Web masters usually place certain web pages such as home pages and index pages in front of others. Under such a design, it is necessary to go through some pages to reach the destination pages, which is similar to the scenario of reaching an inner town of a peninsula through other towns at the edge of the peninsula. In this paper, we try to validate that peninsulas are a universal phenomenon in the World-Wide Web, and clarify how this phenomenon can be used to enhance web search and study web connectivity problems. For this purpose, we model the web as a directed graph, and give a proper definition of peninsulas based on this graph. We also present an efficient algorithm to find web peninsulas. Using data collected from the Chinese web by Tianwang search engine, we perform an experiment on the distribution of sizes of peninsulas and their correlations with PageRank values, outdegrees, or indegrees of the ties with other outside vertices. The results show that the peninsula structure on a web graph can greatly expedite the computation of PageRank values; and it can also significantly affect the link extraction capability and information coverage of web crawlers. 相似文献
Over the last decade, application of soft computing techniques has rapidly grown up in different scientific fields, especially in rock mechanics. One of these cases relates to indirect assessment of uniaxial compressive strength (UCS) of rock samples with different artificial intelligent-based methods. In fact, the main advantage of such systems is to readily remove some difficulties arising in direct assessment of UCS, such as time-consuming and costly UCS test procedure. This study puts an effort to propose four accurate and practical predictive models of UCS using artificial neural network (ANN), hybrid ANN with imperialism competitive algorithm (ICA–ANN), hybrid ANN with artificial bee colony (ABC–ANN) and genetic programming (GP) approaches. To reach the aim of the current study, an experimental database containing a total of 71 data sets was set up by performing a number of laboratory tests on the rock samples collected from a tunnel site in Malaysia. To construct the desired predictive models of UCS based on training and test patterns, a combination of several rock characteristics with the most influence on UCS has been used as input parameters, i.e. porosity (n), Schmidt hammer rebound number (R), p-wave velocity (Vp) and point load strength index (Is(50)). To evaluate and compare the prediction precision of the developed models, a series of statistical indices, such as root mean squared error (RMSE), determination coefficient (R2) and variance account for (VAF) are utilized. Based on the simulation results and the measured indices, it was observed that the proposed GP model with the training and test RMSE values 0.0726 and 0.0691, respectively, gives better performance as compared to the other proposed models with values of (0.0740 and 0.0885), (0.0785 and 0.0742), and (0.0746 and 0.0771) for ANN, ICA–ANN and ABC–ANN, respectively. Moreover, a parametric analysis is accomplished on the proposed GP model to further verify its generalization capability. Hence, this GP-based model can be considered as a new applicable equation to accurately estimate the uniaxial compressive strength of granite block samples.
