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comsol livelink toolbox  (COMSOL Inc)

 
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    COMSOL Inc comsol livelink toolbox
    Comsol Livelink Toolbox, supplied by COMSOL 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/livelink+matlab+transcript+function/comsol+livelink+toolbox/pm37801500-358-14-13
    Average 90 stars, based on 1 article reviews
    comsol livelink toolbox - by Bioz Stars, 2026-09
    90/100 stars

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    other:

    Article Title: Sensitivity distribution simulations of surface electrode configurations for electrical impedance myography
    Article Snippet: The component of the electric current normal to all the outer boundaries was defined to be null to prevent the current flowing out of the boundaries defined by the model. Variations of the model changing subcutaneous fat thickness h sf and electrode array separation were run using Comsol LiveLink toolbox via MATLAB (Mathworks, 2015a, Natick, MA).

    Article Title: Direct isolation of small extracellular vesicles from human blood using viscoelastic microfluidics.
    Article Snippet: After performing flow simulations with COMSOL, data were imported into MATLAB using the COMSOL LiveLink toolbox.



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    livelink matlab transcript function  (MathWorks Inc)
    90
    MathWorks Inc livelink matlab transcript function
    Dynamic process of the study based on the T-junction droplet simulations (A) The COMSOL simulation resulted in a phase description of the final generated droplets, stemming from the settings initialized for interaction between oil and water, which was then conducted using the <t>Livelink</t> <t>MATLAB</t> transcript function of the software to create and store images of phase-defined generated droplets for various input parameters. (B) Images resulting from FEA were then subjected to an image analysis process in which a binary format of those images was used to enhance the performance of measuring parameters visualized in the droplet formation. (C) According to measured variables in binary images, two important output parameters were extracted that emphasize the goal of the current research: Regime and Droplet Length. (D) In each scenario of image creation, four main inputs resulted in two numerical outputs, which were then ordered in a table to create a dataset of 8020 data points. (E) The resulting dataset was then trained with ML and DL methods, including classification models to train the droplet generation regime and regression models with the purpose of training-droplet length. (F) Finally, the trained models were used to estimate the main outputs for the proposed T-junction droplet generation setup.
    Livelink Matlab Transcript Function, 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/livelink+matlab+transcript+function/pmc10951907-77-2-3
    Average 90 stars, based on 1 article reviews
    livelink matlab transcript function - by Bioz Stars, 2026-09
    90/100 stars
    		  Buy from Supplier

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    Dynamic process of the study based on the T-junction droplet simulations (A) The COMSOL simulation resulted in a phase description of the final generated droplets, stemming from the settings initialized for interaction between oil and water, which was then conducted using the Livelink MATLAB transcript function of the software to create and store images of phase-defined generated droplets for various input parameters. (B) Images resulting from FEA were then subjected to an image analysis process in which a binary format of those images was used to enhance the performance of measuring parameters visualized in the droplet formation. (C) According to measured variables in binary images, two important output parameters were extracted that emphasize the goal of the current research: Regime and Droplet Length. (D) In each scenario of image creation, four main inputs resulted in two numerical outputs, which were then ordered in a table to create a dataset of 8020 data points. (E) The resulting dataset was then trained with ML and DL methods, including classification models to train the droplet generation regime and regression models with the purpose of training-droplet length. (F) Finally, the trained models were used to estimate the main outputs for the proposed T-junction droplet generation setup.

    Journal: iScience

    Article Title: Deep learning-augmented T-junction droplet generation

    doi: 10.1016/j.isci.2024.109326

    Figure Lengend Snippet: Dynamic process of the study based on the T-junction droplet simulations (A) The COMSOL simulation resulted in a phase description of the final generated droplets, stemming from the settings initialized for interaction between oil and water, which was then conducted using the Livelink MATLAB transcript function of the software to create and store images of phase-defined generated droplets for various input parameters. (B) Images resulting from FEA were then subjected to an image analysis process in which a binary format of those images was used to enhance the performance of measuring parameters visualized in the droplet formation. (C) According to measured variables in binary images, two important output parameters were extracted that emphasize the goal of the current research: Regime and Droplet Length. (D) In each scenario of image creation, four main inputs resulted in two numerical outputs, which were then ordered in a table to create a dataset of 8020 data points. (E) The resulting dataset was then trained with ML and DL methods, including classification models to train the droplet generation regime and regression models with the purpose of training-droplet length. (F) Finally, the trained models were used to estimate the main outputs for the proposed T-junction droplet generation setup.

    Article Snippet: Through the Livelink MATLAB transcript function, images of the generated droplets were captured and stored ( A).

    Techniques: Generated, Software

    Journal: iScience

    Article Title: Deep learning-augmented T-junction droplet generation

    doi: 10.1016/j.isci.2024.109326

    Figure Lengend Snippet:

    Article Snippet: Through the Livelink MATLAB transcript function, images of the generated droplets were captured and stored ( A).

    Techniques: Software