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Background: The advent of Personalized Medicine (PM) holds significant promise in revolutionizing healthcare by tailoring treatments to individual patients based on their data. However, its successful implementation requires the seamless integration of innovative technologies and presents formidable challenges in terms of sustainability. To tackle these challenges head-on, the International Consortium for Personalized Medicine (ICPerMed) was established, and the IC2PerMed project, as part of this consortium, seeks to foster collaboration between the European Union (EU) and China in the field of Personalized Medicine. Based on the results collected by the project, the objective of this study is to discern the key priorities for the implementation of Personalised Medicine concerning Information and Communication Technologies (ICT) and Big Data and digital solutions, with a particular emphasis on data management and protection. Methods: A Delphi survey was conducted to gather expert's consensus on the main priorities for actions on Information and Communication Technologies (ICT) and Big Data and digital solutions in the field of Personalized Medicine. Results: The survey identified seven priorities in the area of Big Data and digital solutions, including data interoperability, standards, security measures, and international partnerships. Additionally, twelve priorities were identified for the innovation-to-market process, emphasizing cost-effectiveness, need assessment, and value definition in resource allocation. Conclusions: The effective implementation of new technologies in Personalized Medicine research and practice is essential for the advancement of healthcare systems in both the European and Chinese contexts. The identified priorities play a pivotal role in promoting the sustainability of health systems and driving innovation in the implementation of Personalized Medicine. Addressing challenges related to data interoperability, standards, security, international collaboration, cost-effectiveness, and value assessment is of utmost importance in order to propel the progress of Personalized Medicine in healthcare systems. [ABSTRACT FROM AUTHOR]

As a strategic resource, big data has become a key force affecting carbon emission reduction in agriculture. However, its impacts remain controversial, and relevant empirical evidence remains to be explored. Based on quasi-natural experimental analysis, this study explored the impact and mechanism of the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ) on agricultural carbon emissions (ACE) in China and adopted a difference-in-difference (DID) model using China's provincial panel data from 2003 to 2020. The results showed that the ACE in the NBDCPZ establishment area was significantly reduced by 11.91%, a finding that remained robust following the parallel trend test and the placebo test, among others. Mechanism analysis showed that the ACE was reduced through industrial upgrading and technological innovation. Heterogeneity analysis showed that more pronounced policy gains were achieved in China's central-eastern regions as well as in non-major grain-producing areas compared to western and major grain-producing areas. This research provided supporting evidence for the prospect of big data application in ACE and provided useful guidance regarding the promotion of green and sustainable agricultural development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Digital industrialization represented by big data provides substantial support for the high-quality development of the digital economy, but its impact on urban energy conservation development requires further research. To this end, based on the panel data of Chinese cities from 2010 to 2019 and taking the establishment of the national big data comprehensive pilot zone (NBDCPZ) as a quasi-natural experiment, this paper explores the impact, mechanism, and spatial spillover effect of digital industrialization represented by big data on urban energy conservation development using the Difference-in-Differences (DID) method. The results show that digital industrialization can help achieve urban energy conservation development, which still holds after a series of robustness tests. Mechanism analysis reveals that digital industrialization impacts urban energy conservation development by driving industrial sector output growth, promoting industrial upgrading, stimulating green technology innovation, and alleviating resource misallocation. Heterogeneity analysis indicates that the energy conservation effect of digital industrialization is more significant in the central region, intra-regional demonstration comprehensive pilot zones, large cities, non-resource-based cities, and high-level digital infrastructure cities. Additionally, digital industrialization can promote energy conservation development in neighboring areas through spatial spillover effect. This paper enriches the theoretical framework concerning the relationship between digital industrialization and energy conservation development. The findings have significant implications for achieving the coordinated development of digitalization and conservation., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 Elsevier Ltd. All rights reserved.)

Evaluating the sustainable development level and obstacle factors of small towns is an important guarantee for implementing China's new-type urbanization and rural revitalization strategies, and is also a key path to promoting the United Nations Sustainable Development Goal 11 (SDG11). Traditional evaluation methods (such as Analytic Hierarchy Process, AHP, and Technique for Order Preference by Similarity to Ideal Solution, TOPSIS) mainly calculate the comprehensive score of each indicator through weighting. These methods have limitations in handling multidimensional data and system nonlinearity, and they cannot fully reveal the complex relationships and interactions within the sustainability systems of small towns. In contrast, the evaluation model combining Principal Component Analysis (PCA) and Catastrophe Progression Method (CPM) used in this study can better handle multidimensional data and system nonlinear relationships, reducing subjectivity in evaluation and improving the accuracy and reliability of the assessment results. The specific research process is as follows: First, based on the United Nations SDG11 framework, using multi-source big data, a theoretical framework and evaluation index system for the sustainable development of small towns suitable for the Chinese context were established. The impact of county-level factors on the sustainable development of small towns was also considered, and an entropy weight-grey correlation model was used to measure these impacts, resulting in a town-level dataset incorporating county-level influences. Secondly, the sustainability levels of 782 top small towns in China were evaluated using the comprehensive evaluation model based on PCA-CPM Model. Finally, an improved diagnostic model was used to identify obstacles influencing the sustainable development of small towns. The main findings include: 52.69% of the small towns have a sustainable development score exceeding 0.7255, indicating that the overall performance of small towns is at a medium to high development level. The development of small towns exhibits significant differences across regions and types, which are closely linked to county-level effects. Economic and social factors are the main obstacles to the sustainable development of small towns, and the impact of these obstacles intensifies from the eastern to the central, western, and northeastern regions. This study provides valuable insights for policymakers and scholars, promoting a deeper understanding of the sustainable development of small towns., Competing Interests: Declaration of competing interest The authors declare that there is no conflict of interest regarding the publication of this paper., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

In this paper, I theoretically examine the concept of quantified credibility in sociology. I argue that quantified credibility has components of status, reputation, and trust, which are arbitrations of rankings of individuals, public and private institutions to determine their trustworthiness, legitimacy, and access to resources. To illustrate the above, this paper provides an in-depth analysis of China's Social Credit System (SOCS), as it is developing into one of the largest and comprehensive data systems in the world. [ABSTRACT FROM AUTHOR]

This study explores the influencing factors on intelligent transformation and upgrading of China's logistics firms under smart logistics, and designs the corresponding framework to guide the practice of firms. By analyzing the characteristics of smart logistics and the transformation and upgrading needs of traditional logistics, from the micro perspective of logistics firms, this paper constructs influencing factor index system of smart transformation and development from four dimensions: logistics technology innovation, logistics big data sharing, logistics management upgrading and logistics decision-making transformation. Logistics firms are divided into firms with medium scale and above and small and medium-sized firms according to their scale. Then EWIF-AHP model is proposed to measure the weight of index system and score the decision-making, so as to evaluate the impact of various influencing factors on transformation and development of logistics firms. The results show that, for logistics firms above medium scale, logistics technology innovation and logistics big data sharing have the most significant impact on transformation and development, followed by logistics management upgrading and logistics decision-making transformation. For small and medium-sized logistics firms, the biggest factor is the upgrading of logistics management, followed by the upgrading of logistics technology, which is almost as important as the influencing factors of the upgrading of logistics management, and followed by the sharing of logistics big data and the transformation of logistics decision-making. Therefore, corresponding countermeasures and suggestions for intelligent transformation of logistics firms have been put forward., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sixia Fan. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

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

With the rapid development of technologies such as cloud computing and big data, various levels of government departments in the country have successively introduced digital subsidy policies to promote enterprises' digital transformation. However, the effectiveness of these policies and their ability to truly achieve policy objectives have become pressing concerns across society. Against this backdrop, this paper employs a moderated mediation effects model to empirically analyze the incentive effects of financial subsidies on the digital transformation of A-share listed manufacturing companies in the Shanghai and Shenzhen stock markets from 2013 to 2022. The research findings indicate a significant promotion effect of financial subsidies on the digital transformation of manufacturing enterprises, especially demonstrating a notable incentive impact on the digital transformation of large enterprises, non-asset-intensive enterprises, technology-intensive enterprises, and non-labor-intensive enterprises. However, the incentive effect on the digital transformation of small and medium-sized enterprises (SMEs), asset-intensive enterprises, non-technology-intensive enterprises, and labor-intensive enterprises is not significant. Notably, the expansion of financial subsidies positively influences the augmentation of R&D investment within manufacturing enterprises, subsequently providing indirect encouragement for their digital transformation. Additionally, the incorporation of the degree of marketization implies its potential to moderate both the direct and indirect impacts of financial subsidies on enterprise digital transformation. This study enriches the research on the mechanism of the role of financial subsidies in digital transformation and provides empirical evidence on how market participation influences the effects of financial subsidies, thereby assisting policymakers in comprehensively understanding the impact of financial subsidy policies on different types of enterprises., Competing Interests: The authors have declared that no competing interests exist., (Copyright: © 2024 Sun et al. This is an open access article distributed under the terms of the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original author and source are credited.)

Business incubators (BIs) in China have predominantly exhibited a government-led characteristic, recently broadening their spatial and temporal scope and extending reach to the county level. Regarding the inadequacies of county-level analysis scale, this study leverages Points of Interest (POI) big data to overcome them. To comprehend the governmental rationale in the construction of BIs, we examine the evolution dynamics of BIs in conjunction with policies. An economic geography framework is developed, conceptualizing BIs as quasi-public goods and productive services, and incorporating considerations of county-level fiscal operations and industrial structures. Focusing on the Yangtze River Delta (YRD) region as a case study, our findings reveal that over 98% of County Administrative Units (CAUs) have built BIs. Using kernel density estimation and Moran's I, the spatial patterns of CAUs are identified. The CAUs are further classified into three categories of economic levels using the k-means algorithm, uncovering differentiated relationships between industry, finance, and their respective BI. Additionally, we analyze the density relationship between BIs and other facilities at a micro-level, showcasing various site selection rationales. The discussions highlight that while BIs tend to align with wealthier areas and advanced industries, affluent CAUs offer location advantages on BIs, whereas less wealthy CAUs prioritize quantity for political achievements. This paper concludes with recommendations about aligning BIs based on conditions and outlooks on future research. [ABSTRACT FROM AUTHOR]

Since the outbreak of COVID-19 (Corona Virus Disease 2019), the Chinese government has taken strict measures to prevent and control the epidemic. Although the spread of the virus has been controlled, people's daily life and work have been affected and restricted to varying degrees. Thus people have different sentiments, these may affect people's implementation and compliance with the policies, thus affecting the effectiveness of epidemic prevention and control. At present, few pieces of literature have analyzed the relationships between people's feelings, policies, and epidemic trends. The object of this paper is to analyze the text content on social media, to find out the impact of the epidemic blockade policy on the public mood and the concerns expressed by the public about policies changes, and the interaction between policies and epidemic states at different stages of the epidemic. In this paper, we collected the posts of two cities where the epidemic occurred at the same time for analysis and comparative study. On the one hand, we revealed the changes in public attention and attitudes in the two regions during the epidemic, the other hand, it also reflects the differences in public sentiment between the two regions, as well as the correlation between emotions and policies and epidemic trends when different policies are adopted under different circumstances. The obtained results have a certain guiding significance for public health departments to formulate reasonable epidemic prevention policies. [ABSTRACT FROM AUTHOR]

Maritime transport plays a key role in the global and local economy, accounting for 80% of global trade by volume. This makes smooth operation of the maritime transport essential. However, the sector faces the constant risk of various crises and their potential consequences that may significantly impact and disrupt the movement of goods on local, regional, and global levels. In recent years, two notable crises, namely the COVID-19 pandemic and the war in Ukraine, have been observed. This paper aims to analyse how international crises, such as armed conflicts and pandemics, influence maritime traffic and assess their impact on both global and local economies. A comparison is drawn between the periods before and during the COVID-19 pandemic and before and during the war in Ukraine to exemplify the effects of crises. The findings are then extrapolated to apply to potential future crises. Vessel movements are studied using data collected from Automatic Identification Systems (AIS). In our quantitative approach, we analyse big data using dedicated tools and visualisation techniques to gain insights into specific phenomena. The paper identifies economically significant regions for maritime traffic and examines the impact of crises on their performance. Its unique value lies in its flow-based analysis of changes in maritime traffic. The main conclusion is that China's importance for worldwide maritime traffic is increasing. This makes the global economy heavily reliant on China to a substantially greater extent than it is, for example, on Russia. Consequently, any crisis in the China region could exert a dramatic impact on the global economy. The paper also discusses observations of changes in maritime traffic following the outbreak of the war in Ukraine. [ABSTRACT FROM AUTHOR]

Purpose: As the application of artificial intelligence (AI) becomes more prevalent, many high-tech firms have employed AI applications to deal with emerging societal, technological and environmental challenges. Big data analytical capability (BDAC) has become increasingly important in the AI application processes. Drawing upon the resource-based view and the theory of planned behavior, this study aims to investigate how BDAC and collaboration affect new product performance (NPP). Practically, a harmonic working team is particularly important for creating management synergies, this empirical analysis demonstrates the importance of BDAC and collaboration for NPP. Design/methodology/approach: This paper focuses on the performance of firms that applied AI in their operations. This study collected data from firms in Greater China, including China and Taiwan, as Greater China is currently the leading manufacturer of semiconductor, electronic and electric products for AI applications in the manufacturing process. Confirmatory factor analysis and structural equation modeling is employed for statistical analysis. Findings: The analytical results indicate that BDAC positively relates to collaboration capability (CC) in AI applications but not to team collaboration (TC). CC positively correlates with TC, and both CC and TC positively correlate with NPP. Further, the mediating effect was examined using the Sobel t-test, which reveals that CC is a significant mediator in the influence of BDAC on NPP. Practical implications: The strategic implementation of BDAC and collaboration can allow an enterprise to improve its NPP when driven by the external environment to use AI, which further enhances NPP. These processes indicate that AI and BDAC are both crucial for the success of a company's collaboration and for effective management to improve NPP in the face of global competition. Originality/value: This study introduces the concept of BDAC to explain the relationship between CC and TC, as they pertain to NPP. This study presented a discussion of the theoretical and practical implications of the research findings and could provide a framework for managing BDAC. [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]

Ports are essential and strategic nodes of international trade and economic activities. The integration of port resources helps enhance the optimal division of port functions to further promote economic development. This paper explores the evolutionary trends of port integration policy from 2011 to 2021 in China through a text-mining approach. We first used the Latent Dirichlet Allocation (LDA) topic analysis method to analyze the port integration policies and summarize the evolutionary trends of port policies. In addition, an association analysis was conducted to explore port integration policies' impacts by examining their relationships among the environmental protection, digitalization level, and port development scale. Our findings suggested that China's port integration policy has evolved from specific and simple to abstract and complicated at the managerial level. Port development has enriched from infrastructure construction to the ideology of economic development. Our findings show that China's port integration is gradually improving the port operation. The findings of the study contribute to the extant literature by analyzing the port integration policy evolution in China and can be referred to by other countries. • Text mining method was used to explore the port integration policy evolution. • Port integration policy evolved from specific and simple to abstract and complicated. • Port development enriched from basic infrastructure to the governance model. • The evolution of port integration policy was related to reducing carbon emissions in the port sector. • The evolution of port integration policy made the ports intelligent in association with digital technology such as big data and AI. [ABSTRACT FROM AUTHOR]

The digital economy has the characteristics of resource conservation, which can solve China's high carbon emissions problems. The digital economy can quickly integrate with the real economy, forming an integrated economy. However, it is still unclear whether an integrated economy can effectively reduce carbon emissions and achieve China's 'dual carbon goals'. Therefore, this study takes 30 provinces in China as the research object, constructs the integration economy index system through the statistical data from 2011-2021, and explores the spatial effect of the impact of the integration economy on carbon emissions by using principal component analysis, coupled coordination model and spatial econometric model. The research results are as follows. (1) From 2011 to 2021, the comprehensive economy showed a trend of increasing yearly (from 0.667 to 0.828), and carbon emissions showed a slow decrease (from 0.026 to 0.017). (2) Due to the infiltration of China's economic development from the eastern to the western, the spatial distribution of the integrated economy shows a decreasing trend from east to west. The spatial distribution of carbon emissions may be related to China's industrial layout of heavy industry in the northern, and light industry in the southern, showing a trend of low in the south and high in the north. (3) The integrated economy can significantly reduce carbon emissions (the coefficients of influence, -0.146), and the reduction effect will be more obvious if spatial spillover effects are taken into account (-0.305). (4) The eastern coast, the middle reaches of the Yangtze River, and the middle reaches of the Yellow River economic zones all increase carbon emissions at a certain level of significance (0.065, 0.148, and 3.890). The Northeast, South Coastal and Southwest economic zones significantly reduce carbon emissions (-0.220, -0.092, and -0.308). The results of the Northern Coast and Northwest are not significant (-0.022 and 0.095). (5) China should tailor regional economic development policies, such as strengthening investment in digital infrastructure in the Northwest Economic Zone and fully leveraging the spatial spillover effects of integrated economy in the Northeast, Southern Coastal, and Southwest Economic Zones to reduce carbon emissions. [ABSTRACT FROM AUTHOR]

In recent years, technological advancements have been replicated in various industries, including sports medicine. Recent developments, such as big data analytics and data mining, which have revolutionized medical services in sports, are apparent in this transformation. This technological shift is motivated by the need to enhance athletic performance, prevent injuries, and offer individualized health advice. Modern lifestyles have simultaneously increased people's attention to their health, creating a demand for better medical services. However, China's ability to provide superior medical care needs to be improved due to a lack of medical resources and an ever-increasing patient population. To address these challenges, this research paper presents an integrated framework that leverages Spark-based big data analytics and the XGBoost algorithm. The framework aims to provide a robust sports medical service encompassing real-time health monitoring and data-driven insights. Powered by the formidable distributed computing platform Spark, it adeptly manages extensive sports data generated during training and events, facilitating instant health evaluations. Incorporating the XGBoost algorithm for data mining amplifies health prediction and recommendation capabilities. Renowned for its predictive prowess, XGBoost excels in discerning intricate sports data patterns and trends. Its proficiency in tackling intricates feature selection and modeling tasks ensures precision and actionable insights. Empirical findings underscore substantial enhancements in sports medical services. When applied to chronic disease datasets, the XGBoost algorithm garnered an impressive 93% trust rate. In contrast to conventional methods like K-Nearest Neighbors (KNN), Random Forest (RF), Decision Trees (DT), Support Vector Machines (SVM), Naïve Bayes (NB), and Logistic Regression (LR), the proposed framework consistently outperforms these established techniques. This remarkable performance underscores the transformative potential of the integrated framework in revolutionizing sports medical services. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to explore the relationship between big data and supply chain platform in China's retail industry. With the emergence of big data resources and technologies, the business pattern of new retail advocates the combination of online and offline channels. Supply chain platform plays a key role in the implementation of retail activities, which has gradually become a research hotspot in the cross field of operations management and information system. Design/methodology/approach: Through the method of literature review and case study, this study empirically explores how big data shapes supply chain platform to support new forms of online retail by grounded theory. Findings: The model framework is validated by reliability test and coding method to process survey materials. The results identify the overall antecedents of supply chain platform and reveal positive effects between big data and new retail. The findings help firm managers build a big data-driven supply chain to support new retail. Originality/value: There are insufficient studies on theoretical frameworks and interaction relationships among big data, supply chain platform and new retail. [ABSTRACT FROM AUTHOR]

The early diagnosis and treatment of foreign body aspiration (FBA) can significantly improve the overall prognosis of children. There are significant differences in the epidemiology and clinical characteristics of FBA in different regions. Therefore, we conducted a real-world study in the western region of China with over 4000 patients. The aim of this study was to improve the understanding of FBA in terms of its types, the specific months of its occurrence, and the distribution of primary caregiver characteristics in western China. We collected the clinical and epidemiological data of children who were diagnosed with FBA in our hospital over the past 20 years through a big data centre. We matched the data of healthy children who underwent routine physical examinations at the paediatric health clinic during the same period to analyse the differences in the data of actual guardians. A total of 4227 patients from five provinces were included in this study. Foreign bodies were removed by rigid bronchoscopy in 99.4% (4202/4227) of patients, with a median age of 19 months and a median surgical duration 16 min. January was the most common month of onset for 1725 patients, followed by February, with 1027 patients. The most common types of foreign objects were melon peanuts, seeds and walnuts, accounting for 47.2%, 15.3%, and 10.2%, respectively. In the FBA group, the proportion of grandparents who were primary caregivers was 70.33% (2973/4227), which was significantly greater than the 63.05% in the healthy group (2665/4227) (P < 0.01). FBA most commonly occurs in January and February. More than 60% of FBAs occur between the ages of 1 and 2 years, and the incidence of FBA may be greater in children who are cared for by grandparents. A rigid bronchoscope can be used to remove most aspirated foreign bodies in a median of 16 min. [ABSTRACT FROM AUTHOR]

Purpose: The rapid advancement of digital transformation requires a shift in firms' focus from past met needs to both latent future and unmet past needs. However, how boundary-spanning search with future orientation and past orientation affects breakthrough innovation remains unclear. This study thus aims to investigate the relationship between boundary-spanning search and breakthrough innovation from the perspective of search orientation. Design/methodology/approach: In terms of search orientation, this study divides boundary-spanning search into forward-looking search and backward-looking search. Drawing on resource-based view, this study develops a theoretical model in which big data analytics capability moderates the effects of forward-looking and backward-looking searches on breakthrough innovation. Empirical analyses were conducted on data from China's advanced manufacturing firms. Research model and hypotheses were tested through multiple regression. Findings: The results confirm that forward-looking search has a positive effect on breakthrough innovation, and big data analytics capability strengthens this positive effect. Furthermore, backward-looking search has an inverted U-shaped effect on breakthrough innovation. Interestingly, as big data analytics capability increases, this inverted U-shaped curve flattens and becomes almost linear. Originality/value: This study uncovers the different effects of boundary-spanning search with different orientations on breakthrough innovation and extends the research on the relationship between boundary-spanning search and breakthrough innovation by incorporating search orientation. Furthermore, by demonstrating the moderating role of big data analytics capability, this study provides a crucial condition under which boundary-spanning search can enhance breakthrough innovation. [ABSTRACT FROM AUTHOR]

Currently, China has entered the development stage of a "low birth rate" demographically. There is a huge contradiction between the unbalanced and inadequate distribution of preschool education facilities (PEFs) and the people's expectations for "full and good education for children". This study took the Lingui New District, a typical new urban area in West China as the research area, and through the introduction of POI big data and GIS analysis methods, supplemented by GeoDA Bivariate Moran index analyses, established a kindergarten spatial database. The study found that preschool education facilities have problems such as insufficient quantity, uneven quality, low service coverage, poor accessibility, etc. Therefore, it is suggested to increase the proportion of public affordable preschool education facilities including kindergartens and nurseries, optimize the spatial distribution of preschool education, and improve the accessibility of preschool services to promote affordable, safe, and high-quality development of preschool education and to provide reference suggestions for the revision of relevant standards and the adjustment of the layout of preschool education in undeveloped regions of China. [ABSTRACT FROM AUTHOR]

In the context of COVID-19, the efforts undertaken for epidemic control have imposed limitations on the multifaceted development of China. This manuscript utilizes Baidu migration data from 2019 to 2023 to classify the current developmental status of urban agglomerations (UAs) in China. The explication of network structure is achieved through the computation of metrics that capture network structural connectivity and hierarchical attributes. Additionally, an inquiry into the spatio-temporal differentiation of the UAs' network structure is carried out, encompassing three phases: before COVID-19, the normalization stage of COVID-19, and after COVID-19. Furthermore, Quantitative Analysis of Patterns (QAP) is employed to assess the impact of diverse influencing factors. The analysis yields several key findings: ① The impact of COVID-19 on the network structure of China's UAs manifests in two discernible stages—initial impact disruption and subsequent recovery and reconstruction. ② The exploration of pertinent influencing factors during the primary stage of UA development is impeded. ③ The growth stage and the UAs with a high level of development exhibit have a closely intertwined relationship, fostering a more rational hierarchical structure and demonstrating an enhanced capacity for swift recovery. ④ It is discerned that economic development level, medical facility standards, transportation infrastructure capacity, spatial proximity, and innovation accessibility exert a discernible influence on the network structure of UAs. Importantly, the extent of impact varies across different periods and types of UAs. [ABSTRACT FROM AUTHOR]

While the relationship between local housing prices and the urban form and distribution of urban functional zones in a single city is well-discussed, the conclusion is usually sensitive to a particular city context, and cross-city comparative study is limited. This study attempts to examine the influences of urban form and urban functional zone distribution on housing prices within and between cities after controlling the city-wide socio-economic and demographic differences. Based on multiple open-source big data, such as points-of-interest (POI) and historical housing transaction data, the hierarchical linear model is utilized to compare the housing market of 10 extra-large cities in China. Results indicate that the urban form and the urban functional zone distribution significantly influence housing prices after the socio-economic and demographic differences are controlled. For inter-city comparison, an urban form with high compactness, low centrality, low polycentricity, high density, and low dissimilarity in housing development is related to lower city-level housing prices. For intra-city, proximity to work centers, high-quality hospitals, and schools shows positive associations to housing prices. [ABSTRACT FROM AUTHOR]

Smartphone location data provide the most direct field disaster distribution data with low cost and high coverage. The large-scale continuous sampling of mobile device location data provides a new way to estimate the distribution of disasters with high temporal–spatial resolution. On September 5, 2022, a magnitude 6.8 earthquake struck Luding County, Sichuan Province, China. We quantitatively analyzed the Ms 6.8 earthquake from both temporal and geographic dimensions by combining 1,806,100 smartphone location records and 4,856 spatial grid locations collected through communication big data with the smartphone data under 24-hour continuous positioning. In this study, the deviation of multidimensional mobile terminal location data is estimated, and a methodology to estimate the distribution of out-of-service communication base stations in the disaster area by excluding micro error data users is explored. Finally, the mathematical relationship between the seismic intensity and the corresponding out-of-service rate of communication base stations is established, which provides a new technical concept and means for the rapid assessment of post-earthquake disaster distribution. [ABSTRACT FROM AUTHOR]

Administrative regulation is an essential institutional arrangement for governing innovation in big data technology. Administrative penalties are one of its main methods. It is worth investigating whether administrative penalties can serve as an institutional safeguard to promote technological innovation. This study collects data on digital technology administrative penalty (DTAP) and big data technology innovation (BDTI) from 2008 to 2020 in 281 cities in China and empirically evaluates the impact and mechanism of DTAP on BDTI. The findings suggest that the normative impact of the DTAP system can foster a conducive business ecosystem for big data innovation. The incentive mechanism motivates firms to increase their long-term investment in technological innovation, while the deterrence mechanism ensures the existence of a regulated competitive market. These mechanisms play a crucial role in facilitating BDTI. Mechanism tests show that the DTAP has the potential to promote novel business models in the digital economy and accelerate progress in industrial digitalisation, which in turn promotes innovation in big data technologies. The impact of the DTAP on promoting BDTI is diverse, with a significantly greater impact in the first-tier cities. DTAP plays a more important role in fostering BDTI in places where the digital factor-driven industry is still in its early stages of development. [ABSTRACT FROM AUTHOR]

With the rapid development of social media, people can access a large amount of data in a short period of time, and big data technology has emerged. With the vigorous development of cloud computing and big data, the method of mining audience interests through a large amount of data to guide film and television creation has attracted more and more attention from experts and scholars. In order to understand the current situation of film and television drama creation in China and provide suggestions for the shortcomings in the industry, this article mainly analyzes the application of intelligent algorithms for traffic prediction models and big data analysis in film and television creation. This intelligent algorithm can predict the potential audience of movies or TV dramas, helping producers and investors make decisions. This system utilizes artificial intelligence technology to select suitable actors for characters based on their matching degree and past work performance. This article applies intelligent algorithms to big data processing to improve the accuracy of data processing. This article explores the application of intelligent algorithms and big data analysis in film and television creation. Using machine learning algorithms to predict the potential audience of a movie or TV series based on historical data, providing decision-making basis for investors and producers. This system utilizes AI technology to select suitable actors for characters based on their matching degree with characters and past work performance, improving the scientific and accurate selection of roles. The application of these technologies helps to improve production efficiency and quality, reduce costs and risks, and inject new impetus into the sustainable development of the film and television industry. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing technology is required to upgrade existing enterprises' management and production operations. To construct a ground breaking fusion structure, this project unites the theoretical underpinnings, technical breakthroughs, and applications of data analytics, optimisation, and intelligent production engineering. It is driven by China's desire of cutting-edge commodities and efficient growth methods. This research establishes the broad framework for merging optimisation and data analytics. There is a list of data analytics and system optimisation technologies that can address important challenges with intelligent manufacturing. By integrating data analytics and optimisation, businesses may better forecasting and management of new terrain, as well as reveal hidden information to increase decision-making efficacy. [ABSTRACT FROM AUTHOR]

Based on the analysis of data from listed enterprises in China between 2011 and 2022, we investigate the influence of digital transformation on the governance efficiency for minority shareholders. The results show that the extent of digital transformation exert a negative effect on the agency costs incurred from related-party transactions. The mechanism examination elucidates that digital transformation augments the governance efficiency for minority shareholders by boosting attendance at shareholders' meetings and enhancing the exit threat for minority shareholders. Subsequent analysis reveals that non-state-owned enterprises, compared to state-owned enterprises, exhibit a more pronounced effect in diminishing the second type of agency costs through digital transformation. Furthermore, the impact of digital transformation in curtailing agency costs is more significant in the eastern regions than central and western regions. The better the equity checks and balances in listed enterprises, the more effective digital transformation is in reducing agency costs. This study offers valuable insights for bolstering the governance capacity of minority shareholders in the context of digital transformation. [ABSTRACT FROM AUTHOR]

Green finance, an essential tool for high-quality economic development, is valued by policymakers and researchers in line with the growing global concern for environmental protection, climate change, and sustainable development. The banking sector, as a major part of China's green financial system, undertakes significant responsibility for green finance while also confronting the opportunities and requirements of digital transformation. Big data technology is a major driver of digital transformation in the banking sector and can improve the green operational capability of the banking sector. The purpose of this study is to explore the ways in which the extent of big data technology transformation in the banking sector in China affects its ability to operate in a green manner and to analyze the moderating role of green credits, funds, and bonds. For this reason, this study selected A-share listed banks in China from 2015 to 2022 as research subjects and adopted a panel data regression method to study the impact of the degree of big data technology transformation on green operations. The results demonstrate that the degree of big data technology transformation in the banking sector positively influenced green operations. Green credit, funds, and bonds played a moderating role, meaning that financial products strengthened the role of the degree of big data technology transformation in green operations. This study examined the effect of big data technology transformation in the banking sector and enriches research on green finance. This study also provides practical insights for investors and regulators concerned with green development in the banking sector. [ABSTRACT FROM AUTHOR]

Evaluating the rural population hollowing (RPH) caused by the massive out-migration of rural laborers is essential for China's rural land use policy formulation and rural revitalization. Existing studies mainly assessed the RPH based on survey data or statistical data. The former is mainly based on selected local villages and towns, and cannot be replicated on a large scale, while the latter is usually at the county or city scale and cannot reveal the cyclical migration of rural labors at fine spatial resolutions such as townships. To fill this gap, this paper proposed a social media data based-rural population hollowing (SM-RPH) index, which was calculated by Tencent User Density big data on holidays and non-holidays and was used to characterize the "migratory bird" process of rural laborers. Taking Guangdong Province, China as the case, this study employed the proposed novel index to evaluate the RPH at the county and township scales. This was integrated with a GWR model to explore how the important contributing factors of RPH vary across the province. The results showed a trend of transition from the cold spot areas in the core regions of PRD to the hot spot areas around the PRD. Additionally, this study revealed that distance to the nearest prefecture-level city, total nighttime lighting per capita, fiscal expenditure per unit of GDP and road network density were dominant factors in affecting rural population hollowing, and the influence of these four variables vary considerably throughout the province. Further, k-means cluster was used to group the hollowing towns according to the local coefficients of the influencing factors, and policy zoning was suggested to provide differentiated countermeasures and suggestions for rural revitalization. This methodological framework can be easily applied to other regions, and is expected to be used to evaluate the return characteristics of migrant workers in real time to help monitor the progress of rural revitalization in China and provide decision support for policy makers. • This study proposed a social media data-based index for assess rural population hollowing (RPH). • The index is effective in evaluating the RPH at fine spatial resolutions such as townships. • We revealed the spatially non-stationary relationship between RPH and the associated factors. • We further put forwards the policy zoning and planning implications for rural revitalization. [ABSTRACT FROM AUTHOR]

Group travel behaviour widely exists in cities, but has not been well investigated by researchers. To fill this gap, this paper develops a co-existence-based methodological framework to systematically explore the spatiotemporal characteristics of group travel behaviour. We apply our framework to a case study of Shanghai, China, using a one-month tranche of metro smart card data. Results show that most travellers perform a small number of group trips, together with a small number of co-travellers. They usually travel in a dyad or triad group and form far more small social communities than large ones. Group travel behaviour is distinctly different from individual travel in terms of both time and space: group travellers are more likely to travel during weekends, on holidays, and in the afternoons and evenings. They also prefer to perform group behaviour near stations located in the city centre or the centres of new towns in suburban areas, and close to attractions and public facilities. The analysis we present has various potential applications such as improving the management of public events and supporting the design of group ticket policy. [ABSTRACT FROM AUTHOR]

The critical role of high-quality urban development and scientific land use in leveraging big data for air quality enhancement is paramount. The application of machine learning for causal inferences in research related to big data development and air pollution presents considerable potential. This study employs a double machine learning model to explore the impact of big data development on the PM2.5 concentration in 277 prefecture-level cities across China. This analysis is grounded in the quasi-natural experiment named the National Big Data Comprehensive Pilot Zone. The findings reveal a significant inverse relationship between big data development and PM2.5 levels, with a correlation coefficient of −0.0149, a result consistently supported by various robustness checks. Further mechanism analyses elucidate that big data development markedly diminishes PM2.5 levels through the avenues of enhanced urban development and land use planning. The examination of heterogeneity underscores big data's suppressive effect on PM2.5 levels across central, eastern, and western regions, as well as in both resource-dependent and non-resource-dependent cities, albeit with varying degrees of significance. This study offers policy recommendations for the formulation and execution of big data policies, emphasizing the importance of acknowledging local variances and the structural nuances of urban economies. [ABSTRACT FROM AUTHOR]

Considering national big data comprehensive experimental zones' (NBDCEZs) construction as a quasi-natural experiment, we employ a multi-period difference-in-differences method, investigating NBDCEZs' impact on green finance. Based on China's provincial panel data from 2008 to 2020, we find that NBDCEZs have a significant positive impact on green finance. Furthermore, high regional innovation capacity and foreign direct investment can enhance this effect, which differs depending on China's five main economic circles, pilot zone types, and urban characteristics. Besides providing implications on utilizing digital technology for green finance, our study benchmarks China's practices for other emerging countries, looking to develop green finance. [ABSTRACT FROM AUTHOR]

Implementing real-time carbon emissions monitoring at the enterprise level enables the detailed breakdown of carbon neutrality goals for microcosmic enterprises, which is of paramount significance in ensuring the precision of policy formulations. Grounded in China's historical electricity consumption and carbon emissions data, this study utilizes the network approach and input–output methods to compute and predict direct and indirect transmission coefficients of electricity consumption and carbon emissions in each province. We establish a methodology that enables the monitoring of real-time carbon emissions of enterprises based on corporate electricity consumption data. Using the metal smelting industry in Guangxi as an example, our findings are as follows: First, in 2020, the comprehensive carbon emissions of the ferrous metal smelting industry in Guangxi reached 58.84 million tons, marking a notable increase of 42.78% compared to emissions in 2014, indicating that emissions reductions are imperative. Second, significant regional variations in emission coefficients are observed, with values ranging from 14 g CO2/KWh to 940 g CO2/KWh among provinces. Meanwhile, the trends of direct carbon emissions and indirect carbon emissions are totally different, underscoring the importance of comprehensive carbon accounting in informing policy decisions. Third, through the carbon emissions real-time monitoring of 75 metal smelting industry enterprises using electricity big data, we identified that the distribution of emissions across industries, time periods, and regions is uneven. Overall, this method can optimize carbon emission measurement techniques to a higher spatiotemporal resolution and more microscopic monitoring subjects, providing essential numerical foundations for promoting carbon emissions reduction and sustainable development. [ABSTRACT FROM AUTHOR]

Background: The association between lipid and bone metabolism, particularly the role of high-density lipoprotein cholesterol (HDL-C) in regulating bone mineral density (BMD), is of significant interest. Despite numerous studies, findings on this relationship remain inconclusive, especially since evidence from large, sexually diverse Chinese populations is sparse. This study, therefore, investigates the correlation between HDL-C and lumbar BMD in people of different genders using extensive population-based data from physical examinations conducted in China. Methods: Data from a cross-sectional survey involving 20,351 individuals aged > = 20 years drawn from medical records of health check-ups at the Health Management Centre of the Henan Provincial People's Hospital formed the basis of this study. The primary objective was to determine the correlation between HDL-C levels and lumbar BMD across genders. The analysis methodology included demographic data analysis, one-way ANOVA, subgroup analyses, multifactorial regression equations, smoothed curve fitting, and threshold and saturation effect analyses. Results: Multifactorial regression analysis revealed a significant inverse relationship between HDL-C levels and lumbar BMD in both sexes, controlling for potential confounders (Male: β = -8.77, 95% CI -11.65 to -5.88, P < 0.001; Female: β = -4.77, 95% CI -8.63 to -0.90, P = 0.015). Subgroup and threshold saturation effect analyses indicated a stronger association in males, showing that increased HDL-C correlates with reduced lumbar BMD irrespective of age and body mass index (BMI). The most significant effect was observed in males with BMI > 28 kg/m2 and HDL-C > 1.45 mmol/L and in females with a BMI between 24 and 28 kg/m2. Conclusion: Elevated HDL-C is associated with decreased bone mass, particularly in obese males. These findings indicate that individuals with high HDL-C levels should receive careful clinical monitoring to mitigate osteoporosis risk. Trial registration: The research protocol received ethics approval from the Ethics Committee at Beijing Jishuitan Hospital, in conformity with the Declaration of Helsinki guidelines (No. 2015-12-02). These data are a contribution of the China Health Quantitative CT Big Data Research team, registered at clinicaltrials.gov (code: NCT03699228). [ABSTRACT FROM AUTHOR]

The attitude of the Chinese government towards social organizations (SOs) is crucial, as it affects the management rule and development tendency of SOs. To research the rule of SOs' participation in social governance in China, this study used a new historical perspective, the institutional development perspective, to conduct its exploration. This perspective provides an accurate measure of the reality of the SOs' participation, as it involves a mixed research methodology using continuous data from 73 years of reports and content mining, as well as topic clustering analysis to reveal a macroscopic and multi-line picture. Using a co-word analysis of hundreds of reports, from 1949–2021, in the People's Daily, an official newspaper of the Communist Party of China, this study quantified changes in intensity, emotion, and content regarding social organization participation in social governance through topic distribution. Three trends were revealed: (1) "social-oriented character" and "organized-oriented character" were identified during the change in SOs; (2) the extent of being managed gradually strengthened and shifted from the Communist Youth League of China to the Community Party of China; (3) the goals of SOs shifted from general to innovated function in special charitable organizations. The institutional development perspective can complement the focus event perspective, including a new method, co-word analysis, to examine official Chinese media and validate the Administrative Absorption of Society (AAS) theory by identifying two lines of topic clustering trends. The attention distribution analysis in official media from an institutional development perspective can help explore the role of official media reports in analyzing the allocation of national attention and provide new analytical methods for big data mining to establish the social and organizational natures of SOs to optimize their roles. It offers a basis for modern social governance policy innovation in China. [ABSTRACT FROM AUTHOR]

Focusing on the construction of the National Big Data Comprehensive Pilot Zone (NBDCPZ), we adopted the difference-in-differences model to examine the impact of big data on green total factor productivity (GTFP), using data from 276 cities in China for the period of 2012 to 2019. We also conducted mediating effects and moderating effects tests to explore whether a factor exists through which, or relying on it, big data influences the GTFP. The result of the baseline regression showed that the construction of the NBDCPZ significantly increased GTFP. After a series of robustness tests, this conclusion remains valid. In addition, we examined the mediating effects of industrial structure and green innovation and found that the construction of the NBDCPZ increased the level of GTFP, mainly by promoting industrial structure upgrading and green innovation activities. We identified the moderating effects of different types of environmental regulation on the relationship between urban digital transformation and GTFP and found that market incentive environmental regulation (MIER) has a significant positive moderating effect on big data increasing GTFP. Conversely, the moderating effects of command and control environmental regulation (CCER) and social participation environmental regulation (SPER) were found to be insignificant. These findings suggest that urban digital transformation, through its contribution to increasing GTFP, is an important pathway to high-quality development. [ABSTRACT FROM AUTHOR]

The consumer price index (CPI) is an important indicator to measure inflation or deflation, which is closely related to residents' lives and affects the direction of national macroeconomic policy formulation. It is a common method to discuss CPI from the perspective of economic analysis, but the statistical principles and influencing factors related to CPI are often ignored. Thus, the impact of different types of CPI on China's overall CPI was discussed from three aspects: statistical simulation, machine learning prediction and correlation analysis of various types of influencing factors and CPI in this study. Realistic data from the National Bureau of Statistics from 2010 to 2022 were selected as the analysis object. The Statistical analysis showed that in 2015 and 2020, CPI had a fluctuating trend due to the impact of education and transportation. Four types of statistical models including Gauss, Lorentz, Extreme and Pearson were compared. It was determined that the R2 fitted by Extreme model was higher (R2 = 0.81), and the optimal year of simulation was around 2019, which was close to reality. To accurately predict the CPI, the results of Support Vector Machine, Regression decision tree and Gaussian regression (GPR) were compared, and the GPR was determined to be the optimal model (R2 = 0.99). In addition, Spearman matrix analyzed the correlation between CPI and various influencing factors. Herein, this study provided a new method to determine and predict the changing trend of CPI by using big data analysis. [ABSTRACT FROM AUTHOR]

The intensification of the regional water cycle resulting from climate change, coupled with the influence of human activities, has brought about alterations in the frequency, scale, and intensity of droughts. In this study, based on hydrological big data and the standardized runoff drought index (SRI), the multi-scale spatiotemporal evolution of hydrological drought in China from 1948 to 2014 was analyzed using the run-length theory and gravity center model. Meanwhile, the hydrological drought hazard index was constructed to analyze the distribution of the hazard levels of drought in China. The results showed that, during 1948~2014, there was an opposite spatial distribution between the average intensity and the average coverage–duration–frequency of drought in the Yellow River Basin, Haihe River Basin and southeastern river basins. The drought situation in most river basins in China has shown an aggravating trend, among which the southeastern river basins, Haihe River Basin, Songliao River Basin and Pearl River Basin have generally shown an aggravating trend. The drought situation in China was severe in the 1950s and 1960s, gradually reduced in the 1970s, 1980s, and 1990s, and the drought situation was the mildest in the 1990s. After entering the 21st century, the drought situation began to worsen sharply. Meanwhile, from 1948 to 2014, the hazard level of drought generally presented a pattern of high in the west and north, and low in the east and south. The hazard levels of drought in the northwest and northeast were generally higher, and those in the southwest and southeast regions were generally lower. In general, the hazard levels of drought were relatively high in most areas of China. [ABSTRACT FROM AUTHOR]