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Firstly, this paper establishes the principal component regression model to analyze the data quantitatively, based on principal component analysis to get the three principal component factors of flight delays. Then the least square method is used to analyze the factors and obtained the regression equation expression by substitution, and then found that the main reason for flight delays is airlines, followed by weather and traffic. Aiming at the above problems, this paper improves the controllable aspects of traffic flow control. For reasons of traffic flow control, an adaptive genetic queuing model is established for the runway terminal area. This paper, establish optimization method that fifteen planes landed simultaneously on the three runway based on Beijing capital international airport, comparing the results with the existing FCFS algorithm, the superiority of the model is proved. [ABSTRACT FROM AUTHOR]

This paper first builds an agile manufacturing model involving external agility evaluation and internal enabling technology. Based on the model and international manufacturing strategy survey data, factor analysis and analysis of variance are used to compare the agility of manufacturing companies in China and the United States. It is found that China's agility is better than that of the United States in recent years, but the gap is not big. Then use regression analysis and analysis of variance to compare the application of various enabling technologies in the two countries, and explore the causes of the differences in agility between the two countries. The key lies in the in-depth application of specific technologies and the organization and coordination of the upstream and downstream of the supply chain in China. Finally, based on the above analysis, reasonable suggestions for improving the agility of Chinese manufacturing companies are put forward. [ABSTRACT FROM AUTHOR]

In order to explore the influence of center location and indirect connection between provinces and cities in the regional innovation network on their innovation capability, the regional innovation network of 29 provinces and cities in China is represented by using the gravity model adjusted. The centrality is selected to characterize the center location characteristics of provincial nodes. The social network analysis and regression analysis are used to study the relationship between the provincial node centrality and indirect connection of the node and its innovation capability. This study shows that provincial node centrality can promote its innovation capability, and the indirect connection of provincial nodes inhibits the innovation capability of nodes. Moreover, the number plays a u-shaped role in regulating the centrality and innovation capability of provincial nodes. [ABSTRACT FROM AUTHOR]

Unlike private enterprises in developed markets, political influence is profound upon Chinese state-dominated firms. Under this consideration, this paper demonstrates how political impact interferes in Chinese managers’ decisions. State-assigned managers were found to deliberately transfer cash raised via rights issues from the public shareholders to the state by cash dividends in order to please Chinese politicians. Especially, to meet the regulatory requirement of rights issues, managers from firms which distributed more cash dividends in the same year of rights issues were more likely to inflate earnings before rights issues. The earnings inflation which managers use to boost firm’s incomes is defined as “earnings management.” Furthermore, the empirical results also exhibit that firm’s close relationship with the state enables managers to obtain approvals of rights issues easily, which reduces the firm’s earnings management tendency. The manager’s incentives of earnings management is closely attributed to the political intervention. [ABSTRACT FROM AUTHOR]

Nobody on this planet is going to be untouched by the impacts of climate change. The ability to cope with climate change varies from region to region, resulting the difference among its effects on fragility. In this paper, we focus on figuring out how the climate change influences the state’s fragility. First, we build Multilevel Fuzzy Mathematic Appraisement Model to assess the grade of the fragility. Then we identify the climate change as an indirect indicator through studying and discussing. Furthermore, after conducting Multiple Linear Regression Models between quantified climate index and twelve factors, we select the indicators that is relevant to climate change to measure the effect of climate change. Then, apply our model to the countries we choose--Sudan and India. We figure out climate change will increase the fragility index by affecting the economy, brain drain and state legitimacy of Sudan, then we quantize the growth by the multiple liner regression equation. For India, we find that when the temperature raise 1.6°C or the fragility index reach 90, India will become fragile. Then we predict by Back-Propagation neural network, finding that it is hard for India to be fragile within 100 years. Additionally, we apply our model to China, finding that its fragility index decreased with its intervention by decreasing most indicator index. Besides, we measure the total cost of intervention by Markal―Macro Model, getting that the total cost takes up 4% of GDP. Finally, because the indicators affecting the fragility of the cities is different from these affecting states, we modified it by altering the indicators we considered to apply in a small “state”, so it could be effective for cities to reflect the climate change impact on fragility. [ABSTRACT FROM AUTHOR]

An accurate used car price evaluation is a catalyst for the healthy development of used car market. Data mining has been applied to predict used car price in several articles. However, little is studied on the comparison of using different algorithms in used car price estimation. This paper collects more than 100,000 used car dealing records throughout China to do empirical analysis on a thorough comparison of two algorithms: linear regression and random forest. These two algorithms are used to predict used car price in three different models: model for a certain car make, model for a certain car series and universal model. Results show that random forest has a stable but not ideal effect in price evaluation model for a certain car make, but it shows great advantage in the universal model compared with linear regression. This indicates that random forest is an optimal algorithm when handling complex models with a large number of variables and samples, yet it shows no obvious advantage when coping with simple models with less variables. [ABSTRACT FROM AUTHOR]