The global warming problem caused by carbon emissions has significantly influence the sustainable development of human society. Nowadays, more and more countries and regions are taking measures to cope with this problem. Researches indicated that consumers' energy consumption has a significant influence on the carbon emissions. To guide, regulate and motivate consumers' energy consumption behavior is benefit to make a low-carbon energy consumption pattern, which has an important theoretical and realistic significance. Based on the personal carbon trading scheme, we construct an energy use choice model and analyze the market equilibrium of the carbon trading. It is shown that personal carbon trading scheme can motivate consumers to choose clean energy and to make a low-carbon energy consumption pattern. The supply curve of the carbon allowance is different from the general commodity supply curve. When the carbon price increases, the supply quantity of the carbon allowance will decrease. The initial allocated carbon allowance should be set at an appropriate level, thus, the carbon trading among consumers can be achieved.

Concepts revolving around "displaced cones" are used to extend the ideas of nondominated solutions in multiobjective programming. Necessary and sufficient conditions are formed around the additive models of DEA to idelltify nondominated solutions. This provdes a basis for effecting contacts with other disciplines and other parts of DEA.

Based on price duration and volume duration, this paper introduces two indexes to describe the market liquidity. Results show that there is conflict between the two indexes. To find out which is more significant, liquidity ratio is introduced to demonstrate the liquidity situation. Using the new indexes, this paper studies the intraday trend of liquidity and makes model to find out the factors influencing the liquidity of futures market. Empirical results show that both volume and open interest influence the liquidity positively, while absolute yield has significant negative influences. What's more the volume's influence is more significant. The results also show that price spread is not suitable for Chinese market. We should consider the volume when measuring Chinese market's liquidity.

To improve individual customer churn prediction accuracy, integrated model of SMC, rough sets (RS) and least squares support vector machines (LSSVM), i.e., SMC-RS-LSSVM, for E-business customer churn prediction was proposed in this paper. Firstly, customers' active probabilities were obtained by using SMC model to identify customer churn status with the threshold of 0.5. The training and testing samples were formed by the correctly identified customers and incorrectly identified customers respectively. Then the attributes were reduced using rough sets and LSSVM was trained with training samples. Lastly, the trained LSSVM model was used to identify customer churn status of testing samples. Taking 2525 customers in an E-shop as samples, empirical results show that, compared with SMC, BP neural network and LSSVM models, integration model of SMC-RS-LSSVM is an efficient and practical tool for E-business customer churn prediction of testing samples.

本文是世界一流数学家V.Arnold给5至15岁孩子精心挑选的数学题。

Modelling the US West Texas Intermediate (WTI) crude oil spot price structure has received an increasing attention in recent years. However, most of the existing literatures focus on classical econometric methods and point-valued counterfactual analysis, which may suffer from informational loss. This paper proposes a novel interval-based counterfactual analysis to explore the impact of crude oil fluctuation events that appeared on WTI crude oil spot prices in 2011. The interval-based models can be used to simultaneously estimate hypothetical values of level and volatility of WTI price that are not affected by the event. These counterfactual estimations are based on the cross-sectional correlations between WTI and other 10 international crude oil spot markets. Our findings reveal that both the level and volatility of WTI price were lowered by the oversupply of crude oil production caused by shale oil boom, and this effect did not disappear until the end of 2014. The results can provide an important reference for the government, industry practitioners and individual investors.

Emergency resource scheduling of disasters and accidents is an effective means to reduce the loss of lives and property. This paper considers the multi-resource emergency scheduling problem in disaster chain. After the multi-resource and multi-disaster-place scheduling model is proposed, a novel heuristic algorithm is given based on network optimization in graph theory and linear programming optimization. The computational complexity of the algorithm is also analyzed. This algorithm divides the resources into different categories according to emergency resource demand in original and secondary disasters. For common emergency resources both disasters demand in disaster chain, this algorithm reduces the complexity and computation time of the problem by improving the linear relaxation solution of the model. For emergency resources the original or secondary disasters demand, this algorithm finds depots by local search method. Therefore the satisfactory resource scheduling solution is obtained. Finally, a numerical example is presented to show the effectiveness and feasibility of this algorithm. The algorithm is suitable for large-scale problems and meets the real-time requirement.

Chen Jingrun was born on 22 May,1933 in Fu Zhou in Fu Jian province of China.Chen's father Chen Yuanjun was a clerk in postoffice and his mother was passed away in 1947.Chen's family was comparatively poor since the income of his father was lower and the population of his family was comparatively large.

The evidential network model is proposed, on the basis of the Dempster-Shafer theory of evidence and the graph theory, for management problems with uncertainty and complex correlation. The definition and parameter model of evidential network is presented before the advantages of evidential network are concluded. The reasoning problem, reasoning framework and reasoning process are proposed. Then the forward and backward reasoning algorithms are deduced. Finally, a number example is explored to demonstrate the algorithm and process. The evidential network model will be provided as an efficient method for analyzing and resolving the problems in the research fields of decision analysis and system engineering.

In comparison to the US market, we use several methods to study the volatility risk and the volatility risk premium in China's market. We find that there exist anomalies in China's market. First, unlike the US market, the volatility risk in China's market is not a systematic risk on the whole. It is time-varying, and shows significantly negative during crash period while significantly positive during non-crash period. Second, the option-implied volatility risk premium in China is negative although the volatility risk is not a systematic risk, which differs from the US market and does not match the principles of finance. That might mean the options should be continuously overpriced in China. It is proved by the simulation of the Buy-Write strategy.

The vibration of thin circular cylindrical shells has been studied extensively. In this paper, the existence and uniqueness of the solution of this system are proved by means of semigroup theory on the Hilbert space, and the eigenvalue problem is discussed. This offers a theoretical basis for solving this kind of engineering problems.

As a major source of information, news media has significant impacts on the stock market investors, and further influence the stock market dynamics. In order to study the effects of online news reports on the Chinese stock market, this paper uses data collected from the Internet, and analyzes through indicators of media attention and media sentiment. After enriching the sentiment corpus, covering positive sentiment, negative sentiment and neutral sentiment, indicators of media attention and media sentiment are constructed. Based on the empirical studies of Shanghai Composite Index during the period from January 1st, 2012 to April 30th, 2016, we find that the media attention and sentiment both affect the stock market. While the sentiment index is a better indicator to capture media sentiments. Although there are various kinds of sources of online news, 52.8% of these sources provide 99.3% of all the related news reports. The results could improve the analysis and prediction of the Chinese stock market, and provide suggestions for investors.

In order to optimize the investment portfolio of the international financial market, this paper selects the important stock index of the seven major stock markets in the world. First, we use the more flexible APARCH model to describe the "stylized facts" of the stock index return sequence. In the portfolio optimization model, the R-vine Copula, which is selected by the maximum spanning tree (MST) algorithm is used to describe the interdependent structure of the seven stock markets, and then measure portfolio risk under R-vine Copula dependent structure CVaR. Finally, the Mean-CVaR portfolio model was established under the R-vine Copula dependent structure condition. And compare Mean-VaR, Mean-CVaR and Mean-CVaR model based on R-vine Copula dependency structure. The empirical results shows that the interdependence between assets can be used to optimize the portfolio effect, reducing the risk of portfolio risk while increasing the rate of return. The Mean-CVaR model based on the R-vine Copula dependent structure is better than the Mean-CVaR model, while the Mean-VaR model has a relatively poor performance.

Based on a continuous multinomial logit model, this paper develops a stochastic urban system equilibrium to investigate residents' choices of residential location and work place in a linear polycentric city, which takes into account individual taste heterogeneity in terms of location choice. In the urban system, people commute by private car, bus or rail transit between their home and working places. The model is then used to explore the impacts of urban rail transit investment on residential distribution, housing price, and attractiveness of CBDs for employees through comparison of the urban system equilibrium performances before and after the introduction of rail transit. The result reveals that the introduction of urban rail transit makes employment more concentrated in the relatively developed CBD and further widens the gap of attractiveness between the two CBDs. It also finds that improvements of traffic conditions encourage residents to migrate towards the suburbs, and thus leads to a more uniform distribution of residential density and housing price across the city, weakening the advantages of central areas.

Delivery by UAV in the logistics field is becoming a fast and efficient dispatch method and application hotspot. For forward and reverse logistics data, drone dispatch is an important means for large-scale logistics enterprises at home and abroad to implement efficient logistics delivery. A path dynamic programming model (KMG) that integrates the scalable K-Means++ algorithm and genetic algorithm to implement a UAV scheduling strategy including reverse logistics. The KMG model integrates the reverse logistics path into the forward logistics path, using weighted clustering. The algorithm determines the minimum number of dispatched drones required for different attribute packages. For each connected graph of coordinate data, the genetic algorithm is used to solve the TSP problem and the feasible solution is coded, and finally the minimum Euler loop is solved. The simulation results show that the cost of the KMG model is 20.08% lower than that of the independent reverse logistics. The time of using the scalable K-Means++ clustering calculation is 298.85% shorter than the traditional K-Means algorithm.

Based on the natural experiment of the "central management enterprise salary system reform" (pay ceiling order), which was implemented on January 1, 2015, this paper empirically examines the impact of policy on the performance of listed state-owned enterprises. By using the difference-in-differences (DID) model and propensity score matching (PSM) method, we find that "pay ceiling order" has a negative effect on the operating performance of listed companies. Further studies on the dynamic effect of "pay ceiling order" show that the second year is more significant than the first year after implementation. It is also found that the policy has the heterogeneity influence on the performance of different types of enterprises. Results show that performance of central government-owned, high compensation and large-scale enterprises declined more comparing to the local government-owned, low pay and small-scale enterprises. Our findings suggest that executive compensation system of state-owned enterprises should follow the enterprise features and market-oriented reform should be accelerated to maximize the enterprise value.

The medical dispute has been the important contradiction restricting the development of harmonious society, while government regulation will play an important role in strategy choice of both sides when resolving disputes. This article analyzes the influence of the degrees of governmental regulation to doctor-patient strategies by building the doctor-patient evolution game model. The study results indicate that different degrees of regulation result in different strategy profile, different regulation efforts to doctor and patient will determine the operation status and behavior choice separately. The government's improvement of the regulation standard to hospitals and patients will gradually raise the probability of hospitals to operate specifically, regulate patients' routes and ways to safeguard legal rights, and finally normalized the behaviors of both sides. The behaviors of "medical disturbance" are somehow a way to expose the operation status of hospitals; different self-positioning of the government in medical disputes determines different strategies, which resulted in a relatively big difference in the results of the medical disputes. Finally, this article discusses the evolution results under the circumstances of insufficient governmental regulation and improper regulation strategies combing with the Putian Medical Group and Medical Dispute in Nanping, Fujian.

Convertible bond becomes an important way of refinancing for Chinese listed companies recently, and the pricing problem of convertible bond has attracted wide attention. In this paper, we propose a two-factor willow tree method to price Chinese convertible bonds with the consideration of stock price, interest rate, default rate and the trigger conditions of callable and putable clauses embedded in Chinese convertible bonds. By comparing with the existing models, our model matches the market prices better. In empirical research, we estimate the volatility of the stock price process with both historical volatility and implied volatility which is got by calibrating the valuation model with the market price of convertible bond. We find that the accuracy of the valuation model has been greatly improved.

Mutually exclusive products (such as liquids, hazardous chemicals, etc.) cannot be mixed into the same container. Logistics companies usually use multi-compartment trucks to deliver mutually exclusive products to customers. The loading and vehicle routing strategies are the key issues to determine the distribution efficiency and distribution costs. Considering the constraints of loading and unloading sequence and transportation time of mutually exclusive products, a joint optimization model of loading and distribution is constructed to minimize the distribution cost. This paper designs an improved genetic algorithm for solving the model, using the queen evolution and the edge reconstruction crossover operations based on probability to lift the ability of finding the optimal solution. Then we construct the testing examples based on the vehicle routing problem benchmark provided by Augerat to verify the running time and solving efficient of genetic algorithm. The simulation results show that, the solutions obtained by improved genetic algorithm are better than those of classical genetic algorithm, for small-scale examples, the improved genetic algorithm can obtains global optimal solution; for the medium-sized or large size examples with no more than 101 customers, the approximate optimal solution can be obtained in 130 seconds using genetic algorithm. The innovation of this paper lies in the establishment of a mathematical model for a new expended vehicle routing problem and the design of a fast and effective algorithm for solving the model. The mathematical model and algorithm of this paper provide a theoretical basis and algorithmic support for logistics companies to draw up distribution schedule of mutually exclusive products.

Considering the complexity and heterogeneity of weapon nodes in the weapon system of systems, and the diversity of the relationship between equipments, this paper first proposes a modeling method based on heterogeneous networks. Next, by referring to the concept of observe-orient-decide-act (OODA) combat cycle theory and combining the definition of meta-path in heterogeneous networks, the number of attack link is used to evaluate the structural robustness of the weapon system of systems. Then, we do the experiments on structural robustness of weapon system-of-systems under random attack and selective attack strategy using the traditional natural connectivity index and the number of attack link. It is found that the number of attack link with actual semantic information is more effective to evaluate the structural robustness of weapon system of systems. Finally, the invulnerability of the weapon system of systems under certain background is analyzed, which provides assistant decision-making for the attacker's primary attack target and the defender's primary protection object.