Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarp...Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.展开更多
E-commerce is playing an important role in our life. As E-commerce model, SINA is an online media company serv ing China and the global Chinese communities, which is worth studying. In the paper, the advantages of SIN...E-commerce is playing an important role in our life. As E-commerce model, SINA is an online media company serv ing China and the global Chinese communities, which is worth studying. In the paper, the advantages of SINA.com will be intro duced. After that, you can know the competition and strategy of SINA.com. Its future development is discussed at the end.展开更多
Sina Weibo,an online social network site,has gained popularity but lost it in recent years.Now we are still curious on the number of posts in Sina Weibo in its golden age.Besides checking this number in Sina’s operat...Sina Weibo,an online social network site,has gained popularity but lost it in recent years.Now we are still curious on the number of posts in Sina Weibo in its golden age.Besides checking this number in Sina’s operating results,we aim to estimate and verify this number through measurement by using statistical techniques.Existing approaches on measurement always rely on the supported streaming application programming interface(API)which provides proportional sampling.However no such API is available for Sina Weibo.Instead,Sina provides a public timeline API which provides non-proportional sampling but always returns a(nearly)fixed number of s amples.In this paper,we present a novel method utilizing this API and estimate the number of posts in Sina Weibo in its golden age.展开更多
文摘Ethiopia has a mountainous landscape which can be divided into the Northwestern and Southeastern plateaus by the Main Ethiopian Rift and Afar Depression. Debre Sina area is located in Central Ethiopia along the escarpment where landslide problem is frequent due to steep slope, complex geology, rift tectonics, heavy rainfall and seismicity. In order to tackle this problem, preparing a landslide susceptibility map is very important. For this, GISbased frequency ratio(FR) and logistic regression(LR) models have been applied using landslide inventory and the nine landslide factors(i.e. lithology, land use, distance from river & fault, slope, aspect, elevation, curvature and annual rainfall). Database construction, weighting each factor classes or factors, preparing susceptibility map and validation were the major steps to be undertaken. Both models require a rasterized landslide inventory and landslide factor maps. The former was classified into training and validation landslides. Using FR model, weights for each factor classes were calculated and assigned so that all the weighted factor maps can be added to produce a landslide susceptibility map. In the case of LR model, the entire study area is firstly divided into landslide and non-landslide areas using the training landslides. Then, these areas are changed into landslide and non-landslide points so as to extract the FR maps of the nine landslide factors. Then a linear relationship is established between training landslides and landslide factors in SPSS. Based on this relationship, the final landslide susceptibility map is prepared using LR equation. The success-rate and prediction-rate of FR model were 74.8% and 73.5%, while in case of LR model these were 75.7% and 74.5% respectively. A close similarity in the prediction and validation rates showed that the model is acceptable. Accuracy of LR model is slightly better in predicting the landslide susceptibility of the area compared to FR model.
文摘E-commerce is playing an important role in our life. As E-commerce model, SINA is an online media company serv ing China and the global Chinese communities, which is worth studying. In the paper, the advantages of SINA.com will be intro duced. After that, you can know the competition and strategy of SINA.com. Its future development is discussed at the end.
基金This work was supported in part by the National Natural Science Foundation of China under Grants 61602111,61502099,61502100,61532013the Jiangsu Provincial Natural Science Foundation of China under Grants BK20150628,BK20150637+2 种基金the Jiangsu Provincial Scientific and Technological Achievements Transfer Fund,and by the Jiangsu Provincial Key Laboratory of Network and Information Security under Grant BM2003201the Key Laboratory of Computer Network and Information Integration of Ministry of Education of China under Grant 93K-9Collaborative Innovation Center of Novel Software Technology and Industrialization.
文摘Sina Weibo,an online social network site,has gained popularity but lost it in recent years.Now we are still curious on the number of posts in Sina Weibo in its golden age.Besides checking this number in Sina’s operating results,we aim to estimate and verify this number through measurement by using statistical techniques.Existing approaches on measurement always rely on the supported streaming application programming interface(API)which provides proportional sampling.However no such API is available for Sina Weibo.Instead,Sina provides a public timeline API which provides non-proportional sampling but always returns a(nearly)fixed number of s amples.In this paper,we present a novel method utilizing this API and estimate the number of posts in Sina Weibo in its golden age.
