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This article presents a case study of how the Renminbi won the currency war in Shanghai between 1949 and 1950. This case study supports the proposition of chartalism and MMT that money is a creature of the state, and in two ways deepens our understanding of the role of the state in the sovereign currency system. First, taxation is not the only way to drive money. The Shanghai government also drove money by supplying goods. Second, the outcome of a currency war depends on many historical factors, and the government plays an important role by changing the conventions that shape people's monetary behavior. The Shanghai government adopted anti-inflation policies to transform the conventions of valuing commodities and metallic currencies over paper currencies. [ABSTRACT FROM AUTHOR]

This paper takes the establishment of the Green Financial Reform and Innovation Pilot Zone (GFPZ) in 2017 as a natural experiment, adopts the data of a-share industrial listed enterprises in Shanghai and Shenzhen from 2010 to 2020, and utilizes the difference-in-differences (DID) method to carry out empirical tests. The results show that (1) GFPZ policy significantly improves the environmental, social, and governance (ESG) performance of enterprises, and the positive effect is mainly realized by improving the external financing ability and green-technology innovation level of enterprises. (2) There is heterogeneity in the impact of GFPZ policy on the ESG performance of firms with different equity natures and internal control levels. (3) Green finance promotes active corporate social responsibility, and it can further improve environmental governance in the regions where it operates. This paper provides a useful supplement to the comprehensive understanding of green-finance policy effects and ESG impact factors, and it is of great significance in mitigating the negative environmental and social externalities caused by the excessive pursuit of economic benefits by enterprises. [ABSTRACT FROM AUTHOR]

Compared with financial information, whether corporate social responsibility disclosure has impacts on debt financing cost has obviously become a heated topic in the academic. This paper selects A-share listed companies in Shanghai and Shenzhen stock markets of China from 2013 to 2017 as sample to study the impact of corporate social responsibility disclosure on debt financing cost, including bond financing cost and bank loan cost. In addition, this paper divides the sample into voluntary corporate social responsibility disclosure group and mandatory corporate social responsibility disclosure group and studies whether the effects of voluntary corporate social responsibility disclosure and mandatory corporate social responsibility disclosure on bond financing cost and bank loan cost are different. The empirical results are presented as follows: Firstly, enterprise disclosing high-quality corporate social responsibility report is more conducive to reducing the debt financing cost. Specifically, corporate social responsibility disclosure significantly lowers both the bond financing cost and bank loan cost. Moreover, the reduction impact of corporate social responsibility disclosure on bond financing cost is greater than that of bank loan cost. Secondly, enterprises voluntarily disclosing corporate social responsibility reports are more conducive to reducing the debt financing cost than that of enterprises mandatorily disclosing corporate social responsibility reports. Thirdly, non-state-owned enterprises are more conducive to reducing debt financing cost than that of state-owned enterprise. The enterprises with more market competition are more conducive to reducing the debt financing cost than the oligopolistic enterprises. Finally, this paper concludes and gives policy implications. This paper amplifies the literature regarding the economic consequences of corporate social responsibility disclosure and improves the understanding of the impact of corporate social responsibility disclosure on debt financing cost. [ABSTRACT FROM AUTHOR]

China is facing a serious land resource mismatch problem, which will profoundly affect the acceleration of economic growth and technological innovation. Reform of the industrial land allocation system can solve the mismatch of land resources, and that also has an important impact on the promotion of economic and technological development. This paper selects the data of Chinese A-share listed companies in Shanghai and Shenzhen from 2007 to 2020 as the research sample, constructs a double machine learning model, and empirically investigates the impact of a new industrial land use policy on firms' green innovation behavior. The study shows that: (1) the new industrial land use policy significantly promotes firms' substantive and strategic green technological innovation, and the effect on substantive green technological innovation is greater than that on strategic green technological innovation. (2) The enhancement of R&D investment sustainability and the "talent pool" effect are important mechanisms through which the new industrial land use policy influences firms' substantive and strategic green technological innovation. Meanwhile, the new industrial land use policy is conducive to firms' green coinnovation. (3) There is heterogeneity in the effect of the new industrial land use policy, which can significantly promote green technological innovation of firms in the eastern region, while it does not play a significant role in the green innovation behavior of firms in the central and western regions. The above research results enrich the research in the field of industrial land and innovation, help to understand more comprehensively the mechanism of new industrial land affecting firms' green technological innovation, and provide policy insights for strengthening the application of industrial land allocation reform in firms' green innovation. [ABSTRACT FROM AUTHOR]

This paper empirically investigates the impact of digital finance on the breakthrough innovation of enterprises with a sample of A-share listed companies in Shanghai and Shenzhen from 2011 to 2022. It is found that digital finance can promote corporate breakthrough innovation, and presents certain structural heterogeneity characteristics. The mechanism test shows that digital finance has the dual attributes of a financing platform and a social platform, which can promote breakthrough innovation by alleviating corporate financing constraints and expanding corporate social networks. Heterogeneity analysis reveals that the role of digital finance in promoting breakthrough innovation is characterized by regional heterogeneity, with digital finance playing a greater role in promoting breakthrough innovation in provinces with a low level of development of the banking sector, provinces with a high level of development of the capital market sector, and the central region. In addition, the degree of firms' external financing dependence and the degree of product market competition can strengthen the positive effect of digital finance on firms' breakthrough innovation. This paper enriches the related research on the impact of digital finance on enterprise innovation, and provides theoretical basis and policy insights on how digital finance can better assist the innovation-driven development strategy. [ABSTRACT FROM AUTHOR]

This paper investigates the impact of Internet infrastructure construction on enterprise transformation and upgrading and the underlying mechanisms using a progressive double difference model based on a quasi-natural experiment of the Chinese government's "broadband China" policy by matching A-share listed companies and city panel data from 2008–2019 in Shanghai and Shenzhen. The conclusions show that the "broadband China" policy can significantly promote the transformation and upgrading of enterprises in pilot cities. However, its effect shows a diminishing marginal contribution, and the policy is more effective for traditional manufacturing industries during the implementation period. Enhancing human capital and reducing internal transaction costs are two important channels for Internet infrastructure construction to help enterprises transform and upgrade. Combined with the life cycle theory, we find that the "broadband China" policy has the most significant impact on changing and upgrading enterprises in the growth and maturity stages, especially those in the manufacturing industry, but not those in the maturity and decline stages of the service industry. Finally, a series of robustness tests using Monte Carlo simulation, entropy balance method, and instrumental variables method, excluding other factors, show that the findings are still robust. [ABSTRACT FROM AUTHOR]

We are pleased to announce that the June 2022 issue of the IEEE Transactions on Dielectrics and Electrical Insulation (TDEI) will be a special issue on electrets and related phenomena. This issue is open to all authors. Presenters of papers at the IEEE 18th International Symposium on Electrets (ISE18) to be held during September 24–28, 2021 in Shanghai (China) are especially encouraged to submit their work to this special issue. For more information on ISE18 please go to https://ise18.tongji.edu.cn/. [ABSTRACT FROM AUTHOR]

The Omicron pandemic broke out in Shanghai in March 2022, and some infected people spread to some cities in the Yangtze River Delta (YRD) region. To achieve the dynamic zero-COVID target as soon as possible, Shanghai and nine cities that were heavily affected by Shanghai implemented the lockdown measures. This paper aims to quantify the impact of the lockdown on air quality and human health. A difference-in-difference (DID) model was first used to measure the impact of the lockdown on air quality in these ten cities. Based on the results of the DID model, we estimated the PM2.5-related health and economic benefits using the concentration–response function and the value of statistical life method. Results showed that the lockdown has reduced the concentrations of PM2.5, PM10, SO2, NO2, and CO by 9.87 μg/m3, 17.31 μg/m3, 0.75 μg/m3, 9.03 μg/m3, and 0.07 mg/m3, respectively. The number of avoided premature deaths due to PM2.5 reduction was estimated to be 35,342. The resulting economic benefits totaled 18.86 billion US dollars. We investigated the reasons for the air quality improvement in these ten cities and found the "3 + 11" policy has had a great impact on air quality. Compared with the first COVID-19 lockdown in early 2020, the effect of the lockdown in 2022 was smaller. These findings demonstrated that reductions in anthropogenic emissions would achieve substantial air quality improvement and health benefits. This paper re-emphasized continuous efforts to improve air quality are essential to protect public health. [ABSTRACT FROM AUTHOR]

The construction of deep foundation pits in subway stations can affect the settlement of existing buildings adjacent to the pits to varying degrees. In this paper, the Long Short-Term Memory neural network prediction model of building settlement caused by deep foundation pit construction was established using the monitoring data of building settlement around a deep foundation pit project in a metro station in Shanghai, and appropriate hyperparameters including batch size and training set ratio were determined. The accuracy of settlement prediction for single-point and multi-point monitoring of buildings was analyzed. Meanwhile, the effects of construction parameters, engineering geological parameters, and spatial parameters on the accuracy of building settlement prediction were investigated. The results show that the batch size and training set proportion can be taken as 16 and 60%, respectively, when using the Long Short-Term Memory neural network prediction model. The proposed Long Short-Term Memory network model can stably predict the settlement of buildings adjacent to deep foundation pits. The accuracy of settlement prediction at a single point of a building (80%) is lower than the accuracy of coordinated prediction at multiple points (88%). More accurate settlement prediction is achieved with the total reverse construction method. The more detailed the consideration of working conditions, geological parameters, and spatial parameters, the better. The evaluation metrics of the prediction model, RMSE, MAE, and R2, were 0.57 mm, 0.65 mm, and 0.91, respectively. The results of this paper have some practical reference value for analyzing the settlement of buildings caused by foundation pit works. [ABSTRACT FROM AUTHOR]

Digital transformation plays a crucial role in improving the quality development of companies in this era of digital economy with ever-changing technologies. This paper empirically investigates the impact of corporate digital transformation on total factor productivity and the mechanism of action, using A-share listed companies in Shanghai and Shenzhen from 2011–2021 as the research sample, and found that the digital transformation of companies significantly improves total factor productivity, with the plausibility of the findings being verified by a series of robustness tests. Based on the heterogeneity study, it is found that such effect is stronger for private companies, non-high-tech companies, and companies with a high degree of industry competition. The mechanism test indicates that digital transformation facilitates total factor productivity through four ways: strengthening company technological innovation, reducing operational costs, increasing resource allocation efficiency, and improving human capital structure. The findings of this paper support a better understanding of the micro effects of digital transformation and provide empirical evidence for policy formulation and adjustment. [ABSTRACT FROM AUTHOR]

The BL17B beamline at the Shanghai Synchrotron Radiation Facility was first designed as a versatile high‐throughput protein crystallography beamline and one of five beamlines affiliated to the National Facility for Protein Science in Shanghai. It was officially opened to users in July 2015. As a bending magnet beamline, BL17B has the advantages of high photon flux, brightness, energy resolution and continuous adjustable energy between 5 and 23 keV. The experimental station excels in crystal screening and structure determination, providing cost‐effective routine experimental services to numerous users. Given the interdisciplinary and green energy research demands, BL17B beamline has undergone optimization, expanded its range of experimental methods and enhanced sample environments for a more user‐friendly testing mode. These methods include single‐crystal X‐ray diffraction, powder crystal X‐ray diffraction, wide‐angle X‐ray scattering, grazing‐incidence wide‐angle X‐ray scattering (GIWAXS), and fully scattered atom pair distribution function analysis, covering structure detection from crystalline to amorphous states. This paper primarily presents the performance of the BL17B beamline and the application of the GIWAXS methodology at the beamline in the field of perovskite materials. [ABSTRACT FROM AUTHOR]

Based on 908 consumer questionnaire data from 15 urban areas in Shanghai, we use the binary logit model to empirically analyze the impact of traceability label trust on consumers' traceable pork purchase behavior and the moderating effect of food safety identification. After constructing the theoretical analysis framework, this paper verifies it from the two aspects of statistical analysis and econometric analysis and tests the robustness of the final results. The results show that: first, traceability label trust has a significant positive impact on consumers' traceability pork behavior. Second, food safety identification can significantly strengthen and promote this process. Third, consumers' purchasing behavior is significantly positively affected by traceable pork consumption scenarios and price labels, but the permanent elderly in the composition of family members significantly negative impact on it. Therefore, we put forward relevant policy suggestions, such as strengthening the knowledge popularization and publicity based on the advantageous commodity attributes of traceable pork, carrying out food safety knowledge popularization education, and enhancing consumers' risk perception and food safety identification ability. [ABSTRACT FROM AUTHOR]

Prior literature finds senior executives can influence auditor decision making. However, few studies have discussed the impact of employee's personal characteristics. Our research aims to fill the above research gaps by examining the impact of employee level education on audit costs. Taking A-share listed companies in Shanghai and Shenzhen from 2006 to 2021 as the research object, this paper examines the impact of employee education on audit fees. It is found that highly educated employees can effectively reduce the audit fees borne by the company, but the implementation of the Labor Protection Law weakens this inhibitory effect. In the case of low marketization level and weak Confucian culture intensity, employee education level has a more significant inhibitory effect on audit fees of listed companies. This study provides a basis for empirical research on the impact of employee attributes on auditor decision making, provides a new research perspective on the impact of labor protection law at the corporate micro level, and enriches the theoretical research on corporate governance rooted in traditional Chinese culture. We contribute to the practice that implications for evaluating the effectiveness of adopting labor protection. [ABSTRACT FROM AUTHOR]

Enterprise Digital Intelligence Transformation is based on the Digital Conversion of information and process service upgrading, further touching the enterprise's core business, with the goal of building a new business model of Digital Intelligence Transformation at a higher level. Based on dynamic capability theory, this paper conducts an in-depth study on the mechanism of enterprise Digital Intelligence Transformation and firm performance. This paper selects manufacturing companies listed in China's Shanghai and Shenzhen A-shares from 2013 to 2022 as the research sample, and analyzes and tests the sample data using empirical research methods in order to explore the actual impact of Digital Intelligence Transformation on firm performance, including the specific pathways of action and moderating effects. This study helps enterprises to positively face the megatrend of Digital Intelligence Transformation and upgrading and the challenges it brings, and to grasp the new opportunities in the digital era. This study finds that enterprises carry out digital empowerment transformation and development strategies, and implement information Digital Conversion, service upgrading, and Digital Intelligence Transformation to promote firm performance to different degrees. Enterprise innovation resilience has a mechanism effect between information digitalization conversion enterprise performance and process service upgrading enterprise performance. The higher the environmental uncertainty, the greater the positive contribution of information digitalization to firm performance. Digital Conversion is the base and service upgrading is the process. The current sample enterprises have limited years of data collection, and most of them have only carried out the strategic implementation of Digital Conversion or servitization, and have not reached the high-level stage of digital and intellectual transformation. Therefore, it is found that enterprise innovation resilience has not yet shown a significant role mechanism effect between digital–intelligent transformation and enterprise performance at present. And environmental uncertainty has not yet shown a significant moderating effect in the stage of Servitization Upgrading and digital–intelligent transformation. The marginal contributions of this paper are mainly reflected in the following: (1) This study introduces the dynamic capability theory to explore the role of Digital Intelligence Transformation (Digital Conversion + service upgrading) on enterprise performance. (2) This paper investigates the role of Digital Intelligence Transformation in influencing the performance of enterprises from the Digital Conversion and service upgrading phases, and enriches the relevant role and regulatory mechanisms. (3) This study provides new ideas and strategic suggestions on Digital Intelligence Transformation for enterprises with different factor intensities, at different stages of development, and in different regions. [ABSTRACT FROM AUTHOR]

Buildings increase the urban surface roughness and reduce near‐surface wind speeds in the urban canopy due to the drag effect. Urban heat storage and other effects cause urban warming as well, which decreases the urban boundary layer stability and enhances the turbulence exchange between upper and lower layer. As upper momentum is transported downward, the wind speed of urban canopy increases. Quantitative descriptions of these mechanisms are still lacking currently. This paper presents high‐resolution numerical simulation results of a mega city, Shanghai, China from 2016 to 2020 using the building effect parameterization in WRF (WRF‐BEP) with urban morphological parameters. The dynamic and thermal effects of building morphology on urban canopy wind speed were separated and their quantitative expression functions were given. The results indicate that the influence of building morphology on urban canopy wind speed is mainly dynamic resulting in a wind speed attenuation of approximately 50% and nearly constant. The thermal effect of building morphology on urban canopy wind speed increases with the urban heat island intensity, and the thermal effect could increase urban canopy wind speed by about 30% under the condition of strong urban heat island. The relative contributions of the dynamic and thermal effects of building morphology to urban canopy wind speed change with the wind speed. As wind speed increases, the contribution of the thermal effect of building morphology to urban canopy wind speed gradually decreases. This paper provides a quantitative relationship between the urban canopy wind speed variation and urban morphology, as well as urban heat island intensity. Plain Language Summary: Urbanization changes the dynamic and thermal properties of urban and creates new dynamic and thermal processes in urban areas. Buildings increase the urban surface roughness and reduce urban canopy wind speed due to the drag effect. At the same time, urban warming causes the downward transportation of momentum, which increases the wind speed. Quantitative descriptions of these mechanisms are lacking currently. This study investigated the impact of building morphology on urban canopy wind speed using WRF‐BEP model with high‐resolution urban morphological parameters. The dynamic and thermal effects of building morphology on urban canopy wind speed were separated and their quantitative expression functions were given. Key Points: The dynamic and thermal effects of building morphology on urban canopy wind speed are separatedThe quantitative expression functions of the dynamic and thermal effects of building morphology on wind speed are givenThe relative contributions of the dynamic and thermal effects on urban canopy wind speed are investigated [ABSTRACT FROM AUTHOR]

Purpose: This paper examines firm-level accountability and performance implications under a state-dominated institutional environment, China, for firms engaged in the space economy. Extant studies on the rapidly evolving civil space economy predominantly focus on developed Western economies at national or sector levels, frequently ignoring alternative institutional contexts. Additionally, limited attention has been given to firm-level empirical evidence and analysis, including corporate social responsibility (CSR) practice-R&D quality relationships in the space economy. The paper addresses each of these areas. Design/methodology/approach: This paper utilises multiple regression, propensity score matching and split sampling methods applied to a proprietary dataset of Shanghai and Shenzhen Stock Exchange-listed A-share firms. Results are robust to endogeneity issues, alternative measurement of dependent variables and sampling. Findings: China's space firms demonstrate superior CSR performance to their counterparts in other sectors, supporting CSR's role in maintaining legitimacy. Their CSR practices also positively contribute to firm patent quality. The link is more pronounced among firms facing higher economic policy uncertainty and for state-owned enterprises (SOEs). The latter is due to SOEs' government support, advantages in financing and attracting and retaining a high-quality workforce. Originality/value: This paper adds to discussion on major space power's, by examining China's state-dominated civil space sector. It also addresses a lack of empirical firm-level evidence on space firm behaviour by examining the impact of firm-level CSR practices on R&D quality outcomes, areas in which there is a limited literature. [ABSTRACT FROM AUTHOR]

In response to the intensification of urbanization, characterized by increasingly dense land use, the proliferation of high-rise residences has emerged as a predominant housing solution, particularly evident in developed cities like those in China. With a primary aim of enhancing the residential living environment within high-rise structures, this study advocates for a comprehensive whole life cycle perspective. By employing digital technology and conducting experiments centered on a representative case study, this paper focuses on optimizing the functional layout of public activity spaces within high-rise residential projects. Specifically, this article aims to address challenges such as the inadequate configuration and articulation of various public spaces, along with the underutilization of peripheral activity areas. Central to the optimization efforts is the utilization of the Kruskal algorithm, which enables the analysis of flow lines and functions, ultimately leading to the derivation of optimal solutions. Through a detailed analysis of our case study, this paper provides actionable insights into enhancing the feasibility and effectiveness of public spaces within high-rise residential projects. Recognizing the pivotal role of public space layout in shaping residents' living experiences, the importance of addressing this issue early in the design phase was emphasized. By integrating scientifically driven digital technology solutions, smart, inclusive, and convenient communities that cater to the diverse needs of their inhabitants are aspired to be created. [ABSTRACT FROM AUTHOR]

The purpose of this paper is to identify the medical risk of applying big data technology and to build a medical big data risk (MBDR) control process and manage medical big data risk (MBDR) from a systematic perspective. In this process, we firstly used systematic literature reviews (SLRs) method to systematically search 322 papers in web of science with the topics of "medical risk" and "big data risk" to build a dimensional system of medical big data risk (MBDR) from the theoretical level. Based on a case study of a hospital in Shanghai, we explored the formation mechanism and interaction effect of medical big data risk (MBDR) by using Bayesian belief networks (BBNs) method, and built a systematic risk control process. This paper finally finds that: the dimensional system of medical big data risk (MBDR) includes 24 subdimensions and 5 major categories of dimensions, which helps to explore the medical application of big data technology from a risk perspective. In addition, the medical big data risk (MBDR) control process constructed in this paper includes: risk prediction, reverse reasoning, risk control, and risk prevention in 4 aspects, which is important for hospitals to actually carry out medical big data risk (MBDR) control. [ABSTRACT FROM AUTHOR]

This paper presents the analysis of the results of a scientific and research work for the group of technical-technological faculties, University of Belgrade, in the post-accreditation period (2008 - 2010), measured through a number of published papers in journals with an impact factor and citations, generated by SCOPUS database. A number of papers and a number of citations for the observed period (3 years) are presented by categories of teachers for each faculty separately. Quality Index was calculated as the ratio of cited and published papers, for each faculty, and according to the number of researchers, which eliminates the influence of the faculties' size on total results. Multicriteria PROMETHEE II - GAIA analysis defines the clusters of faculty, which determine the importance of actual contributions of technical faculties (University of Belgrade) on the way to the Shanghai list of 500 first-ranked universities in the world [ABSTRACT FROM AUTHOR]

To explore the relationship between air pollution and total factor productivity and new pathways, This paper examines the impact of air pollution on total factor productivity of A-share listed companies in Shanghai and Shenzhen between 2015 and 2019. It investigates this relationship by considering two pathways: investor sentiment and government attention. The findings indicate that air pollution suppresses total factor productivity of firms. However, air pollution stimulates investor sentiment, which in turn increases R&D investment and total factor productivity, reducing to some extent the dampening effect of air pollution on total factor productivity. There exists a notable positive correlation between air quality and government attention, acting as a mediating variable. This implies that air pollution has the potential to capture the attention of governmental entities, leading to the implementation of appropriate measures aimed at managing and mitigating the occurrence of air pollution caused by industrial enterprises.And the relevant governments should formulate a series of policies to meet the different needs of different enterprises. These two approaches have varying impacts depending on the type of enterprises, thus governments should develop laws to cater to the various demands of different types of enterprises. [ABSTRACT FROM AUTHOR]

Background: Public health emergencies are characterized by uncertainty, rapid transmission, a large number of cases, a high rate of critical illness, and a high case fatality rate. The intensive care unit (ICU) is the "last line of defense" for saving lives. And ICU resources play a critical role in the treatment of critical illness and combating public health emergencies. Objective: This study estimates the demand for ICU healthcare resources based on an accurate prediction of the surge in the number of critically ill patients in the short term. The aim is to provide hospitals with a basis for scientific decision-making, to improve rescue efficiency, and to avoid excessive costs due to overly large resource reserves. Methods: A demand forecasting method for ICU healthcare resources is proposed based on the number of current confirmed cases. The number of current confirmed cases is estimated using a bilateral long-short-term memory and genetic algorithm support vector regression (BILSTM-GASVR) combined prediction model. Based on this, this paper constructs demand forecasting models for ICU healthcare workers and healthcare material resources to more accurately understand the patterns of changes in the demand for ICU healthcare resources and more precisely meet the treatment needs of critically ill patients. Results: Data on the number of COVID-19-infected cases in Shanghai between January 20, 2020, and September 24, 2022, is used to perform a numerical example analysis. Compared to individual prediction models (GASVR, LSTM, BILSTM and Informer), the combined prediction model BILSTM-GASVR produced results that are closer to the real values. The demand forecasting results for ICU healthcare resources showed that the first (ICU human resources) and third (medical equipment resources) categories did not require replenishment during the early stages but experienced a lag in replenishment when shortages occurred during the peak period. The second category (drug resources) is consumed rapidly in the early stages and required earlier replenishment, but replenishment is timelier compared to the first and third categories. However, replenishment is needed throughout the course of the epidemic. Conclusion: The first category of resources (human resources) requires long-term planning and the deployment of emergency expansion measures. The second category of resources (drugs) is suitable for the combination of dynamic physical reserves in healthcare institutions with the production capacity reserves of corporations. The third category of resources (medical equipment) is more dependent on the physical reserves in healthcare institutions, but care must be taken to strike a balance between normalcy and emergencies. [ABSTRACT FROM AUTHOR]

In recent years, the rapid growth of vehicles has imposed a significant burden on urban road resources. To alleviate urban traffic congestion in intelligent transportation systems (ITS), real-time and accurate traffic flow prediction has emerged as an effective approach. However, selecting relevant parameters from traffic flow information and adjusting hyperparameters in intelligent algorithms to achieve high prediction accuracy is a time-consuming process, posing practical challenges in dynamically changing traffic conditions. To address these challenges, this paper introduces a novel prediction architecture called Multiple Variables Heuristic Selection Long Short-Term Memory (MVHS-LSTM). The key innovation lies in its ability to select informative parameters, eliminating unnecessary factors to reduce computational costs while achieving a balance between prediction performance and computing efficiency. The MVHS-LSTM model employs the Ordinary Least Squares (OLS) method to intelligently reduce factors and optimize cost efficiency. Additionally, it dynamically selects hyperparameters through a heuristic iteration process involving epoch, learning rate, and window length, ensuring adaptability and improved accuracy. Extensive simulations were conducted using real traffic flow data from Shanghai to evaluate the enhanced performance of MVHS-LSTM. The prediction results were compared with those of the ARIMA, SVM, and PSO-LSTM models, demonstrating the innovative capabilities and advantages of the proposed model. [ABSTRACT FROM AUTHOR]

While the benefits of land-use mix have been widely analyzed with regard to transportation, public health, and economic development, relatively little attention has been paid to empirically investigating the impact of land-use mix on technological innovation at the intra-urban level. Drawing upon a database of geo-coded patents that are used to reflect the capacity of technological innovation, this paper takes Shanghai as a case study and analyzes how the intra-urban distribution of technological innovation has been associated with land-use mix at the 1 km × 1 km grid cell level. Empirical results, which are robust when the grids are divided at the 2 km × 2 km level, show that the degree of land-use mix is positively associated with the number of patents for a given grid, suggesting that grids with a higher level of land-use mix are likely to have more patents, ceteris paribus. Moreover, the results demonstrate an inverted U-curve relationship between land-use mix and technological innovation, indicating that a too much higher level of land-use mix could lead to a smaller number of patents for a certain grid. In addition, the empirical results suggest the existence of spatial dependence in the effect of land-use mix on technological innovation. [ABSTRACT FROM AUTHOR]

Although there is extensive research demonstrating the significant loss and fragmentation of urban spaces caused by rapid urbanization, to date, no empirical research in Shanghai has investigated the spatiotemporal dynamics of urban open spaces using a comprehensive set of integrated geospatial techniques based on long-sequence time series. Based on the Google Earth Engine (GEE) platform and using the Random Forest (RF) classifier, multiple techniques, namely landscape metrics, trend analysis, open space ratio, transition matrix, Normalized Difference Vegetation Index (NDVI), and fractal dimension analysis, were applied to analyze the Landsat satellite data. Next, Geographic Detector (GeoDetector) methods were used to investigate the driving forces of such spatial variations. The results showed that (1) the RF classification algorithm, supported by the GEE, can accurately and quickly obtain a research object dataset, and that calculating the optimal spatial grain size for open space pattern was 70 m; (2) open spaces exhibited declining and contracting trends; and open spaces in the city experienced a decline from 91.83% in 1980 to 69.63% in 2020. Meanwhile, the degree of open spaces in each district increased to different extents, whilst connectivity markedly decreased. Furthermore, the open space of city center districts showed the lowest rate of decrease, with open space patterns fragmenting due to encroaching urbanization; (3) the contribution of socioeconomic factors to the spatial–temporal changes in open space continually has increased over the past 40 years, and were also higher than natural geographic factors to some extent. Apart from offering policy insights guiding the future spatial planning and development of the city, this paper has contributions from both methodological and empirical perspectives. Based on integrated remote sensing and geographic information science (GIS) techniques, this paper provides updated evidence and a clearer understanding of the spatiotemporal variations in urban spaces and their influencing mechanisms in Shanghai. [ABSTRACT FROM AUTHOR]

Currently, countries worldwide are embracing digital strategies, enabling enterprises to utilize digital technology, digital supply chains, blockchain, and additional digital measures to increase their competitiveness. This paper analyzed the correlation between the digital transformation of manufacturing export enterprises and their business and export performance, focusing on China's manufacturing export enterprises through empirical analysis. The study investigated the influence of digital transformation on enterprise performance. Using the Resource Based View theory and Trade theory, hypotheses were proposed and regression models were developed to analyze a sample of 1007 enterprises listed on the Shanghai and Shenzhen Stock markets from 2012 to 2019. The study conducted regression analysis, intermediate effect test, robustness test, stage lag, and heterogeneity analysis. The study found that (1) Manufacturing export enterprises listed in the stock market implemented digital transformation, leading to a significant positive impact on their overall performance. (2) Digital transformation led to cost reduction, improved R&D intensity, and enhanced human resources, among other benefits for enterprise performance. (3) According to the fractal analysis, non-state-owned enterprises exhibited more favorable effects on enterprise performance, and the digital transformation of manufacturing export companies in developed regions had a more significant impact on their performance. Finally, the study's empirical results yielded pertinent proposals for digital transformation. [ABSTRACT FROM AUTHOR]

With a large number of new energy vehicles being put into use, it is the general trend for traditional fuel vehicles to withdraw from the market in an orderly manner. Determining the optimal ratio between them in this process is of great significance to the low-carbon sustainable development of cities. Therefore, considering the constraints of urban automobile development planning and air pollution prevention and control policies, a multi-objective model to minimize pollutants and costs is constructed in this paper. Through model calculation and sensitivity analysis of dynamic impact relationship of different types of vehicles, it is determined that when new energy vehicles account for around 36% in Beijing, 57% in Shanghai and 46% in Guangzhou, the pollutant emissions can be minimized without causing a significant increase in social costs. Additionally, compared with 2030, Beijing, Shanghai and Guangzhou can achieve emission reductions of 320,000 tons, 200,000 tons and 250,000 tons, respectively, in 2050 if they implement the policy of banning the sale and delisting of fuel vehicles, which could provide suggestions for the guidance of the low-carbon development plan of the automobile industry. [ABSTRACT FROM AUTHOR]

This paper aims to solve the decision-making problem of value chain reconstruction of Chinese port enterprises under the background of the Free Trade Zone policy. Based on value chain theory and system dynamics method, this paper constructs a simulation model that can simulate the value-added change process of port enterprises under different combination input conditions. Furthermore, it conducts simulation case studies. The research indicates that the Free Trade Zone policy has a significant promoting effect on the restructuring of port enterprise value chains and the transformation and upgrading of enterprises. Moreover, considering the impact of the Free Trade Zone policy and limited resources, the overall benefits to port enterprises from combined investments are superior to those from single-factor investments. According to the value chain theory, the business segments of a port are decomposed into ancillary value activities, basic value activities, and external value activities. The investments in these three types of value activities play roles, respectively, in enhancing the operational efficiency of port enterprises, expanding the business scope of port enterprises, and strengthening the core competitiveness of port enterprises. From the overall perspective of the system, Shanghai Port can obtain the maximum operating profit when the endogenous factor input rate is 13%, the basic factor input rate is 4%, and the exogenous factor input rate is 13%. The findings of this research provide a decision-making reference for Chinese port enterprises to realize value reconstruction, transformation, and upgrading in the context of the Free Trade Zone policy. [ABSTRACT FROM AUTHOR]

Water resources are a fundamental natural and strategic economic resource and are closely related to high-quality economic and societal development. This paper uses the pilot implementation of the water resource tax reform to explore the impact of that reform on the green innovation and total factor productivity of enterprises. The study sample includes data for high water-consuming A-share listed enterprises in Shenzhen and Shanghai, China, from 2007 to 2021; the double-difference method was used for the analysis. Study findings indicate that replacing water resource fees with taxes significantly improves the green innovation level and total factor productivity of enterprises. Green innovation has a significant partial mediating effect between the water resource tax reform and total factor productivity. The water resource tax reform promotes green innovation in enterprises, enhancing total factor productivity. When considering different types of enterprise property rights, the economic effect of the water resource tax reform is more pronounced in non-state-owned enterprises, compared to state-owned enterprises. This paper provides empirical evidence for expanding the pilot scope of the water resource tax reform. [ABSTRACT FROM AUTHOR]

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Based on the sample of major asset restructuring transactions of Shanghai and Shenzhen A-share listed companies from 2009 to 2018, this paper uses Logit model to examine the impact of auditors ' cooperation experience with independent financial advisors on the reliability of performance commitments, and to examine the moderating effect of management overconfidence on the relationship between the two. The results show that the cooperation experience between auditors and independent financial advisors is significantly positively correlated with the reliability of performance commitment, and the closer the cooperation experience is, the easier the performance commitment will be realized, this has played a financial intermediary team effect. Further analysis shows that in the context of management overconfidence, the positive relationship between financial intermediaries ' cooperation experience and the reliability of performance commitment is stronger. The research results enrich the research on the influencing factors of M & A performance commitment and the economic consequences of financial intermediaries ' cooperation, and help the transaction subjects to rationally view performance commitment and regulators to improve policies and regulations. [ABSTRACT FROM AUTHOR]

While development zones are the main locations of the urban industrial economy and sources of energy consumption, studies on the interactive relationship between mixed land use and CO2 emissions in these zones have not attracted much attention. In this paper, the development zone is selected as the research object, and a honeycomb grid with a side length of 50 meters is used as the unit to measure multiple dimensions of mixed land use. The efficiency and intensity of CO2 emissions are estimated for these units, and the coupling model is used to analyse the interactive relationship between these two factors. The results show that (1) the mixed land use degree of different types of development zones differs; the bonded zone has the highest degree, while the economic and technological development zone and the high-tech development zone have higher degrees than the industrial development zones. (2) The CO2 emissions capacities of economic and technological development zones and high-tech development zones are better than those of industrial development zones and bonded areas. (3) There is a strong interaction between the mixing degree of land use and the intensity of CO2 emissions; the relationship between the two may be diverse and complex in different development zones. (4) The coordinated development level between mixed land use and CO2 emissions in economic and technological development zones and bonded areas is better than that in high-tech development zones, which in turn is better than that in industrial development zones. Based on existing measurements of mixed land use, the index of land use intensity is introduced in this paper and the land use classification in development zones is refined to supplement the land use compatibility matrix. The results of this study have implications for the development zone to enhance mixed land use and low-carbon development. [ABSTRACT FROM AUTHOR]

Introduction: The purpose of this paper is to empirically test the impact of CEO's financial background on industrial AI transformation of manufacturing enterprises based on upper echelons theory and imprinting theory. Methods: The paper preliminarily takes listed manufacturing companies in Shanghai and Shenzhen stock markets that are affiliated to enterprise groups from 2014 to 2020 as samples, and manually collects and collates datas of CEO's financial background and industrial AI transformation. The research hypotheses are tested by stata 15.0 software*. Results: It is found that CEO's financial background significantly inhibits the industrial AI transformation of manufacturing enterprises, and when the CEO works part-time in the parent company, it will strengthen the negative impact of CEO's financial background on industrial AI transformation. Further research shows that enterprise financialization plays a partial intermediary role between CEO's financial background and industrial AI transformation; Compared with private enterprise groups, the inhibiting effect of CEO financial background on industrial AI transformation is stronger in state-owned enterprise groups; CEOs with non-banking financial background have a stronger inhibitory effect on industrial AI transformation. Discussion: Firstly, based on the process of making business decisions, it verifies and clarifies the action mechanism of CEO's financial background on industrial AI transformation through internal driving mechanism, which expands the research horizon of industrial AI transformation, and further applies the Imprinting Theory in biology to the research of business decision-making, which forms a beneficial complement to the relevant research on economic consequences of CEO's financial background. Secondly, different from the research of single independent company, this paper focuses on the special situation of parent-subsidiary corporate governance, and explores the mechanism of action, deepening the research on the synergy of enterprise groups. Finally, this paper further explores the influence of CEO's financial background on industrial AI transformation, which is conducive to a deeper understanding of the heterogeneity of managers except manpower and capital factors in the industrial AI transformation practice of manufacturing enterprises, and provides a new idea and a more comprehensive analysis perspective for industrial AI transformation. [ABSTRACT FROM AUTHOR]

For large-scale 3D building reconstruction, there have been several approaches to utilizing multi-view satellite imagery to produce a digital surface model (DSM) for height information and extracting building footprints for contour information. However, limited by satellite resolutions and viewing angles, the corresponding DSM and building footprints are sometimes of a low accuracy, thus generating low-accuracy building models. Though some recent studies have added GIS data to refine the contour of the building footprints, the registration errors between the GIS data and satellite images are not considered. Since OpenStreetMap (OSM) provides a high level of precision and complete building polygons in most cities worldwide, this paper proposes an automatic single building reconstruction method that utilizes a DSM from high-resolution satellite stereos, as well as building footprints from OSM. The core algorithm accurately registers the building polygons from OSM with the rasterized height information from the DSM. To achieve this goal, this paper proposes a two-step "coarse-to-fine registration" algorithm, with both steps being formulated into the optimization of energy functions. The coarse registration is optimized by separately moving the OSM polygons at fixed steps with the constraints of a boundary gradient, an interior elevation mean, and variance. Given the initial solution of the coarse registration, the fine registration is optimized by a genetic algorithm to compute the accurate translations and rotations between the DSM and OSM. Experiments performed in the Beijing/Shanghai region show that the proposed method can significantly improve the IoU (intersection over union) of the registration results by 69.8%/26.2%, the precision by 41.0%/15.5%, the recall by 41.0%/16.0%, and the F1-score by 42.7%/15.8%. For the registration, the method can reduce the translation errors by 4.656 m/2.815 m, as well as the rotation errors by 0.538°/0.228°, which indicates its great potential in smart 3D applications. [ABSTRACT FROM AUTHOR]

The common production functions include the Leontief production function, the Cobb-Douglas (C-D) production function, the constant elasticity of substitution (CES) production function, the variable elasticity of substitution (VES) production function and so on. With different elasticity of substitution of factor, the production functions have different ranges of applications. In the production functions, the C-D production function is used the most widely because of its simple form, while the CES production function and the VES production function have limitations in applications due to their complicated forms. However, the C-D production function has the elasticity of substitution of factors of 1, and the CES production function has the elasticity of substitution of factors which is not 1 but a constant, so the two production functions both have limitations in applications. The VES production function with the variable elasticity of substitution is more practical in some application cases. This paper studies the applications of the VES production function model and gives a method of calculating the contribution rates of economic growth factors scientifically. As for the parameter estimation of the model, this paper gives an improved Sine Cosine Algorithm (SCA) to enhance the convergence rate and precision. Finally, the paper makes an empirical analysis on the contribution rates of economic growth factors of Shanghai City, China, using the method proposed. [ABSTRACT FROM AUTHOR]

In the spring of 2022, a new round of epidemic broke out in Shanghai, causing a shock to the Shanghai carbon trading market. Against this background, this paper studied the impact of the new epidemic on the price of Shanghai carbon emission rights and tried to explore the prediction model under the unexpected event. First, because a model based on point value data cannot capture the information hidden in inter-day price fluctuation, based on the interval price of Shanghai carbon emission rights (SHEA) and its influencing factors, an autoregressive conditional interval model with jumping and exogenous variables (ACIXJ) was established to explore the influence of the Russian–Ukrainian conflict and COVID-19 on the interval price of SHEA, respectively. The empirical results show that the conflict between Russia and Ukraine has no obvious influence on the price of SHEA, but COVID-19 led to a decline in the price trend of SHEA over four days before the city was closed, and the volatility changed significantly on the day before the city was closed. The price fluctuation was the strongest within 3 days after the city was closed; In addition, in order to accurately predict the interval data of SHEA against the background of COVID-19, based on the interval data decomposition algorithm (BEMD), a hybrid forecasting model of NDGM-ACIXJ/CNN-LSTM was constructed, in which the discrete gray model of approximate nonhomogeneous exponential series (NDGM) combined with the ACIXJ model is used to predict the high-frequency sub-interval, and the convolution neural network long-term and short-term memory model (CNN-LSTM) is used to predict the low-frequency sub-interval. The empirical results show that the prediction model proposed in this article has higher prediction precision than the reference models (ACIX, ACIXJ, NDGM-ACIXJ, BEMD-ACIX/CNN-LSTM, BEMD-ACIXJ/CNN-LSTM). [ABSTRACT FROM AUTHOR]

Since 2012, China's economic growth rate has been much higher than the world average; its economic aggregate ranks second in the world, people's living standards have risen significantly, and absolute poverty has been historically eliminated, laying a solid foundation for solid promotion of common prosperity. However, China is still in the primary stage of socialism, and the problem of unbalanced and inadequate development has not been fundamentally solved. It is urgent to monitor the status of common prosperity construction in all regions. By constructing an evaluation index system of common prosperity, this paper uses spatial correlation analysis model and geographically weighted regression model to assess the comprehensive development level of common prosperity quantitatively, reveal the spatiotemporal evolution characteristics and analyze the main influencing factors. The results show that the comprehensive development level of China's common prosperity has been continuously improved. It gradually presents a spatial distribution pattern with Beijing-Shanghai as the development pole and the eastern coastal area as the highland, and there is a significant positive spatial correlation, but the local spatial heterogeneity has increased. The development level of common prosperity is affected by multiple factors, and the increase in per capita GDP is the leading factor in promoting common prosperity. Therefore, we should focus on promoting high-quality economic development, coordinated regional development and expanding middle-income groups, so that all people can enjoy the fruits of reform and development and ultimately achieve common prosperity. [ABSTRACT FROM AUTHOR]

The conflict between agricultural production and environmental protection, especially vegetable production, is one of the world's most prominent concerns. The acceptance degree and possible response of new agricultural operation subjects, represented by Farmers Professional Cooperatives (FPCs), to green production policies are a core issue that must be considered when designing environmental policies in the future. Based on field survey data of 192 FPCs of vegetables in Shanghai, China, this paper uses a choice experiment method to test the willingness of cooperatives to adopt green production behaviors under different policy settings and to identify the differences under different operation scales. The results show that both mandatory policies and incentive policies have an impact on the willingness of cooperatives to adopt green production behaviors, but significant differences exist among FPCs of different operation scales. Specifically, the willingness of large-scale FPCs to adopt green production behavior is affected by key technology training and income subsidies, whereas the willingness of small-scale FPCs is only affected by income subsidies; the willingness of medium-scale FPCs is simultaneously affected by key technology training, income subsidies, and penalties. Therefore, when formulating relevant policies in the future, policy-makers should consider local conditions, including the endowment heterogeneity of the production and operation subjects, so as to enhance the level of green production. [ABSTRACT FROM AUTHOR]

Recent research has played a pivotal role in advancing our understanding of cloisonné enamel production in China during the 19th and 20th centuries. However, Christian workshops, whether operating under missionary subcontracting or owned by the Catholic Church, have yet to be accurately identified and contextualized. This article delves into three significant contexts. Firstly, it identifies and contextualizes the Christian connections and interactions of the Decheng private cloisonné workshop, involving the French Lazarist Bishop Alphonse Favier, in Beijing. Secondly, it identifies the cloisonné workshop stablished by the Lazarists in the Beitang complex in Beijing and elucidates the role it played. Finally, this paper presents new evidence concerning cloisonné Christian objects crafted by the Tushanwan Jesuit workshop in Shanghai. Some of the primary works of these three workshops are identified for the first time. Additionally, this paper shows that certain cloisonné crosses, some of them thought to be originally Japanese, including those referred to as Namban, were, in fact, crafted in Beijing during the 1920s. These preliminary results will contribute to placing Chinese Christian cloisonné within the history of Chinese Art and its interactions at a global level in the context of evangelization in China, the production of the so-called export art, and the processes of indigenization carried out by the Lazarists and the Jesuits. [ABSTRACT FROM AUTHOR]

Under the background of the rapid development of digital economy, this paper empirically analyzes the impact of digital transformation on enterprise innovation and selects the panel data of China's Shanghai and Shenzhen A-share listed companies from 2013 to 2021 as the research objective is to study the impact of digital transformation on enterprise innovation from theoretical and empirical perspectives. First, we find that digital transformation accelerates enterprise innovation, a conclusion that has been validated through robustness testing. Second, digital transformation impacts enterprise innovation by enhancing productivity and information transparency. Third, financing constraints and financial redundancy play distinct regulatory roles in the process. Fourth, heterogeneity analysis finds that the role of digital transformation in promoting enterprise innovation has different effects in state-owned and non-state-owned enterprises, high-tech and non-high-tech enterprises, and enterprises with different life cycles. Finally, the functional analysis suggests that further investigation is needed to determine whether digital transformation can significantly promote the sustainable development of enterprises through innovation while also recognizing that this function may have a lag effect. Overall, this study contributes to a deeper understanding of digital transformation and innovation-driven practices and encourages more significant integration of the real and digital economies. [ABSTRACT FROM AUTHOR]

On March 15, 2022, the volume of trade of the Shanghai Stock Exchange (SSE) 50 ETF option contracts and the CSI 300 ETF option contracts exceeded 10 million for the first time, of which 5,707,400 50 ETF options were traded, and SSE 50 ETF options, as the main force, has become one of the most active ETF option varieties in the world after seven years of vigorous development. The SSE 50 ETF options receive highlights in risk-free arbitrage, hedging, risk management and other aspects. In order to give full play to the function of the SSE 50 ETF options, it is necessary to conduct studies on their pricing. This paper adopts the traditional classical models for pricing European-style options, the BSM model and the volatility model, to price call options and put options of the SSE ETF, and meanwhile analyzes the volatility of the SSE 50 ETF. The empirical results suggest that (1) the volatility of SSE 50 ETF has a weak leverage effect or no leverage effect, which converges with the existence of the inverse leverage effect of the SSE index; (2) the BSM model will underestimate the price of SSE 50 ETF options and is only ideal for pricing in-the-money (ITM) options; while out-of-the-money (OTM) options are highly influenced by time value and therefore cannot be accurately priced. [ABSTRACT FROM AUTHOR]

By using dynamic game analysis, a theoretical framework is constructed to investigate the impact of environmental regulations on corporate financial performance. A sample of 3021 A-share listed companies, listed on the Shanghai and Shenzhen stock exchanges from 2010 to 2018, is used for empirical testing with GMM model. Through the matching of regional data and enterprise data, this paper expands the measurement of environmental regulation from meso to micro and then, systematically estimates the impact and mediating effect of environmental regulation on corporate financial performance. The results show that environmental regulation policies and instruments are important factors influencing corporate financial performance and that the main way to improve the efficiency of environmental regulation is to encourage firms to carry out pollution control through technological innovation. The empirical results not only show that environmental regulations can facilitate the upgrading of corporate financial performance, but also that technological innovation, especially green technology innovation, also partially mediates the effect of environmental regulations in promoting corporate financial performance. At the same time, the impact of environmental regulations on corporate financial performance is both constrained and influenced by corporate ownership, industry classification and city level. Environmental regulations are more likely to improve the corporate financial performance of non-state-owned companies than that of state-owned companies. Environmental regulation significantly boosts corporate financial performance in clean industries, but significantly depresses corporate financial performance in polluting industries. Compared with higher-level cities, environmental regulations only facilitate improvements to corporate financial performance in ordinary prefecture-level cities. [ABSTRACT FROM AUTHOR]

The key to solving the problem of redundant financial indicators in addressing financial warning issues is to reduce the dimensionality of the original financial indicators. This paper proposes a model based on the whale optimization algorithm with mixed strategy (IWOA) combined with support vector machine (SVM), namely, the IWOA-SVM early warning model, which simultaneously performs index optimization and dimensionality reduction, and financial risk early warning identification. This paper takes a total of 302 enterprises specially treated in Shanghai and Shenzhen stock exchanges and normal enterprises of the same specification as the research samples to design the model. The results show that the improved whale optimization algorithm has better optimization speed and accuracy and improves the search ability of the original algorithm for the optimal solution. Compared with other dimensionality reduction methods, the IWOA-SVM model has the lowest index dimension after dimensionality reduction and has more excellent recognition effect. The dimensionality reduction results have certain universality for different classifiers, which provides a new idea for the selection of indicators for financial early warning. [ABSTRACT FROM AUTHOR]

Evaluation of the spatial distribution of urban emergency shelters can effectively identify defects in the current distribution of urban emergency shelters and weaknesses in the overall evacuation service capacity of the city and provide reference for improving the level of urban emergency shelters and evacuation and disaster relief capacity. At present, evaluation of the spatial distribution of urban emergency shelters is mainly carried out on three aspects: effectiveness, accessibility, and safety. However, there are problems, such as individual evaluation scales and incomplete indicator systems, unreasonable allocation of indicator weights, and ignoring the influence of fuzzy incompatibility between different indicator attributes on the evaluation results. In this paper, we start from two scales, the individual emergency shelter and the regional groups of emergency shelters. Based on the five criteria of effectiveness, accessibility, safety, suitability, and fairness, the evaluation indicator system of the spatial distribution of urban emergency shelters was constructed. It was combined with AHP, CRITIC, the optimal weight coefficient solution method based on the maximum deviation sum of squares theory, and fuzzy optimization theory to construct a multi-indicator evaluation model. Further, the spatial distribution condition of the existing emergency shelter in Shanghai was evaluated. The results show that: among the existing ninety-one emergency shelters in Shanghai, there are nine places with unreasonable spatial distribution; nineteen places are comparatively unreasonable. From the scale of regional groups, there is one district (Pudong New District) with unreasonable spatial distribution: its relative superiority value is far lower than other districts, and there are three districts that are comparatively unreasonable. Further, the evaluation scores of the spatial distribution reasonableness of emergency shelters in each region of Shanghai show a high–low–middle distribution from the downtown area of Shanghai outward. The evaluation indicator system and evaluation method used in this paper can effectively reflect the deficiencies in the spatial distribution of urban emergency shelters, thus providing a reference for the relevant departments to improve and plan emergency shelters. [ABSTRACT FROM AUTHOR]

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

Digital technology has already permeated the production and operations of organizations, with many incorporating it into employee performance evaluations. However, there remains a research gap in identifying effective approaches to achieving digital performance. Furthermore, while scholars have often mentioned the differences in employee attitudes, and employee dynamic capability (EDC) in the dynamic digital market, empirical evidence supporting this notion is scarce. This paper investigates the influence of high-involvement human resource management practices (HI-HRMPs) on employee digital performance (EDP) within Chinese small and medium-sized enterprises (SMEs), focusing on specific cultural regions in China. Using purposive sampling, four representative regions (Guangzhou, Shanghai, Guizhou, and Anhui) were selected for Survey. Structural equation modeling (AMOS) was employed to examine the relationships between HI-HRMPs as the independent variable and employee competitive attitudes, employee competitive behaviors, and employee dynamic capabilities as mediating variables, with a focus on their impact on employee digital performance. Contrary to direct effects, the study reveals that HI-HRMPs do not directly influence employee digital performance. Instead, employee competitive attitudes and behaviors, along with their dynamic capabilities, emerge as significant mediating variables in this relationship. Specifically, employee competitive attitudes and behaviors, as well as employee dynamic capabilities, play crucial roles in mediating the relationship between HI-HRMPs and employee digital performance. Furthermore, the findings indicate that employee dynamic capabilities exhibit greater influence on employee digital performance compared to employee competitive attitudes and behaviors, particularly in the context of a rapidly evolving digital marketplace. These results underscore the importance of fostering employee dynamic capabilities within SMEs to enhance digital performance in the ever-changing business landscape. [ABSTRACT FROM AUTHOR]

The express/local mode of municipal rail transit provides passengers with multiple alternatives to achieve more efficient and superior travel, in contrast to the conventional all-stop operation mode. However, the various route choices (including direct express trains, direct local trains, or transfers) covering different passenger groups pose a significant challenge to passenger flow assignment. To understand route choice behavior, it is crucial to measure the passenger heterogeneity (variability in individual and trip attributes) in order to propose targeted solutions for operation schemes and service planning. This paper proposes a hybrid model by integrating structural equation modeling and the mixed logit model under express/local mode to estimate the impact of passenger heterogeneity on route choice. An empirical study with revealed preference and stated preference surveys carried out in Shanghai revealed how individual and trip attributes quantitatively impact the sensitivity of factors in route choice. The results show that age and trip purpose are more significant factors. Compared to the control group, the probability of express trains is reduced by 10.22% for the elderly and by 11.36% for non-commuters. Our findings can provide support for more reasonable operation schemes and more targeted services. [ABSTRACT FROM AUTHOR]

The bogie frame, as one of the most critical load-bearing structures of the Electric Multiple Unit (EMU), is responsible for bearing and transmitting various loads from the car body, wheelsets, and its own installation components. With the increasing operating speed of high-speed EMUs, especially when the design and operational speeds exceed 400 km/h, the applicability of current international standards is uncertain. The load spectrum serves as the foundation for structural reliability design and fatigue evaluation. In this paper, the measured loads of the bogie frame of a CR400AF high-speed train on the Beijing–Shanghai high-speed railway are obtained, and the time-domain characteristic of the measured loads is analyzed under different operating conditions. Then, through the Weibull distribution of three parameters, the Weibull parameters at the 450 km/h speed level are fitted, and the maximum load and cumulative frequency under the speed level are derived. Finally, the load spectrum of the bogie frame at the 450 km/h speed level is established, which provides a more realistic load condition for accurately evaluating the fatigue strength of bogie frames at higher speed levels. [ABSTRACT FROM AUTHOR]

In shield tunneling, the joint is one of the most vulnerable parts of the segmental lining. Opening of the joint reduces the overall stiffness of the ring, leading to structural damage and issues such as water leakage. Currently, the Winkler method is commonly used to calculate structural deformation, simplifying the interaction between segments and soil as radial and tangential Winkler springs. However, when introducing connection springs or reduction factors to simulate the joint stiffness of segments, the challenge lies in determining the reduction coefficient and the stiffness of the springs. Currently, the hyperstatic reflection method cannot simulate the discontinuity effect at the connection of the tunnel segments, while the state space method overlooks the nonlinear interaction between the tunnel and the soil. Therefore, this paper proposes a numerical simulation method considering the interaction between the tunnel and the soil, which is subjected to compression rather than tension, and the discontinuity of the joints between the segments. The model structure and external load are symmetrical, resulting in symmetrical calculation results. This method is based on the soft soil layers and shield tunnel structures of the Shanghai Metro, and the applicability of the model is verified through deformation calculations using three-dimensional laser scanning point clouds of sections from the Shanghai Metro Line 5. When the subgrade reaction coefficient is 5000 k N / m 3 , the model can effectively simulate the deformation of operational tunnels. By adjusting the bending stiffness of individual connection springs, we investigate the influence of bending stiffness reduction on the bending moment, radial displacement, and rotational displacement of the ring. The results indicate that a decrease in joint bending stiffness significantly affects the mechanical response of the ring, and the extent and degree of this influence are correlated with the joint position and the magnitude of joint bending stiffness. [ABSTRACT FROM AUTHOR]

Accurately predicting the short-term passenger flow of urban subways is very important for urban subway stations to formulate passenger flow organization and evacuation plans effectively and rationally plan passenger travel routes. This paper establishes a novel framework to predict the hourly inbound and outbound passenger flow of subway stations based on WOA-GMM station classifiers, CEEMD-SE noise reduction, and BiGRU optimized by attention. Firstly, this paper classifies subway stations using the improved Gaussian mixture model (GMM) with Whale Optimization Algorithm (WOA) to realize the feature extraction of different types of subway stations. Secondly, this paper uses the Complementary Ensemble Empirical Mode Decomposition (CEEMD) to decompose the noise reduction of each station's hourly inbound and outbound passenger flow. It combines the empirical modal components by calculating the sample entropy (SE), which makes the time series stable and reduces the time cost of forecasting. Finally, a Bidirectional Gated Recurrent Unit (BiGRU) model improved by attention mechanism (AM) is established for each station's inbound and outbound passenger flow. The prediction model established in this paper is verified by subway passenger flow data in Shanghai, China, within 24 days. Finally, it is concluded that the model can predict the passenger flow of subway stations. Compared with the traditional Backpropagation Neural Network (BP), the Long Short-Term Memory (LSTM), and the normal BiGRU model, the model proposed in this paper has an average reduction of 65.90%, 64.54%, and 49.06% in Mean Absolute Percentage Error (MAPE) in the prediction of the hourly inbound and outbound passenger flow of each type of station, respectively. [ABSTRACT FROM AUTHOR]

With the establishment and rapid development of the China (Shanghai) Pilot Free Trade Zone (FTZ), the scale of enterprises in the zone has grown rapidly. This paper takes the actual needs of the Shanghai FTZ as the background, extracts key information such as entities from the Big Data related to Internet enterprises, further constructs the enterprise knowledge graph, and applies it to the supervision and service of the FTZ. The enterprise knowledge graph is constructed using the Neo4j graph database. To verify the effectiveness of the named entity identification and relationship extraction methods proposed in this paper, experiments were conducted to validate them, and both achieved good results. [ABSTRACT FROM AUTHOR]