Narrow Results

We exclusively analyze the onset and condition of formation of modulated waves in a diffusive FitzHugh–Nagumo model for myocardial cell excitations. The cells are connected through gap junction coupling. An additive magnetic flux variable is used to describe the effect of electromagnetic induction, while electromagnetic radiation is imposed on the magnetic flux variable as a periodic forcing. We used the discrete multiple scale expansion and obtained, from the model equations, a single differential-difference amplitude nonlinear equation. We performed the linear stability analysis of this equation and found that instability features are importantly influenced by the induced electromagnetic gain. We present the unstable and stable regions of modulational instability (MI). The resulting analytic predictions are confirmed by numerical experiments of the generic equations. The results reveal that due to MI, an initial steady state that consisted of a plane wave with low amplitude evolves into a modulated localized wave patterns, soliton-like in shape, with features of synchronization. Furthermore, the formation of periodic pulse train with breathing motion presents a disappearing pattern in the presence of electromagnetic radiation. This could provide guidance and better understanding of sudden heart failure exposed to heavily electromagnetic radiation.

Exercise has long been known to improve cardiovascular health, energy metabolism, and well-being. However, myocardial cell responses to exercise are complex and multifaceted due to their molecular pathways. To understand cardiac physiology and path physiology, one must understand these pathways, including energy autophagy. In recent years, deep learning techniques, IoT devices, and cloud computing infrastructure have enabled real-time, large-scale biological data analysis. The objective of this work is to extract and analyze autophagy properties in exercise-induced cardiac cells in a cloud-IoT context using deep learning, more especially an autoencoder. The Shanghai University of Sport Ethics Committee for Science Research gave its approval for the data collection, which involved 150 male Sprague–Dawley (SD) rats that were eight weeks old and in good health. The $Z$-score normalization method was used to standardize the data. Fractal optimization methods could be applied to these algorithms. For example, fractal-inspired optimization techniques might be used to analyze deep learning with Autoencoder, the autography energy of exercise myocardial cells within a cloud-IoT. To capture the intricate myocardial energy autophagy during exercise, we introduced the DMO-GCNN-Autoencoder, a Dwarf Mongoose Optimized Graph Convolutional Neural Network. The results showed that the proposed network’s performance matches that of the existing methods.