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adapted open-source matlab script  (MathWorks Inc)


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    MathWorks Inc adapted open-source matlab script
    Adapted Open Source Matlab Script, 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/open-source+script/pmc11948067-207-13-14
    Average 90 stars, based on 1 article reviews
    adapted open-source matlab script - by Bioz Stars, 2026-10
    90/100 stars

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    other:

    Article Title: Musical Auditory Alpha Wave Neurofeedback: Validation and Cognitive Perspectives
    Article Snippet: In NBT (Owen et al., ), 8 letters of the alphabet were presented by a PC using the open-source Matlab script, ‘N-Back-for-Matlab’ (Layden, ), in a random order with an interval of 1.8 or 2.0 s for two- and three-back tests, respectively.

    Article Title: Responses of candidate intensity measures to different mental and motor load levels using upper limb exergames in typically developing children and adolescents
    Article Snippet: We used an open-source MATLAB® script that band-pass filtered, resampled, and summed raw 3-dimensional acceleration data [ ].


    Article Title: Investigating the effects of N-terminal acetylation on KFE8 self-assembly with 2D IR spectroscopy.
    Article Snippet: Peptide self-assembly is an exciting and robust approach to create novel nanoscale materials for biomedical applications.. However, the complex interplay between intraand intermolecular interactions in peptide aggregation means that minor changes in peptide sequence can yield dramatic changes in supramolecular structure.. Here, we use two-dimensional infrared spectroscopy to study a model amphiphilic peptide, KFE8, and its N-terminal acetylated counterpart, AcKFE8.

    Article Title: Spider Silk‐Inspired Conductive Hydrogels for Enhanced Toughness and Environmental Resilience via Dense Hierarchical Structuring
    Article Snippet: The number of pores and their size were analyzed by using an adapted open‐source MATLAB script.



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    Overview of the proposed workflow for data collection, classification, and explanation in automated landing pattern recognition. A The single-leg landing movements of the subjects before and after the fatigue intervention were collected, and <t>the</t> <t>three-dimensional</t> kinematics and kinetics data of the landing leg during the landing phase were used as the input data of the model. B The three-dimensional kinematics and kinetics data as input signals to explore the recognizability of the two class landing patterns by three classical classification and recognition algorithm models and ZeroR classifier. C The ANN with the best performance in classification and recognition accuracy between classes was used as the forward propagation classifier to compute the input signals, and the output signals of ANN were used as the input of LRP to calculate the RS that can explain the difference of landing patterns through backward propagation. D The application of <t>1-SPM</t> to evaluate the LRP results from a statistical perspective. E The results of these two aspects were evaluated and discussed together.
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    Overview of the proposed workflow for data collection, classification, and explanation in automated landing pattern recognition. A The single-leg landing movements of the subjects before and after the fatigue intervention were collected, and the three-dimensional kinematics and kinetics data of the landing leg during the landing phase were used as the input data of the model. B The three-dimensional kinematics and kinetics data as input signals to explore the recognizability of the two class landing patterns by three classical classification and recognition algorithm models and ZeroR classifier. C The ANN with the best performance in classification and recognition accuracy between classes was used as the forward propagation classifier to compute the input signals, and the output signals of ANN were used as the input of LRP to calculate the RS that can explain the difference of landing patterns through backward propagation. D The application of 1-SPM to evaluate the LRP results from a statistical perspective. E The results of these two aspects were evaluated and discussed together.

    Journal: Heliyon

    Article Title: A new method applied for explaining the landing patterns: Interpretability analysis of machine learning

    doi: 10.1016/j.heliyon.2024.e26052

    Figure Lengend Snippet: Overview of the proposed workflow for data collection, classification, and explanation in automated landing pattern recognition. A The single-leg landing movements of the subjects before and after the fatigue intervention were collected, and the three-dimensional kinematics and kinetics data of the landing leg during the landing phase were used as the input data of the model. B The three-dimensional kinematics and kinetics data as input signals to explore the recognizability of the two class landing patterns by three classical classification and recognition algorithm models and ZeroR classifier. C The ANN with the best performance in classification and recognition accuracy between classes was used as the forward propagation classifier to compute the input signals, and the output signals of ANN were used as the input of LRP to calculate the RS that can explain the difference of landing patterns through backward propagation. D The application of 1-SPM to evaluate the LRP results from a statistical perspective. E The results of these two aspects were evaluated and discussed together.

    Article Snippet: For the implementation of SPM, the open-source MATLAB script (paired-samples T-test) of One-Dimensional SPM (SPM 1D) was employed to test the statistical differences, and the significance threshold was set as 0.05 [ , ].

    Techniques: