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Showing 1 - 20 results of 31 for search '(((("data modeling algorithm") OR ("based clustering algorithm"))) OR ("element ree algorithms"))', query time: 0.40s Refine Results
  1. 1
    Topographic map of the Central China. The filled red triangles represent the lightning sensors within the domain. Elevation data obtained from NASA's Shuttle Radar Topography Mission (SRTM).

    Topographic map of the Central China. The filled red triangles represent the lightning sensors within the domain. Elevation data obtained from NASA's Shuttle Radar Topography Missi... by Manxing Shi (22367181)

    Published 2025
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  2. 2
    Similar to Fig 10, but for 0100–0400 BJT (Beijing Time) on 17 July 2015.

    Similar to Fig 10, but for 0100–0400 BJT (Beijing Time) on 17 July 2015. by Manxing Shi (22367181)

    Published 2025
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  3. 3
    Similar to Fig 7, but for the year 2014.

    Similar to Fig 7, but for the year 2014. by Manxing Shi (22367181)

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  4. 4
    Sample lightning stroke data.

    Sample lightning stroke data. by Manxing Shi (22367181)

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  5. 5
    The proposed adaptive clustering algorithm determines the optimal distance parameters for two lightning events, <i>p</i><sub>1</sub> and <i>p</i><sub>2</sub>.

    The proposed adaptive clustering algorithm determines the optimal distance parameters for two lightning events, <i>p</i><sub>1</sub> and <i>p</i><sub>2</sub>. by Manxing Shi (22367181)

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  6. 6
    A multi-scale spatiotemporal neighborhood-based approach for identifying lightning clusters.

    A multi-scale spatiotemporal neighborhood-based approach for identifying lightning clusters. by Manxing Shi (22367181)

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  7. 7
    Workflow for determining adaptive clustering parameters.

    Workflow for determining adaptive clustering parameters. by Manxing Shi (22367181)

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  8. 8
    Similar to Fig 7, but for the year 2015.

    Similar to Fig 7, but for the year 2015. by Manxing Shi (22367181)

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  9. 9
    Monthly counts of lightning strokes and grid clusters and the proportion of grid clusters with more than 500 strokes during 2013–2015.

    Monthly counts of lightning strokes and grid clusters and the proportion of grid clusters with more than 500 strokes during 2013–2015. by Manxing Shi (22367181)

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  10. 10
    Fine-scale identification results of lightning clusters in the study area during 2013–2015 using adaptive-parameter spatiotemporal clustering (CSCAP) and fixed-parameter spatiotemporal clustering (CSCGP) methods.

    Fine-scale identification results of lightning clusters in the study area during 2013–2015 using adaptive-parameter spatiotemporal clustering (CSCAP) and fixed-parameter spatiotemp... by Manxing Shi (22367181)

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  11. 11
    Two representative subsets of (A) high-flash-rate and (B) low-flash-rate lightning data identified by the CC3D algorithm.

    Two representative subsets of (A) high-flash-rate and (B) low-flash-rate lightning data identified by the CC3D algorithm. by Manxing Shi (22367181)

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  12. 12
    Hierarchical relationships among key terms used in this study.

    Hierarchical relationships among key terms used in this study. by Manxing Shi (22367181)

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  13. 13
    Thunderstorm clouds were identified and extracted by the CSCAP algorithm from 0000–0600 BJT (Beijing Time) on 29 May 2015.

    Thunderstorm clouds were identified and extracted by the CSCAP algorithm from 0000–0600 BJT (Beijing Time) on 29 May 2015. by Manxing Shi (22367181)

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  14. 14
    Statistical properties of high-flash-rate (≥500 lightning strokes) grid clusters identified in 2013 within the study region: (A) Spatial distribution of cluster centroids; (B, C) Probability distribution functions (PDFs) of the number of grids per cluster and the number of lightning strokes per grid, respectively.

    Statistical properties of high-flash-rate (≥500 lightning strokes) grid clusters identified in 2013 within the study region: (A) Spatial distribution of cluster centroids; (B, C) P... by Manxing Shi (22367181)

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  15. 15
    Summary of key parameters used in the CSCAP algorithm.

    Summary of key parameters used in the CSCAP algorithm. by Manxing Shi (22367181)

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  16. 16
    Distribution of (A) temporal difference set (DistT) and (B) spatial distance set (DistS) values by sample number, for the lightning data subset (Fig 3(B)) identified by the CC3D algorithm on 22 March 2015.

    Distribution of (A) temporal difference set (DistT) and (B) spatial distance set (DistS) values by sample number, for the lightning data subset (Fig 3(B)) identified by the CC3D al... by Manxing Shi (22367181)

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  17. 17
    Reconstructed haplotype trees showed different topologies across individuals.

    Reconstructed haplotype trees showed different topologies across individuals. by Lele Zhao (532080)

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  18. 18
    Ocean plot of windows with strong evidence for the maintenance of population structure after treatment.

    Ocean plot of windows with strong evidence for the maintenance of population structure after treatment. by Lele Zhao (532080)

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  19. 19
    Haplotype phylogenies and iSNV frequency trajectories across sampling time points (patient A and J).

    Haplotype phylogenies and iSNV frequency trajectories across sampling time points (patient A and J). by Lele Zhao (532080)

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  20. 20
    A. Distributions of the observed (red), 1000 Poisson (green) and 1000 negative binomial (blue) model generated expected counts of synonymous mutations per genome per day. B. Estimated rates of lineages from single lineage individuals, and multi-lineage individuals, while distinguishing between lineages where sampling time point(s) was missing or not.

    A. Distributions of the observed (red), 1000 Poisson (green) and 1000 negative binomial (blue) model generated expected counts of synonymous mutations per genome per day. B. Estima... by Lele Zhao (532080)

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