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In this work, SEM images analysis and fractal dimension calculations are performed on the fracture surfaces for N50-type with low coercivity, 30 EH-type with high coercivity, and isotropic Nd-Fe-B permanent magnets. It is discovered for the first time that the difference between the fractal dimensions of fracture surface parallel and perpendicular to the c-axis D ∥ and D ⊥ for N50-type magnet with low coercivity is large, and the difference between the fractal dimensions of D ∥ and D ⊥ for 30 EH-type magnet with high coercivity is small. However, there is almost no difference in the fractal dimension of D ∥ and D ⊥ for isotropic magnet. The correlation between anisotropic fractal dimension of the fracture surface and coercivity is revealed. The difference between the fractal dimension of fracture surface D ∥ and D ⊥ not only reflects the difference in micro morphology, but also serves as an important performance parameter to characterize the coercivity of the magnet, which provides new insights for studying the relationship between the magnetic properties and the microstructure of permanent magnetic materials.

With an increasing number of Geographical Information System (GIS) services publicly available on the Web, the discovery of composite GIS services is promising when novel requirements are to be satisfied. GIS services in the repository like ArcGIS software are organized in a tree hierarchy, where a parent node represents a categorial GIS service with a coarser-granularity than its child GIS services, while leaf nodes correspond to atomic and exercisable GIS services. In this setting, discovering appropriate atomic GIS services is challenging. To remedy this issue, this paper proposes a composite GIS service discovery mechanism. Specifically, for the given requirement, select the parent nodes that take the given input parameters as input and remove their inactivated children. Use remaining children to build the network and repeat the previous operation until finding the services that contain the required output. Then record the semantic similarity degree, calculated by services functional description, in this network. By using the simulated annealing algorithm, a composite GIS services solution will be recommended from this semantic network. Evaluation results demonstrate that our approach could give more significant solution compared with the state-of-the-art techniques.

It is a common phenomenon that GIS service, a convenient tool, helps people to solve the problem in various fields. However, single GIS service can no longer meet the diverse needs of users. To address this challenge, a GIS services composition recommendation framework based on semantic and heuristic optimization algorithms is proposed in this paper. The Normalized Google Distance (NGD), as an indicator of invoking between two services, is used to construct dynamic semantic network. In order to save processing time, we use the hierarchical structure of ArcGIS services. In addition, we use the improved heuristic optimization algorithm to find the solution with the highest semantic value quickly. Consequently, once the initial parameters set and the end parameters set are given by the user, our GIS services composition recommendation framework will find the most appropriate Directed Acyclic Graph (DAG) to the user. The result of evaluation proves that our method could give more meaningful solution, compared with others.