hyo-xtm: a set of hyper-graph operations on xml topic map toward knowledge management

Knowledge management is a critical issue for the next-generation web application, because the next-generation web is becoming a semantic web, a knowledge-intensive network. XML Topic Map (XTM), a new standard, is appearing in this field as one of the structures for the semantic web. It organizes information in a way that can be optimized for navigation. In this paper, a new set of hyper-graph operations on XTM (HyO-XTM) is proposed to manage the distributed knowledge resources.HyO-XTM is based on the XTM hyper-graph model. It is well applied upon XTM to simplify the workload of knowledge management.The application of the XTM hyper-graph operations is demonstrated by the knowledge management system of a consulting firm. HyO-XTM shows the potential to lead the knowledge management to the next-generation web.

etl workflow analysis and verification using backwards constraint propagation

University of Twente; Centre for Telematics and Information Technology; Netherlands Organisation for Scientific Research; Jacquard; Capgemini

气泡/液滴运动的Level Set方法模拟研究

利用Level Set方法，结合投影法求解了描述气泡/液滴运动的Navier-Stokes方程。对地面常重力场中不同大小的空气泡在高黏度糖浆溶液中的自由上升运动现象，数值模拟结果与实验观测结果符合甚好，表明该方法能够计算大密度比和黏度比$(>1000:1)$情况下的气液两相流动。而对等密度液滴的热毛细迁移现象的数值模拟结果同样能够与实验结果相一致，表明该方法同样适于研究具有Marangoni效应的两相流动现象，特别是在空间微重力环境中的气液两相传热现象中的局部流动与传热问题。

Application of rough set-based analysis to extract spatial relationship indicator rules: An example of land use in Pearl River Delta

Spatial relations, reflecting the complex association between geographical phenomena and environments, are very important in the solution of geographical issues. Different spatial relations can be expressed by indicators which are useful for the analysis of geographical issues. Urbanization, an important geographical issue, is considered in this paper. The spatial relationship indicators concerning urbanization are expressed with a decision table. Thereafter, the spatial relationship indicator rules are extracted based on the application of rough set theory. The extraction process of spatial relationship indicator rules is illustrated with data from the urban and rural areas of Shenzhen and Hong Kong, located in the Pearl River Delta. Land use vector data of 1995 and 2000 are used. The extracted spatial relationship indicator rules of 1995 are used to identify the urban and rural areas in Zhongshan, Zhuhai and Macao. The identification accuracy is approximately 96.3%. Similar procedures are used to extract the spatial relationship indicator rules of 2000 for the urban and rural areas in Zhongshan, Zhuhai and Macao. An identification accuracy of about 83.6% is obtained.

A Unified Tensor Level Set for Image Segmentation

This paper presents a new region-based unified tensor level set model for image segmentation. This model introduces a three-order tensor to comprehensively depict features of pixels, e.g., gray value and the local geometrical features, such as orientation and gradient, and then, by defining a weighted distance, we generalized the representative region-based level set method from scalar to tensor. The proposed model has four main advantages compared with the traditional representative method as follows. First, involving the Gaussian filter bank, the model is robust against noise, particularly the salt-and pepper-type noise. Second, considering the local geometrical features, e. g., orientation and gradient, the model pays more attention to boundaries and makes the evolving curve stop more easily at the boundary location. Third, due to the unified tensor pixel representation representing the pixels, the model segments images more accurately and naturally. Fourth, based on a weighted distance definition, the model possesses the capacity to cope with data varying from scalar to vector, then to high-order tensor. We apply the proposed method to synthetic, medical, and natural images, and the result suggests that the proposed method is superior to the available representative region-based level set method.

A Relay Level Set Method for Automatic Image Segmentation

This paper presents a new image segmentation method that applies an edge-based level set method in a relay fashion. The proposed method segments an image in a series of nested subregions that are automatically created by shrinking the stabilized curves in their previous subregions. The final result is obtained by combining all boundaries detected in these subregions. The proposed method has the following three advantages: 1) It can be automatically executed without human-computer interactions; 2) it applies the edge-based level set method with relay fashion to detect all boundaries; and 3) it automatically obtains a full segmentation without specifying the number of relays in advance. The comparison experiments illustrate that the proposed method performs better than the representative level set methods, and it can obtain similar or better results compared with other popular segmentation algorithms.

A set of polymorphic microsatellite loci for the bay scallop, Argopecten irradians

The method of creating enriched microsatellite libraries can supply an abundant source of microsatellite sequences at a considerably reduced cost. Here we report the development of 15 polymorphic microsatellite loci from the bay scallop, Argopecten irradians, using enrichment protocol. Polymorphism was assessed in a sample of hatchery population (n = 38) revealing three to seven alleles per locus. The expected and observed heterozygosities ranged from 0.198 to 0.813 and from 0.083 to 0.833, respectively. These markers will be useful for genetic variation monitoring and parentage analysis.