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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]

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]

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]

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]

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]

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]

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]

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]

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]

This paper investigates whether foreign capital is smarter money using multifractal cross-correlation analysis (MFCCA) and nonlinear Granger Causality test. Using multifractal detrended fluctuation analysis (MF-DFA) method, we find that time series of stock returns, foreign-capital inflow from Shanghai–Hong Kong Stock Connect (SHKSC), and domestic-capital flow (proxied by margin-trading activities capital) exhibit strong multifractality. In addition, MFCCA results show that there exists a strong persistent cross-correlation between stock returns and foreign-capital inflow, but anti-persistent cross-correlation between stock returns and domestic-capital flow. Moreover, using nonlinear Granger Causality test, we find that foreign-capital inflow is the granger cause of stock returns. Our findings provide empirical evidence that foreign-capital inflow is positively associated with future stock returns, i.e., foreign capital is smarter money. [ABSTRACT FROM AUTHOR]

High noise and strong volatility are the typical characteristics of financial time series. Combined with pseudo-randomness, nonsteady and self-similarity exhibiting in different time scales, it is a challenging issue for the pattern analysis of financial time series. Different from the existing works, in this paper, financial time series are converted into granular complex networks, based on which the structure and dynamics of network models are revealed. By using variable-length division, an extended polar fuzzy information granule (FIGs) method is used to construct granular complex networks from financial time series. Considering the temporal characteristics of sequential data, static networks and temporal networks are studied, respectively. As to the static network model, some features of topological structures of granular complex networks, such as distribution, clustering and betweenness centrality are discussed. Besides, by using the Markov chain model, the transfer processes among different granules are investigated, where the fluctuation pattern of data in the coming step can be evaluated from the transfer probability of two consecutive granules. Shanghai composite index and foreign exchange data as two examples in real life are applied to carry out the related discussion. [ABSTRACT FROM AUTHOR]

In order to predict more accurately the structural vibration and noise of elevated tracks induced by moving trains, a new prediction method based on the scaled model test is proposed in this paper. A 32-m simply supported box girder bridge used in the Beijing–Shanghai high-speed railway is selected as the prototype for designing and constructing a scaled model test with 10:1 geometric similarity ratio. Both experimental tests and finite element analyses were carried out to verify the similarity relationship between the model and prototype. The test result shows that the scaled model can predict the structural vibration and noise of the prototype, as long as the similarity constants between the prototype and scaled model are correctively determined. Furthermore, a standard finite element analysis model for the scaled model is built. Based on the sensitivity analysis, the model parameters for finite element analysis are updated by minimizing the errors between the measured and calculated modes. The computational results show that the updated model based on the local parameters partitioning works best, and the precision of the modal frequency calculated is noticeably improved after updating, with the average relative error reduced from 5.46% to 3.09%, and the difference of the peak values reduced from 0. 3 5 8 × 1 0 3 m/s2 to 0. 1 8 9 × 1 0 3 m/s2. The calculated dynamic response of the finite element model after updating is basically in line with experimental results, indicating that the updated model can better reflect the dynamic properties of the scaled box girder model. The updated finite element model is useful both for verification with the model test result and for reliable prediction of the dynamic characteristics of the prototype. [ABSTRACT FROM AUTHOR]

In China, the evolution of inter-provincial migration patterns is from policy orientation to individual selection and hotspot attraction during 1950–2010. These years, China's abundant regional labor flow brings prosperity to several famous high-growth regions, but also causes the problem of unbalanced development. It needs systematic analysis to do effective guidance and management of population mobility. This paper tries to analyze the driving and dragging force in China's inter-provincial migration flows with improved multilateral migration model stemming from individual behavior of potential migrants. It revealed the problem of imbalanced regional labor flow has been exacerbated in China since the 1990s, and the driving force for typical hotspots is different, as high income force for Beijing, Shanghai, and low barrier for Guangzhou, Zhejiang. The results of analyzing indicates the the distinct regional mobility barriers help to alleviate the problem of labor loss in some provinces since 1980s, but the regional imbalance in population migration continues to grow in recent years. Finally, with regression, we reveal the essential element affecting regional mobility barriers, and it could explain 70.8% of the influence factors. It also suggests that, for the provinces with labor loss, improving economic, welfare environment will mitigate the problem effectively. [ABSTRACT FROM AUTHOR]

Forecasting stock price indexes has been regarded as a challenging task in financial time series analysis. In order to improve the prediction accuracy, a novel hybrid model that integrates fractal interpolation with support vector machine (SVM) models has been developed in this paper to forecast the time series of stock price indexes. For this, a new method to calculate the vertical scaling factors of the fractal interpolation iterated function system is first proposed and an improved fractal interpolation model is then established. The improved fractal interpolation model and the SVM model are integrated to predict the every 5-min high frequency index data of Shanghai Composite Index. The experimental results show that the hybrid model is suitable for forecasting the stock index time series with fractal characteristics. In addition, a comparison of the prediction accuracy is carried out among the hybrid model and other three commonly used models. The results show that the prediction performance of the hybrid model is superior to that of other three models. [ABSTRACT FROM AUTHOR]

This paper studies the subway-induced vibrations on two adjacent masonry structures in Shanghai with focus on the vibration level and frequency. The results show that: (1) the vibration levels of the two masonry structures in three directions are all higher during the peak traffic hours than the off-peak hours and are higher in the daytime than at night. In addition, they are higher at the mid-span of the floor than the wall-floor junctions and staircase. (2) The parameters of the numerical model were calibrated by the measured vibration response. The slab properties, room size, and other factors can affect the vibration response distribution. (3) The vertical vibrations in the rooms with precast slab are greater than those with cast- in-situ slab. The vertical vibrations at the ash seam between the precast slabs are amplified. In addition, the room depth has a small effect on the vibration intensity. The relationship between the room depth and the vibration intensity depends on the relative width and depth of the room. (4) The width of the room has a significant effect on the vertical vibration. The floor corner and surrounding wall constraint conditions greatly affect the vibration intensity. [ABSTRACT FROM AUTHOR]

In this paper, using a moving window to scan through every stock price time series over a period from 2 January 2001 to 11 March 2011 and mutual information to measure the statistical interdependence between stock prices, we construct a corresponding weighted network for 501 Shanghai stocks in every given window. Next, we extract its maximal spanning tree and understand the structure variation of Shanghai stock market by analyzing the average path length, the influence of the center node and the p-value for every maximal spanning tree. A further analysis of the structure properties of maximal spanning trees over different periods of Shanghai stock market is carried out. All the obtained results indicate that the periods around 8 August 2005, 17 October 2007 and 25 December 2008 are turning points of Shanghai stock market, at turning points, the topology structure of the maximal spanning tree changes obviously: the degree of separation between nodes increases; the structure becomes looser; the influence of the center node gets smaller, and the degree distribution of the maximal spanning tree is no longer a power-law distribution. Lastly, we give an analysis of the variations of the single-step and multi-step survival ratios for all maximal spanning trees and find that two stocks are closely bonded and hard to be broken in a short term, on the contrary, no pair of stocks remains closely bonded for a long time. [ABSTRACT FROM AUTHOR]

In this paper, we apply IAAFT to generate surrogate time series of measured multivariate time series. A quantitative method to detect nonlinearity in multivariate time series is proposed using the generalized redundancy and linear redundancy as the significance test statistic. The null hypothesis of a multivariate linear Gaussian random process is tested using the multivariate surrogate data. The validity of this method is demonstrated using two types models (linear and nonlinear) and applied to Shanghai stock market. [ABSTRACT FROM AUTHOR]