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| 1 | Effects of fencing on vegetation and soil restoration in a degraded alkaline grassland in northeast China显示文摘In order to restore a degraded alkaline grassland, the local government implemented a large restoration project using fences in Changling county, Jilin province, China, in 2000. Grazing was excluded from the protected area, whereas the grazed area was continuously grazed at 8.5 dry sheep equivalent(DSE)/hm2. In the current research, soil and plant samples were taken from grazed and fenced areas to examine changes in vegetation and soil properties in 2005, 2006 and 2008. Results showed that vegetation characteristics and soil properties improved significantly in the fenced area compared with the grazed area. In the protected area the vegetation cover, height and above- and belowground biomass increased significantly. Soil pH, electrical conductivity and bulk density decreased significantly, but soil organic carbon and total nitrogen concentration increased greatly in the protected area. By comparing the vegetation and soil characteristics with pre-degraded grassland, we found that vegetation can recover 6 years after fencing, and soil pH can be restored 8 years after fencing. However, the restoration of soil organic carbon, total nitrogen and total phosphorus concentrations needed 16, 30 and 19 years, respectively. It is recommended that the stocking rate should be reduced to 1/3 of the current carrying capacity, or that a grazing regime of 1-year of grazing followed by a 2-year rest is adopted to sustain the current status of vegetation and soil resources. However, if N fertilizer is applied, the rest period could be shortened, depending on the rate of application. | Qiang LI DaoWei ZHOU YingHua JIN MinLing WANG YanTao SONG GuangDi LI | 2014 | Journal of Arid Land2014，6，4： | 9 |
| 2 | 基于体波有限频层析成像的青藏高原南部和中部下方印度大陆岩石层俯冲和撕裂的三维图像显示文摘使用TIBET-31N无源地震台阵以及以前临时地震台阵记录到的远震体波数据进行了有限频层析成像反演,对青藏高原南部及中部的三维速度结构成像。在喜马拉雅和拉萨地块下方存在向北倾角40°的高速体。我们把这些高速异常区域解释为俯冲的印度大陆岩石层(ICL)。印度大陆岩石层似乎在青藏高原中部比东部向北延伸更多——沿85°E在31°N到达350km深处,而沿91°E则是在30°N到达350km深度。P波和S波低速异常区在当惹雍错裂谷、亚东—谷露裂谷和错那裂谷下方从下地壳延伸至≥180km深度,这表明青藏高原南部的裂谷可能包含了整个岩石层的变形。当惹雍错裂谷下方的异常区向下延伸到约180km,而亚东—谷露裂谷西部和错那裂谷东部的异常延伸到了超过300km的深度。亚东—谷露裂谷西部的低速区上地幔延伸至最北,并且似乎与青藏高原中部下方广阔的上地幔低速区相连。于是,北向俯冲的印度板块沿着南北走向的裂缝撕裂。这些裂缝允许或者导致的软流层上涌同青藏高原北部上地幔相类似。 | Xiaofeng Liang Yun Chen Xiaobo Tian Yongshun John Chen James Ni Andrea Gallegos Simon L.Klemperer Minling Wang Tao Xu Changqing Sun Shaokun Si Haiqiang Lan Jiwen Teng 左思成 | 2016 | 世界地震译丛2016，47，6： | 8 |
| 3 | A comparison of intrauterine balloon, intrauterine contraceptive device and hyaluronic acid gel in the prevention of adhesion reformation following hysteroscopic surgery for Asherman syndrome: a cohort study显示文摘 | Xiaona Lin Minling Wei T.C. Li Qiongxiao Huang Dong Huang Feng Zhou Songying Zhang | 2013 | European Journal of Obstetrics and Gynecology2013，，： | 2 |
| 4 | Multilabel neural networks with applications to functional genomics and text categorization 显示文摘 | Zhang Minling Zhou Zhihua | 2006 | IEEE Trans on Knowledge and Data Engineering2006，18，10： | 1 |
| 5 | Ml-knn: A lazy learning approach to multi-label learning 显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 6 | ML-KNN: A lazy learning approach to multi-label learning显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 7 | Muhi-label neural networks with applications to functional genomics and text categorization 显示文摘 | ZHANG Minling ZHOU Zhihua | 2006 | IEEE Transactions on Knowledge and Data Engineering2006，18，10： | 1 |
| 8 | ML - Knn: a lazy learning approach to multi - label learning 显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 9 | Multi- instance multi-label learning 显示文摘 | Zhou Zhihua Zhang Minling Huang Shengjun | 2012 | Artificial Intelligence2012，176，1： | 1 |
| 10 | Multi-label neural networks with applications to functional genomics and text categorization显示文摘 | ZHANG Minling ZHOU Zhihua | | 0，，10： | 1 |
| 11 | ML-KNN: a lazy learning approach to multi-label learning显示文摘 | ZHANG Minling ZHOU Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 12 | ML-RBF: RBF neural networks for multi-label learning 显示文摘 | ZHANG Minling | 2009 | Neural Processing Letters2009，29，： | 1 |
| 13 | ML-KNN:A lazy learning approach to multi-label learning显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 14 | ML-kNN:A lazy learning approach to multi-label leaming显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattem Recognition2007，，7： | 1 |
| 15 | ML-KNN: A Lazy Learning Approach to Multi-label Learning显示文摘 | Zhang Minling Zhou Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 16 | Feature selection formulti-label naive Bayes classification显示文摘 | Zhang Minling Pena J M Robles V | 2009 | Information Sciences2009，179，19： | 1 |
| 17 | ML-KNN: a lazy learning approach to multi-label learning 显示文摘 | ZHANG Minling ZHOU Zhihua | 2007 | Pattern Recognition2007，40，7： | 1 |
| 18 | ML-KNN: A lazy learning approach to multi-label learning显示文摘 | ZHANG Minling ZHOU Zhihua | 2007 | Pattern Recognition (PRJ)2007，40，7： | 1 |
| 19 | Multi instance multi-label learning 显示文摘 | Zhou Zhihua Zhang Minling Huang Shengjun | 2012 | Artificial Intelligence2012，176，1： | 1 |
| 20 | ML-RBF: RBF neural networks for multi-label learning显示文摘 | ZHANG Minling | 2009 | Neural Process Letter2009，29，3： | 1 |
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