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Heart disease is the leading cause of death for men and women globally. The residual network (ResNet) evolution of electrocardiogram (ECG) technology has contributed to our understanding of cardiac physiology. We propose an artificial intelligence-enabled ECG algorithm based on an improved ResNet for a wearable ECG. The system hardware consists of a wearable ECG with conductive fabric electrodes, a wireless ECG acquisition module, a mobile terminal App, and a cloud diagnostic platform. The algorithm adopted in this study is based on an improved ResNet for the rapid classification of different types of arrhythmia. First, we visualize ECG data and convert one-dimensional ECG signals into two-dimensional images using Gramian angular fields. Then, we improve the ResNet-50 network model, add multistage shortcut branches to the network, and optimize the residual block. The ReLu activation function is replaced by a scaled exponential linear units (SELUs) activation function to improve the expression ability of the model. Finally, the images are input into the improved ResNet network for classification. The average recognition rate of this classification algorithm against seven types of arrhythmia signals (atrial fibrillation, atrial premature beat, ventricular premature beat, normal beat, ventricular tachycardia, atrial tachycardia, and sinus bradycardia) is 98.3%. [ABSTRACT FROM AUTHOR]

This study presents two meso-mechanical methods of analyzing the asphalt concrete’s dynamic modulus, including the transient dynamic method (implicit dynamic method) based on the modified Newmark numerical differential algorithm and the steady-state dynamic method based on the perturbation theory. The results indicate that compared to the transient dynamic method based on the modified Newmark numerical differential algorithm, the steady-state dynamic method based on the perturbation theory is much more efficient in calculating dynamic modulus of asphalt concrete while maintaining similar accuracy. Furthermore, the random aggregate model of asphalt concrete coupled with the steady-state dynamic method is also employed to obtain the asphalt concrete’s dynamic modulus. The numerical solution was found in a good agreement with the corresponding experiment results, which indicates the proposed steady-state dynamic method an efficient and effective way to predict the dynamic modulus of asphalt concrete. [ABSTRACT FROM AUTHOR]