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2020b platform  (MathWorks Inc)


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    MathWorks Inc 2020b platform
    2020b Platform, supplied by MathWorks Inc, used in various techniques. Bioz Stars score: 96/100, based on 3261 PubMed citations. ZERO BIAS - scores, article reviews, protocol conditions and more
    https://www.bioz.com/product/simulation+modeling+tool+matlab/Optimization+Toolbox/ppr0800511-200-8-7
    Average 96 stars, based on 3261 article reviews
    2020b platform - by Bioz Stars, 2026-11
    96/100 stars

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    Article Title: Refined modelling of hypervelocity impact crater size predictions
    Article Snippet: The parameters were identified through non-linear least-squares optimization using the MATLAB function lsqcurvefit.

    Article Title: Solid-state NMR dipolar recoupling in the presence of large chemical shielding anisotropies by quaternion-based effective Hamiltonian optimal control.
    Article Snippet: Dipolar recoupling is a key element in magic-angle-spinning (MAS) solid-state NMR spectroscopy with reintroduced dipole–dipole coupling interactions providing information about internuclear distances and enabling transfer of polarization between spins in resolution-enhancing multiple-dimensional experiments.. Such methods may be challenged in many important applications by the presence of large anisotropic nuclear spin interactions such as chemical shielding anisotropy.. In this paper, we address this challenge by presenting quaternion-based optimal control.

    Article Title: Sparse time-delay and amplitude estimation of multipath arrivals in ocean acoustics.
    Article Snippet: We estimate multipath arrival time delays and amplitudes with high resolution for accurate characterization of ocean acoustic channels.. Although matched filtering (MF) is robust and widely used, its output represents a superposition of contributions from multiple arrivals, which can bias delay and amplitude estimates.. More advanced high-resolution (HR) methods exist but are usually computationally expensive.

    Article Title: Resistance Profile of Listeria monocytogenes and Escherichia coli to High Power Ultrasound in Juice Matrices.
    Article Snippet: Regression analyses were performed to obtain the parameter estimation of the primary and secondary model using Matlab Version 6.1 (MathWorks, MA, USA) while the parameter optimization was performed with the optimization function of Isqnonlin of the Matlab Optimisation Toolbox.

    Article Title: Optimization of the Volumetric Overall Mass Transfer Coefficient in an L-Shaped Pulsed Sieve Plate Extraction Column
    Article Snippet: Nonlinear regression and optimization were performed in MATLAB R2022b using the Optimization Toolbox (Version 9.4) and the lsqnonlin solver.

    Article Title: Trade-off between flux efficiency and metabolic control shapes enzyme allocation.
    Article Snippet: As the whole procedure computes pathway flux J from kcat ;KS;KP;K;E; Sin1⁄2 ; 1⁄2Sout , the pathway flux J can also be written as a function of these parameters and concentrations: J 1⁄4 Jðkcat ;KS;KP;K ;E; Sin1⁄2 ; 1⁄2Sout Þ Optimal enzyme allocation profile maximizing flux efficiency under a given parameter set was computed using the MATLAB constrained optimization solver fmincon[] to maximize the pathway flux with the upper bound of total enzyme abundance set to 1: max E Jðkcat ;KS;KP;K ;E; Sin1⁄2 ; 1⁄2Sout Þ s:t: 0 1⁄2Ei 1Pn i1⁄41 Ei1⁄2 1

    Article Title: Network-Irreducible Multiparty Entanglement in Quantum Matter.
    Article Snippet: [27] MOSEK Optimization Toolbox for MATLAB 10.2.17, MOSEK ApS (2024), https://docs.mosek.com/10.2/toolbo x/index.html.

    Article Title: Protocol for thermodynamically optimal construction and analysis of metabolic networks using ThermOptCOBRA.
    Article Snippet: Other MATLAB toolboxes to be installed are Optimization Toolbox, Statistics and Machine Learning Toolbox, and Parallel Computing Toolbox.



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    The minimal physiologically‐based pharmacokinetic ( mPBPK ) models that are used in ATLAS mPBPK. ( a ) mPBPK model for small molecules. ( b ) mPBPK model for large molecules with no target binding. ( c ) mPBPK model for large molecules with target binding in plasma (interstitial fluid). ( d ) mPBPK model for large molecules with target binding in peripheral tissues. Parameters' names and typical values are shown in Table . EV, extravascular; IV, intravenous; PO, oral.

    Journal: CPT: Pharmacometrics & Systems Pharmacology

    Article Title: ATLAS mPBPK: A MATLAB‐Based Tool for Modeling and Simulation of Minimal Physiologically‐Based Pharmacokinetic Models

    doi: 10.1002/psp4.12441

    Figure Lengend Snippet: The minimal physiologically‐based pharmacokinetic ( mPBPK ) models that are used in ATLAS mPBPK. ( a ) mPBPK model for small molecules. ( b ) mPBPK model for large molecules with no target binding. ( c ) mPBPK model for large molecules with target binding in plasma (interstitial fluid). ( d ) mPBPK model for large molecules with target binding in peripheral tissues. Parameters' names and typical values are shown in Table . EV, extravascular; IV, intravenous; PO, oral.

    Article Snippet: In this tutorial, we present a MATLAB‐based tool for the modeling and simulation of mPBPK models (ATLAS mPBPK).

    Techniques: Binding Assay, Clinical Proteomics

    Concordance of simulated ATLAS mPBPK small‐molecule model values based on pharmacokinetic data of beta‐lactams. Figure is not an output of ATLAS mPBPK but was constructed by processing ATLAS mPBPK simulation output using MATLAB.

    Journal: CPT: Pharmacometrics & Systems Pharmacology

    Article Title: ATLAS mPBPK: A MATLAB‐Based Tool for Modeling and Simulation of Minimal Physiologically‐Based Pharmacokinetic Models

    doi: 10.1002/psp4.12441

    Figure Lengend Snippet: Concordance of simulated ATLAS mPBPK small‐molecule model values based on pharmacokinetic data of beta‐lactams. Figure is not an output of ATLAS mPBPK but was constructed by processing ATLAS mPBPK simulation output using MATLAB.

    Article Snippet: In this tutorial, we present a MATLAB‐based tool for the modeling and simulation of mPBPK models (ATLAS mPBPK).

    Techniques: Construct

    Demonstration of ATLAS mPBPK large‐molecule model based on PK data of Trastuzumab (target binding in peripheral tissues) and Mavrillimumab (target binding in plasma) for a range of doses. Figures are not an output of ATLAS mPBPK but were constructed by processing ATLAS mPBPK simulation output using MATLAB. c‐TMDD, Target mediated drug disposition in plasma (central); hr, hour; mPBPK, minimal physiologically‐based pharmacokinetic modeling; p‐TMDD, target‐mediated drug disposition in peripheral tissues.

    Journal: CPT: Pharmacometrics & Systems Pharmacology

    Article Title: ATLAS mPBPK: A MATLAB‐Based Tool for Modeling and Simulation of Minimal Physiologically‐Based Pharmacokinetic Models

    doi: 10.1002/psp4.12441

    Figure Lengend Snippet: Demonstration of ATLAS mPBPK large‐molecule model based on PK data of Trastuzumab (target binding in peripheral tissues) and Mavrillimumab (target binding in plasma) for a range of doses. Figures are not an output of ATLAS mPBPK but were constructed by processing ATLAS mPBPK simulation output using MATLAB. c‐TMDD, Target mediated drug disposition in plasma (central); hr, hour; mPBPK, minimal physiologically‐based pharmacokinetic modeling; p‐TMDD, target‐mediated drug disposition in peripheral tissues.

    Article Snippet: In this tutorial, we present a MATLAB‐based tool for the modeling and simulation of mPBPK models (ATLAS mPBPK).

    Techniques: Binding Assay, Clinical Proteomics, Construct