matlab® component runtime engine Search Results


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Principle component biplot of JIP test parameters based on variations of 5 levels of light spectra under ( A ) control (non-stress); ( B ) salinity stress; ( C ) alkalinity stress. P1: ABS/RC; P2: Dio/RC; P3: TRo/RC; P4: Eto/RC; P5: REo/RC; P6: φP O ; P7: ΨE O ; P8: φE O ; P9: δR O ; P10: φR O ; P11: PI ABS ; P12: PI total ; P13: V j ; P14: V i ; P15: S m . Principal component analysis was performed using MATLAB software version <t>R2015b</t> ( https://www.mathworks.com ).
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Principle component biplot of JIP test parameters based on variations of 5 levels of light spectra under ( A ) control (non-stress); ( B ) salinity stress; ( C ) alkalinity stress. P1: ABS/RC; P2: Dio/RC; P3: TRo/RC; P4: Eto/RC; P5: REo/RC; P6: φP O ; P7: ΨE O ; P8: φE O ; P9: δR O ; P10: φR O ; P11: PI ABS ; P12: PI total ; P13: V j ; P14: V i ; P15: S m . Principal component analysis was performed using MATLAB software version <t>R2015b</t> ( https://www.mathworks.com ).
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Flowchart of <t>CEEMDAN–BO–BiLSTM</t> model.
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Image Search Results


Principle component biplot of JIP test parameters based on variations of 5 levels of light spectra under ( A ) control (non-stress); ( B ) salinity stress; ( C ) alkalinity stress. P1: ABS/RC; P2: Dio/RC; P3: TRo/RC; P4: Eto/RC; P5: REo/RC; P6: φP O ; P7: ΨE O ; P8: φE O ; P9: δR O ; P10: φR O ; P11: PI ABS ; P12: PI total ; P13: V j ; P14: V i ; P15: S m . Principal component analysis was performed using MATLAB software version R2015b ( https://www.mathworks.com ).

Journal: Scientific Reports

Article Title: The effect of supplementary light on the photosynthetic apparatus of strawberry plants under salinity and alkalinity stress

doi: 10.1038/s41598-022-17377-8

Figure Lengend Snippet: Principle component biplot of JIP test parameters based on variations of 5 levels of light spectra under ( A ) control (non-stress); ( B ) salinity stress; ( C ) alkalinity stress. P1: ABS/RC; P2: Dio/RC; P3: TRo/RC; P4: Eto/RC; P5: REo/RC; P6: φP O ; P7: ΨE O ; P8: φE O ; P9: δR O ; P10: φR O ; P11: PI ABS ; P12: PI total ; P13: V j ; P14: V i ; P15: S m . Principal component analysis was performed using MATLAB software version R2015b ( https://www.mathworks.com ).

Article Snippet: Principal component analysis was performed using MATLAB software version R2015b ( https://www.mathworks.com ).

Techniques: Software

Flowchart of CEEMDAN–BO–BiLSTM model.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: Flowchart of CEEMDAN–BO–BiLSTM model.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques:

CEEMDAN decomposition.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: CEEMDAN decomposition.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques:

Relative error table for  CEEMDAN–BO–BiLSTM  model prediction.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: Relative error table for CEEMDAN–BO–BiLSTM model prediction.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques:

Table of indicators for the four forecasting models.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: Table of indicators for the four forecasting models.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques:

Average time for each model run.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: Average time for each model run.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques:

Nonparametric test results.

Journal: Scientific Reports

Article Title: A monthly temperature prediction based on the CEEMDAN–BO–BiLSTM coupled model

doi: 10.1038/s41598-024-51524-7

Figure Lengend Snippet: Nonparametric test results.

Article Snippet: In this study, the MATLAB software component CEEMDAN model is utilized to decompose the monthly mean temperature time series, and five modal components and one residual component are finally obtained .

Techniques: