[Trade-off and Synergy of Ecosystem Services in the Yangtze River Economic Belt and Its Driving Factors].

Ecosystem services (ESs) and their changes are complex processes driven by multiple factors. Understanding the trade-off and synergy between ESs and their driving factors is essential for achieving effective management of ESs and human well-being. Taking the Yangtze River Economic Belt as the research area, this study analyzed the temporal and spatial variation characteristics of four ESs including water yield, soil conservation, carbon sequestration, and food supply from 2000 to 2020. Correlation analysis and geographically weighted regression were used to identify and quantify the trade-off and synergy between ESs. On this basis, the partial least squares structural equation model was used to explore the impact of natural and human activities on ESs, and then the driving mechanism of ESs relationship change was analyzed via GeoDetector. The results showed that:① During the 20 years, the average annual carbon sequestration increased from 946.14 t·km-2 to 1 202.73 t·km-2, and the average food supply increased from 32.73×104 Yuan·km-2 to 127.22×104 Yuan·km-2. Water yield and soil conservation increased to a lesser degree. ② On the whole, carbon sequestration and soil conservation and food supply and water yield showed synergy, and other ESs were trade-offs. The relationship between ESs varied in different regions. ③ Terrain and climate were important driving factors for ESs and the trade-off and synergy of multiple ESs. Among them, structural equation model results showed that climate had a positive impact on water yield (S=0.73), and terrain had a negative impact on food supply (S=-0.57). GeoDetector results revealed that the main driving factors affecting the spatial relationship between carbon sequestration and water yield were elevation (q=0.38) and precipitation (q=0.19). The results of this study can provide a scientific reference for the sustainable management of ESs in the Yangtze River Economic Belt and the realization of the coordinated development of ecological environment protection and social economy in the region.

The carbon budget of China: 1980-2021.

As one of the world's largest emitters of greenhouse gases, China has set itself the ambitious goal of achieving carbon peaking and carbon neutrality. Therefore, it is crucial to quantify the magnitude and trend of sources and sinks of atmospheric carbon dioxide (CO2), and to monitor China's progress toward these goals. Using state-of-the-art datasets and models, this study comprehensively estimated the anthropogenic CO2 emissions from energy, industrial processes and product use, and waste along with natural sources and sinks of CO2 for all of China during 1980-2021. To recognize the differences among various methods of estimating greenhouse emissions, the estimates are compared with China's National Greenhouse Gas Inventories (NGHGIs) for 1994, 2005, 2010, 2012, and 2014. Anthropogenic CO2 emissions in China have increased by 7.39 times from 1980 to 12.77 Gt CO2 a-1 in 2021. While benefiting from ecological projects (e.g., Three Norths Shelter Forest System Project), the land carbon sink in China has reached 1.65 Gt CO2 a-1 averaged through 2010-2021, which is almost 15.81 times that of the carbon sink in the 1980s. On average, China's terrestrial ecosystems offset 14.69% ± 2.49% of anthropogenic CO2 emissions through 2010-2021. Two provincial-level administrative regions of China, Xizang and Qinghai, have achieved carbon neutrality according to our estimates, but nearly half of the administrative regions of China have terrestrial carbon sink offsets of less than 10% of anthropogenic CO2 emissions. This study indicated a high level of consistency between NGHGIs and various datasets used for estimating fossil CO2 emissions, but found notable differences for land carbon sinks. Future estimates of the terrestrial carbon sinks of NGHGIs urgently need to be verified with process-based models which integrate the comprehensive carbon cycle processes.

Spreading Dynamics of Capital Flow Transfer in Complex Financial Networks.

The financial system, a complex network, operates primarily through the exchange of capital, where the role of information is critical. This study utilizes the transfer entropy method to examine the strength and direction of information flow among different capital flow time series and investigate the community structure within the transfer networks. Moreover, the spreading dynamics of the capital flow transfer networks are observed, and the importance and traveling time of each node are explored. The results imply a dominant role for the food and drink industry within the Chinese market, with increased attention towards the computer industry starting in 2014. The community structure of the capital flow transfer networks significantly differs from those constructed from stock prices, with the main sector predominantly encompassing industry leaders favored by primary funds with robust capital flow connections. The average traveling time from sectors such as food and drink, coal, and utilities to other sectors is the shortest, and the dynamic flow between these sectors displays a significant role. These findings highlight that comprehension of information flow and community structure within the financial system can offer valuable insights into market dynamics and help to identify key sectors and companies.

Quantitative assessment of the effects of massive nucleic acid testing in controlling a COVID-19 outbreak.

BACKGROUND: From 20 July to 26 August 2021, local outbreaks of COVID-19 occurred in Nanjing City and Yangzhou City (Jiangsu Province, China). We analyzed the characteristics of these outbreaks in an effort to develop specific and effective intervention strategies. METHODS: Publicly available data on the characteristics of the COVID-19 outbreaks in Jiangsu Province were collected. Logistic regression was used to assess the association of age and sex with clinical severity. Analysis of onset dates, generation time distributions, and locations were used to estimate the mean transmission distance. A branching process model was used to evaluate different management strategies. RESULTS: From 20 July to 26 August 2021, 820 patients were diagnosed with COVID-19 in Jiangsu Province, with 235 patients (28.7%) from Nanjing, 570 (69.5%) from Yangzhou, and 15 (1.8%) from other cities. Overall, 57.9% of the patients were female, 13.7% were under 20 years-old, and 58.3% had moderate disease status. The mean transmission distance was 4.12 km, and closed-loop management of the area within 2.23 km of cases seemed sufficient to control an outbreak. The model predicted that the cumulative cases in Yangzhou would increase from 311 to 642 if the interval between rounds of nucleic acid amplification testing (NAAT) increased from 1 to 6 days. It also predicted there would be 44.7% more patients if the NAAT started 10 days (rather than 0 days) after diagnosis of the first case. The proportion of cases detected by NAAT would increase from 11.16 to 44.12% when the rounds of NAAT increased from 1 to 7 within 17 days. When the effective vaccine coverage was 50%, the outbreak would be controlled even when using the most relaxed non-pharmaceutical interventions. CONCLUSIONS: The model predicted that a timely closed-loop management of a 2.23 km area around positive COVID-19 cases was sufficient to control the outbreak. Prompt serial NAAT is likely to contain an outbreak quickly, and our model results indicated that three rounds of NAAT sufficiently controlled local transmission. Trial registration We did not involve clinical trial.

Cluster randomised controlled trial to assess a tailored intervention to reduce antibiotic prescribing in rural China: study protocol.

INTRODUCTION: Up to 80% of patients with respiratory tract infections (RTI) attending healthcare facilities in rural areas of China are prescribed antibiotics, many of which are unnecessary. Since 2009, China has implemented several policies to try to reduce inappropriate antibiotic use; however, antibiotic prescribing remains high in rural health facilities. METHODS AND ANALYSIS: A cluster randomised controlled trial will be carried out to estimate the effectiveness and cost effectiveness of a complex intervention in reducing antibiotic prescribing at township health centres in Anhui Province, China. 40 Township health centres will be randomised at a 1:1 ratio to the intervention or usual care arms. In the intervention group, practitioners will receive an intervention comprising: (1) training to support appropriate antibiotic prescribing for RTI, (2) a computer-based treatment decision support system, (3) virtual peer support, (4) a leaflet for patients and (5) a letter of commitment to optimise antibiotic use to display in their clinic. The primary outcome is the percentage of antibiotics (intravenous and oral) prescribed for RTI patients. Secondary outcomes include patient symptom severity and duration, recovery status, satisfaction, antibiotic consumption. A full economic evaluation will be conducted within the trial period. Costs and savings for both clinics and patients will be considered and quality of life will be measured by EuroQoL (EQ-5D-5L). A qualitative process evaluation will explore practitioner and patient views and experiences of trial processes, intervention fidelity and acceptability, and barriers and facilitators to implementation. ETHICS AND DISSEMINATION: Ethical approval was obtained from the Biomedical Research Ethics Committee of Anhui Medical University (Ref: 20180259); the study has undergone due diligence checks and is registered at the University of Bristol (Ref: 2020-3137). Research findings will be disseminated to stakeholders through conferences and peer-reviewed journals in China, the UK and internationally. TRIAL REGISTRATION NUMBER: ISRCTN30652037.

Regional trends and drivers of the global methane budget.

The ongoing development of the Global Carbon Project (GCP) global methane (CH4 ) budget shows a continuation of increasing CH4 emissions and CH4 accumulation in the atmosphere during 2000-2017. Here, we decompose the global budget into 19 regions (18 land and 1 oceanic) and five key source sectors to spatially attribute the observed global trends. A comparison of top-down (TD) (atmospheric and transport model-based) and bottom-up (BU) (inventory- and process model-based) CH4 emission estimates demonstrates robust temporal trends with CH4 emissions increasing in 16 of the 19 regions. Five regions-China, Southeast Asia, USA, South Asia, and Brazil-account for >40% of the global total emissions (their anthropogenic and natural sources together totaling >270 Tg CH4  yr-1 in 2008-2017). Two of these regions, China and South Asia, emit predominantly anthropogenic emissions (>75%) and together emit more than 25% of global anthropogenic emissions. China and the Middle East show the largest increases in total emission rates over the 2000 to 2017 period with regional emissions increasing by >20%. In contrast, Europe and Korea and Japan show a steady decline in CH4 emission rates, with total emissions decreasing by ~10% between 2000 and 2017. Coal mining, waste (predominantly solid waste disposal) and livestock (especially enteric fermentation) are dominant drivers of observed emissions increases while declines appear driven by a combination of waste and fossil emission reductions. As such, together these sectors present the greatest risks of further increasing the atmospheric CH4 burden and the greatest opportunities for greenhouse gas abatement.

Levels of urinary metabolites of benzene compounds, trichloroethylene, and polycyclic aromatic hydrocarbons and their correlations with socioeconomic, demographic, dietary factors among pregnant women in six cities of China.

Maternal exposure to benzene and related compounds, trichloroethylene, and polycyclic aromatic hydrocarbons has been suggested as risk factors for reproductive and developmental problem. In different countries and groups, there are differences in socioeconomic circumstances, consumer products, dietary habits, lifestyles, and so on, resulting in different exposure risks from these chemicals. This study investigated the correlation between their metabolite concentrations and socioeconomic, demographic, and dietary factors to explore the possible exposure source of the concerned pollutants in pregnant women. We conducted biological monitoring to assess the exposure of these chemicals using urine samples from 590 to 639 pregnant women during 2nd or 3rd trimester of pregnancy in six cities of China. Socioeconomic and demographic characteristics and dietary habits were collected from questionnaires. The detection rate was over 74% of the urine samples for all metabolites. Compared with the Fourth National Report on Human Exposure to Environmental Chemical concentrations for females (FNRHEEC, the US Centers for Disease Control and Prevention), the metabolite concentrations of benzene compounds and trichloroethylene, excluding MA reported here, were higher. Principal component analysis results showed that SBMA and MHA could be proxy of the principal sources of metabolites for benzene compounds. The concentrations of SBMA and MA were higher in Fuzhou and Wuhan, respectively. The concentration of DCVMA was higher in Shenzhen, Xi'an, and Nanning, and the concentration of PGA was higher in Fuzhou, Wuhan, and Xi'an. Also, the 1-OHPG concentration in Wuhan is higher than that in Fuzhou and Shenzhen. Unhealthy dietary habits, using cosmetics and indoor exposure, contacting chemical solvent during pregnancy were associated with increased benzene compounds, trichloroethylene, and PAH exposure. There were significant positive associations between 1-OHPG level and maternal BMI, low education status, and cooking without a range hood. Pregnant women in China may be at a greater risk of exposure to most of the target compounds than US females, and their exposure levels varied in different regions. Some adverse environmental and behavioral factors may increase the exposure of environmental toxins, which can urge people to take measures to reduce the health risk to pregnant women during pregnancy.

Outpatient Antibiotic Prescribing Patterns and Appropriateness for Children in Primary Healthcare Settings in Beijing City, China, 2017-2019.

(1) Background: Few studies have focused on antibiotic use and appropriateness in children in primary health institutions (PHIs). This study aimed to identify the patterns and appropriateness of antibiotic use for children in PHIs in Beijing, China. (2) Methods: Outpatient prescriptions of 327 PHIs from 2017 to 2019 for patients <18 years old were collected. Prescriptions were described using quantity indicators. Antibiotics were categorized according to ATC classification J01 and Access, Watch, Reserve grouping. Appropriateness was reviewed by experts using three subtypes of irrational prescriptions (irregular, inappropriate, and abnormal). (3) Results: 20,618 prescriptions were collected in total. The antibiotic prescription rate (APR) was 15.1% (N = 3113). Among antibiotic prescriptions, J01FA Macrolides were the most used (N = 1068, 34.9%). The Watch group constituted 89.0% (N = 2818) of total antibiotic use. Bronchitis (N = 1059, 35.2%) was the most common diagnosis. A total of 292 instances of irrational antibiotic use were identified, with inappropriate prescriptions being the most prevalent subtype (N = 233, 79.8%). (4) Conclusion: Although APR for children in PHIs in Beijing was relatively low, the pattern of antibiotic use differed from other countries. Further studies are needed to optimize antibiotic use for children in PHIs under different levels of economic development.

Antibiotic use and irrational antibiotic prescriptions in 66 primary healthcare institutions in Beijing City, China, 2015-2018.

OBJECTIVES: To identify the patterns of antibiotic use and irrational antibiotic prescriptions in primary healthcare institutions (PHIs) in Dongcheng District of Beijing, China. MATERIALS AND METHODS: All primary healthcare institutions (7 community healthcare centres and 59 community healthcare stations in total) in Dongcheng District were included in the study. Prescription data from January 2015 to December 2018 was derived from the Beijing Prescription Reviewing System of Primary healthcare institutions and analysed retrospectively. The antibiotic prescription rate was calculated and cases of irrational antibiotic prescriptions were identified. RESULTS: We extracted 11,166,905 prescriptions from the database. Only 189,962 prescriptions were included in the study, among which 9167 (4.8%) contained antibiotics. The antibiotic prescription rate fell from 5.2% in 2015 to 4.1% in 2018 while irrational antibiotic prescription rate increased from 10.4 to 11.8%. Acute Bronchitis was the most prevalent diagnosis (17.6%) for antibiotic prescriptions, followed by Unspecified Acute Respiratory Tract Infection (14.4%), Acute Tonsillitis (9.9%), and Urinary Tract Infection (6.4%). Around 10% of the prescriptions for the top 7 diagnoses identified were rated as irrational. Cephalosporins, fluoroquinolones, and macrolides were the most prescribed antibiotics, which accounted for 89.3% of all antibiotic prescriptions. Of all the antibiotic prescriptions, 7531 were reviewed, among which 939 (12.5%) were rated as irrational because of antibiotic use. Among all the irrational prescriptions, prescriptions with inappropriate antibiotic use and dosage accounted for the majority (54.4%). CONCLUSION: Although a relatively low level of antibiotic utilization was found in PHIs in Dongcheng District of Beijing, the utilization patterns differed considerably from developed countries and irrational prescriptions remained. Considering the imbalanced allocation of medical resources between primary healthcare setting and secondary and tertiary hospitals, there need to be more efforts invested in regions with different levels of economic development.

The impacts of Chinese drug volume-based procurement policy on the use of policy-related antibiotic drugs in Shenzhen, 2018-2019: an interrupted time-series analysis.

BACKGROUND: In 2019, Chinese government implemented volume-based procurement of 25 drugs in 4 municipalities and 7 sub-provincial cities, i.e. "4 + 7" policy. Competitive bidding was conducted by the government based on the annual agreed procurement volume submitted by each public medical institution in pilot cities. Pilot cities were required to implement bid winning results in March 2019 and the use volume of bid winning products was examined to ensure the completion of agreed procurement volume. In the policy, an oral antibiotic (cefuroxime) was included. Given the current condition of the irrational use of antibiotics in China, this study aims to evaluate the impact of "4 + 7" policy on the use of policy-related antibiotics. METHODS: This study used drug purchase data from the Centralized Drug Procurement Survey in Shenzhen 2019, covering 24 months from January 2018 to December 2019. Oral antibiotic drugs related to "4 + 7" policy were selected as study samples, including cefuroxime and 12 antibiotic drugs that have an alternative relationship with cefuroxime in clinical use. Purchase volume and expenditures were selected as outcome variables, and were measured using Defined Daily Doses (DDDs) and Chinese yuan, respectively. Segmented linear regression analysis with interrupted time series was adopted to examine the effect of "4 + 7" policy. RESULTS: After the implementation of "4 + 7" policy, the overall volume of cefuroxime and its alternative drugs increased from 9.47 million DDDs to 13.42 million DDDs, with an increase of 41.8 %. The results of segmented linear regression showed that the volume of cefuroxime significantly increased 161.16 thousand DDDs after "4 + 7" policy (95 % CI: 59.43 to 262.90, p-value = 0.004). The volume of alternative drugs significantly increased 273.65 thousand DDDs (95 % CI: 90.17 to 457.12, p-value = 0.006). The overall "4 + 7" policy-related antibiotics significantly increased 436.31 thousand DDDs (95 % CI: 190.81 to 681.81, p-value = 0.001) after "4 + 7" policy. CONCLUSIONS: This study provides evidence that the implementation of "4 + 7" volume-based procurement policy was associated with significant increases in the volume of policy-related antibiotic drugs. The increase in antibiotic use after the policy needs special attention and vigilance.

Costs and Outcomes of Patients Admitted to the Intensive Care Unit With Cancer.

INTRODUCTION: Cancer is associated with significant health-care expenditure, but few studies have examined the cost of patients with cancer in the intensive care unit (ICU). We aimed to describe the costs and outcomes of patients admitted to the ICU with cancer. METHODS: We conducted a retrospective cohort study of patients admitted between 2011 and 2016 to 2 tertiary-care ICUs. We included patients with a cancer-related most responsible diagnosis using International Classification of Disease, 10th Revision, Canada codes. We compared costs and outcomes of patients having cancer with noncancer controls matched for age, sex, and Elixhauser comorbidity score. We used logistic regression to determine predictors of mortality among patients with cancer. RESULTS: There were 1022 patients with cancer during the study period. Mean age was 63.2 years and 577 (56.5%) were male. Inhospital mortality for all patients with cancer was 24.0%. Total cost per patient was higher for patients with cancer compared to noncancer patients (CAD$57 084 vs CAD$40 730; P < .001) but there were no differences in the cost per day (CAD$2868 vs CAD$2887; P = .76) or ICU cost (CAD$30 495 vs CAD$29 382; P = .42). Among patients with cancer, the cost per day was higher for nonsurvivors (CAD$3477 vs CAD$2677; P < .001). Liver disease (odds ratio [OR]: 2.96; 95% confidence interval [CI]: 1.22-7.81), mechanical ventilation (OR: 1.73; 95% CI: 1.25-2.39), hematologic malignancy (OR: 3.88; 95% CI: 2.31-6.54), and unknown primary site (OR: 2.13; 95% CI: 1.36-3.35) were independently associated with mortality in patients with cancer. CONCLUSION: Patients admitted to the ICU with cancer did not differ in cost per day, ICU cost, or mortality compared to matched noncancer controls. Among patients with cancer, nonsurvivors had significantly higher cost per day compared to survivors. Hematologic and unknown primaries, liver disease, and mechanical ventilation were independently associated with mortality in patients with cancer.

Near-real-time monitoring of global CO2 emissions reveals the effects of the COVID-19 pandemic.

The COVID-19 pandemic is impacting human activities, and in turn energy use and carbon dioxide (CO2) emissions. Here we present daily estimates of country-level CO2 emissions for different sectors based on near-real-time activity data. The key result is an abrupt 8.8% decrease in global CO2 emissions (-1551 Mt CO2) in the first half of 2020 compared to the same period in 2019. The magnitude of this decrease is larger than during previous economic downturns or World War II. The timing of emissions decreases corresponds to lockdown measures in each country. By July 1st, the pandemic's effects on global emissions diminished as lockdown restrictions relaxed and some economic activities restarted, especially in China and several European countries, but substantial differences persist between countries, with continuing emission declines in the U.S. where coronavirus cases are still increasing substantially.

Disparity in clinical outcomes between pure and combined pulmonary large-cell neuroendocrine carcinoma: A multi-center retrospective study.

OBJECTIVES: The 2015 World Health Organization classification defines pulmonary large-cell neuroendocrine carcinoma (LCNEC) as a high-grade neuroendocrine carcinoma. However, the clinical characteristics and prognostic factors of pure LCNEC and combined LCNEC remain unclear. Hence, we performed a multi-center retrospective study to compare the clinical outcomes of pure versus combined LCNEC. MATERIALS AND METHODS: Data from 381 patients with pulmonary LCNEC admitted to 17 Chinese institutes between 2009 and 2016 were collected retrospectively. Clinical characteristics and prognosis were analyzed among patients receiving adjuvant (adjuvant group; n = 56) and first-line (first-line group; n = 146) chemotherapy, as well as among patients receiving small cell lung cancer (SCLC) and non-SCLC (NSCLC) chemotherapy regimens. The Kaplan-Meier method and multivariable Cox regression were used to identify clinicopathological variables that might influence patient outcomes. RESULTS: Expression levels of neuroendocrine markers (synaptophysin, chromogranin-A, CD56) were associated with patients' prognosis in the total study cohort. In the adjuvant group, median disease-free survival was non-significantly longer for SCLC-based regimens than for NSCLC-based regimens (P = 0.112). In the first-line group, median progression-free survival was significantly longer for SCLC-based regimens than for NSCLC-based regimens (11.5 vs. 7.2 months, P = 0.003). Among patients with combined LCNEC, adenocarcinoma was the most common combined component, accounting for 70.0 % of cases. Additionally, median overall survival was non-significantly shorter for combined LCNEC than for pure LCNEC (P = 0.083). CONCLUSION: The SCLC regimen is a more effective choice, as either first-line or adjuvant chemotherapy, when compared to the NSCLC regimen for LCNEC treatment. Further studies are needed to clarify the survival differences between patients with pure-, and combined LCNEC.

Inequality of household consumption and air pollution-related deaths in China.

Substantial quantities of air pollution and related health impacts are ultimately attributable to household consumption. However, how consumption pattern affects air pollution impacts remains unclear. Here we show, of the 1.08 (0.74-1.42) million premature deaths due to anthropogenic PM2.5 exposure in China in 2012, 20% are related to household direct emissions through fuel use and 24% are related to household indirect emissions embodied in consumption of goods and services. Income is strongly associated with air pollution-related deaths for urban residents in which health impacts are dominated by indirect emissions. Despite a larger and wealthier urban population, the number of deaths related to rural consumption is higher than that related to urban consumption, largely due to direct emissions from solid fuel combustion in rural China. Our results provide quantitative insight to consumption-based accounting of air pollution and related deaths and may inform more effective and equitable clean air policies in China.

Infrastructure Shapes Differences in the Carbon Intensities of Chinese Cities.

The carbon intensity of economic activity, or CO2 emissions per unit GDP, is a key indicator of the climate impacts of a given activity, business, or region. Although it is well-known that the carbon intensity of countries varies widely according to their level of economic development and dominant industries, few studies have assessed disparities in carbon intensity at the level of cities due to limited availability of data. Here, we present a detailed new inventory of emissions for 337 Chinese cities (every city in mainland China including 333 prefecture-level divisions and 4 province-level cities, Beijing, Tianjin, Shanghai, and Chongqing) in 2013, which we use to evaluate differences of carbon intensity between cities and the causes of those differences. We find that cities' average carbon intensity is 0.84 kg of CO2 per dollar of gross domestic product (kgCO2 per $GDP), but individual cities span a large range: from 0.09 to 7.86 kgCO2 per $GDP (coefficient of variation of 25%). Further analysis of economic and technological drivers of variations in cities' carbon intensity reveals that the differences are largely due to disparities in cities' economic structure that can in turn be traced to past investment-led growth. These patterns suggest that "carbon lock-in" via socio-economic and infrastructural inertia may slow China's efforts to reduce emissions from activities in urban areas. Policy instruments targeted to accelerate the transition of urban economies from investment-led to consumption-led growth may thus be crucial to China meeting both its economic and climate targets.

Fluctuation-driven price dynamics and investment strategies.

Investigation of the driven mechanism of the price dynamics in complex financial systems is important and challenging. In this paper, we propose an investment strategy to study how dynamic fluctuations drive the price movements. The strategy is successfully applied to different stock markets in the world, and the result indicates that the driving effect of the dynamic fluctuations is rather robust. We investigate how the strategy performance is influenced by the market states and optimize the strategy performance by introducing two parameters. The strategy is also compared with several typical technical trading rules. Our findings not only provide an investment strategy which extends investors' profits, but also offer a useful method to look into the dynamic properties of complex financial systems.

Exploring Market State and Stock Interactions on the Minute Timescale.

A stock market is a non-stationary complex system. The stock interactions are important for understanding the state of the market. However, our knowledge on the stock interactions on the minute timescale is limited. Here we apply the random matrix theory and methods in complex networks to study the stock interactions and sector interactions. Further, we construct a new kind of cross-correlation matrix to investigate the correlation between the stock interactions at different minutes within one trading day. Based on 50 million minute-to-minute price data in the Shanghai stock market, we discover that the market states in the morning and afternoon are significantly different. The differences mainly exist in three aspects, i.e. the co-movement of stock prices, interactions of sectors and correlation between the stock interactions at different minutes. In the afternoon, the component stocks of sectors are more robust and the structure of sectors is firmer. Therefore, the market state in the afternoon is more stable. Furthermore, we reveal that the information of the sector interactions can indicate the financial crisis in the market, and the indicator based on the empirical data in the afternoon is more effective.

Intrinsic Multi-Scale Dynamic Behaviors of Complex Financial Systems.

The empirical mode decomposition is applied to analyze the intrinsic multi-scale dynamic behaviors of complex financial systems. In this approach, the time series of the price returns of each stock is decomposed into a small number of intrinsic mode functions, which represent the price motion from high frequency to low frequency. These intrinsic mode functions are then grouped into three modes, i.e., the fast mode, medium mode and slow mode. The probability distribution of returns and auto-correlation of volatilities for the fast and medium modes exhibit similar behaviors as those of the full time series, i.e., these characteristics are rather robust in multi time scale. However, the cross-correlation between individual stocks and the return-volatility correlation are time scale dependent. The structure of business sectors is mainly governed by the fast mode when returns are sampled at a couple of days, while by the medium mode when returns are sampled at dozens of days. More importantly, the leverage and anti-leverage effects are dominated by the medium mode.

Agent-based model with multi-level herding for complex financial systems.

In complex financial systems, the sector structure and volatility clustering are respectively important features of the spatial and temporal correlations. However, the microscopic generation mechanism of the sector structure is not yet understood. Especially, how to produce these two features in one model remains challenging. We introduce a novel interaction mechanism, i.e., the multi-level herding, in constructing an agent-based model to investigate the sector structure combined with volatility clustering. According to the previous market performance, agents trade in groups, and their herding behavior comprises the herding at stock, sector and market levels. Further, we propose methods to determine the key model parameters from historical market data, rather than from statistical fitting of the results. From the simulation, we obtain the sector structure and volatility clustering, as well as the eigenvalue distribution of the cross-correlation matrix, for the New York and Hong Kong stock exchanges. These properties are in agreement with the empirical ones. Our results quantitatively reveal that the multi-level herding is the microscopic generation mechanism of the sector structure, and provide new insight into the spatio-temporal interactions in financial systems at the microscopic level.

How volatilities nonlocal in time affect the price dynamics in complex financial systems.

What is the dominating mechanism of the price dynamics in financial systems is of great interest to scientists. The problem whether and how volatilities affect the price movement draws much attention. Although many efforts have been made, it remains challenging. Physicists usually apply the concepts and methods in statistical physics, such as temporal correlation functions, to study financial dynamics. However, the usual volatility-return correlation function, which is local in time, typically fluctuates around zero. Here we construct dynamic observables nonlocal in time to explore the volatility-return correlation, based on the empirical data of hundreds of individual stocks and 25 stock market indices in different countries. Strikingly, the correlation is discovered to be non-zero, with an amplitude of a few percent and a duration of over two weeks. This result provides compelling evidence that past volatilities nonlocal in time affect future returns. Further, we introduce an agent-based model with a novel mechanism, that is, the asymmetric trading preference in volatile and stable markets, to understand the microscopic origin of the volatility-return correlation nonlocal in time.