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Corporate social responsibility (hereinafter referred to as "CSR") sometimes does not directly affect enterprise performance but through some mediator variable. Corporate reputation and brand value have chain-mediating effects in the influence mechanism of CSR on enterprise performance. Theoretically, this paper broadens the scope of study on CSR and enterprise performance, deeply explores the internal mechanism between them, and opens the "black box" in the process of transforming CSR into enterprise performance. Practically, it can encourage Chinese enterprises to actively undertake CSR, strengthen reputation management, enhance brand value, and finally promote the sustainable development of enterprises. A-share listed companies (listed companies in the Chinese A stock market) in Shanghai and Shenzhen stock exchanges from 2008 to 2020 in "China's 500 Most Valuable Brands List" by World Brand Lab are studied in this paper. The results of the empirical study based on relevant data show a significant positive relationship between CSR and enterprise performance. It investigates the mediating chain effects of corporate reputation and brand value in the influence of CSR on enterprise performance. Listed companies must disclose the annual CSR report. CSR reports as an effective supplement to corporate financial information, which provides an important communication channel for the enterprise and stakeholders. Stakeholders can timely understand their rights and interests through the CSR information and have a more comprehensive and deeper understanding of enterprises. In turn, the enterprise can also win a good reputation, establish a good brand image, and get sustainable development. [ABSTRACT FROM AUTHOR]

As the subject of pollutant emissions, enterprises play an important role in environmental governance and should consciously fulfil their environmental responsibility. So, can the enterprise perform environmental responsibility to increase their investment in research and development (R&D)? Based on the data from China's Shanghai and Shenzhen A-share listed enterprises in 2011–2019, this paper explores the relationship between corporate environmental responsibility and R&D investment and examines the dual moderating effects of public attention and intellectual property protection. It is found that the implementation of corporate environmental responsibility can promote R&D investment. Public attention moderates the positive relationship between corporate environmental responsibility and R&D investment; it seeks that the positive relationship becomes higher with public attention. With higher regional intellectual property protection levels, the moderating effect of public attention on corporate environmental responsibility and R&D investment will be more obviously higher. This research provides theoretical support for enterprises to fulfil their environmental responsibilities and a new perspective for revealing the factors of enterprises' R&D investment, which can offer a foundation for future research and practices. [ABSTRACT FROM AUTHOR]

In this paper, we first proposed a statistical test for the detrended multiple moving average cross-correlation coefficient DMMC (s). The DMMC (s) mainly was used to analyze the correlation between the dependent variable y and other n independent variables x i . We proved that DMMC (s) approximately obeys the chi-square distribution. We studied the statistical properties of the DMMC (s) between normally distributed random sequences and power-law ARFIMA long memory random sequences. Furthermore, we discussed the influence of the cross-correlation among the target variable and independent variables on DMMC (s). Finally, we further study the application of DMMC (s) to China's stock markets and China carbon emission trading markets to investigate multiple cross-correlation. The empirical results show that there is a strong multiple correlation between China's Shanghai, Shenzhen and Hong Kong stock markets, while the correlation between China's carbon markets is not significant. This paper provides new ideas and theoretical support for exploring the correlation between multiple variables, which has implications for investors and policymakers. [ABSTRACT FROM AUTHOR]

With the trend for smart maintenance for the electric multiple unit (EMU), there is a critical need for cross-organisational data sharing amongst multiple stakeholders. Traditional centralised solutions may not ensure sufficient trust for data sharing. In the maintenance field, blockchain technology has been introduced to improve security and privacy. However, the current blockchain solutions for data sharing are solely based on public chain or consortium chain, which may have the limitation of low transaction processing rate. Moreover, multiple levels of decentralisation are required in cross-organisational data sharing. For the purpose of providing multiple types of ledger's governance and a performance improvement, this paper presents a method for developing EMU maintenance data sharing solution based on double blockchain. The method is validated using a case study in Shanghai EMU depot. The proposed solution adopts practical byzantine fault tolerance (PBFT) as the consensus mechanism. Interplanetary file system (IPFS) is introduced to reduce the payload of big data storage. [ABSTRACT FROM AUTHOR]

The COVID-19 outbreak and the implementation of peak and carbon neutral policies have severely impacted oil price volatility and the industrial sector. Exploring the impact mechanisms between oil prices and industries is particularly important for accurate forecasting of crude oil prices. As one of the world's largest commodity consumers, China's crude oil market is more representative and susceptible to external factors than that of developed countries. In this paper, we propose an analytical forecasting framework based on the causal effects between Shanghai crude oil prices and various industries in China to improve the forecasting accuracy of crude oil prices. Information geometric causal inference (IGCI) is applied to detect causal relationships between 31 different industries in China and Shanghai crude oil prices in the three time periods before, during and after COVID-19, and industries with strong causal information effects on crude oil prices in the long run are screened out as additional features. An oil price forecasting model based on Gray Wolf Optimization and Extreme Learning Machine (GWO-ELM) is proposed. Considering the small amount of data for Shanghai crude oil, this paper proposes a cross-learning data approach to solve the problem. Experimental results show that the GWO-ELM model outperforms RF, LSTM, GRU, and migration learning-based Tr-LSTM and Tr-Adaboost models in the task of Shanghai crude oil futures price prediction, and find that industry characteristics with long-term causal effects on oil prices can improve the model prediction accuracy. Our proposed analytical prediction can capture the oil price trend more accurately through the information of the industry and solve the problem of insufficient training data for the model. The application of this framework is expected to provide new methods and ideas for data mining of crude oil and other futures prices. [ABSTRACT FROM AUTHOR]

The uncertainty, immediate hazards, severe consequences and broad impact of medical emergencies are far beyond the scope of ordinary medical treatment, calling for special attention. The importance of constructing hospital emergency management system has been increasingly reflected. The system not only needs the practical solution of hospital management at the present stage, but also the working direction of the hospital management in the future. In this paper, the emergency satisfaction survey was conducted on the patients in two first-class tertiary hospitals in Shanghai. The service satisfaction scores of a hospital with integrated traditional Chinese medicine (TCM) and Western medicine as well as a general hospital were 4. 9 4 ± 0. 3 2 and 4. 9 9 ± 0. 1 4 , respectively. There was a significant difference in the service satisfaction scores between the two hospitals (p < 0. 0 0 0 1). Poor network signal was a vulnerability problem in the dimension of medical auxiliary facilities in both hospitals, accounting for the highest proportion. For the dimension of medical building, the main vulnerability problems were wall cracking and uneven ground, accounting for a high proportion. In the dimension of medical process, the parking inconvenience of a hospital with integrated traditional Chinese and Western medicine was particularly serious, accounting for 81.7%. In the dimension of medical service, the vulnerability of "medical staff overwork" in a general hospital was more prominent, accounting for 39.10%, which was significantly higher than that in a hospital with integrated TCM and Western medicine. In terms of service attitude, the vulnerability of the two hospitals was represented as the poor service attitude of doctors, nurses and managers. In the medical safety dimension of the two hospitals, "lack of assistance in emergencies" and "hospital equipment failures" were the main vulnerability issues. From the perspective of patients, this paper analyzed the current vulnerability problems of the hospital, identified the actual and potential risks of hospital emergency management and put forward suggestions on adjusting the spatial layout, strengthening internal management and comprehensively improving employees' emergency awareness and skills. This will provide a reference for the hospital's emergency management system to make necessary adjustments to meet the practical needs. [ABSTRACT FROM AUTHOR]

A majority of Chinese high-tech firms were founded in development zones established by the Chinese government. It is necessary to identify the impacts of development zones on firms' innovation (hereinafter referred to as "innovation effects") as well as the possible knowledge spillover of development zones. Different types of zones, due to their different administrative levels and original purposes of establishment, may have different innovation effects. In this paper, the innovation effects of the Shanghai development zones are identified by using the geographic range of Shanghai development zones as well as geographic coordinates, patents, and research and development (R&D) data of manufacturing firms. The research results robustly show that there are significant and positive innovation effects of development zones. Through the use of regression analysis and by using firm data on both sides of zone boundaries, we observe knowledge spillovers. We also test a possible mechanism for the innovation effects of development zones. [ABSTRACT FROM AUTHOR]

The ongoing COVID-19 shocked financial markets globally, including China's crude oil future market, which is the third-most traded crude oil futures after WTI and Brent. As China's first crude oil futures are accessible to foreign investors, the Shanghai crude oil futures (SC) have attracted significant interest since launch at the Shanghai International Energy Exchange. The impact of COVID-19 on the new crude oil futures is an important issue for investors and policy makers. Therefore, this paper studies the short-term influence of COVID-19 pandemic on SC via multifractal analysis. We compare the market efficiency of SC before and during the pandemic with the multifractal detrended fluctuation analysis and other commonly used random walk tests. Then, we generate shuffled and surrogate data to investigate the components of multifractal nature in SC. And we examine cross-correlations between SC returns and other financial assets returns as well as SC trading volume changes by the multifractal detrended cross-correlation analysis. The results show that market efficiency of SC and its cross-correlations with other assets increase significantly after the outbreak of COVID-19. Besides that, the sources of its multifractal nature have changed since the pandemic. The findings provide evidence for the short-term impacts of COVID-19 on SC. The results may have important implications for assets allocation, investment strategies and risk monitoring. [ABSTRACT FROM AUTHOR]

In rail transit systems, improving transportation efficiency has become a research hotspot. In recent years, a new method of train control system based on virtual coupling has attracted the attention of many scholars. Virtual coupled train is a typical nonlinear system. How to control the nonlinear system with high precision is the key to realize the safe and efficient operation of virtual coupled train. Therefore, this paper proposes a virtual coupling scheme based on local leader–follower method. On this basis, the controller based on artificial potential field with variable parameter mixed is applied to carry out the collaborative and anti-collision control for the virtual coupled train system with nonlinear dynamics. Finally, a section of the Beijing–Shanghai high-speed railway is taken as the background to verify the effectiveness of artificial potential field with variable parameter mixed controller. The results show that the variable parameter artificial potential field controller with changing the weight can reduce the average error of the stop by 0.4304 m compared with the traditional controller. [ABSTRACT FROM AUTHOR]

Providing a scientific basis and method to ensure the smooth functioning of the Chinese crude oil market would be hugely significant to China's future economic development and security. To this end, attempts have been made to both internationalize the Shanghai crude oil market and minimize financial risks. This paper selects three crude oil markets (INE, WTI and Brent) and five Chinese financial markets (the futures, bond, fund, stock and foreign exchange markets) as the research objects. The Diebold and Yilmaz spillover index model and the multifractal asymmetric detrended cross-correlation analysis (MF-ADCCA) method are used to study the volatility spillover effect and the asymmetric cross-correlation between crude oil markets and financial markets. When the volatility spillover effect and the asymmetric relationship that exists between the financial markets are examined, the volatility spillover of the oil market to the financial market is found to be significantly higher than that of the financial market to the oil market. In particular, the spillover effect was even more significant from late-2019 to early-2020. Analysis demonstrates an asymmetric cross-correlation between crude oil markets and the abovementioned five Chinese financial markets. In particular, the impact of the Chinese crude oil market on the stock market is greatest, especially with respect to the Brent and WTI crude oil markets. Except for the bond market, when the INE and Brent markets are increasing, the risk exposure to financial markets is more significant. Among financial markets, INE-Bond market asymmetry is stronger than WTI-bond market asymmetry, but weaker than that of the Brent-Bond market when there are large fluctuations. [ABSTRACT FROM AUTHOR]

The volatility and cross-correlations of the energy market and the stock market during the COVID-19 pandemic have been paid close attention by scholars and investors. In this paper, we use the asymmetric multifractal analysis methods to study the fluctuation characteristics, market risks and cross-correlations of the Chinese energy futures market (EFM) and two energy stock markets before and after the COVID-19 outbreak, while the return series of Shanghai fuel oil futures, CSI Energy Index and CSI Mainland New Energy Theme Index are considered. The empirical evidences indicate that the auto- and cross-correlations of the three markets have the asymmetric multifractality, and that the multifractality of the cross-correlations is mainly caused by the fat-tailed distribution of the original series. After the COVID-19 outbreak, the risks of both the traditional energy stock market in the uptrend and the entire new energy stock market become larger, while those of the entire EFM become smaller. In addition, the COVID-19 pandemic has increased the multifractality of the cross-correlations between the energy futures and energy stock markets when the EFM is in downward trend. [ABSTRACT FROM AUTHOR]

As the aging population continues to grow, the increasing number of disabled elderly individuals poses ongoing challenges to society in terms of healthcare and medical burdens. Grading disability is of significant importance for improving medical services and allocating social resources. Traditional grading methods using a single assessment scale tend to be subjective and cannot comprehensively and accurately reflect the real situation. This paper presents the NES (Needs assessment of Elderly care in Shanghai) scale, based on 33 quantified scoring items from basic surveys, to enhance the objectivity and accuracy of disability grading. Correlation analysis of observed items using the C4.5 decision tree algorithm is conducted, followed by an analysis and evaluation of the scale's performance. The data used in this study are derived from 266 questionnaires collected from eight regions in Shanghai and categorized into three classes: physiological (JKOM/BADL), psychological (BDI/SDS/SAS), and cognitive (CDR). The analysis results show that the NES scale exhibits better robustness in physiological assessment compared to the BADL and JKOM scales. In psychological assessment, the NES scale performs similarly to the SDS and SAS scales. In cognitive assessment, the NES scale's grading level is slightly higher than that of the CDR scale, demonstrating good comprehensive disability assessment capabilities. [ABSTRACT FROM AUTHOR]

Although human society has developed to an unprecedented height in terms of politics, economy and culture, it is still impossible to avoid local wars, epidemics, earthquakes, fires, floods, snow disasters, mudslides, mine accidents, major traffic accidents, building collapses, and other disasters, causing heavy casualties and property losses. In the face of a sudden disaster, external support is impossible to arrive immediately, and the government rescue department is relatively short of manpower, requiring social rescue forces as an important supplement. Therefore, this paper will analyze the current situation of socialized emergency rescue organizations at home and abroad, and put forward some suggestions on the management policies of socialized emergency rescue organizations in Shanghai. [ABSTRACT FROM AUTHOR]

The dynamic performance of high-speed maglev trains, a next-generation rapid transit system, has received continuous attention in recent years. In this study, a dynamic reliability analysis method for a high-speed maglev train–guideway coupled system was proposed. First, a refined model of the maglev vehicle–bridge interaction system was established, where the vehicle subsystem was simulated as a rigid body-spring-damper model with 101 degrees of freedom. The guideway subsystem was simulated as a finite element model, and these two subsystems were coupled as an entire system through a magnet–rail interaction model with a proportional-derivative (PD) controller. Second, a dimension-reduction method for the simulation of representative samples of track irregularities was developed, and thus the number of random variables in the system was reduced to four. Finally, an efficient method for the calculation of the dynamic reliability of a maglev train–guideway coupled system was proposed using the probability density evolution method-based equivalent extreme value principle. With numerical examples, the accuracy of the maglev train–guideway interaction model was verified by comparing it with field measurement data from the Shanghai high-speed maglev line. The accuracy of the proposed dynamic reliability analysis method was confirmed by comparing three types of results, that is, the mean value time-history curve, the probability density function, and the cumulative distribution function of extreme values, obtained by the Monte Carlo method. Finally, the dynamic reliability of the running safety and stability of the maglev vehicle at a speed of 430 km/h and the variation laws of the dynamic reliabilities with train speed were examined in detail. [ABSTRACT FROM AUTHOR]

In this study, we focus on exploring the propagation characteristics of particle swarms in social networks and analyze the diffusion process of viruses among populations based on system dynamics. The article mainly discusses three propagation influence mechanisms, including individual attributes, group attributes, and particle swarm attributes, and delves into the modeling of diffusion processes based on network structures. Firstly, we adopt the main roads in the transportation network (hub nodes) as the initial network backbone. On this basis, by introducing branch networks with small-world characteristics and scale-free characteristics, we construct a transportation network that integrates multiple properties. Using this network, we conducted a detailed simulation and analysis of the COVID-19 transmission process and compared and verified it with the infection dynamic data of COVID-19 in Shanghai from March to September 2022. The verification results reveal that our proposed model can significantly improve prediction accuracy. Compared with other existing dynamic models, our model demonstrates excellent performance, possessing high practical application value. This study provides robust theoretical support for the propagation characteristics of particle swarms in social networks and lays the foundation for further research and application in related fields. [ABSTRACT FROM AUTHOR]

In 2022, highly restrictive measures were imposed in China to contain the less deadly Omicron variant of SARS-CoV-2 virus or the COVID-19 virus. This study argues that these measures stemmed from a move away from pragmatic authoritarianism and towards totalitarianism since 2013 and that the lockdown of scores of cities such as Shanghai in 2022 carried quintessential features of totalitarianism. Revived totalitarianism in China has helped explain the longest and likely the most comprehensive strict epidemic control among all nations during the COVID-19 pandemic. The politics and the end of zero-COVID policy in China in 2022 are analysed. [ABSTRACT FROM AUTHOR]

With the continuous digital transformation of Shanghai, the problem of illegal use of data has become prominent. This study examines the relationship between urban digital transformation and enterprises' personal data protection from both micro- and macro-viewpoints. Enterprises establishing personal data protection system (PDPS) can gain public support in the process of urban digital transformation, accelerate urban digital transformation, and develop the whole market. Public support, corporate governance, and government guidance interact to provide the impetus for urban digital transformation. This study provides a theoretical basis for the establishment of a smart city under emerging markets. Meanwhile, it provides practical implications for governance guidelines for promoting the digital transformation of cities. [ABSTRACT FROM AUTHOR]