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This paper focuses on landslide susceptibility prediction in Nanchuan, a high-risk landslide disaster area. The evidential belief function (EBF)-based function tree (FT), logistic regression (LR), and logistic model tree (LMT) were applied to Nanchuan District, China. Firstly, an inventory with 298 landslides was compiled and separated into two parts (70%: 209; 30%: 89) as training and validation datasets. Then, based on the EBF method, the Bel values of 16 conditioning factors related to landslide occurrence were calculated, and these Bel values were used as input data for building other models. The receiver operating characteristic (ROC) curve and the values of the area under the ROC curve (AUC) were used to evaluate and compare the prediction ability of the four models. All the models achieved good results and performed well. In particular, the LMT model had the best performance (0.847 and 0.765, obtained from the training and validation datasets, respectively). This paper also demonstrates the superiority of integration and optimization of models in landslide susceptibility evaluation. Finally, the best classification method was selected to draw landslide susceptibility maps, which may be helpful for government administrators and engineers to carry out land design and planning. [ABSTRACT FROM AUTHOR]

In this paper, considering the financial performance of China's listed companies as the dependent variable, a computational intelligence method based on genetic algorithms and discriminant analysis is employed to screen variables that influence financial performance and forecast the change of financial performance. Specifically, a new model based on genetic algorithms is developed to screen factors that influence financial performance of Chinese listed companies. The empirical results show that variables selected by genetic algorithms can predict financial performance well. Copyright © 2009 John Wiley & Sons, Ltd. [ABSTRACT FROM AUTHOR]

The article offers information on several conferences related to nondestructive testing (NDT) for 2012-2013 including the China (Beijing) International Exposition of Application Technique of NDT to be held on May 10-12, 2012 in Beijing, China, the 8th European Conference on Joining Technology (EUROJOIN) to be held in Pula, Croatia on May 24-26, 2012 and the IEEE Symposium Series on Computational Intelligence-IEEE SSCI 2013 to be held in Singapore on April 15-19, 2013.

Reasonably forecasting demands of natural gas in China is of significance as it could aid Chinese government in formulating energy policies and adjusting industrial structures. To this end, a self-adapting intelligent grey prediction model is proposed in this paper. Compared with conventional grey models which have the inherent drawbacks of fixed structure and poor adaptability, the proposed new model can automatically optimize model parameters according to the real data characteristics of modeling sequence. In this study, the proposed new model, discrete grey model, even difference grey model and classical grey model were employed, respectively, to simulate China's natural gas demands during 2002–2010 and forecast demands during 2011–2014. The results show the new model has the best simulative and predictive precision. Finally, the new model is used to forecast China's natural gas demand during 2015–2020. The forecast shows the demand will grow rapidly over the next six years. Therefore, in order to maintain the balance between the supplies and the demands for the natural gas in the future, Chinese government needs to take some measures, such as importing huge amounts of natural gas from abroad, increasing the domestic yield, using more alternative energy, and reducing the industrial reliance on natural gas. [ABSTRACT FROM AUTHOR]

With the development of science and technology, resource consumption has increasingly become a social problem. The circular economy is an economic form of a comprehensive utilization of resources. At present, China's circular economy industry has begun to develop towards intensification, scale and specialization. With the deepening of research on the value of a comprehensive utilization of resources, the traditional economic field has undergone tremendous changes. According to the current situation of circular economy industry, an intelligent decision-making system is designed using artificial intelligence technology. Empirically, using the circular economy model and combining the sequence parameters to determine the sequence parameter indicators of the scientific and technological innovation subsystem and the energy saving and emission reduction subsystem, the correlation analysis of each indicator is carried out, and the order degree of the two subsystems is obtained respectively, so as to obtain for the enterprise the degree of synergy of the evolution system of the circular economy synergy of industries and industrial clusters. After repeated exploratory tests, it is found that the calculation has achieved over 98% of the precision of an astute direction, and has specific use and value because of the enormous number of preparing modes and trial dependability. Making a specific commitment to the circular economy is a trusted and logical solution. [ABSTRACT FROM AUTHOR]

No abstract received. [ABSTRACT FROM AUTHOR]

The upgrading of the digital economy (DE) is crucial to building an intelligent society. The rapid development of computational intelligence in recent years has also brought vitality to the development of the DE. However, with the deepening of international trade, the issue of trade security has become more and more prominent. Therefore, this study aimed to construct a DE trade security evaluation system. For computational intelligence, this study proposes a support vector machine based on improved particle swarm optimization. Such an algorithm can play a key role in the construction of an evaluation system. It is built for the evaluation system. This study selects five indicators of market competitiveness, trade control, trade dependence, market concentration, and trade legal system evaluation. The experimental results of this study prove that the evaluation system proposed in this study is effective. In its test for some countries in the world, the UK trade security index was the highest at 0.582, while China was relatively low at 0.492. [ABSTRACT FROM AUTHOR]

The construction industry is a labor-intensive industry in China. In recent years, as people's living standards have risen, so have their requirements for the functionality, appearance, and comfort of buildings. The amount of information attached to construction projects is also increasing, especially for some modern large-scale construction projects. Due to the long construction period and a large amount of information in the construction process, the difficulty of project engineering management has also increased significantly. At the same time, with the gradual development and improvement of China's market economy system, the prices of labor and materials in the construction industry continue to rise, and the huge consumption of energy and resources no longer meets the strategic requirements of sustainable development. Therefore, construction project cost management is facing a huge impact, the actual effect of construction project cost management work is not good, and the phenomenon of uncontrolled construction project investment commonly occurs. The use of BIM software to build a building information model can integrate information from all stages of the project and facilitate the participation and cooperation of all project entities. Cost management is carried out before the actual construction of the project, thus realizing the whole process of cost management and effectively avoiding the occurrence of actual costs exceeding the budget after the project is completed. The study analyses the suitability of applying BIM technology to the whole process of construction project cost management with computational intelligence and explains the value and advantages of BIM in the whole process of construction project cost management. On this basis, this research provides a specific analysis of the application of BIM technology in the process of cost management at various stages of the whole process of construction projects and thus puts forward suggestions that can solve the obstacles that may be encountered in the whole process of cost management of construction projects in China. [ABSTRACT FROM AUTHOR]

Information about several papers discussed at the multiconference on Computational Engineering in Systems Applications (CESA) in Beijing, China on October 4-6, 2006 is presented. The conference has aimed to bring together the scholars and practitioners from academia and industries to exchange the latest development in theories and applications of computational techniques. The conference was co-sponsored by the International Association for Mathematics and Computers in Simulation (IMACS).

In this study, hybrid integration of MultiBoosting based on two artificial intelligence methods (the radial basis function network (RBFN) and credal decision tree (CDT) models) and geographic information systems (GIS) were used to establish landslide susceptibility maps, which were used to evaluate landslide susceptibility in Nanchuan County, China. First, the landslide inventory map was generated based on previous research results combined with GIS and aerial photos. Then, 298 landslides were identified, and the established dataset was divided into a training dataset (70%, 209 landslides) and a validation dataset (30%, 89 landslides) with ensured randomness, fairness, and symmetry of data segmentation. Sixteen landslide conditioning factors (altitude, profile curvature, plan curvature, slope aspect, slope angle, stream power index (SPI), topographical wetness index (TWI), sediment transport index (STI), distance to rivers, distance to roads, distance to faults, rainfall, NDVI, soil, land use, and lithology) were identified in the study area. Subsequently, the CDT, RBFN, and their ensembles with MultiBoosting (MCDT and MRBFN) were used in ArcGIS to generate the landslide susceptibility maps. The performances of the four landslide susceptibility maps were compared and verified based on the area under the curve (AUC). Finally, the verification results of the AUC evaluation show that the landslide susceptibility mapping generated by the MCDT model had the best performance. [ABSTRACT FROM AUTHOR]