In this work, we have used knowledge visualization technologies to evaluate the global scientific production and trend of e-government research. It was found that the U.S. has the most cooperation, and its publication number and the centrality are now among the world's highest, while China has no large-scale domestic cooperation relations, and China's e-government research paper mainly come from the east and south part. The hotspots of the e-government research include the enhancement of e-government cross-sectorial collaboration, the construction of e-government, the security infrastructure design of digital government in a complicated environment, while the performance evaluation has become the research frontier in this field.
This paper examines the effect of gasoline and diesel prices on passenger transportation demand. To solve the urban haze problem caused by the consumption of petroleum products by transportation, pricing policies are examined as a primary way to adjust supply and demand. Koyck's distributed lag model is used to analyze the long-term relationship between oil prices and passenger transportation demand, and a simple linear equation is presented to analyze the short-term relationship. In addition, using an estimation based on monthly data from 2010 to 2012, price decomposition techniques are employed to separately analyze the demand when prices rise or fall. The results indicate that passenger transportation demand decreases more rapidly when price rises than when the price falls. However, the demand increases when the maximum historical price increases. Further, passenger traffic demand adjusts more slowly when the diesel price changes than when the gasoline prices change, which indicates that more attention should be paid to gasoline prices when analyzing passenger traffic demand.
Customer credit scoring is an important concern for numerous domestic and global industries. It is difficult to achieve satisfactory performance by traditional models constructed on the assumption that the training and test data are subject to the same distribution, because the customers usually come from different districts and may be subject to different distributions in reality. This study combines ensemble learning and transfer learning, and proposes a clustering and selecting based dynamic transfer ensemble (CSTE) model to transfer the related source domains to target domain for assisting in modeling. The experimental results in a large customer credit scoring dataset show that CSTE model outperforms two traditional credit scoring models, as well as three existing transfer learning models.
This paper argues that objective and subjective evaluation should be applied simultaneously to evaluate scientific journal. Moreover, the evaluation is often made by groups and inevitably contains evaluators' subjective judgments. Accordingly, this paper develops a hybrid fuzzy multi-criteria group evaluation and statistics method which considers objective and subjective evaluations, i.e., bibliometric measures and peer review opinions simultaneously. A model is established to achieve maximum group consensus. To determine the criteria weights, this paper applies an intuitionistic fuzzy weighted averaging operator and to obtain the evaluator weights, a fuzzy distance-based method is used. Thereafter, this paper uses TOPSIS method to aggregate the objective and subjective ratings. Finally, a practical case study is presented.
To provide a support in suggesting possible research directions on port operations, this paper surveys the current state of the art in relevant literature. Particular focus is put on resource allocation problems in seaport container terminals which involves berth allocation, quay crane scheduling, storage yard allocation, vehicle dispatching, human resources management and so on. As separated problems have been fully studied regarding port operations, this paper reveals the need for integrated optimization on different resource allocation problems under uncertain environment. Online optimization is also an important need in terms of unexpected disruptions.
As we all know, the possibility of heavy earthquake is tiny, however, the destructiveness is serious, so it is important to improving the veracity of predicting destructive earthquake. This paper using the database of the destructive earthquake precursor, by defining the small probability even and estimating the parameter of success rate in small probability event prediction and changing the parameter, to transform the small probability event to conditional probability event. This paper attempts to find a way to improving the possibility of destructive earthquake which belongs to small probability events.
The Excel spreadsheet built-in a lot of function, is widely applied in the actual work area, such as accounting, financial management. This paper introduces two commonly used Excel function: the IF function and VLOOKUP function, and use these functions to solve the actual financial problems. Using the If function, for example, the seven layers of nested solved the problem of calculation of individual income tax. By using VLOOKUP function in Excel data list, we find the target data and references.
Since the global financial tsunami, Exchange Traded Funds have attracted much attention, as investors are increasingly concerned with the underlying risk exposure and transparency in their investments. However, much is yet to be learned about its behavioral characteristics, in addition to the traditional risk and return factors. In this paper, we take a behavioral finance perspective to investigate additional behavioral factors during the ETF investment process. We have found some behavioral characteristics, that are unique for the ETF investment and the Hong Kong regions.
In order to discover the most significant factors which affect the selecting of the young consumer on their mobile operators, a questionnaire is used in this paper to get some information about the mobile users. And based on the perspective of consumer behavior, consumer behavior model is used in this research. To test and verify this model, this paper use PCA (principal component analysis) in the data analysis and then get two significant factors: the Experience and the Outside-effects, and then put forward the strategy of family binding. This paper also explains the other data which come from the questionnaire and give some suggestions to the three mobile operators for their market share in the 3G and even 4G users compete.
In order to solve the problem of slow convergence of the basic firefly algorithm, a modified method was proposed. In the modified firefly algorithm, the parameters of the algorithm can be adjusted adaptively according to the lightness variance of the firefly population. And the random movement step can be determined in accordance with the distance of two fly flies. Meanwhile, the method of autonomous flight was also proposed to improve the performance of the basic algorithm. The performance of the modified firefly algorithm was proved though simulation experiments.
The authors use risk-as-feelings hypothesis and the affect heuristic theory to illustrate that consumers' panic will affect their risk perception when the product-harm crisis has happened. The study investigates the amplification effect of consumers' perceived probability on objective probability for the harm of crises. Besides, there is a positive correlation between panic and perceived probability. Furthermore, we also find the divergence of perceived probability among consumers with distinct degrees of involvement when they confront distinct objective probabilities for the harm of crisis.
In this paper, we focus on a optimal production planning problem with carbon emission trading. We build a bilevel decision-making model with taking into account the participation of the government. In this model, the government is the leader and the firm is the follower. Since the uncertain market factors, the demand of the products and the prices of allowance of carbon dioxide (CO 2 ) emission are assumed as random variables. The technique of chance-constrained programming are used to deal with the randomness. After that, the bilevel structure of the model is transformed into single model through Karush-Kuhn-Tucker (KKT) algorithm. A numerical example, as additional discussion illustrated the effectiveness of the model and algorithm proposed in this paper.
This paper proposes a public opinion propagation model on social networks based on SEIR. This model consider impacts of the node degree, social networks peculiar dissemination rules and users' habits, utilizes epidemiology and complex network theories, and establishes the dynamic evolution equations by building mathematical models of informed probability and disseminate probability. Simulation results show that pages of updating information can affect the propagation behavior of S, E nodes directly, the value of spread probability is the key impact factor of propagation velocity and scale. Additional, the number of infected nodes should be maintained at an appropriate range in order to make the maximum range of public opinion propagation.
Volatility is a very important factor of measuring financial risk. This paper introduces the volatility measurement method of high frequency financial time series involving the nonnegative-Multiplicative Error Model. This paper takes the high frequency data of HS300 index of Chinese stock market as the research object, building the TARCH model according to leverage, and uses the "realized volatility" to build ARFIMA model, multiplicative error model respectively, then carries on the comparative analysis on accuracy after using the three models to predict with the mean square error method. The analysis results show that the multiplicative error model gives the best prediction effects, and ARFIMA model is the second.
The paper constructed the information extraction method to extract description characteristic of execution process from the description text to provide data support for the evaluation of enterprise strategy execution effectiveness. The constructed information extraction method included two stages, i.e., Feature information extraction and information integration. It overcame some weaknesses of the current information extraction technology, such as its mere use for extracting time, address and other named entities. Firstly, the information extraction process of descriptive information is designed through establishing substantial GATE vocabularies and compiling ordinary and special extraction rules to avoid the disadvantages of the identification of GATE in Chinese named entity. Then, rules of integration of description characteristics information are established on the basis of semantic relation, positional relation to structurally represent the process of executing strategic activities.
In order to study the relationship between the foreign final demand and China's output, this paper develops the induction coefficient and dependence degree based on the inter-regional input-output model, and calculates them using the world input-output table with seven regions, i.e. EU, US, China, Japan, South Korea, other East Asia countries and rest of the world from 1995 to 2011. The results show that: final demand from the US and the EU contribute most to China's output. China's output has larger dependence degree on foreign consumption demand of other countries while smaller dependence degree on foreign investment. However, the former has trend of decline while the later is on the rise.
In this paper, we consider portfolio adjusting problem in the environment with multiple uncertainties. We establish two kinds of mean-variance adjusting models. The first one is formulated by only taking into account the transaction costs, and the second one is established by simultaneously considering transaction costs and minimum transaction lots. In the situation that all the returns are symmetrical triangular fuzzy random variables, these two models are converted into equivalent deterministic forms which are mixed-integer nonlinear programming models. Finally, a numerical example is given to illustrate the modelling idea.
Viewing the role of senior managers in organizations, the human resource managers suffer from the lack of hands-on tools for the performance evaluation of senior managers. This study reports the design of expert systems for senior managers' performance evaluation. Different from the normal information systems for employee performance appraisal, several modular components for this special case are designed under the guidelines of designing expert systems in previous literature.
The Nash nonlinear grey Bernoulli model (NNGBM(1,1)) is a flexible grey system model that can be used to forecast nonlinear data. In order to better forecast the fluctuations contained in the original data, a Fourier Nash nonlinear grey Bernoulli model (FNNGBM(1,1)) is proposed in this research. The parameters optimization of FNNGBM(1,1) is formulated as a combinatorial optimization problem and is solved collectively using the concept of Nash equilibrium. The simulation and practical application to fluctuation data both prove that FNNGBM(1,1) could offer a more precise forecast than NNGBM(1,1) and the Fourier residual GM(1,1) (FGM(1,1)). Thus, FNNGBM(1,1) is selected to forecast the export, import, trade balance and trade specialization coefficient of Chinese high-tech products during the period 2012 to 2014. The forecasting results show that import/export data will maintain rapid growth, with corresponding trade balance enlargement; however, there will be a concomitant decrease in the trade specialization coefficient.
User interest modeling is an important way for P2P document sharing systems to improve the level of information service such as personalized information retrieval and document recommendation. Based on K-medoids clustering, the paper presents a method of user interest modeling for P2P document sharing systems. Staring from the perspective of the shared document, the proposed approach creates the initial user interest model with k-mediods clustering algorithm. Then, combining with the related results of user's historical queries, the initial user interest model is improved and complete user interest model is obtained.
