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matlab's built-in functions for handling graphs with directed edges and weighted adjacency matrices  (MathWorks Inc)


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    MathWorks Inc matlab's built-in functions for handling graphs with directed edges and weighted adjacency matrices
    Matlab's Built In Functions For Handling Graphs With Directed Edges And Weighted Adjacency Matrices, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 90/100, based on 1 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/matlab+matrices/pm40419173-65-10-0
    Average 90 stars, based on 1 article reviews
    matlab's built-in functions for handling graphs with directed edges and weighted adjacency matrices - by Bioz Stars, 2026-09
    90/100 stars

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    Article Snippet: These matrices are readable by MATLAB software in matrix form and utilized for simulation.

    Article Title: Interactions between motor cortical forelimb regions and their influence on muscles reorganize across behaviors
    Article Snippet: Principal components analysis (PCA) was applied to resultant 600 ms x N units matrices for each area (Matlab function ‘pca’) and the top 3 PCs were selected.

    Article Title: An Independent Coding Scheme for Distance versus Position in the Hippocampus
    Article Snippet: The multidimensional scaling on such matrices was then performed with the MATLAB mdscale function using the metricstress criterion and a number of replicates equal to 50.

    Article Title: 2024 World Molecular Imaging Congress Program.
    Article Snippet: 2024 World Molecular Imaging Congress Program Published online: 7 February 2025 © The Author(s), under exclusive licence to World Molecular Imaging Society 2025 Selected abstracts from the World Molecular Imaging Congress 2024 meeting of the World Molecular Imaging Society WMIC 2024, Montreal, Canada Sponsorship: Publication of this supplement was funded by the World Molecular Imaging Society.. All content was reviewed and approved by the 2024 WMIC Program Committee, which held full responsibility for the abstract selections.. Presenting authors have asterisks in the contributor lists.

    Article Title: Research on modeling method for connectors in the train-track-bridge system based on multi-timestep explicit-implicit co-simulation
    Article Snippet: For the response of the train-track-bridge system under extreme environment, an increasing number of studies employ co-simulation strategies for detailed modeling and calculation of the structure.. However, there is a lack of research on the module connectors, which significantly impact simulation accuracy and stability.. In this paper, the theoretical differences between weak, moderate, and strong coupling methods in dealing with co-simulation module interface connectors are discussed.

    Article Title: Cortical circuits for cross-modal generalization.
    Article Snippet: Across all fits reported uncertainties (i.e. confidence intervals) for best-fit parameter values were extracted from fit covariance matrices (fitting was performed using Matlab function “nlinfit”).

    Article Title: Exponential admissibility and <mml:math xmlns:mml="http://www.w3.org/1998/Math/MathML" display="inline" id="d1e531" altimg="si11.svg"><mml:msub><mml:mrow><mml:mi>H</mml:mi></mml:mrow><mml:mrow><mml:mi>∞</mml:mi></mml:mrow></mml:msub></mml:math> control for nonlinear singular discrete systems with mixed time-varying delays
    Article Snippet: This study addresses the issue of exponential admissibility analysis and H∞ control for nonlinear singular discrete-time systems with mixed time-varying delays (NSDS-MTVD).. By developing a set of improved Lyapunov-Krasovskii functionals, new delay-dependent criteria are constructed in terms of linear matrix inequalities (LMIs) to ensure the regularity, causality, and exponential admissibility with an H∞ performance level−γ of the NSDS-MTVD.. Subsequently, the state feedback H∞ controller design approach is also presented as an application of the obtained result.

    Residue:

    Article Title: Molecular basis for the increased fusion activity of the Ebola virus glycoprotein epidemic variant A82V: Insights from simulations and experiments
    Article Snippet: .. Residue correlation matrices were calculated using the MDToolbox in Matlab (MathWorks, Natick, MA). ..



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    Radiomic features SERA calculates in each feature family. Different 2D, 2.5D, and  3D  configurations are explained in detail in the IBSI guideline. Users can set to return only a selected subset of these features. Standardized Environment for Radiomics Analysis (SERA) is our standardized in-house-developed radiomics framework.

    Journal: Cureus

    Article Title: Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

    doi: 10.7759/cureus.43343

    Figure Lengend Snippet: Radiomic features SERA calculates in each feature family. Different 2D, 2.5D, and 3D configurations are explained in detail in the IBSI guideline. Users can set to return only a selected subset of these features. Standardized Environment for Radiomics Analysis (SERA) is our standardized in-house-developed radiomics framework.

    Article Snippet: Our workflow then exported the 3D SPECT image and these 22 contours as 3D MATLAB ® matrices for analysis.

    Techniques:

    List of radiomics features calculated by SERA for Configuration D of IBSI CT phantom. SERA shows great compliance with IBSI with 99.8% match in calculated results. Refer to [ <xref ref-type= 28 ] for the standardized definitions of radiomic features and acronyms based on the Image Biomarker Standardization Initiative (IBSI) guidelines. Standardized Environment for Radiomics Analysis (SERA) is our standardized in-house-developed radiomics framework." width="100%" height="100%">

    Journal: Cureus

    Article Title: Standardized Radiomics Analysis of Clinical Myocardial Perfusion Stress SPECT Images to Identify Coronary Artery Calcification

    doi: 10.7759/cureus.43343

    Figure Lengend Snippet: List of radiomics features calculated by SERA for Configuration D of IBSI CT phantom. SERA shows great compliance with IBSI with 99.8% match in calculated results. Refer to [ 28 ] for the standardized definitions of radiomic features and acronyms based on the Image Biomarker Standardization Initiative (IBSI) guidelines. Standardized Environment for Radiomics Analysis (SERA) is our standardized in-house-developed radiomics framework.

    Article Snippet: Our workflow then exported the 3D SPECT image and these 22 contours as 3D MATLAB ® matrices for analysis.

    Techniques: Biomarker Discovery, Dispersion