how molting occurred in some of the very earliest arthropod relatives, a trait which enabled continuous growth in animals with hard exoskeletons. [ABSTRACT FROM AUTHOR]

Crowdfunding is a new type of financing favored by entrepreneurs in need of capital. Financing performance is a key concern for crowdfunding project initiators. Although a growing number of studies have investigated the factors that affect the financing performance of crowdfunding projects, there are still some issues that need to be further clarified. How does the investment behavior of backers, as the supply side of finance, affect the financing performance of project in reward-based crowdfunding? What are the moderating mechanisms of this influence by initiator characteristics and project attributes? Based on a panel data set from Zhongchou, a famous agri-food crowdfunding platform in China, this paper finds that the investment speed, the investment intensity, the number of early backers, the backers' comments, and the number of selfless backers all have significant effects on financing performance. The core trust factors of initiator characteristics and project attributes play a moderating role in the relationship between backer investment behavior and financing performance, but there are differences in the moderating mechanisms. Based on the research conclusions, practical enlightenment is proposed for initiators, crowdfunding platforms, and regulators. [ABSTRACT FROM AUTHOR]

Bayesian network (BN) is an important tool in probabilistic seismic risk analysis (PSRA) due to its holistic nature and powerful probabilistic inference capabilities. However, while the information that can be stored by BN includes variable values, probability distributions, and relationships between variables, it does not involve event quantities. The results obtained from PSRA and probabilistic seismic hazard analysis (PSHA) are commonly frequency‐related (the number of occurrences per unit time), fundamentally describing event numbers. BN assumes a fixed event number relationship between nodes, but the aftershock counts vary for different mainshock magnitudes. This limitation further restricts its effectiveness in lifespan PSRA for structures. Therefore, this study aims to propose an enhanced BN (EBN) for modeling aftershock PSHA (APSHA) considering frequency information. This paper first introduces the modeling method using BN, including PSHA and a simplified model of APSHA using Båth's law. Next, an EBN that can effectively address frequency issues between the mainshock and aftershocks is presented for modeling APSHA considering the modified Omori's law, while the mathematical principles of both forward and backward inferences for the EBN are elaborated in detail. Then, the article carefully analyzes the utilization of the EBN for APSHA modeling under different scenarios where the information regarding the mainshock is known to varying extents, followed by the extension of the EBN in a time domain to obtain time‐dependent aftershock hazard. Finally, an example of APSHA in a specific location in China is illustrated. [ABSTRACT FROM AUTHOR]

On the basis of "Carbon Peak and Carbon Neutral" goals, carbon sequestration projects are increasing in China. The integrity of cement sheaths, as an important factor affecting carbon sequestration projects, has also received more attention and research. When CO2 is injected into the subsurface from sequestration wells, the cement sheath may mechanically fail due to the pressure accumulated inside the casing, which leads to the sealing of the cement sheath failing. The elasticity and strength parameters of the cement sheath are considered in this paper. The critical bottom-hole injection pressures of inclined well sections under anisotropic formation stresses at different depths were calculated for actual carbon-sealing wells in the X block—the CO2 sequestration target block. The sensitivity factors of the critical bottom-hole injection pressure were also analyzed. It was found that the cement sheath damage criterion was tensile damage. The Young's modulus and tensile strength of the cement sheath are the main factors affecting the mechanical failure of the cement sheath, with Poisson's ratio having the second highest influence. An increase in the Young's modulus, Poisson's ratio, and tensile strength of the cement sheath can help to improve the mechanical stability of cement sheaths in CO2 sequestration wells. This model can be used for the design and evaluation of cement in carbon sequestration wells. [ABSTRACT FROM AUTHOR]

In recent years, the State Grid of China has placed significant emphasis on the monitoring of noise in substations, driven by growing environmental concerns. This paper presents a substation noise monitoring system designed based on an end-network-cloud architecture, aiming to acquire and analyze substation noise, and report anomalous noise levels that exceed national standards for substation operation and maintenance. To collect real-time noise data at substations, a self-developed noise acquisition device is developed, enabling precise analysis of acoustic characteristics. Moreover, to subtract the interfering environmental background noise (bird/insect chirping, human voice, etc.) and determine if noise exceedances are originating from substation equipment, an intelligent noise separation algorithm is proposed by leveraging the convolutional time-domain audio separation network (Conv-TasNet), dual-path recurrent neural network (DPRNN), and dual-path transformer network (DPTNet), respectively, and evaluated under various scenarios. Experimental results show that (1) deep-learning-based separation algorithms outperform the traditional spectral subtraction method, where the signal-to-distortion ratio improvement (SDRi) and the scale-invariant signal-to-noise ratio improvement (SI-SNRi) of Conv-TasNet, DPRNN, DPTNet and the traditional spectral subtraction are 12.6 and 11.8, 13.6 and 12.4, 14.2 and 12.9, and 4.6 and 4.1, respectively; (2) DPTNet and DPRNN exhibit superior performance in environment noise separation and substation equipment noise separation, respectively; and (3) 91% of post-separation data maintains sound pressure level deviations within 1 dB, showcasing the effectiveness of the proposed algorithm in separating interfering noises while preserving the accuracy of substation noise sound pressure levels. [ABSTRACT FROM AUTHOR]

This paper introduces an innovative cooperative model for energy efficiency retrofitting that incorporates carbon emission rights, addressing critical financial constraints in Energy Performance Contracting (EPC). By employing the fuzzy analytic hierarchy process (F-AHP) to evaluate risk assessment indicators and stakeholder contributions and utilizing the enhanced Shapley method for equitable benefit distribution, the model demonstrates significant improvements in financing and efficiency for energy conservation projects. The findings are as follows: (1) the energy efficiency retrofit model, which integrates carbon emission rights, effectively alleviates the financial constraints and fosters energy conservation and emission reduction in guaranteed-savings EPC projects; (2) the enhanced Shapley method is deemed appropriate for the equitable distribution of energy-saving benefits among stakeholders; (3) when compared with the traditional model and the benefit allocation-absent carbon rights, the energy-saving benefits of the energy efficiency retrofit model incorporating carbon emission rights are higher in individual and overall terms. The findings of this study offer a viable solution to financing challenges faced by stakeholders in such projects and delineate a pragmatic approach for enterprises to enhance energy efficiency and reduce emissions. [ABSTRACT FROM AUTHOR]

A non-periodic "step-like" variation in displacement is exhibited owing to the repeated instability of expansive soil landslides. The dynamic prediction of deformation for expansive soil landslides has become a challenge in actual engineering for disaster prevention and mitigation. Therefore, a support vector regression prediction (AMPSO-SVR) model based on adaptive mutation particle swarm optimization is proposed, which is suitable for small samples of data. The shallow displacement is decomposed into a trend component and fluctuating component by complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN), and the trend displacement is predicted by cubic polynomial fitting. In this paper, the multiple disaster-inducing factors of expansive landslides and the time hysteresis effect between displacement and its influencing factors are fully considered, and the crucial influencing factors which eliminate the time lag effect and state factors are input into the model to predict the fluctuation displacement. Monitoring data in the Ningming area of China are employed for the model validation. The predicted results are compared with those of the traditional model. The model performance is evaluated through indicators such as the goodness of fit R2 and root mean square error RMSE. The results show that the prediction RMSE of the new model for three monitoring stations can reach 2.6 mm, 6.6 mm, and 2.5 mm, respectively. Compared with the common Grid search support vector regression (GS-SVR), the Particle Swarm Optimization Support Vector Regression (PSO-SVR) and Back Propagation Neural Network (BPNN) models have average improvements of 58.4%, 38.1%, and 25.2% respectively. The goodness of fit R2 is superior to 0.99 in the new method. The proposed model can effectively be deployed for the displacement prediction of non-periodic stepped expansive soil landslides driven by multiple influencing factors, providing a reference idea for the deformation prediction of expansive soil landslides. [ABSTRACT FROM AUTHOR]

The timely and accurate acquisition of information on the distribution of the crop planting structure in the Loess Plateau of eastern Gansu Province, one of the most important agricultural areas in Western China, is crucial for promoting fine management of agriculture and ensuring food security. This study uses multi-temporal high-resolution remote sensing images to determine optimal segmentation scales for various crops, employing the estimation of scale parameter 2 (ESP2) tool and the Ratio of Mean Absolute Deviation to Standard Deviation (RMAS) model. The Canny edge detection algorithm is then applied for multi-scale image segmentation. By incorporating crop phenological factors and using the L1-regularized logistic regression model, we optimized 39 spatial feature factors—including spectral, textural, geometric, and index features. Within a multi-level classification framework, the Random Forest (RF) classifier and Convolutional Neural Network (CNN) model are used to classify the cropping patterns in four test areas based on the multi-scale segmented images. The results indicate that integrating the Canny edge detection algorithm with the optimal segmentation scales calculated using the ESP2 tool and RMAS model produces crop parcels with more complete boundaries and better separability. Additionally, optimizing spatial features using the L1-regularized logistic regression model, combined with phenological information, enhances classification accuracy. Within the OBIC framework, the RF classifier achieves higher accuracy in classifying cropping patterns. The overall classification accuracies for the four test areas are 91.93%, 94.92%, 89.37%, and 90.68%, respectively. This paper introduced crop phenological factors, effectively improving the extraction precision of the shattered agricultural planting structure in the Loess Plateau of eastern Gansu Province. Its findings have important application value in crop monitoring, management, food security and other related fields. [ABSTRACT FROM AUTHOR]

Although atmospheric CO2 concentrations collected by satellites play a crucial role in understanding global greenhouse gases, the sparse geographic distribution greatly affects their widespread application. In this paper, a hybrid CNN and spatiotemporal Kriging (CNN-STK) model is proposed to generate a monthly spatiotemporal continuous XCO2 dataset over China at 0.25° grid-scale from 2015 to 2020, utilizing OCO-2 XCO2 and geographic covariates. The validations against observation samples, CAMS XCO2 and TCCON measurements indicate the CNN-STK model is effective, robust, and reliable with high accuracy (validation set metrics: R2 = 0.936, RMSE = 1.3 ppm, MAE = 0.946 ppm; compared with TCCON: R2 = 0.954, RMSE = 0.898 ppm and MAE = 0.741 ppm). The accuracy of CNN-STK XCO2 exhibits spatial inhomogeneity, with higher accuracy in northern China during spring, autumn, and winter and lower accuracy in northeast China during summer. XCO2 in low-value-clustering areas is notably influenced by biological activities. Moreover, relatively high uncertainties are observed in the Qinghai-Tibet Plateau and Sichuan Basin. This study innovatively integrates deep learning with the geostatistical method, providing a stable and cost-effective approach for other countries and regions to obtain regional scales of atmospheric CO2 concentrations, thereby supporting policy formulation and actions to address climate change. [ABSTRACT FROM AUTHOR]

The current deformation and stable state of slopes with historical shatter signs is a concern for engineering construction. Suspected landslide scarps were discovered at the rear edge of the Genie slope on the Tibetan Plateau during a field investigation. To qualitatively determine the current status of the surface deformation of this slope, this study used high-resolution optical remote sensing, airborne light detection and ranging (LiDAR), and interferometric synthetic aperture radar (InSAR) technologies for comprehensive analysis. The interpretation of high-resolution optical and airborne LiDAR data revealed that the rear edge of the slope exhibits three levels of scarps. However, no deformation was detected with differential InSAR (D-InSAR) analysis of ALOS-1 radar images from 2007 to 2008 or with Stacking-InSAR and small baseline subset InSAR (SBAS-InSAR) processing of Sentinel-1A radar images from 2017 to 2020. This study verified the credibility of the InSAR results using the standard deviation of the phase residuals, as well as in-borehole displacement monitoring data. A conceptual model of the slope was developed by combining field investigation, borehole coring, and horizontal exploratory tunnel data, and the results indicated that the slope is composed of steep anti-dip layered dolomite limestone and that the scarps at the trailing edges of the slope were caused by historical shallow toppling. Unlike previous remote sensing studies of deformed landslides, this paper argues that remote sensing results with reliable accuracy are also applicable to the study of undeformed slopes and can help make preliminary judgments about the stability of unexplored slopes. The study demonstrates that the long-term consistency of InSAR results in integrated remote sensing can serve as an indicator for assessing slope stability. [ABSTRACT FROM AUTHOR]

Thunderstorm gusts are a common form of severe convective weather in the warm season in North China, and it is of great importance to correctly forecast them. At present, the forecasting of thunderstorm gusts is mainly based on traditional subjective methods, which fails to achieve high-resolution and high-frequency gridded forecasts based on multiple observation sources. In this paper, we propose a deep learning method called Thunderstorm Gusts TransU-net (TG-TransUnet) to forecast thunderstorm gusts in North China based on multi-source gridded product data from the Institute of Urban Meteorology (IUM) with a lead time of 1 to 6 h. To determine the specific range of thunderstorm gusts, we combine three meteorological variables: radar reflectivity factor, lightning location, and 1-h maximum instantaneous wind speed from automatic weather stations (AWSs), and obtain a reasonable ground truth of thunderstorm gusts. Then, we transform the forecasting problem into an image-to-image problem in deep learning under the TG-TransUnet architecture, which is based on convolutional neural networks and a transformer. The analysis and forecast data of the enriched multi-source gridded comprehensive forecasting system for the period 2021–23 are then used as training, validation, and testing datasets. Finally, the performance of TG-TransUnet is compared with other methods. The results show that TG-TransUnet has the best prediction results at 1–6 h. The IUM is currently using this model to support the forecasting of thunderstorm gusts in North China. [ABSTRACT FROM AUTHOR]

Employing non-family CEOs in Chinese family firms has become a universal phenomenon. The existing research mainly focuses on the direct effect of non-family CEOs on corporate operation, but ignores the capital market influence despite stock price crash has a more serious damage for family firms due to their reputation concern. To fill this research gap, this paper shed light on the important but ignored capital market effect of non-family members by exploring the role of nonfamily CEOs on stock price crash risk. Based on 12,561 firm-year observations of listed family firms in China from 2004 to 2020, this study estimates using correlation analysis, multiple regression analysis, entropy balancing matching, treatment effect model and granger causality test. The results show that family firms with non-family CEOs are negatively related with future stock price crash risk in Chinese family firms, and this relationship tends to be stronger when CEOs have more power. Further, to explore the effect mechanism, we isolate the formation of stock price crash risk into two stages: bad news generation and bad news disclosure, and find that non-family CEOs reduce bad news generation but have no impact on bad news disclosure. Additionally, nonfamily CEOs decrease stock price crash risk when governance mechanisms in family firms are weaker, industry environments are poorer and macroeconomic conditions are more positive. This study highlights the negative effect of non-family CEOs on stock price crash risk in Chinese family firms, firstly clarifies the effect mechanism based on the two stages of stock price crash formation and finds that CEO power strengthens the role of non-family CEOs in lowering stock price crash risk in family firms, which differs from previous studies. Finding of this study also have important implications for the managers and investors of family firms. Family firms are expected to increase the introduction of non-family members and strengthen supervision to formalize governance structure. Meanwhile, investors should also consider the ability and experience of top management teams in family firms. [ABSTRACT FROM AUTHOR]

Flash drought is characterized by rapid onset and short-duration drought conditions caused by a combination of factors, including high evaporation, high temperature, and prolonged periods of little to no precipitation, leading to a sudden and severe decrease in soil moisture levels. In comparison to conventional drought, it is more susceptible to the effects of global warming and has the potential to become a common drought phenomenon in the coming years, necessitating further research. In this paper, we focused on flash drought events, specifically in forest parts of northeastern China that are included within the Greater Khingan Mountains (GKM), Lesser Khingan Mountains (LKM), and Changbai Mountains (CM), using daily soil moisture data as well as SPOT- VEGETATION NDVI satellite data from 2000 to 2020 and determined their impact on the forest NDVI. Our major findings are as follows. (1) The forest within GKM had the maximum area being affected by flash drought events. (2) The frequency ranged from 1 to 2 times, whereas the total duration varied between 20 and 55 days over the study area in a 21-year period. (3) Flash drought was most common in the plant-growing seasons. (4) The flash drought events had a negative influence on the forest NDVI. Our study contributes to a deeper understanding of the flash drought dynamics in forest areas of northeast China for flash drought monitoring, prediction, and management strategies in this region. [ABSTRACT FROM AUTHOR]

CO2 capture from coal power plants is an important and necessary solution to realizing carbon neutrality in China, but CCS demonstration deployment in power sector is far behind expectations. Hence, the reduction potential of energy consumption and cost for CCS and its competitiveness to renewable powers are very important to make roadmaps and policies toward carbon neutrality. Unlike the popular recognition that capturing CO2 from flue gases is technically and commercially mature, this paper notes that it has been proved to be technically feasible but far beyond technology maturity and high energy penalty leads to its immaturity and therefore causes high cost. Additionally, the potential energy penalty reduction of capture is investigated thermodynamically, and future CO2 avoidance cost is predicted and compared to renewable power (solar PV and onshore wind power). Results show that energy penalty for CO2 capture can be reduced by 48%-57%. When installation capacity reaches a similar scale to that of solar PV in China (250 GW), CO2 capture cost in coal power plants can be reduced from the current 28-40 US$/ton to 10-20 US$/ton, and efficiency upgrade contributes to 67%-75% in cost reduction for high coal price conditions. In China, CO2 capture in coal power plants can be cost competitive with solar PV and onshore wind power. But it is worth noting that the importance and share of CCS role in CO2 emission reduction is decreasing since renewable power is already well deployed and there is still a lack of large-scale CO2 capture demonstrations in China. Innovative capture technologies with low energy penalties need to be developed to promote CCS. Results in this work can provide informative references for making roadmaps and policies regarding CO2 emission reductions that contribute towards carbon neutrality. [ABSTRACT FROM AUTHOR]

Myanmar is a key neighbor for China and an important link in advancing the "Belt and Road" initiative, contributing to both domestic and international economic flows. Despite the border closures and restrictions imposed during the COVID-19 pandemic, the New China-Myanmar Indian Ocean Corridor has seen substantial progress. However, this development has not garnered the attention it deserves, as both national and Yunnan provincial governments continue to prioritize the China-Myanmar Economic Corridor (CMEC). This oversight results from an incomplete understanding of the changes in Myanmar's geopolitical landscape since 2000. Using a framework for national geopolitical landscape analysis, this study examines Myanmar's basic national conditions, principal relationships, and inherent contradictions, revealing how Myanmar's geopolitical landscape has evolved due to the interplay of internal and external factors, cross-field interactions, and strategic games played by multiple geopolitical actors. Specifically, the study discusses the period from 2000 to 2010, characterized by external pressure and internal stability, and the years from 2011 to 2021, marked by external conflict and internal turmoil. The evolving geopolitical landscape in Myanmar has created favorable conditions for building the New China-Myanmar Indian Ocean Corridor. From a geopolitical perspective, this paper explores the reasons behind the necessity of this new corridor and suggests a re-evaluation of China's spatial planning for major infrastructure projects in Myanmar given the country's shifting geopolitical context. The corridor's feasibility--whether measured by distance, time, costs, spatial distribution of domestic ethnic armed conflicts, or Myanmar's post-pandemic economic trends--suggests it is highly workable. In the short term, the new corridor can complement the China-Myanmar Economic Corridor, progressing concurrently; in the long term, it could gradually replace it as the main route for China-Myanmar trade. This study not only enhances understanding of the New China-Myanmar Indian Ocean Corridor but also provides a scientific rationale for its vigorous promotion. [ABSTRACT FROM AUTHOR]

The declining trend in China's fertility rate is very pronounced, and since 2023, the population has entered a phase of negative growth, significantly constraining economic development. At the same time, income inequality, which creates many problems, remains a serious issue. Existing research does not discuss the impact of income inequality on fertility rates in China currently. This paper depends on both macro and micro perspectives to examine the relationship between income inequality and fertility. We used the macro perspective to study the impact of urban-rural income inequality on the birth rate. The results show that the widening income gap between urban and rural areas will reduce the birth rate, but this effect declines with increasing of the birth rate; this negative effect is the strongest in the eastern region and the weakest in the western region. We used micro perspective to study the effect of the Gini coefficient on fertility motivation. It was found that for every one percent increase of the Gini coefficient, the fertility motivation decreased by about 0.08%, indicating that income inequality also impacts fertility behavior negatively. Comparing different income groups, income inequality has no impact on the fertility motivation of low-income groups, but has a significant negative and positive impact, respectively, on the middle and high-income groups. The mediating effect model shows that income inequality can negatively affect fertility behavior by reducing social trust and subjective well-being. Therefore, the empirical results from China suggest that income inequality negatively affects fertility behavior. [ABSTRACT FROM AUTHOR]

This paper aims to analyze the relationship between China's maritime security policy and territorial disputes in the gray zone in the South China Sea. The study examines this relationship through the strategy and means of Chinese maritime security policy, aiming to clarify the factors that influenced this policy to drag its territorial disputes into the gray zone. The article argues that the rivalries between countries in the region fostered by the United Nations Convention on the Law of the Sea (UNCLOS) operated as an external pressure on China. Furthermore, geopolitical factors and the perception of the Chinese ruling elite have contributed to considering the UNCLOS as a threat to China's maritime interests in the South China Sea. Consequently, the Chinese maritime security policy seeks to shift the status quo in the region to establish China as a hegemonic regional power with its sphere of influence while avoiding an open war with its neighbors and the US. [ABSTRACT FROM AUTHOR]

Border issues, such as sovereignty, territorial claims, and security, play a significant role in transboundary river management. Borders can act as physical barriers that impede cooperation and coordination, and disputes over ownership and control of river resources can exacerbate tensions and lead to conflicts. Transboundary river management is a complex and critical issue that has gained increasing attention in recent years. The management of rivers that cross international borders raises numerous challenges, including political, economic, social, and environmental concerns. Effective management requires the cooperation of all countries sharing the river basin, and it involves the development of legal and institutional frameworks that promote sustainable use, equitable sharing, and peaceful resolution of conflicts. This paper aims to explore the intricate relationship between India's persistent border disputes with China and their far-reaching implications for the management of transboundary rivers. Their unresolved territorial disputes have profound implications for the shared river basins, such as the Brahmaputra Basin, and pose significant challenges to the implementation of effective transboundary river management strategies. Through an interdisciplinary examination of historical, geopolitical, and hydrological factors, this study sheds light on the multifaceted dimensions of India's border disputes with China and elucidates their detrimental impacts on the use of transboundary river resources. [ABSTRACT FROM AUTHOR]

The superiority of BWM over other weighting methods for obtaining the weight values of the attributes is that it achieves high-confidence results with a reasonable number of pairwise comparisons. Although the best-worst method (BWM) is a well-known multi-criteria decision-making (MCDM) method that has been successfully utilized in almost all scientific areas to solve challenging real-life problems, no research has comprehensively examined the state-of-the-art in this regard. The present study depicts a detailed overview of publications concerned with BWM during the period 2015–2022. Based on the information obtained from the Scopus database, this work presents a big picture of current research on BWM. In other words, this paper analyzes the existing literature about BWM and identifies thematic contexts, application areas, emerging trends, and remaining research gaps to shed light on future research agendas aligning with those gaps. Further, the most recent BWM research is analyzed in the top ten scientific areas, from engineering to materials science. “Engineering”, “computer science”, and “business, management, and accounting” are the hottest fields of BWM research. China is the most active country regarding “engineering” and “computer science”, whereas India is the leader in “business, management, and accounting”. The study also reveals that there are still many research gaps in BWM research. The big picture taken in this study will not only showcase the current situation of BWM research but will also positively impact the direction and quality of new research. [ABSTRACT FROM AUTHOR]

High precision ionospheric Total Electron Content(TEC) prediction is of great significance for improving the accuracy of Global Navigation Satellite System(GNSS), preventing natural disasters, and ensuring wireless communication. Given the varying frequencies of TEC signals, a hybrid CEEMDAN-BiLSTM-PSO-LSSVM-FE model for predicting ionospheric TEC content is proposed in this paper. Firstly, complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) is used to decompose the extracted hourly TEC sequence and calculate the fuzzy entropy (FE) of the subsequences. Then, the signal is divided into high-frequency and low-frequency parts based on the fuzzy entropy value, for the high-frequency component, Bidirectional Long Short-Term Memory network (BiLSTM) is used for prediction; for the low-frequency component, Particle Swarm Optimization-based Least Squares Support Vector Machine (PSO-LSSVM) is used for prediction. The hourly TEC values collected from six evenly distributed GPS stations in China are used as the main input variable for the proposed model, with solar and geomagnetic activity data used as auxiliary data, using the TEC data from the previous 48 h to forecast the TEC content for the next hour. The performance of the model is evaluated by comparing it with other models under different geographical locations, seasons, solar and geomagnetic activity conditions. Experimental results show that the proposed hybrid model outperforms other models, with a correlation coefficient R2 and root mean square error (RMSE) of 0.99 and 0.20 TECU, respectively. The model effectively overcomes the problem of low TEC prediction accuracy and can provide more precise ionospheric delay correction services. [ABSTRACT FROM AUTHOR]

This paper presents new evidence on knowledge flows in the Beijing-Tianjin-Hebei region of China, involving 43 cities (districts) in the Beijing-Tianjin-Hebei region, based on the invention patent transfer data from the State Intellectual Property Office of China. First, the characteristics of technology flows in the Beijing-Tianjin-Hebei region are analyzed in terms of changes in the number of flows, types of flowing subjects and spatial distribution characteristics. Then, a multi-level patent technology flow network in the Beijing-Tianjin-Hebei region was constructed, and the structural characteristics and node characteristics of each level network were explored separately. The key findings of the study are as follows. (1) The number of patented technology flows has been growing over time, showing obvious phase characteristics during the study period. As a whole, the intra-city (district) technology flow in the Beijing-Tianjin-Hebei region is higher than the inter-city (district). (2) The multi-level patent technology flow network in the Beijing-Tianjin-Hebei region shows dynamic characteristics, with more and more mobile subjects participating in the patent technology flow network, some network nodes becoming closer to each other, and the trend of small group technology flow increasing significantly. (3) Enterprises are the core hub of the patent technology flow network in Beijing-Tianjin-Hebei region. Individual invention patent technology transfer also occupies a high proportion and the participation of universities and colleges in the patent technology flow network in the Beijing-Tianjin-Hebei region is gradually increasing. (4) Over time, the flow of patent technology in the 43 cities (districts) in the Beijing-Tianjin-Hebei region has gradually become active and no longer relies excessively on a particular city (district) for patent technology transfer. [ABSTRACT FROM AUTHOR]

This paper studied an integrated process planning and scheduling problem from a machining workshop for large-size valves in a valve manufacturing plant. Large-size valves usually contain several key parts and are generally produced in small-series production. Almost all the parts need to be manufactured in the same workshop at the same time in the plant. Facilities have to handle various items in one order, including different models, sizes, and types. It is a classical NP-hard problem on a large scale. An improved NSGA-II algorithm is suggested to obtain satisfactory solutions for makespan and manufacturing costs, which involve large optimization parameters and interactions. A two-section encoding method and an inserting greedy decoding method are chosen to enable the algorithm. The dynamic population update strategy based on dynamic population update and the adaptive mutation technique depending on the population entropy changing rate are selected for enhancing both the solution quality and population diversity. The methodology was successfully implemented in a real-life case at a major valve machining workshop operated by Yuanda Valve Company in China. By taking into account realistic factors and restrictions that have been identified from a real-world manufacturing setting, this technique aids in bridging the knowledge gap between present IPPS research and practical valve production implementations. [ABSTRACT FROM AUTHOR]

The new development pattern has identified two key avenues for the sustained advancement of high-quality agricultural and rural development: digitalisation and low-carbon development. The measurement of the digital economy and the agricultural carbon emission performance, and their spatial and temporal heterogeneity, is a crucial step in promoting the spatial coordination and sustainable development of digitalisation and low-carbon agriculture. This paper employs the entropy value method, SBM model, and coupling coordination degree model to investigate the coupling coordination measurement and spatial-temporal heterogeneity of the performance of the digital economy and agricultural carbon emissions. The data used are provincial panel data from 2013 to 2021. The simulation results demonstrate that, between 2013 and 2021, the digital economy of all provinces exhibited varying degrees of growth, yet the development of the digital economy between provinces exhibited a more pronounced tendency to diverge. Concurrently, the agricultural carbon emission efficiency in China exhibited a fluctuating upward trend. The development of the digital economy and the efficiency of agricultural carbon emission were found to be highly coupled. Their coupling and coordination relationship showed a downward trend followed by an upward trend. In general, it is suggested that we should increase investment in digital economy infrastructure and technology, promote digital agricultural applications, strengthen policy guidance and financial support, establish a coupling coordination mechanism and strengthen farmers' digital literacy and environmental awareness. [ABSTRACT FROM AUTHOR]

China is transitioning into the digital economy era. The advancement of the digital economy could offer a fresh mechanism to attain carbon peak and carbon neutrality objectives. Applications of the digital economy, such as smart energy management, intelligent transport systems, and digital agricultural technologies, have significantly reduced carbon emissions by optimizing resource use, reducing energy waste, and improving production efficiency. This research does so by devising a theoretical model that looks into the multi-faceted power of the digital economy under a two-sector paradigm. Utilising a panel model, a mediation effect model and a spatial Durbin model to assess the digital economy's power on carbon emissions. This research has determined that the digital economy can significantly diminish carbon emissions, with green tech innovations and industrial transformation being key contributors. The spatial spillover effect was used for the digital economy to aid in lowering carbon emissions in adjacent districts and upgrading better environmental stewardship. The influence of the digital economy has better performance in lowering carbon emissions in mid-western China than in the eastern area. This paper deepens understanding of the drivers of low-carbon growth and the significance, mechanism and regional disparities of the digital economy's effect on reducing carbon emissions. It offers valuable policy insights and guidance for globally achieving digital economy growth, reducing carbon emissions and reaching carbon peak and neutrality goals. [ABSTRACT FROM AUTHOR]

Background: Stroke stands as a significant macrovascular complication among individuals with Type 2 diabetes mellitus (T2DM), often resulting in the primary cause of mortality and disability within this patient demographic. Presently, numerous studies have been conducted to investigate the underlying causes of stroke in individuals with T2DM, yet the findings exhibit inconsistencies. Objective: This paper aims to consolidate and summarize the available evidence concerning the influential factors contributing to stroke among patients diagnosed with T2DM. Methods: We conducted a comprehensive search across multiple databases, including Cochrane Library, PubMed, Web Of Science, Embase, China Biology Medicine (CBM), China National Knowledge Infrastructure (CNKI), Wanfang and Weipu up to August 2023. Google Scholar was also searched to retrieve gray literature. We calculated odds ratios (OR) and 95% confidence intervals (CI) using Stata software. Results: Our analysis encompassed 43 observational studies, exploring factors across sociodemographic, biochemical, complications, and hypoglycemic agent categories. The findings identified several risk factors for stroke in patients with T2DM: age, gender, T2DM duration, hypertension, body-mass index (BMI), smoking, Glycated hemoglobin (HbA1c), estimated Glomerular Filtration Rate (eGFR), albuminuria, Triglycerides (TG), Low density lipoprotein cholesterol (LDL-C), Coronary heart disease (CHD), Atrial fibrillation (AF), diabetic retinopathy (DR), Peripheral vascular disease (PVD), and carotid plaque. Conversely, exercise, High density lipoprotein cholesterol (HDL-C), metformin (MET), pioglitazone, and metformin combination therapy emerged as protective factors. Conclusion: This study underscores the multitude of influencing factors contributing to stroke in people with T2DM patients, among which the microvascular complications of T2DM play an most important role. Therefore, we emphasize the importance of screening for microvascular complications in patients with T2DM. However, due to limitations arising from the number of articles reviewed, there remain areas where clarity is lacking. Further research efforts are warranted to expand upon and reinforce our current findings. [ABSTRACT FROM AUTHOR]

Architectural heritage health assessment is the basis of scientific repair and maintenance. However, existing methods do not adequately take into account the fuzziness, randomness and uncertainties unique to architectural heritage assessment. In this paper, a new evaluation model of VM-NCM is constructed by combining variable weight theory and normal cloud model theory. The model enables the combination of qualitative ratings and quantitative calculation, deals with the fuzziness in the assessment process, and resolves the randomness and reflects the uncertainty to a certain extent. Based on constructing the index system combining qualitative and quantitative indexes, the structural index values are acquired by the synergistic coupling of the fine laser point cloud model and finite element structural analysis model. The acquisition of surface index values is completed by the hyperspectral intelligent detection technology of surface materials and diseases. These reduce the generation of ambiguous information in the index detection process. An evaluation study is conducted using the Yingxian wooden pagoda in China as an example. The results show that this method takes into account the fuzziness and randomness in the evaluation process, and obtains more scientific and reliable evaluation results, which provides a research paradigm for assessing the architectural heritage health status. [ABSTRACT FROM AUTHOR]

Background: To control resurging infectious diseases like mumps, it is necessary to resort to effective control and preventive measures. These measures include increasing vaccine coverage, providing the community with advice on how to reduce exposure, and closing schools. To justify such intervention, it is important to understand how well each of these measures helps to limit transmission. Methods: In this paper, we propose a simple SEILR (susceptible-exposed-symptomatically infectious-asymptomatically infectious-recovered) model by using a novel transmission rate function to incorporate temperature, humidity, and closing school factors. This new transmission rate function allows us to verify the impact of each factor either separately or combined. Using reported mumps cases from 2004 to 2018 in the mainland of China, we perform data fitting and parameter estimation to evaluate the basic reproduction number R 0 . As a wide range of one-dose measles, mumps, and rubella (MMR) vaccine programs in China started only in 2008, we use different vaccination proportions for the first Stage I period (from 2004 to 2008) and the second Stage II period (from 2009 to 2018). This allows us to verify the importance of higher vaccine coverage with a possible second dose of MMR vaccine. Results: We find that the basic reproduction number R 0 is generally between 1 and 3. We then use the Akaike Information Criteria to assess the extent to which each of the three factors contributed to the spread of mumps. The findings suggest that the impact of all three factors is substantial, with temperature having the most significant impact, followed by school opening and closing, and finally humidity. Conclusion: We conclude that the strategy of increasing vaccine coverage, changing micro-climate (temperature and humidity), and closing schools can greatly reduce mumps transmission. [ABSTRACT FROM AUTHOR]

In the context of global aging, promoting the health of the elderly has become a critical issue. However, whether the development of smart cities can impact the health of older adults remains to be further validated. In this paper, based on panel data from the China Health and Retirement Longitudinal Study (CHARLS), a difference in difference model is used to empirically investigate whether smart city construction improves the health of older people in the region. The results show that smart city construction enhances the health of the elderly. Specifically, the construction achieved a significant improvement in the physical health of the elderly who did not live with their children. The health promotion effect of the smart city was more significant for the urban elderly than for the rural elderly. The elucidated mechanisms of influence suggest that smart cities bring about their effects through the promotion of urban leisure infrastructure, enhancement of medical service provision, advancement in urban environmental protection and stimulation of urban information and communication technology infrastructure development. [ABSTRACT FROM AUTHOR]