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livelink matlab transcript function  (MathWorks Inc)


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    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

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    1) Product Images from "Deep learning-augmented T-junction droplet generation"

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

    Journal: iScience

    doi: 10.1016/j.isci.2024.109326

    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.
    Figure Legend 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.

    Techniques Used: Generated, Software


    Figure Legend Snippet:

    Techniques Used: Software

    Related Articles

    Generated:

    Article Title: Deep learning-augmented T-junction droplet generation
    Article Snippet: .. Through the Livelink MATLAB transcript function, images of the generated droplets were captured and stored ( A). ..

    Article Title: Deep learning-augmented T-junction droplet generation
    Article Snippet: T-junction droplet simulations were employed using COMSOL Multiphysics software, where initial settings governed oil-water interaction. .. Utilizing Livelink MATLAB, phase-defined droplet images were generated and stored for various parameters. ..

    other:

    Article Title: Herringbone micromixers for particle filtration
    Article Snippet: Simulation outputs were consolidated using Matlab livelink.

    Article Title: Experimental and numerical studies on fluid flow through fractured rock masses based on an enhanced three-dimensional discrete fracture network model.
    Article Snippet: Fluid flow through rock fracture networks was experimentally and numerically studied based on an enhanced discrete fracture network (DFN) model that explicitly characterizes 3D void geometry within rough-walled fracture.. Fluid flow tests under different hydraulic gradients J were conducted on a series of DFN samples created by a 3D printer.. Meanwhile, numerical simulations were performed based on the enhanced DFN model solving the NS equations and conventional DFN model solving the Reynolds equation, respectively.

    Article Title: Investigating the synergistic effects of immunotherapy and normalization treatment in modulating tumor microenvironment and enhancing treatment efficacy.
    Article Snippet: We developed a comprehensive mathematical model of cancer immunotherapy that takes into account: i) Immune checkpoint blockers (ICBs) and the interactions between cancer cells and the immune system, ii) characteristics of the tumor microenvironment, such as the tumor hydraulic conductivity, interstitial fluid pressure, and vascular permeability, iii) spatial and temporal variations of the modeled components within the tumor and the surrounding host tissue, iv) the transport of modeled components through the vasculature and between the tumor-host tissue with convection and diffusion, and v) modeling of the tumor draining lymph nodes were the antigen presentation and the development of cytotoxic immune cells take place.. Our model successfully reproduced experimental data from various murine tumor types and predicted immune system profiling, which is challenging to achieve experimentally.. It showed that combination of ICB therapy and normalization treatments, that aim to improve tumor perfusion, decreases interstitial fluid pressure and increases the concentration of both innate and adaptive immune cells at the tumor center rather than the periphery.

    Transferring:

    Article Title: A multi-step parameter identification of a physico-chemical lithium-ion battery model with electrochemical impedance data
    Article Snippet: While a single EIS simulation with the COMSOL P2D model took around 20 s, the complete impedance-driven P2D parameter identification took approximately 7 days to complete. .. As most of the total computational time was spent on data transferring through the Matlab Livelink communication bridge (between Matlab and COMSOL), this computational time can be significantly reduced in several folds by implementing the P2D model in the Matlab environment directly, which is planned as future work. ..

    Control:

    Article Title: The Impacts of Micro‐Porosity and Mineralogical Texture on Fractured Rock Alteration
    Article Snippet: The Darcy Brinkman Stokes equation was solved in COMSOL for the flow field, which was passed to CrunchTope to solve the advection‐diffusion‐reaction equation and update the εf field. .. Matlab LiveLink was used as the application interface to control the iteration between COMSOL and CrunchTope. ..



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    livelink matlab transcript function  (MathWorks Inc)
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    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

    Image Search Results


    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