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Matlab v r2022a

Manufactured by MathWorks
Sourced in United States

MATLAB R2022a is a software package developed by MathWorks for numerical computation, visualization, and programming. It provides a high-level programming language and a powerful suite of tools for a wide range of applications, including signal processing, image analysis, control system design, and data analysis. MATLAB R2022a offers a user-friendly interface and a comprehensive set of built-in functions and libraries, allowing users to efficiently solve complex problems and prototype their ideas.

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5 protocols using matlab v r2022a

1

Evaluating Muscular Performance Changes

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Statistics and graphs were performed and built using SPSS v. 27 (IBM, Armonk, New York, United States of America) and MATLAB v. R2022a (MathWorks, Natick, United States of America). Descriptive results were based on mean values and the standard deviation (±). Participants were excluded from further analysis if the z-transformed values reached 2.5. Repeated measures mixed multivariate analysis of variance (MANOVA) were used to show overall differences. The factors were group (Intervention and control), time (pre and post), mode (eccentric and concentric), and speed (30°/s and 60°/s). Post-hoc analysis was performed using repeated measures univariate analysis of variance (ANOVA) and t-tests for within group pre-post comparisons. The level of significance was set to α = 0.05. Further, to compare the morphology of the angle-torque and passive torque-angle trajectories, the interpolated curves were compared using SPM analysis. For this purpose, an ANOVA with repeated measures was chosen.
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2

Statistical Analysis of Experimental Data

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All fitting and statistical analyses were performed in MATLAB vR2022a (MathWorks, Natick, MA, USA) and R (http://www.R-project.org/); all mentions of MATLAB function calls are italicized. Hypothesis testing was performed using a significance level of 0.05. Data are reported as grand means [95% confidence interval, CI] computed from means of individual specimen means, unless stated otherwise. We use N to denote the number of individual specimens and n for number of trials.
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3

Biomechanical Effects of Exercise Intervention

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MATLAB v.R2022a (MathWorks, Natick, USA) and SPSS v.27 (IBM, Armonk, New York, USA) were used for statistics. Descriptive results were based on mean and standard deviation (±). Participants were excluded from further analysis if the data distribution showed outliers and z-transformed values exceeded 2.5. Repeated measures multivariate analysis of variance (MANOVA) was used to show main effects and interactions between different factors. For strength statistics, values are normalized to body mass and the factors were time (pre and post), mode (eccentric and concentric), and speed (30°/s, 60°/s and 180°/s). For flexibility parameters, the factors were time (pre and post) and test (aROM and pROM). For muscle parameters, the factor for statistical analysis were time (pre and post). Statistics were separated for the muscles.
For further post-hoc mean comparisons, paired t-tests were used. The p-value was set at 0.05. Differences between the torque-angle curves were calculated using SPM1d integrated in MATLAB.
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4

Acoustic Occlusion Characterization

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The experimental setup is presented in Fig. 2a. The occluder was realized by a pair of acoustic drywall plates with two layers of Suprema—Tecsound pallet sandwiched between them. This 3 cm thick occluder was placed perpendicularly to the wall at a distance of 45 cm. Noise was generated by playing two different Gaussian random white noises through two audio speakers (MIYAKO Ltd, SL-800). The microphone array consisted of 16 condenser microphones (BOYA, BY-M1) placed at a spacing of 4 cm, and were sampled simultaneously at 40 kHz with 16-bit depth using a multichannel DAQ device (National Instruments, PXIe-6363). The array was placed at a distance of 53 cm from the wall, in parallel to it, and the rightmost microphone was at a distance of 5 cm from the occluder. A human subject served as the target in all experiments. The figures were created using MATLAB V. R2022a (https://www.mathworks.com/) and INKSCAPE V. 1.2 (https://inkscape.org/).
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5

Electrical Characterization and Thermal Analysis

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Voltage and current processing and impedance extractions were conducted in MATLAB v.R2022a (MathWorks Inc., Natick, MA, USA). Impedance measurements were transmitted to GraphPad Prism (GraphPad Software, San Diego, CA, USA) for fitting to a one-phase decay line to remove noise, and then sent back to MATLAB for difference calculations. Similarly, collected temperature and impedance measurements were loaded into GraphPad Prism for quadratic line fitting to remove noise and then imported back into MATLAB for calculation of temperature changes. All further data processing and statistical analysis were conducted in GraphPad Prism.
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