Showing 5,501 - 5,520 results of 5,809 for search '"spy"', query time: 0.64s Refine Results
  1. 5501
  2. 5502

    Individual Tree Height Increment Model for <i>Quercus mongolica</i> Secondary Forest in the Northeastern China Using Generalized Nonlinear Two-Level Mixed-Effects Model by Xuefan Hu, Yingshan Jin, Xiaohong Zhang, Huiru Zhang

    Published 2023-10-01
    “…Two-level nonlinear mixed-effects models were constructed to predict the height of a single tree using sample plots and tree species as random effects, combined with a variety of tree size factors, site factors, and competitive factors as independent variables. (3) Results: The significant factors related to the height increment of Mongolian oak secondary forest are the initial diameter at breast height as the size of the tree itself (<i>DBH</i>), height (<i>H</i>), crown height ratio (<i>CR</i>), and site productivity index reflecting site quality (<i>SPI</i>). The distance-dependent and distance-independent competition indexes have no significant effect on tree height increment. …”
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    Article
  3. 5503
  4. 5504
  5. 5505
  6. 5506
  7. 5507

    Detection and Molecular Identification of <i>Salmonella</i> Virulence Genes in Livestock Production Systems in South Africa by Thobeka P. Mthembu, Oliver T. Zishiri, Mohamed E. El Zowalaty

    Published 2019-08-01
    “…Virulence genes that were screened from livestock-associated <i>Salmonella</i> were <i>invA</i>, <i>iroB</i>, <i>spiC</i>, <i>pipD</i> and <i>int1</i>. Statistically significant associations (<i>p</i> &lt; 0.05) were established between the virulence genes, sampling location, animal host as well as the season when samples were collected. …”
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    Article
  8. 5508

    Drought risk analysis and rainfall index insurance model development by Chen, Wen

    Published 2015
    “…This research project is the first in the field of Standardized Precipitation Index (SPI)–based drought-risk studies to use diffusion kernel density estimation (DKDE) to estimate the bivariate probability density functions (PDFs) and the joint return period (RP). …”
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    Thesis
  9. 5509
  10. 5510
  11. 5511

    Optimization of landslide conditioning factors using very high-resolution airborne laser scanning (LiDAR) data at catchment scale by Jebur, Mustafa Neamah, Pradhan, Biswajeet, Tehrany, Mahyat Shafapour

    Published 2014
    “…The first dataset was derived from high-resolution airborne laser scanning data (LiDAR), which contains eight landslide conditioning factors: altitude, slope, aspect, curvature, stream power index (SPI), topographic wetness index (TWI), topographic roughness index (TRI), and sediment transport index (STI). …”
    Article
  12. 5512

    Application of GIS based data driven evidential belief function model to predict groundwater potential zonation by Nampak, Haleh, Pradhan, Biswajeet, Abd Manap, Mohammad

    Published 2014
    “…Those twelve groundwater conditioning factors are elevation, slope, curvature, stream power index (SPI), topographic wetness index (TWI), drainage density, lithology, lineament density, land use, normalized difference vegetation index (NDVI), soil and rainfall. …”
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    Article
  13. 5513

    Earthquake induced landslide susceptibility mapping using an integrated ensemble frequency ratio and logistic regression models in West Sumatera Province, Indonesia by Umar, Zahrul, Pradhan, Biswajeet, Ahmad, Anuar, Jebur, Mustafa Neamah, Tehrany, Mahyat Shafapour

    Published 2014
    “…The landslide conditioning factors consist of: altitude, curvature, river, SPI, rainfall, soil type, soil texture, land use/cover (LULC), peak ground acceleration (PGA), geology, slope, aspect, lineament and topographic wetness index (TWI). …”
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    Article
  14. 5514

    Genome sequencing and analysis of Salmonella enterica subsp. enterica serovar Stanley UPM 517: insights on its virulence-associated elements and their potentials as vaccine candida... by Ashari, Khalidah Syahirah, Roslan, Najwa Syahirah, Omar, Abdul Rahman, Bejo, Mohd Hair, Ideris, Aini, Mat Isa, Nurulfiza

    Published 2019
    “…There were seven Salmonella pathogenicity islands (SPIs) within the draft genome, which contained the virulence factors associated with Salmonella infection (except SPI-14). Five intact prophage regions, mostly comprising of the protein encoding Gifsy-1, Fels-1, RE-2010 and SEN34 prophages, were also encoded in the draft genome. …”
    Article
  15. 5515

    Improvement in Organogenesis and the Development of a Transformation Procedure for Cucumber and Muskmelon by Mohi Uddin, A. K. M.

    Published 1998
    “…The regeneration rate was further enhanced when these two ethylene inhibitors were added to both SGM and SPI media. Furthermore, the number of shoots induced from explants of both Spring Swallow and Tasty Green cultivars was also enhanced upon the same treatment. …”
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  16. 5516

    Influence of soil pile interaction on seismic behavior in sandy condition by Khari, Mahdy

    Published 2014
    “…Damages of structures supported by deep foundations due to complete or partial collapse have demonstrated paramount importance of the understanding of Soil-Pile Interaction (SPI). Kinematic interaction is due to the presence of pile foundation in the ground. …”
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    Thesis
  17. 5517

    Landslide susceptibility mapping using remote sensing data and geographic information system-based algorithms by Mohammadi, Ayub

    Published 2019
    “…Seventeen triggering and conditioning factors, namely slope, aspect, elevation, distance to road, distance to river, proximity to fault, road density, river density, Normalized Difference Vegetation Index (NDVI), rainfall, land cover, lithology, soil types, curvature, profile curvature, Stream Power Index (SPI) and Topographic Wetness Index (TWI), were extracted from satellite imageries, digital elevation model (DEM), geological and soil maps. …”
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    Thesis
  18. 5518

    Hybrid model of spatio-temporal assessment of landslide hazards by using airborne laser altimetry data by Bibi, Tehmina

    Published 2019
    “…The spatial dataset initially constructed landslide inventory maps for these two sample areas through virtual mapping technique. 21 landslide-causative factors which included slope angle, aspect, altitude, TWI, SPI, TST, TSC, TSConv, soil, lithology, land use, rainfall, seismicity and distance to roads, rivers, roads, and faults were derived from different sources including Airborne Laser Altimetry (ALS) data. …”
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    Thesis
  19. 5519

    Modification of landslide susceptibility mapping using optimized PSO-ANN technique by Moayedi, Hossein, Mehrabi, Mohammad, Mosallanezhad, Mansour, A. Rashid, Ahmad Safuan, Pradhan, Biswajeet

    Published 2019
    “…The input dataset includes elevation, slope aspect, slope degree, curvature, soil type, lithology, distance to road, distance to river, distance to fault, land use, stream power index (SPI) and topographic wetness index (TWI), where the output was taken landslide susceptibility value. …”
    Article
  20. 5520