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A scientific paper's citation represents its influence, which is the most intuitive indicator to access the quality of papers. This paper mainly adopts the social network analysis method, using the authors and the keywords of sports industry papers in China to constitute the networks of collaboration and knowledge, to explore effects of the degree centrality of authors and keywords and the structural hole of authors and keywords on the citation of papers in the collaboration and knowledge networks and draw the following conclusions: (1) as for collaboration networks, the degree centrality at the paper level is positively correlated with citations; (2) in the collaboration network, the positive correlation between the structural hole at the paper level and citations does not exist; (3) within knowledge networks, an inverted- U shape was found between degree centrality and paper's citation; and (4) within knowledge networks, a positive correlation is in existence between the structural hole of papers and their citation. This study synthesizes the already widely used collaboration network with the knowledge network constructed through keywords, distinguishes from the previous network features focusing at the author level, and explores research projects of Chinese Sports Industry from the paper level, providing a new perspective for the research of sports industry in China, complementing the methods and ideas of sports industry research, as well as providing a reference for the research in other disciplinary fields. [ABSTRACT FROM AUTHOR]

With the increasing frequency of scientific cooperation, research on the impact of scientific cooperation on scholars' scientific performance has become a hot topic in academic research. This paper mainly uses quantitative research methods such as bibliometrics and social network analysis to analyze the correlation between degree centrality, L-index, and scientific performance of the Chinese author of Physical Education and Training Science and draws the following conclusions: (1) degree centrality is positively correlated with the number of papers, average citations per paper, and H-index and (2) L-index is positively correlated with the number of papers, average citations per paper, and H-index. Unlike previous studies on scientific collaboration networks and scholars' performance that focused on traditional network characteristics indicators, this study introduces a new network characteristics indicator, L-index, through which the degree of collaboration between authors and other important authors in the research field can be better assessed, which also provides a new direction for research related to research collaboration networks and scholars' performance; at the same time, it also provides a theoretical basis for subsequent research on the utility of scientific collaboration, a reference for the assessment of the research performance of scholars in other disciplines, and a theoretical reference for more scientific and comprehensive prediction of scholars' research performance in the future. [ABSTRACT FROM AUTHOR]

The digital economy has great potential to sustain China's high-quality economic growth and substantially strengthen urban innovation capacity. This paper developed a digital economy index using city-level data from China and measured the level of urban innovation with patents per capita. We used a spatial econometric model to explore the spatial implications of the digital economy on urban innovation, probed into the mechanism by which the digital economy affects urban innovation, and further measured the spatial spillover distance and threshold of the digital economy on urban innovation. The findings suggest that China's digital economy and urban innovation are characterized by spatial aggregation, and the spatial distribution varies from region to region. The digital economy, with strong spatial spillover effects on the innovation capacity of cities in China, may not only enhance the innovation capacity of one city but also drive a simultaneous growth of the innovation capacity in peripheral cities. The analysis of mechanisms indicates that the digital economy enhances local innovation capacity directly through promoting human resources and increasing science and technology spending and drives the improvement of the innovation capacity in peripheral cities through the spatial spillover of human resources and science and technology spending. The effects of the latter one outweigh those of the former one. The analysis of heterogeneity shows that the central, western, and northern regions, where the digital economy is relatively less developed, have the latecomer advantage, and the digital economy has more prominent effects on innovation capacity. Calculating the spillover distance and threshold demonstrates that the digital economy influences urban innovation within a spatial spillover range and threshold of approximately 500 kilometers. Within 500 kilometers, the positive spatial spillover effects prevail, while beyond 500 kilometers, the negative siphon effect prevails. Therefore, it is necessary to consider the differences in the impact and role of the digital economy on urban innovation from a spatial perspective. [ABSTRACT FROM AUTHOR]

With the increasing frequency of scientific cooperation, research on the impact of scientific cooperation on scholars' scientific performance has become a hot topic in academic research. This paper mainly uses quantitative research methods such as bibliometrics and social network analysis to analyze the correlation between degree centrality, L-index, and scientific performance of the Chinese author of Physical Education and Training Science and draws the following conclusions: (1) degree centrality is positively correlated with the number of papers, average citations per paper, and H-index and (2) L-index is positively correlated with the number of papers, average citations per paper, and H-index. Unlike previous studies on scientific collaboration networks and scholars' performance that focused on traditional network characteristics indicators, this study introduces a new network characteristics indicator, L-index, through which the degree of collaboration between authors and other important authors in the research field can be better assessed, which also provides a new direction for research related to research collaboration networks and scholars' performance; at the same time, it also provides a theoretical basis for subsequent research on the utility of scientific collaboration, a reference for the assessment of the research performance of scholars in other disciplines, and a theoretical reference for more scientific and comprehensive prediction of scholars' research performance in the future. [ABSTRACT FROM AUTHOR]

Regional poverty is one of the most serious challenges facing the world today. Poverty, antipoverty, and poverty alleviation are the focus of the attention of scholars and the public. This paper takes China's counties as the research unit, selects the influencing factors of poverty from natural and socio-economic factors, establishes an evaluation index system, simulates the natural poverty index and socio-economic poverty eradication index of each county, and clarifies the distribution characteristics of spatial poverty using GIS spatial analysis and BP artificial neural network. The results indicate that natural factors are the main cause of poverty in Chinese counties, with 710 counties having a high natural poverty index, accounting for nearly 30% of the total number of counties in the country. The national county-level natural poverty index shows a clear strip distribution pattern along latitude and longitude, with a strip distribution from north to south and from west to east; socio-economic factors have played a certain role in poverty alleviation, with as many as 1521 counties with low socio-economic poverty alleviation indices, accounting for approximately 64% of the total number of counties in the country. The spatial distribution of the county-level socio-economic poverty alleviation index is relatively fragmented. Through spatial scanning statistics, a total of 44 county poverty pressure index risk clusters reached a statistical significance level, involving 243 counties and districts. In poverty reduction practice, the internal counties and districts of contiguous poverty-stricken areas should strengthen cooperation and exchange. In the process of poverty alleviation and development, targeted poverty alleviation and economic development should be carried out based on the poverty-dominant type and self-development ability of the county, in order to improve efficiency. Regions that are relatively prosperous and have taken the lead in poverty reduction should play a leading and exemplary role in strengthening the radiation power of regional central cities. The prominent feature of this study is the comprehensive utilization of multisource data and the use of new spatial analysis methods (flexible spatial scanning method is widely used in the field of infectious disease prevention and control research). By constructing a multidimensional poverty measurement system that includes natural and social factors, it distinguishes the differences between the factors that cause poverty and the factors that eliminate poverty in regional poverty. At the same time, the flexible spatial scanning detection method was used to detect the differentiation mechanism of poverty spatial patterns. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of cross-border investment of domestic venture firms (VCs) on the overseas listings of their domestic portfolio companies. Using a sample of 1,439 domestic VCs' first-round domestic investment events collected by Crunchbase and PEDATA, we find that the more cross-border investment experience domestic VCs have, the more likely their domestic portfolio companies are to go public outside China. The findings remain robust after using the instrumental variable method to eliminate endogeneity, the Heckman two-stage regression method to eliminate sample selection bias, and the exclusion of a portion of the sample for reregression. In addition, we further find that foreign VCs participating in follow-on financing play a mediating role in the relationship between the cross-border investments of domestic VCs and the overseas listings of their portfolio companies. This paper reveals the critical path for domestic VCs to go out and bring in foreign VCs to promote overseas listings of domestic firms. The findings of this paper are critical to the layout of domestic VCs' internationalization strategy and the sustainable development of domestic firms. [ABSTRACT FROM AUTHOR]

Dragon boat sport is a traditional activity in China. In recent years, dragon boat sport has become more and more popular around the world. In order to face more challenges, it is urgent for athletes to enhance their own strength. Scientific training methods are particularly important for athletes, and accurate training data are the basis to support scientific training. Traditional mathematical statistic methods neither can sample signals accurately nor can they do real-time analysis and feedback the characteristics to each athlete. In this paper, we use the wearable device with a triaxial accelerometer and heart rate sensor builtin to sample the speed signals and heart rate signals of athletes in various stages of men's 1000m straight race. Based on the complex network theory, we regard the 23 dragon boat athletes in the dragon boat race as 23 nodes so as to establish a network with 23 nodes and reflect the importance of nodes by measuring the impact of node deletion on the results of the race. The neural network multilayer perceptron (MLP) model is used for training to obtain the optimal combined value with speed and heart rate for each race stage. The optimal value will be used in the simulated race as the target value to verify if it can help to improve the training efficiency. Experimental results show that the optimal value obtained by this method has a positive effect on the results of the dragon boat race which is beneficial to sports training and tactics planning. [ABSTRACT FROM AUTHOR]

Hub economy is a kind of emerging economic form. Developing a hub economy is essential to strengthen domestic and foreign connectivity and build a powerful country in transportation. This paper designs a two-stage analysis framework for the evaluation and impact study of the input-output efficiency of the hub economy based on the super SBM model, Malmquist model, and Tobit model. In the first stage, the Super SBM and Malmquist models are used to measure the static and dynamic efficiency of the hub economy. In the second stage, the Tobit model is used to analyze the factors influencing the efficiency of the hub economy. Among them, the explained variable in the second stage is the measurement result of technical efficiency in the first stage. The empirical results of 30 provinces and cities in China from 2012 to 2021 show that (1) the technical efficiency (TE), pure technical efficiency (PTE), and scale efficiency (SE) of China's hub economy are 0.585, 0.740, and 0.820, respectively, which do not reach the effective state; (2) the technical efficiency change index (Effch), technical progress change index (Techch), and total factor productivity change index (Tfpch) of China's hub economy are 0.994, 0.945, and 0.939, respectively, indicating that the corresponding efficiencies show a downward trend; and (3) industrial structure, innovation, and technology are significantly and positively correlated with the efficiency of the hub economy; policy and enterprises are significantly negatively correlated with the efficiency of the hub economy; and education does not correlate with the efficiency of the hub economy. [ABSTRACT FROM AUTHOR]

International experiences have underscored the dual implications of financial openness. Given China's unique circumstances and its escalating level of financial openness, it is crucial to assess potential impacts on the country's bank systemic risk. This paper uses the quarterly data of 37 listed banks in China from 2010 to 2022 to explore the relationship between financial openness and systemic risk of the banking system, the mechanism of action, and the moderating effect of macroprudential policy on the two. The findings indicate an inverted "U"-shaped correlation between financial openness and bank systemic risk. On one side of this shape, financial openness primarily exacerbates funding mismatch, thereby increasing the systemic risk of banks. Conversely, on the other side, it primarily alleviates systemic risk by optimizing capital management. Moreover, with the help of macroprudential supervision, the inverted "U"-shaped relationship between financial openness and bank systemic risk leads to a lower level of systemic risk and, at the same time, promotes the early arrival of the inverted "U"-shaped inflection point between financial openness and bank systemic risk. Notably, the impact of financial openness on the systemic risk of joint-stock commercial banks, urban commercial banks, and rural commercial banks is more significant. The above research results provide a regulatory reference for effectively preventing and resolving systemic risk while achieving high-quality openness to the outside world. In deepening financial openness, the banking industry needs to pay attention to the funding mismatch and the efficiency of capital management and implement differential risk supervision and prevention mechanisms for banks with different ownership, which is conducive to the reduction of bank systematic risk. [ABSTRACT FROM AUTHOR]

Venture capital syndication (VCS), an investment model, is gaining increasing attention. This paper uses a new perspective on knowledge transfer to explore the influence mechanism of knowledge heterogeneity in VCS on the innovation performance (IP) of entrepreneurial firms. We use firm-year panel data from the Shenzhen Stock Exchange for 2000–2017 and find that the knowledge heterogeneity of VCS includes, at least, management knowledge heterogeneity, investment knowledge heterogeneity, and technical knowledge heterogeneity. In particular, management knowledge heterogeneity and technical knowledge heterogeneity positively affect the IP of entrepreneurial firms. We further find that the intellectual capital (IC) of entrepreneurial firms mediates these positive relationships. Moreover, the equity concentration of VCS positively moderates the relationship between knowledge heterogeneity and IP and negatively moderates the relationship between knowledge heterogeneity and IC. This paper provides a new theoretical explanation of the relationship between VCS and IP of entrepreneurial firms from a knowledge transfer perspective and explores the impact paths of this relationship. [ABSTRACT FROM AUTHOR]

The traditional innovation model has been unable to adapt to high-speed development, so the role of the innovation ecosystem has become more important. In this paper, we introduce ecology into industrial innovation and construct the symbiotic model to study the symbiotic evolution process of the high-tech industrial innovation ecosystem. This paper takes China's national high-tech industrial park as a case to study its symbiotic efficiency through empirical research, which uses a stochastic frontier analysis as a research method, constructs a complete index evaluation system, and analyzes the influencing factors. According to the results, we find that an environment conducive to the symbiotic efficiency has emerged, but development and efficiency of high-tech ecosystems in different regions of China are highly dispersed and unbalanced. There is room for improvement in symbiosis efficiency, but the difficulty is gradually increasing. Based on the evaluation of symbiotic efficiency of innovation ecosystem of high-tech industry and the consideration of influencing factors such as policy, economy, society, and technology, this paper puts forward the countermeasures of high-tech industry supporting regional economy. [ABSTRACT FROM AUTHOR]

This paper analyzes the survey data and qualitative evaluation information of the internal control audit of some universities directly under the Ministry of Education of China (MEC) by using the principal component analysis. The qualitative evaluation information is given by experts and presented by the probabilistic hesitant fuzzy elements. The analysis results show that the internal control audit system and process are not perfect, the professional knowledge and skills of internal auditors in universities are not very adaptable to internal control audit, and the effectiveness of internal control audit management conducted by external firms is not good. These are the three most serious defects of the current internal control audit in universities. The most critical factors influencing the quality of internal control audit in universities are, in order, the difficulty of rational use of the results of internal control audit by universities, the poor support of the leadership for internal control audit, and the insufficient professional knowledge and skills of internal auditors to conduct internal control audit. Thus, this paper proposes that the optimization suggestions for the internal control audit of universities are to establish and improve their internal control audit system and process to continually promote the application of internal control audit results, to increase the degree of attention of the leadership, and to strengthen the construction of the internal audit teams. [ABSTRACT FROM AUTHOR]

China is located in the intersection area of two seismic zones. Due to this special geographical location, earthquake disasters occur frequently in China. Earthquake emergency rescue work is one of the key construction works of disaster prevention and mitigation in China. This paper mainly studies the earthquake disaster rescue model based on the complex adaptive system theory and establishes the earthquake disaster rescue model by analyzing the complex adaptive system theory and combining the earthquake rescue process. In this paper, through the task allocation mechanism task, the disaster rescue task is divided into simple task and complex task, and the executive task subject is divided into single task subject and multitask subject. On the basis of considering the shortest emergency rescue time goal and the goal of maximizing the deployment utility of rescue team, the reasonable deployment of a rescue team is realized through a complex adaptive system, that is, the deployment utility of the rescue team is maximized. In this paper, the simulation experiment and comparison of the earthquake disaster rescue model based on the complex adaptive system theory are carried out. The experimental results show that the model used in this paper is better than the other two models in terms of algorithm convergence, rescue number, and overall score; in different scenarios, the relative survival probability of the model in this paper is 58.92%, 67.85%, and 77.46%, and the proportion of the wounded rescued is 66.31%, 76.45%, and 83.06%, which were higher than those of the other two models. The earthquake disaster rescue model based on the complex adaptive system theory proposed in this paper provides an effective theoretical basis and method system for postdisaster emergency rescue decision making and enhances and improves the emergency response ability to deal with large-scale geological disaster events. [ABSTRACT FROM AUTHOR]

In the current performance evaluation works of commercial banks, most of the researches only focus on the relationship between a single characteristic and performance and lack a comprehensive analysis of characteristics. On the other hand, they mainly focus on causal inference and lack systematic quantitative conclusions from the perspective of prediction. This paper is the first to comprehensively investigate the predictability of multidimensional features on commercial bank performance using boosting regression tree. The dimensionality in the financial-related fields is relatively high. There are not only observable price data, financial fundamentals data, etc., but also many unobservable undisclosed data and undisclosed events; more sources of income cannot be explained by existing models. Aiming at the characteristics of commercial bank data, this paper proposes an adaptively reduced step size gradient boosting regression tree algorithm for bank performance evaluation. In this method, a random subsample sampling is performed before training each regression tree. The adaptive reduction step size is used to replace the reduction step size setting of the original algorithm, which overcomes the shortcomings of low accuracy and poor generalization ability of the existing regression decision tree model. Compared to the BIRCH algorithm for classification of existing data, our proposed gradient boosting regression tree algorithm with adaptively reduced step size obtains better classification results. This paper empirically uses data from rural banks in 30 provinces in China to classify the different characteristics of rural banks' performance in order to better evaluate their performance. [ABSTRACT FROM AUTHOR]

To protect energy resources and alleviate environmental pollution, many countries attach great importance to the transformation of traditional industries into clean energy industries. In this paper, fuel vehicles (FVs), hybrid vehicles (HVs), and electric vehicles (EVs) are included in the research. Then, based on the aging chain and coflow theory of SDs, we construct a dynamic matching model of the auto industry upgrade process and its energy consumption attributes. The simulation results of China's auto industry show that (1) the upgrading of the auto industry is an evolutionary process from high energy consumption and high pollution to low energy consumption and no pollution and the transition from FVs and HVs to EVs will undergo two adjustments; (2) simply reducing energy supply does not have the expected impact on vehicle size and vehicle energy consumption intensity and only by adjusting the energy supply and upgrade ratios together, energy utilization efficiency can be improved; (3) market screening time has an impact on auto industry upgrade speed by affecting vehicle market share and dwell time; (4) China's auto industry upgrade process should adhere to "problem-oriented" and strengthen consumer guidance, technology innovation, and infrastructure construction. The conclusions can provide references for industrial policy adjustment and industrial structure optimization. [ABSTRACT FROM AUTHOR]

Autonomous detection and fishing by underwater robots will be the main way to obtain aquatic products in the future; sea urchins are the main research object of aquatic product detection. When the classical Single-Shot MultiBox Detector (SSD) algorithm is applied to the detection of sea urchins, it also has disadvantages of being inaccurate to small targets and insensitive to the direction of the sea urchin. Based on the classic SSD algorithm, this paper proposes a feature-enhanced sea urchin detection algorithm. Firstly, according to the spiny-edge characteristics of a sea urchin, a multidirectional edge detection algorithm is proposed to enhance the feature, which is taken as the 4th channel of image and the original 3 channels of underwater image together as the input for the further deep learning. Then, in order to improve the shortcomings of SSD algorithm's poor ability to detect small targets, resnet 50 is used as the basic framework of the network, and the idea of feature cross-level fusion is adopted to improve the feature expression ability and strengthen semantic information. The open data set provided by the National Natural Science Foundation of China underwater Robot Competition will be used as the test set and training set. Under the same training and test conditions, the AP value of the algorithm in this paper reaches 81.0%, 7.6% higher than the classic SSD algorithm, and the confidence of small target analysis is also improved. Experimental results show that the algorithm in this paper can effectively improve the accuracy of sea urchin detection. [ABSTRACT FROM AUTHOR]

Technological innovation has an important impact on environmental pollution. In this paper, first, we analyze the influence mechanism of technological innovation on environmental pollution and then design the index system of technological innovation. Then, we use the entropy method to calculate the technological innovation level of different regions in China based on provincial panel data from 2004 to 2016. Finally, the panel vector autoregression model (PVAR) is adopted, and taking the discharge of sewage, solid waste, and exhaust gas as the research objects, the impact of technological innovation on them is empirically analyzed. The results show that China's technological innovation level is steadily improving, but there are significant differences in the impact of technological innovation on wastewater, waste gas, and solid waste. Specifically, technological innovation can contribute to an increase in wastewater and solid waste emissions. However, the impact of this technological innovation on them is not equal. Secondly, the impact of technological innovation on exhaust emissions is to inhibit exhaust emissions in the short term and promote exhaust emissions in the long term. Finally, there are clear differences between them in terms of the specific impact of changes in wastewater, solid waste, and exhaust emissions. Changes in wastewater discharges and solid waste generation are largely derived from their own effects, while the role of technological innovation is supportive and insignificant. The change in exhaust emissions is initially influenced by itself, but in the long run, the influence of technological innovation gradually increases and eventually exceeds its own influence. Based on these research results, this paper puts forward corresponding policy suggestions to speed up environmental pollution control. [ABSTRACT FROM AUTHOR]

This paper investigates the relationship between technological finance, high-quality economic growth, and financial stability. Based on data of 30 provinces (including autonomous regions and municipalities) collected between 2004 and 2017, this paper adopts the method of factor analysis to construct comprehensive indexes of technological finance and financial stability before calculating green total factor productivity as the index of high-quality development, using the CRS Multiplicative Model. Then it constructs the spatial SAC model and PVAR model for analyses of the just-mentioned relationship based on the total sample of the nation and regional samples in eastern, middle, and western China, respectively. The results reveal that (1) All samples, whether the total national samples or regional samples of eastern, middle, and western China demonstrate the positive influence of technological finance on high-quality economic development, with an obvious spatial spillover effect. The impact factor is the highest in the eastern region, while the western region holds the lowest factor among the three. (2) Judging by the general national sample, technological finance has an obvious negative shock effect on financial stability within a short period, but the effect gradually dwindles as time goes by. This rule applies to the sample of the eastern region, as its technological finance poses a short-time negative shock effect on financial stability, before gradually diminishing to 0. Neither western nor middle regions have displayed an obvious shock impact on financial stability. [ABSTRACT FROM AUTHOR]

People's production and life have been inseparable from the consumption of various products, which often directly or indirectly release CO2. As CO2 emissions can transfer among industries, the identification and classification of the industries that release CO2 directly or indirectly can contribute to curbing the CO2 emissions. This paper proposes an input-output-based methodology to measure CO2 emissions transfer caused by linkages between industries in an economy and constructs the network topology in terms of the remarkable coefficients of interindustry CO2 emissions transfer. We classify all industries according to the role played in the emissions transfer process, and the network is represented by a "Bow-Tie" structure. In the visualization expression, it is easy to find the star nodes and the transmission paths of CO2 emissions among industries. Finally, the method is applied to the case of China. Empirical results indicate that the method developed in this paper provides new tools for the study of industrial CO2 emissions theory. [ABSTRACT FROM AUTHOR]

Attractions of overseas high-level returned talents have become a widely practiced talent policy, and the spatial structures of city-related interactions are drawing the attention of researchers from various fields. China is particularly an interesting case in point, as it has moved toward an innovation-oriented economy. Based on the movement trajectories of 2,846 returnees in a program entitled "Young Thousand Talents Plan" during 2011–2016, this paper identifies three city types in China: national core city, regional excellent city, and regional special city. Subsequently, this paper employs spatial visualization and qualitative interviews. This paper also goes on to show that the national core cities that possess dense research resources that coincide with premium life conditions and have objectively more favorable conditions, wider opportunities for attracting highly qualified specialists. Moreover, the backflow pattern can be divided into circle route and linear route, which is driven by social proximity and innovation context. [ABSTRACT FROM AUTHOR]

Improving the coordination between technological innovation in high-tech industries and regional economic development is an important measure for all provinces (cities) to implement the innovation-driven development strategy. Based on the analysis of the mechanism of high-tech industrial technological innovation and regional economic development, this paper constructs the measurement index system of high-tech industrial technological innovation and regional economic development, and the chain network DEA model, entropy weight method, coupling coordination model, and exploratory spatial data analysis technology are comprehensively used to empirically analyze the technological innovation efficiency of high-tech industries, regional economic development level, and the coupling coordination relationship between them and the spatiotemporal evolution trend in 24 provinces (cities) of China from 2009 to 2018. The results show that (1) there are significant differences in the average level of technological innovation efficiency of high-tech industry in each stage, which is the most prominent in the technology research and development stage, followed by the industrialization stage, and finally in the technology transformation stage. (2) The degree of coupling and coordination between high-tech industrial technological innovation and regional economic development is increasing year by year, which is in the adaptation stage of high-tech industrial technological innovation and regional economic development. (3) From the perspective of spatial pattern, there is a positive spatial autocorrelation between the coupling coordination degree of high-tech industrial technological innovation and regional economic development. The number of provinces (cities) with low coupling coordination degree is much larger than that with high coupling coordination degree, showing a serious imbalance of regional heterogeneity and forming two spatial aggregation areas of "high-high" diffusion effect and "low-low" low-speed growth. [ABSTRACT FROM AUTHOR]

The transplanted firm is an important force to promote the network evolution and cluster transformation and upgrading of the undertaking firm. From the micro-analytic perspective of firm network, this paper puts forward a theoretical framework with "relationship-network-evolution" as the main line. Taking the refrigeration industry cluster in Minquan County of China as a case study and keeping the firm networks of economic relation, technical cooperation, and social communication firm network in 2009, 2013, and 2017 as the research objects, this paper analyzes the structure and evolution characteristics of regional firm network system, proposes the degree and effect of the local embeddedness of transplanted firms, and discusses their differences between international and interregional transplanted firms. The results revealed that: (1) the local embeddedness of transplanted firms significantly promotes the development of refrigeration industry network. (2) The network power of large-scale transplanted firms in the cluster is increasing day by day, and the network presents a multi-core trend. (3) The local embeddedness of some transplanted firms is not high, and the overall connectivity of the network is not strong. (4) Network intermediary nodes have strong heterogeneity, and the intermediary role of some large transplanted firms in the network needs to be improved. (5) The three network systems have similar structural characteristics, and social capital plays an important role in the local embeddedness of transplanted firms and the development of regional firm network. (6) Compared with international transplanted firms, interregional transplanted firms are more adaptable in terms of local embeddedness. The research results provide a reference for the construction of similar industrial clusters in China and other developing countries. [ABSTRACT FROM AUTHOR]

This paper investigates the drivers of hot money in China. It develops a model based on expectation-variance utility theory in the theoretical analysis section. The model considers a foreign investor who faces the question of how to distribute his wealth between foreign and domestic assets. The model's analysis suggests that economic variations, such as expected domestic currency appreciation, rise in domestic asset return, drop in foreign asset return, domestic economic growth, decrease in domestic inflation, and rise in foreign asset risk will cause foreign investors to distribute more wealth in domestic assets. Therefore, hot money flows in, and vice versa. In the empirical analysis section, the paper estimates structural VAR models using data from 2000 to 2019 in China. The impulse response functions are consistent with the theoretical predictions: when there is a positive domestic inflation shock, hot money outflows increase (inflows decline) in the current period, but the response is not significant. When there is a positive domestic growth rate shock or positive domestic asset return rate shock, hot money inflows increase (outflows decline) in the current period, and the response reaches its peak in the next period. Furthermore, when there is a positive expected exchange rate shock, hot money outflows increase (inflows decline) in the current period. Of these drivers, the expected exchange rate has the largest impact on hot money, and the domestic growth rate has the most enduring effect. [ABSTRACT FROM AUTHOR]

This paper mainly analyzes the current situation of e-commerce in domestic SMEs and points out that there are limited initial investment and difficulty in financing in China's SMEs; e-commerce control is not scientific; e-commerce personnel of SMEs are not of high quality, in the case of improper setting of the e-commerce sector and shortage of talents, rigid management model, and outdated management concepts. By using the loss function and the value chain management theory of the deep learning in the stationary wavelet domain residual learning model, the e-commerce model of SMEs is newly constructed, and the e-commerce department as the core department of the enterprise is proposed. By training the optimal parameters of the deep residual network and comparing the results with other models, the method of this paper has a good effect against the sample. The original loss function based on the residual learning model deep learning is modified to solve the original model fuzzy problem, which improves the effect and has good robustness. Finally, based on the wavelet residual depth residual evaluation method, this paper evaluates the application effect of this model and proposes relevant suggestions for improving this model, including rationalizing and perfecting the external value chain coordination mechanism, establishing the e-commerce value chain sharing center, and promoting integration of e-commerce business, strengthening measures and recommendations in various aspects of e-commerce information construction. At last, taking the business activities of a company as an example, applying the theory described in this paper to specific practice proves the feasibility and practical value of the theory. [ABSTRACT FROM AUTHOR]

With the improvement of citizens' risk perception ability and environmental protection awareness, social conflicts caused by environmental problems in large-scale construction projects are becoming more and more frequent. Traditional social risk prevention management has some defects in obtaining risk data, such as limited coverage, poor availability, and insufficient timeliness, which makes it impossible to realize effective early warning of social risks in the era of big data. This paper focuses on the three environments of diversification of stakeholders, risk media, and big data era. The evolution characteristics of the social risk of environmental damage of large-scale construction projects are analyzed from the four stages of incubation, outbreak, mitigation, and regression in essence. On this basis, a social risk early warning model is constructed， and the multicenter network governance mode of social risk of environmental damage in large-scale construction projects and practical social risk prevention strategies in different stages are put forward. Experiments show that the long short-term memory neural network model is effective and feasible for predicting the social risk trend of environmental damage of large-scale construction projects. Compared with other classical models, the long short-term memory model has the advantages of strong processing capability and high early warning accuracy for time-sensitive data and will have broad application prospects in the field of risk control research. By using the network governance framework and long short-term memory model, this paper studies the environmental mass events of large-scale construction projects on the risk early warning method, providing reference for the government to effectively prevent and control social risk of environmental damage of large-scale construction project in China. [ABSTRACT FROM AUTHOR]

With the rapid development of China's Internet finance industry and the continuous growth of transaction amount in recent years, a variety of financial risks have increased, especially credit risk in the financial industry. Also, the credit risk evaluation is usually made by using the application card scoring model, which has the shortcomings of strict data assumption and inability to process complex data. In order to overcome the limitations of the credit card scoring model and evaluate credit risk better, this paper proposes a credit evaluation model based on extreme gradient boosting tree (XGBoost) machine learning (ML) algorithm to construct a credit risk assessment model for Internet financial institutions. At the same time, an Internet lending company in China is taken as a case study to compare the performance of the traditional credit card scoring model and the proposed machine learning (ML) algorithm model. The results show that ML algorithm has a very significant advantage in the field of Internet financial risk control, it has more accurate prediction results and has no particularly strict assumptions and restrictions on data, and the process of processing data is more convenient and reliable. We should increase the application of ML in the field of financial risk control. The value of this paper lies in enriching the related research of financial technology and providing a new reference for the practice of financial risk control. [ABSTRACT FROM AUTHOR]

This article innovatively builds the infrastructure of farmer credit rating index system into a multilevel unidirectional network structure. First, according to the logical structure of the three-level credit rating index system, a four-level unidirectional network is constructed, and the credit rating calculation formulas of all indexes at the four-level network are established. Furthermore, the special cases of the credit rating formula with the first- and second-level farmer credit rating index system are discussed. On this basis, it is extended to a credit rating index system with more than four levels, and the corresponding credit rating formula is established. Finally, the general formula of credit rating formula of the farmer credit rating index system from first level to multilevel is obtained. In order to solve the problem of farmers' credit rating, this paper also designs a linear segmentation classifier to classify the results of multilayer unidirectional network, establishes the rules of farmers' credit rating and the unidirectional network linear segmentation evaluation model of farmers' credit rating, and discusses the properties of bank credit based on farmers' credit rating. Finally, the model established in this paper is applied to the credit rating of farmers in A County, Guangdong Province in China. When the credit rating of 160 farmers is carried out, the evaluation results are in line with the actual credit rating of farmers in A County, with an accuracy of 100%. This research has the maneuverability to carry on the scientific credit rating to the countryside. This study has important method guidance and operability for rural credit rating. [ABSTRACT FROM AUTHOR]

The difference between urban and rural development levels (URDL) and the deficiency of rural development level have become the weak points for China to achieve balanced and high-quality development. In order to reveal the changing trend of urban-rural differences in China over the years and provide a reference for the policy-making of the balanced development of urban and rural areas and high-quality economic development, this paper uses the United Nations Development Program-adjusted Human Development Index (HDI) calculation method to calculate the urban and rural areas, based on 1995–2017 national time series data and provincial panel data. On this basis, this paper uses the Logarithmic Mean Weight Divisia to decompose the dynamic changes of the difference of URDL and analyzes the spatial equilibrium of the change trend of the differences of URDL, supplementing the shortcomings of the existing literature which only focuses on income, education, and other local areas and lacks continuity and comparison. The research finds that 1. based on the time series analysis of the difference of URDL, this paper firstly proposes the "inverted U" curve for the difference of URDL in China, which shows that the difference of URDL in China has experienced a process from expansion (1995–2001) to high fluctuation (2001–2011) to continuous convergence (2011–2017). 2. From the factor decomposition effect of the difference of URDL, the difference expansion period is caused by the increase in the gap between the Health Index, the Education Index, and the Income Index. With the decline in the gap between education levels and life expectancy, the growth trend of China's urban-rural gap has been suppressed, and it has entered a high platform period with relative small fluctuation. After 2011, benefiting from the large decline in income level gaps, China's urban-rural difference has entered a period of convergence. 3. From the perspective of the spatial evolution characteristics of the gap of URDL, the overall coefficient of variation of the country has shown a downward trend. The degree of spatial equilibrium is gradually increasing, and the overall changes in the east, west, and northeast are the same as the overall trend of the country. The decline in the northeast is the largest, the west is the second, and the east is the least. The middle shows a slight upward trend, but the value of each year is always smaller compared with other regions. Generally speaking, the gap of URDL is relatively good in the middle and northeast, followed by the east, and there is still much room for improvement in the balance of the west. [ABSTRACT FROM AUTHOR]

Currently, China is in the period of social transformation. Such transformation continuously results in high group polarization behaviors, which attracts many attentions. In order to explore the evolutionary mechanism and formation process of group polarization behavior, this paper proposes a group polarization model which is integrated into the Susceptible-Infected-Recovered-Susceptible (SIRS) epidemic model. In this paper, firstly, the SIRS epidemic model and the factors of relationship strength are introduced based on the J-A model (proposed by Jager and Amblard) to enhance the information transmission and interaction among individuals. In addition, the BA network (proposed by Barabasi and Albert) model is used as the agent adjacency model due to its closeness to the real social network structure. After that, the Monte Carlo method is applied to conduct experimental simulation. Subsequently, this paper analyzes the simulation results in threefold: (1) comparison of polarization processes with and without integration of the SIRS epidemic model; (2) adjusting the immune recovery parameter γ and the relationship strength z to explore the role of these two parameters in the polarization process; and (3) comparing the polarization effects of different network structures. Through the experiments, we find that BA network is more polarized than small-world network in the same scale. Finally, corresponding measures are proposed to prevent and mitigate the occurrence of group polarization. [ABSTRACT FROM AUTHOR]

To consider the jump problem of the Chinese stock market, this paper takes the CSI 300 Index from April 2005 to November 2015 as the research object, uses the rescaled range analysis (R/S analysis) method to examine the fractal characteristics of the Chinese stock market in the past ten years, and deduces the possibility of multiple bubbles in the Chinese stock market. Based on this, combined with the log-periodic power law (LPPL) model, the stock market bubbles are identified in different periods. The results show that China's stock market has some anomalies in terms of positive bubbles, negative bubbles, and reverse bubbles, as well as the cross occurrence of reverse-negative bubbles. Besides, through a comparison with the major foreign stock markets, it is found that the fluctuation range of the Chinese stock market is much larger than that of the Dow Jones Industrial Average and the FTSE 100 indices in the same period and there are also more types of multibubbles, which is a connotative anomaly that makes the Chinese stock market different from other major stock markets. Furthermore, the bubble phenomenon in the Chinese stock market during the periods of 2005/4–2007/10 and 2015/6–2015/11 is studied, and it is found that there is a jump anomaly in the Chinese stock market. Finally, based on the above empirical analysis and the current state of the stock market, this paper provides some suggestions for improving the mechanism of the Chinese stock market. [ABSTRACT FROM AUTHOR]

To consider the jump problem of the Chinese stock market, this paper takes the CSI 300 Index from April 2005 to November 2015 as the research object, uses the rescaled range analysis (R/S analysis) method to examine the fractal characteristics of the Chinese stock market in the past ten years, and deduces the possibility of multiple bubbles in the Chinese stock market. Based on this, combined with the log-periodic power law (LPPL) model, the stock market bubbles are identified in different periods. The results show that China's stock market has some anomalies in terms of positive bubbles, negative bubbles, and reverse bubbles, as well as the cross occurrence of reverse-negative bubbles. Besides, through a comparison with the major foreign stock markets, it is found that the fluctuation range of the Chinese stock market is much larger than that of the Dow Jones Industrial Average and the FTSE 100 indices in the same period and there are also more types of multibubbles, which is a connotative anomaly that makes the Chinese stock market different from other major stock markets. Furthermore, the bubble phenomenon in the Chinese stock market during the periods of 2005/4–2007/10 and 2015/6–2015/11 is studied, and it is found that there is a jump anomaly in the Chinese stock market. Finally, based on the above empirical analysis and the current state of the stock market, this paper provides some suggestions for improving the mechanism of the Chinese stock market. [ABSTRACT FROM AUTHOR]

To benefit from users' co-creation knowledge in online communities, enterprises need effective methods to discover and manage co-creation users and knowledge as suggested in existing research. However, the existing methods still have their own limitations when analysing different relationships and networks. Therefore, first, this paper proposes a co-creation cyberspace super-network model for product innovation from a "user-knowledge-product" perspective to integrate the co-creation information of users, knowledge, and products. Second, to increase the accuracy of discovering, managing, and using the co-creation knowledge, three subnetworks including co-creation user network, co-creation knowledge network, and co-creation product network as well as the relationships among them are constructed and improved. Third, a well-known co-creation community in China is selected as a case to verify the feasibility and validity of the proposed model for enterprise's product innovation. Three subnetworks and the mapping relationships among them are constructed and visualized based on the introduced User-Knowledge-Product Cyberspace model. The results from the case study validate the co-creation cyberspace constructed in this paper and provide enterprises with a comprehensive and detailed integration framework analysis model. Hence, the enterprises can use this model for user discovery, product innovation knowledge mining, and dynamic innovation knowledge basement and forecast hotspot trend in co-creation communities. [ABSTRACT FROM AUTHOR]

This paper tries to investigate the future development tendency of five typical industries in China and find out whether there exists a different innovation strategy preference between Chinese firms of low- and high-knowledge density industry in the background of low-carbon sustainable development. First, this paper finds that the innovation driven-based trend of industrial development is further accelerated in China. Firms in industries with high knowledge and technology density, such as specialized-supplier, scale-intensive, and science-based industries, are more likely to choose exploratory innovation strategy than exploitative innovation strategy. Firms in industries with low knowledge and technology density like service- and supplier-dominated industries are more likely to choose the exploitative innovation strategy. Second, results indicate that exploratory innovation can bring higher technological innovation performance than exploitative innovation. And the effect of the ambidextrous strategy is better than the single exploitative strategy in industries with high knowledge and technology density like specialized-supplier, scale-intensive, and science-based industries. Third, this paper suggests that innovation strategy preference plays a mediating role between industry type and technology innovation and a moderating effect of knowledge management capacity between innovation strategy and firms' technology innovation. [ABSTRACT FROM AUTHOR]

Aiming to improve the commercialization efficiency of scientific innovative achievements, this paper utilizes the time series visualization method to construct the time series network of each subsystem. After that, the network similarity is calculated by the cosine similarity theorem. On this basis, a new multilayer network adjacency matrix is obtained. With the adoption of k-core technology, the critical nodes can be identified to study the transformation efficiency of the innovation value in the network. Finally, according to the provincial innovation value transformation data of China from 1998 to 2016, an empirical study was carried out to calculate and analyze the transformation efficiency of innovation achievements in 30 provinces. The results indicate that (1) the transformation efficiency of innovation value can be expressed by the structure of the time series network constructed by the input-output vectors; (2) the mapping relationship of the value transformation vectors could be reflected by the cosine similarity of the time series network, while the transformation efficiency of innovation value could be identified using the k-core; and (3) the transformation efficiency of innovation value in three coastal provinces is relatively higher, while that of the rest of the provinces is roughly the same among the 30 provinces. [ABSTRACT FROM AUTHOR]

What is the interplay of high-tech industrial agglomeration and urban innovation? How does high-tech industrial agglomeration affect urban innovation? What are the heterogeneous effects of high-tech industry agglomeration on urban innovation in different conditions? To answer these questions, this paper analyzes the interrelationship between high-tech industry agglomeration and urban innovation based on panel data of China's Yangtze River Delta urban agglomeration from 2010 to 2019. We discuss the influence mechanism of high-tech industrial agglomeration on urban innovation by exploring the mediating effect of industrial structure optimization and the threshold effect of industrial attributes. The heterogeneous impact of high-tech industry agglomeration on urban innovation is also been further studied. We find that (1) the interaction relationship between high-tech industry agglomeration and urban innovation output is positive. (2) The advancement of industrial structure plays a positive intermediary role between high-tech industrial agglomeration and urban innovation output, while the rationalization of industrial structure shows a suppressing effect. (3) There are different threshold effects between capital intensity and technology intensity. The influence of high-tech agglomeration on urban innovation is positive only when the capital intensity exceeds 1.125. However, the influence is always positive in different levels of technology intensity, significantly. When the technology intensity is higher than 9.012E − 06, the degree and significance of this positive impact would decrease. (4) There are heterogeneous impacts of high-tech industry agglomeration on urban innovation output in the Yangtze River Delta urban agglomeration in different time stages, urban innovation development stages, and urban circles. [ABSTRACT FROM AUTHOR]

Considering the reward and punishment mechanism of the management committee and the complexity of innovation path selection of high-tech and general enterprises, this paper constructs an evolutionary game model of independent innovation incentive mechanism in the National Independent Innovation Demonstration Zone of China. Meanwhile, the equilibrium points of the strategy selection are solved for the three. In addition, this paper adopts numerical simulation to analyze the influence of each decision variable on different players' strategic selections. The results show that (1) the initial willingness of the management committee, high-tech and general enterprises has different influences on each other, and these factors such as independent innovation cost, technology spillover coefficient, and patent royalty significantly affect the strategic selection of enterprises; (2) the reward and punishment mechanism of the management committee can enhance the innovation willingness of high-tech and general enterprises, in which these punitive measures can promote further the independent innovation of the two; (3) the greater the innovation subsidy provided by the management committee to high-tech enterprises, the heavier the punishment for general enterprises, and the better the effect of independent innovation incentives. The results can provide theoretical guidance and practical reference for the management committee to formulate the independent innovation incentive policies in the National Independent Innovation Demonstration Zone. [ABSTRACT FROM AUTHOR]

As new energy vehicle (NEV) is the future of automobile development, it is of great significance to dig deeper into the technical topics and development trends of new energy vehicles for accurately understanding the technical trends of the new energy vehicle industry, grasping development opportunities, and scientifically formulating strategic plans. This paper takes the patent texts in the field of new energy vehicles from 2000 to 2020 in the patent database of CNKI as the data source, identifies 25 technical topics implied in the patent texts by using the LDA (Latent Dirichlet Allocation) topic model, analyzes the evolution trend of the 25 technical topics in terms of importance and popularity, and predicts the popularity and development trend of each technical topic in new energy vehicles from 2021 to 2025 by constructing the ARIMA model. The popularity and development trend of each technology topic of new energy vehicles in China from 2021 to 2025 are predicted by constructing ARIMA model. Drawing on quantitative evidence, the study found that there are top five technical topics in terms of importance in this field, namely, Topic 8 (Installation and Fixation), Topic 5 (Heat Dissipation), Topic 14 (Vehicle Data Monitoring), Topic 9 (Charging Pile), and Topic 15 (Damping). From 2014 to 2020, the importances of Topic 5 (Heat Dissipation), Topic 8 (Installation and Fixation), Topic 6 (Electric Drive System), Topic 9 (Charging Pile), and Topic 15 (Damping) are gradually rising. In terms of popularity of technical topics, from 2014 to 2020, the first to fifth topics are Topic 20 (Safety), Topic 8 (Installation and Fixation), Topic 3 (Cable Insulation Materials), Topic 15 (Damping), and Topic 10 (Pump Cooling). Based on the prediction of ARIMA model, it is found that the popularity of these five technical topics is steadily increasing from 2021 to 2025, among which the popularity of Topic 20 (Safety) will increase from 63.58 to 113.07, the largest increase in popularity among all technical topics. The paper provides implications for countries dedicated to developing the new energy vehicle industry. [ABSTRACT FROM AUTHOR]

e-commerce has promoted the application of the Internet by traditional enterprises. While this phenomenon has deepened industrial embeddedness, it has also profoundly affected the development of traditional manufacturing and retail industries. Based on the embeddedness theory, this paper takes the e-commerce industrial chain in Xiong'an New Area of China as an example, uses location entropy method and local spatial autocorrelation to analyze its spatial characteristics and influencing factors, and also discusses the features of embeddedness from each dimension of industrial chains. The results show the following: ① The supply chain of industry-dependent e-commerce is dominated by production wholesalers, and suppliers with cost advantages and commercial traditions are embedded in local characteristic industries; the e-commerce value chain is dominated by production and processing activities, and the spatial orientation of the business model is affected by regional industrial functions; the e-commerce enterprise chain is dominated by B2B enterprises, the high-level agglomeration of which reflects the absolute scale advantage provided by the e-commercialization of traditional industry. ② The distribution of the e-commerce industrial chains of Xiong'an New Area is characterized by spatial heterogeneity and has obvious spatial agglomeration. The industrial chains are concentrated in Xiong County, followed by Rongcheng County, and Anxin County has the weakest agglomeration. ③ The main factors that cause the uneven distribution of the e-commerce industrial chains in Xiong'an New Area include three aspects: transportation location advantages, traditional industrial foundation, and government policy regulation. Research can provide a scientific reference for accurately identifying the spatial development characteristics of the industrial chains from various perspectives and promoting the sustainable development and e-commercialization of traditional industries. [ABSTRACT FROM AUTHOR]

Machine learning methods have been used in multifactor stock strategy for years. This paper uses three machine learning methods and linear regression method to find the most appropriate approach. First, a framework is established and 10 style factors and 30 industry factors are chosen. Second, four methods are used to forecast portfolio returns and compared by predicting returns, successful rate, and Sharpe ratio. Finally, this paper draws conclusion. The main findings are as follows: the support vector regression has the most stable successful rate for predicting, while ridge regression and linear regression have the most unstable successful rate with more extreme cases; algorithm of support vector regression fitting higher-degree polynomials in Chinese A-share market is optimized, compared with the traditional linear regression both in terms of stock return and retracement control; the results of support vector regression significantly outperforming the CSI 500 index prove further. [ABSTRACT FROM AUTHOR]

Faster R-CNN architecture is used to solve the problems of moving path uncertainty, changeable coverage, and high complexity in cold-air induced large-scale intensive temperature-reduction (ITR) detection and classification, since those problems usually lead to path identification biases as well as low accuracy and generalization ability of recognition algorithm. In this paper, an improved recognition method of national ITR (NITR) path in China based on faster R-CNN in complicated meteorological systems is proposed. Firstly, quality control of the original dataset of strong cooling processes is carried out by means of data filtering. Then, according to the NITR standard and the characteristics of NITR, the NITR dataset in China is established by the intensive temperature-reduction areas located through spatial transformation. Meanwhile, considering that the selection of regularization parameters of Softmax classification method will cause the problem of probability calculation, support vector machine (SVM) is used for path classification to enhance the confidence of classification. Finally, the improved faster R-CNN model is used to identify, classify, and locate the path of NITR events. The experimental results show that, compared to other models, the improved faster R-CNN algorithm greatly improves the performance of NITR's path recognition, especially for the mixed NITR paths and single NITR paths. Therefore, the improved faster R-CNN model has fast calculation speed, high recognition accuracy, good robustness, and generalization ability of NITR path recognition. [ABSTRACT FROM AUTHOR]

This paper uses the 5-five-minute high-frequency data of energy-listed companies in China's A-share market to extract the jump of energy stock prices and build a dynamic stock price jump complex network. Then, we analyze the clustering effect of the complex network. The research shows that the energy stock price jump is an important part of stock price volatility, and the complex network of energy stock jump risk has obvious time-varying characteristics. However, the infection problem of stock price jump risks needs specific analysis. China's coal industry has an important influence on the development of China's energy industry. According to the clustering analysis results of the network community, the clustering effect of the network community has time-varying characteristics. After October 2017, the clustering effect of the jumping risk of the coal industry and the new energy industry is obvious. The risk contagion within the new energy industry community is a key point for the development of the new energy industry. [ABSTRACT FROM AUTHOR]

In this paper, we investigate the impact of economic policy uncertainty (EPU) on the conditional dependence between China and U.S. stock markets by employing the Copula-mixed-data sampling (Copula-MIDAS) framework. In the case of EPU, we consider the global EPU (GEPU), the American EPU (AEPU), and the China EPU (CEPU). The empirical analysis based on the Shanghai Stock Exchange Composite (SSEC) index in China and the S&P 500 index in the U.S. shows that the tail dependence between China and U.S. stock markets is symmetrical, and the t Copula outperforms alternative Copulas in terms of in-sample goodness of fit. In particular, we find that the t Copula-MIDAS model with EPU dominates the traditional time-varying t Copula in terms of in-sample fitting. Moreover, we observe that both the GEPU and AEPU have a significantly positive impact on the conditional dependence between China and U.S. stock markets, whereas CEPU has no significant impact. The tail dependence between China and U.S. stock markets exhibits an increasing trend, particularly in the recent years. [ABSTRACT FROM AUTHOR]

The development of China's new urbanization has driven the rapid increase in large complex engineering projects, which have the characteristics of large-scale investment, long-term construction, and wide social influence, easily causing benefit conflicts among relevant stakeholders, and breaking out social stability risks. In the previous research, the risks of large complex engineering projects mainly concentrated on the assessment of economic risk, schedule risk, etc. However, there were few studies on social risks, and they did not consider how the risks spread on the complex networks based on the social connections such as interpersonal relationship. From the subject of social stability risk diffusion of large complex engineering projects, this paper constructs a related risk diffusion model based on the SIR model to analyze risk diffusion mechanism. through Net Logo simulation platform, the model is placed under a small-world network environment that is closest to the topology structure of real social interpersonal relationship network for simulation research, aiming to find out key factors of social stability risk intervention for large complex engineering projects, which greatly contributes to the social stability risk management of large complex engineering projects. [ABSTRACT FROM AUTHOR]

An enterprise must be able to conduct in-depth analysis of the existing data as the information has certain grey characteristics, if it wants to occupy a dominant position in the fierce market competition. In this paper, a compound three-dimensional grey Lotka–Volterra model is developed to carry out the grey transformation of the original data, so that the data can have better simulation accuracy, and the observation noise of the original data can be reduced. The competitive situation analysis based on the three-dimensional grey Lotka–Volterra model can help enterprises better understand the market situation. This paper takes the luxury brand automobile market in mainland China as an example to conduct a competitive analysis and a balanced development simulation. It can be found that, based on Three Species System Analysis, there is a symbiotic relationship among automobile enterprises and that the three species model can be adopted in analyzing the competition and cooperation among enterprises. Through balanced development of a Symbiotic System Analysis, the results of symbiotic optimization under the achievable equilibrium state of three populations are obtained and they show that the proposed method can be used effectively to conduct the market competition analysis. It is thus of great importance to study the relationships among enterprises as it is helpful for enterprises to make strategic policies. [ABSTRACT FROM AUTHOR]

With the advent of Industry 4.0, economic development has become a rapid information age. The content of macroeconomic forecast is very extensive, and the existence of big data technology can provide the government with multilevel, diversified, and complete information and comprehensively process, integrate, summarize, and classify these pieces of information. This paper forecasts the CPI value in the next 12 months according to the CPI in China in the recent 20 years. Compared with the traditional forecasting methods, the forecasting results have higher accuracy and timeliness. At the same time, the trend of growth rate of industrial value-added is analyzed, and the experiments on MAE and RMSE show that the method proposed in this paper has obvious advantages. It also analyzes the disadvantages of traditional psychological decision-making behavior analysis, introduces the development status and advantages of big data-driven psychological decision-making behavior analysis, and opens up new research ideas for psychological decision-making analysis. [ABSTRACT FROM AUTHOR]

Cognitive ability is an important aspect of children's development, but there is still room for discussion about the impact of preschool education on children's cognitive ability. Based on the data of China Urbanization and Children Development Survey (CUCDS) of Tsinghua University, this paper categorizes cognitive ability into Chinese language cognition and mathematical cognition. It is discovered that the impact of preschool education on children's cognitive development differs depending on the cognitive ability and the length of time. In particular, preschool education has both short-term and long-term effects on children's Chinese cognitive ability, while there is only a short-term effect on the development of children's mathematical cognitive ability without long-term effect. [ABSTRACT FROM AUTHOR]

Agricultural enterprises play a significant role in China's economic development. However, compared with other enterprises, agricultural enterprises are facing serious financial problems. Financing difficulty is essentially a question of financing efficiency. Based on the DEA method, this paper evaluates the financing efficiency of 39 agricultural listed companies in China from 2013 to 2017. The results suggest that the financing efficiency is generally low, and the Total Factor Productivity of agricultural enterprises' financing has a tendency to decrease first and then increase. The influencing factors of financing efficiency are analyzed using the Tobit regression model and the random forest regression model. And we find the following: (1) The random forest regression model significantly outperformed the Tobit regression model, with determination coefficients (R2) greater than 0.9 in full sample sets. (2) Total liability, financial expenses, return on total assets, and inventory turnover rate are important factors affecting financing efficiency of agricultural listed companies. (3) Return on total assets and inventory turnover rate promote the financing efficiency, while total liability and financial expenses reduce financing efficiency. Finally, the paper makes some suggestions for the financing of agricultural enterprises. [ABSTRACT FROM AUTHOR]

Intraday range (the difference between intraday high and low prices) is often used to measure volatility, which has proven to be a more efficient volatility estimator than the return-based one. Meanwhile, a growing body of studies has found that economic policy uncertainty (EPU) has important impact on stock market volatility. In this paper, building on the range-based volatility model, namely, the conditional autoregressive range (CARR) model, we introduce the CARR-mixed-data sampling (CARR-MIDAS) model framework by considering intraday information to investigate the impact of EPU on the volatility of Chinese stock market and to explore the predictive ability of EPU for Chinese stock market. The empirical results show that both the China EPU (CEPU) and global EPU (GEPU) have a significantly negative effect on the long-run volatility of Chinese stock market. Furthermore, we find that taking into account the CEPU and GEPU leads to substantial improvement in the ability to forecast the volatility of Chinese stock market. We also find that the CEPU provides superior volatility forecasts compared to the GEPU. Our findings are robust to different forecasting windows. [ABSTRACT FROM AUTHOR]

Intelligent manufacturing capability evaluation is the key for enterprises to scientifically formulate the implementation path and continuously improve the level of intelligent manufacturing. To help manufacturing enterprises diagnose the level of intelligent manufacturing capability, this paper conducts research on intelligent manufacturing capability maturity evaluation based on maturity theory. The evaluation problem is a complex nonlinear problem, and BP neural network is particularly suitable for solving such complex mapping problems. Aiming at the problem that the BP neural network is sensitive to initial weights and thresholds, the sparrow search algorithm (SSA) is used to optimize the initial weights and thresholds of the BP neural network. In order to overcome the shortcoming of SSA that it is easy to fall into the local optimum, the firefly disturbance strategy is introduced to improve it, a new sparrow search algorithm (FASSA) is proposed, and on this basis, an intelligent manufacturing capability maturity evaluation model based on the FASSA-BP algorithm is constructed. Finally, a large battery manufacturing enterprise in China is selected for empirical research, and the comparison experiments are carried out on the FASSA-BP model, BP model, SSA-BP model, and PSO-BP model in terms of accuracy, stability, etc. The results show that the evaluation of intelligent manufacturing capability maturity through this model can effectively help companies diagnose problems in the construction of intelligent manufacturing and provide a reference for companies to accurately improve their intelligent manufacturing capabilities. [ABSTRACT FROM AUTHOR]

At present, urban traffic congestion is a common problem of urban development in China. Therefore, China's government issued the policy of opening the gated communities in 2016, hoping to alleviate traffic pressure to some extent. But, at present, the quantitative empirical research on the effect of the policy implementation is less and more idealistic. In order to complete the leap from research on local isolated traffic capacity of static gated communities to research on global coupling traffic capacity of dynamic multilayer network and, to a certain extent, reflect the innovation of the research model and method, we make a quantitative analysis of the effect of the policy more consistent with the actual situation, which provides a quantitative management basis for the implementation of the policy. Based on literature review, this paper carried out two stages of research. Stage one consists of constructing traffic capacity assessment model—Road Capacity Assessment (RCA) model based on BPR (Bureau of Public Roads) impedance function from a simple local static perspective. Considering the factors such as delay of signalized intersection and travel time, through modeling analysis and empirical test, it is found that about 12% of local traffic capacity can be improved after open community. Stage two consists of constructing a global capacity model based on multilayer complex network coupling from the perspective of complex global dynamics. Considering the various network nodes of pedestrians, nonmotor vehicles, and other factors, we construct a multilayer complex network and dynamic superposition coupling. The empirical data show that the overall traffic capacity of the open community is improved by at least 11.3%. Finally, it can be concluded that a significant increase in the global traffic capacity of complex urban road networks means that the losses caused by traffic congestion will be reduced. In some first-tier cities, the direct monthly income of the open-gated community is as high as 230 million yuan, not to mention its overall economic, social, and environmental benefits. [ABSTRACT FROM AUTHOR]