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Arrhythmia is a prevalent cardiovascular disease, which has garnered widespread attention due to its age‐related increases in mortality rates. In the analysis of arrhythmia, the electrocardiogram (ECG) plays an important role. Arrhythmia classification often suffers from a significant data imbalance issue due to the limited availability of data for certain arrhythmia categories. This imbalance problem significantly affects the classification performance of the model. To address this challenge, data augmentation emerges as a viable solution, aiming to neutralize the adverse effects of imbalanced datasets on the model. To this end, this paper proposes a novel Multimodality Data Augmentation Network (MM‐DANet) for arrhythmia classification. The MM‐DANet consists of two modules: the multimodality data matching‐based data augmentation module and the multimodality feature encoding module. In the multimodality data matching‐based data augmentation module, we expand the underrepresented arrhythmia categories to match the size of the largest category. Subsequently, the multimodality feature encoding module employs convolutional neural networks (CNN) to extract the modality‐specific features from both signals and images and concatenate them for efficient and accurate classification. The MM‐DANet was evaluated on the MIT‐BIH Arrhythmia Database and achieving an accuracy of 98.83%, along with an average specificity of 98.87%, average sensitivity of 92.92%, average precision of 91.05%, and average F1_score of 91.96%. Furthermore, its performance was also assessed on the St. Petersburg INCART arrhythmia database and the MIT‐BIH supraventricular arrhythmia database, yielding AUC values of 81.98% and 90.93%, respectively. These outstanding results not only underscore the effectiveness of MM‐DANet but also indicate its potential for facilitating reliable automated analysis of arrhythmias. [ABSTRACT FROM AUTHOR]

Thermosetting polymers have higher heat resistance, chemical resistance, mechanical characteristics and structural integrity than thermoplastic polymers due to their three-dimensional networks of covalent bonds. The current status and challenges in the field of thermosetting polymer modified asphalts are reviewed in this article. Firstly, classification and application of thermosetting polymers are briefly introduced, and modification approaches of polymer modified asphalts are compared. Crucial factors in selecting polymers for asphalt modification are then analyzed. Further, the cure mechanisms, preparation methods and properties of thermosetting polymers, including epoxy resin, polyurethane, polyurea, unsaturated polyester resin, phenolic resin, furan resin and amino resin, for asphalt modification are discussed. Especially, the effect of components of epoxy resin and polyurethane on the properties of modified asphalts is described. To develop eco-friendly and sustainable asphalt materials, this article also reviews the use of bio-based thermosetting polymers, including epoxy resin, polyurethane and phenolic resin, in asphalt modification. Finally, self-healing and recyclable thermosetting polymer modified asphalts are highlighted as promising solution to the challenges of healability and recyclability of thermosetting polymers in asphalt modification after long-term service. [ABSTRACT FROM AUTHOR]

Driving fatigue is a physiological phenomenon that often occurs during driving. When the driver enters a fatigue state, they will become distracted and unresponsive, which can easily lead to traffic accidents. The driving fatigue detection method based on a single information source has poor stability in a specific driving environment and has great limitations. This work helps with being able to judge the fatigue state of the driver more comprehensively and achieving a higher accuracy rate of driving fatigue detection. This work mainly introduces research into different signal fusion methods to detect fatigue drive. This work will take the normal driver's breathing signal, eye signals, and steering wheel signal as research objects and collect and isolate the characteristics of the fatigue detection signal. Research was then conducted on different signal fusion methods for the detected depth of breath. Change of steering angle, eyelid closure, and blinking marks and the fatigue driving experiment was designed to evaluate the results of different data fusion methods. Experimental results show that the detection accuracy of the heterogeneous signal fusion method in fatigue detection is as high as 80%., Author(s): Qingjun Wang [1,2]; Zhendong Mu (corresponding author) [3] 1. Introduction 1.1. Research Background and Significance With the continuous improvement of domestic infrastructure construction and the continuous improvement of people's [...]

This paper deals with the impact of polymer loading on the microstructure, viscosity, thermal properties and mechanical performance of recycled polyethylene (rPE) modified epoxy asphalt binder. With this aim, rPE was used as a modifier to improve the toughness of a hot-mix epoxy asphalt binder. rPE particles were found to be highly swollen by the asphalt. Double phase separation was observed in rPE modified epoxy asphalt binders: asphalts dispersed in the continuous epoxy phase and rPE particles distributed in the discontinuous asphalt phase. The viscosity of epoxy asphalt binders increased with more rPE contents but the allowable construction time for pavement remained long. Results revealed that the thermal stability of epoxy asphalt binders was slightly improved with the addition of rPE. Furthermore, rPE lowered the glass transition temperature and increased the storage modulus at the rubbery state. All rPE modified epoxy asphalt binders performed better damping behaviours. In addition, at low rPE concentrations, the mechanical properties of epoxy asphalt binders were improved while at rPE loading higher than 2%, the results were opposite. [ABSTRACT FROM AUTHOR]