This study evaluated the effectiveness of behavioral skills training (BST) to teach peer-trainers to implement video feedback (Study 1) and evaluated the effectiveness of peer-implemented video feedback to increase proper deadlifting form across 3 participants (Study 2). A nonconcurrent, multiple baseline design across participants was employed to evaluate BST and peer implemented video feedback. Results demonstrate that BST was effective for teaching peertrainers to implement video feedback and video feedback led to improvement of deadlifting form across all participants.

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The battery as an electrochemical system can be considered a Blackbox with no way of observing processes occurring within it in a nondestructive manner1. However, most physical, and chemical processes in electrochemical systems are shown to be distinguishable by their distinct characteristic time constant. Electrochemical impedance spectroscopy (EIS) can therefore be a valuable tool to distinguish between internal processes within batteries based on their frequency response2–4. Numerical approaches for understanding battery systems have long been explored by researchers in academia and industry alike2,4,5. Generally, it is computationally expensive to develop a high fidelity multiscale and multiphysics model with high predictive capability that can estimate critical transport parameters and material decay. Thus, model development traditionally starts with simplistic models where more physics is added as per requirements. Many approaches to mathematical modelling of the battery at different length scales and complexity are available today6. The impedance response of the battery can also be simulated coupled with various other parameters using the battery models7. Basic equivalent circuit models of the battery are commonly used for this but they suffer from lack of physical interpretability and model degeneracy8. The impedance response based on models are obtained in a physics-based model by linearizing the non-linear models and transforming equations in the time-domain into the frequency domain5,9. This generally is achieved by applying a Laplace transform to the system of linearized equations. Now, based on the complexity of the model, these equations can be solved analytically or after separating the real and imaginary parts for numerical simulation. Therefore, there are varied solution strategies appropriate for individual battery models that depend on underlying scale and physics involved. Through this work we aim to comprehensively compile these solution strategies and present the best approach for the individual electrochemical model based on criteria such as faster convergence times and error minimization. References: R. S. Robinson, INTELEC, Int. Telecommun. Energy Conf., 654–661 (1996). M. Gaberšček, Nat. Commun., 12, 19–22 (2021). J. Xu, C. C. Mi, B. Cao, and J. Cao, J. Power Sources, 233, 277–284 (2013). S. E. Li, B. Wang, H. Peng, and X. Hu, J. Power Sources, 258, 9–18 (2014). J. P. Meyers, M. Doyle, R. M. Darling, and J. Newman, J. Electrochem. Soc., 147, 2930 (2000). V. Ramadesigan et al., J. Electrochem. Soc., 159, R31–R45 (2012). D. Andre et al., J. Power Sources, 196, 5349–5356 (2011). S. Fletcher, J. Electrochem. Soc., 141, 1823–1826 (1994). M. Pathak et al., J. Electrochem. Soc., 165, A1324–A1337 (2018).