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This paper aims to demonstrate the impact of coal enterprises’ vertical integration on the performance of innovation corporate social responsibility (ICSR) and to elaborate its specific transmission path through financing structure and market power. This paper takes the data of A-share listed coal company from 2008 to 2017 as samples, uses input-output table method to measure the degree of vertical integration, and explores the relationship between the four factors through multiple linear regression and Bootstrap method. We found that: (1) the vertical integration of coal enterprises has positive incentives for the promotion of ICSR. (2) Financing structure and market power play a chain intermediary role in this process of incentive. (3) In areas with high marketization process, vertical integration has more significant incentive effect on ICSR. The paper extends the research on the relationship between vertical integration and innovation, which provides a reference for the improvement of China’s supply-side reform and corporate social responsibility (CSR) fulfillment policy. It is helpful to promote the sustainable development of the coal industry, stimulate the innovation vitality of enterprises, and improve the fulfillment of CSR. [ABSTRACT FROM AUTHOR]

Background: Daodi-herb is a part of Chinese culture, which has been naturally selected by traditional Chinese medicine clinical practice for many years. Sweet wormwood herb is a kind of Daodi-herb, and comes from Artemisia annua L. Artemisinin is a kind of effective antimalarial drug being extracted from A. annua. Because of artemisinin, Sweet wormwood herb earns a reputation. Based on the Pharmacopoeia of the People's Republic of China (PPRC), Sweet wormwood herb can be used to resolve summerheat-heat, and prevent malaria. Besides, it also has other medical efficacies. A. annua, a medicinal plant that is widely distributed in the world contains many kinds of chemical composition. Research has shown that compatibility of artemisinin, scopoletin, arteannuin B and arteannuic acid has antimalarial effect. Compatibility of scopoletin, arteannuin B and arteannuic acid is conducive to resolving summerheat-heat. Chemical constituents in A. annua vary significantly according to geographical locations. So, distribution of A. annua may play a key role in the characteristics of efficacy and chemical constituents of Sweet wormwood herb. It is of great significance to study this relationship. Objectives: We mainly analyzed the relationship between the chemical constituents (arteannuin B, artemisinin, artemisinic acid, and scopoletin) with special efficacy in A. annua that come from different provinces in china, and analyzed the relationship between chemical constituents and spatial distribution, in order to find out the relationship between efficacy, chemical constituents and distribution. Methods: A field survey was carried out to collect A. annua plant samples. A global positioning system (GPS) was used for obtaining geographical coordinates of sampling sites. Chemical constituents in A. annua were determined by liquid chromatography tandem an atmospheric pressure ionization-electrospray mass spectrometry. Relationship between chemical constituents including proportions, correlation analysis (CoA), principal component analysis (PCA) and cluster analysis (ClA) was displayed through Excel and R software version2.3.2(R), while the one between efficacy, chemical constituents and spatial distribution was presented through ArcGIS10.0, Excel and R software. Results: According to the results of CoA, arteannuin B content presented a strong positive correlation with artemisinic acid content (p = 0), and a strong negative correlation with artemisinin content (p = 0). Scopoletin content presented a strong positive correlation with artemisinin content (p = 0), and a strong negative correlation with artemisinic acid content (p = 0). According to the results of PCA, the first two principal components accounted for 81.57% of the total accumulation contribution rate. The contribution of the first principal component is about 45.12%, manly including arteannuin B and artemisinic acid. The contribution of the second principal component is 36.45% of the total, manly including artemisinin and scopoletin. According to the ClA by using the principal component scores, 19 provinces could be divided into two groups. In terms of provinces in group one, the proportions of artemisinin are all higher than 80%. Based on the results of PCA, ClA, percentages and scatter plot analysis, chemical types are defined as "QHYS type", "INT type" and "QHS type." Conclusion: As a conclusion, this paper shows the relationship between efficacy, chemical constituents and distribution. Sweet wormwood herb with high arteannuin B and artemisinic acid content, mainly distributes in northern China. Sweet wormwood herb with high artemisinin and scopoletin content has the medical function of preventing malaria, which mainly distributes in southern China. In this paper, it is proved that Sweet wormwood Daodi herb growing in particular geographic regions, has more significant therapeutical effect and higher chemical constituents compared with other same kind of CMM. And also, it has proved the old saying in China that Sweet wormwood Daodi herb which has been used to resolve summerheat-heat and prevent malaria, which distributed in central China. But in modern time, Daodi Sweet wormwood herb mainly has been used to extract artemisinin and prevent malaria, so the Daod-region has transferred to the southern China. [ABSTRACT FROM AUTHOR]

South Korea is ranked as 4th among 34 nations of the Organization for Economic Cooperation and Development with 102 deaths in road accidents per one million population. This paper aims to investigate the factors associated with road accidents in South Korea. The rainfall data of the Korea Meteorological Administration and road accidents data of Traffic Accident Analysis System of Korea Road Traffic Authority is analyzed for this purpose. In this connection, multivariate regression analysis and ratio analysis with the descriptive analysis are performed to uncover the catastrophic factors involved. In turn, the results reveal that traffic volume is the leading factor in road accidents. The limited road extension of 1.47% compared to the 4.14% per annum growth of the vehicles is resulting in road accidents at such a large scale. The increasing proportion of passenger cars accelerate road accidents as well. 56% of accidents occur by the infringement of safety driving violations. The drivers with higher driving experience tend to have a higher accident ratio. The collected data is analyzed in terms of gender, driver experience, type of violations and accidents as well as the associated time of the accidents when they happen. The results indicate that 36.29% and 53.01% of accidents happen by male drivers in the day and night time, respectively. 29.15% of crashes happen due to safety infringement and violations of 41 to 60 years old drivers. The results demonstrate that population density is associated with the accidents frequency and lower density results in an increased number of accidents. The necessity of the state-of-the-art regulations to govern the urban road traffic is beyond dispute, and it becomes even more crucial for citizens’ relief since in our daily lives road accidents are getting more diverse. [ABSTRACT FROM AUTHOR]

The adoption of the shared prosperity goal by the World Bank in 2013 and Sustainable Development Goal 10, on inequality, by the United Nations in 2015 should strengthen the focus of development interventions and cooperation on the income growth of the bottom 40 percent of the income distribution. This paper contributes to the incipient literature on within-country allocations of development institutions and assesses the geographic targeting of World Bank projects to the bottom 40 percent. Bivariate correlations between the allocation of project funding approved over 2005–14 and the geographical distribution of the bottom 40 as measured by survey income or consumption data are complemented by regressions with population and other potential factors affecting the within-country allocations as controls. The correlation analysis shows that, of the 58 countries in the sample, 41 exhibit a positive correlation between the shares of the bottom 40 and World Bank funding, and, in almost half of these, the correlation is above 0.5. Slightly more than a quarter of the countries, mostly in Sub-Saharan Africa, exhibit a negative correlation. The regression analysis shows that, once one controls for population, the correlation between the bottom 40 and World Bank funding switches sign and becomes significant and negative on average. This is entirely driven by Sub-Saharan Africa and not observed in the other regions. Hence, the significant and positive correlation in the estimations without controlling for population suggests that World Bank project funding is concentrated in administrative areas in which more people live (including the bottom 40) rather than in poorer administrative areas. Furthermore, capital cities receive disproportionally high shares of World Bank funding on average. [ABSTRACT FROM AUTHOR]

To discover regularities in human mobility is of fundamental importance to our understanding of urban dynamics, and essential to city and transport planning, urban management and policymaking. Previous research has revealed universal regularities at mainly aggregated spatio-temporal scales but when we zoom into finer scales, considerable heterogeneity and diversity is observed instead. The fundamental question we address in this paper is at what scales are the regularities we detect stable, explicable, and sustainable. This paper thus proposes a basic measure of variability to assess the stability of such regularities focusing mainly on changes over a range of temporal scales. We demonstrate this by comparing regularities in the urban mobility patterns in three world cities, namely London, Singapore and Beijing using one-week of smart-card data. The results show that variations in regularity scale as non-linear functions of the temporal resolution, which we measure over a scale from 1 minute to 24 hours thus reflecting the diurnal cycle of human mobility. A particularly dramatic increase in variability occurs up to the temporal scale of about 15 minutes in all three cities and this implies that limits exist when we look forward or backward with respect to making short-term predictions. The degree of regularity varies in fact from city to city with Beijing and Singapore showing higher regularity in comparison to London across all temporal scales. A detailed discussion is provided, which relates the analysis to various characteristics of the three cities. In summary, this work contributes to a deeper understanding of regularities in patterns of transit use from variations in volumes of travellers entering subway stations, it establishes a generic analytical framework for comparative studies using urban mobility data, and it provides key points for the management of variability by policy-makers intent on for making the travel experience more amenable. [ABSTRACT FROM AUTHOR]

Poverty affects many people, but the ramifications and impacts affect all aspects of society. Information about the incidence of poverty is therefore an important parameter of the population for policy analysis and decision making. In order to provide specific, targeted solutions when addressing poverty disadvantage small area statistics are needed. Surveys are typically designed and planned to produce reliable estimates of population characteristics of interest mainly at higher geographic area such as national and state level. Sample sizes are usually not large enough to provide reliable estimates for disaggregated analysis. In many instances estimates are required for areas of the population for which the survey providing the data was unplanned. Then, for areas with small sample sizes, direct survey estimation of population characteristics based only on the data available from the particular area tends to be unreliable. This paper describes an application of small area estimation (SAE) approach to improve the precision of estimates of poverty incidence at district level in the State of Bihar in India by linking data from the Household Consumer Expenditure Survey 2011–12 of NSSO and the Population Census 2011. The results show that the district level estimates generated by SAE method are more precise and representative. In contrast, the direct survey estimates based on survey data alone are less stable. [ABSTRACT FROM AUTHOR]

Accurate reporting and forecasting of PM2.5 concentration are important for improving public health. In this paper, we propose a partial differential equation (PDE) model, specially, a linear diffusive equation, to describe the spatial-temporal characteristics of PM2.5 in order to make short-term prediction. We analyze the temporal and spatial patterns of a real dataset from China’s National Environmental Monitoring and validate the PDE-based model in terms of predicting the PM2.5 concentration of the next day by the former days’ history data. Our experiment results show that the PDE model is able to characterize and predict the process of PM2.5 transport. For example, for 300 continuous days of 2016, the average prediction accuracy of the PDE model over all city-regions is 93% or 83% based on different accuracy definitions. To our knowledge, this is the first attempt to use PDE-based model to study PM2.5 prediction in both temporal and spatial dimensions. [ABSTRACT FROM AUTHOR]

Spatial scan statistics with circular or elliptic scanning windows are commonly used for cluster detection in various applications, such as the identification of geographical disease clusters from epidemiological data. It has been pointed out that the method may have difficulty in correctly identifying non-compact, arbitrarily shaped clusters. In this paper, we evaluated the Gini coefficient for detecting irregularly shaped clusters through a simulation study. The Gini coefficient, the use of which in spatial scan statistics was recently proposed, is a criterion measure for optimizing the maximum reported cluster size. Our simulation study results showed that using the Gini coefficient works better than the original spatial scan statistic for identifying irregularly shaped clusters, by reporting an optimized and refined collection of clusters rather than a single larger cluster. We have provided a real data example that seems to support the simulation results. We think that using the Gini coefficient in spatial scan statistics can be helpful for the detection of irregularly shaped clusters. [ABSTRACT FROM AUTHOR]

Rainy weather conditions could result in significantly negative impacts on driving on freeways. However, due to lack of enough historical data and monitoring facilities, many regions are not able to establish reliable risk assessment models to identify such impacts. Given the situation, this paper provides an alternative solution where the procedure of risk assessment is developed based on drivers’ subjective questionnaire and its performance is validated by using actual crash data. First, an ordered logit model was developed, based on questionnaire data collected from Freeway G15 in China, to estimate the relationship between drivers’ perceived risk and factors, including vehicle type, rain intensity, traffic volume, and location. Then, weighted driving risk for different conditions was obtained by the model, and further divided into four levels of early warning (specified by colors) using a rank order cluster analysis. After that, a risk matrix was established to determine which warning color should be disseminated to drivers, given a specific condition. Finally, to validate the proposed procedure, actual crash data from Freeway G15 were compared with the safety prediction based on the risk matrix. The results show that the risk matrix obtained in the study is able to predict driving risk consistent with actual safety implications, under rainy weather conditions. [ABSTRACT FROM AUTHOR]

Based on our site investigation conducted in 2013, we found that the pedestrian red light crossing at the midblock connecting the campus of Southwest University and living area was low, where most of pedestrians are university students and staff. This paper reports a supplementary work applying the Theory of Planned Behaviour (TPB) to identify any psychological differences toward pedestrian red light crossing between university students and their peers. Three social groups participated in the investigation. The first group is the university students in Grade one (Group 1), the other two groups are their previous senior middle school classmates who are now working full time (Group 2) or who are now out of work and school (Group 3). The statistical results indicated The TPB components accounted for 42.9%, 55.3% and 55.4% of the variance of red signal crossing intention for Group 1, Group 2 and Group 3 in the depicted road crossing scenario. The data also showed that there are obvious differences among the participants’ responses to “refrain from crossing” between university students and others, and the subsequent regression analysis revealed the ability to “refrain from crossing” played the most important role in the intention of red light crossing in the depicted scenario. [ABSTRACT FROM AUTHOR]

In geo-statistics, the Durbin-Watson test is frequently employed to detect the presence of residual serial correlation from least squares regression analyses. However, the Durbin-Watson statistic is only suitable for ordered time or spatial series. If the variables comprise cross-sectional data coming from spatial random sampling, the test will be ineffectual because the value of Durbin-Watson’s statistic depends on the sequence of data points. This paper develops two new statistics for testing serial correlation of residuals from least squares regression based on spatial samples. By analogy with the new form of Moran’s index, an autocorrelation coefficient is defined with a standardized residual vector and a normalized spatial weight matrix. Then by analogy with the Durbin-Watson statistic, two types of new serial correlation indices are constructed. As a case study, the two newly presented statistics are applied to a spatial sample of 29 China’s regions. These results show that the new spatial autocorrelation models can be used to test the serial correlation of residuals from regression analysis. In practice, the new statistics can make up for the deficiencies of the Durbin-Watson test. [ABSTRACT FROM AUTHOR]