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Abstract Myocardial infarction (MI) remains a significant contributor to global mortality and morbidity, necessitating accurate and timely diagnosis. Current diagnostic methods encounter challenges in capturing intricate patterns, urging the need for advanced automated approaches to enhance MI detection. In this study, we strive to advance MI detection by proposing a hybrid approach that combines the strengths of ResNet and Vision Transformer (ViT) models, leveraging global and local features for improved accuracy. We introduce a slim-model ViT design with multibranch networks and channel attention mechanisms to enhance patch embedding extraction, addressing ViT’s limitations. By training data through both ResNet and modified ViT models, we incorporate a dual-pathway feature extraction strategy. The fusion of global and local features addresses the challenge of robust feature vector creation. Our approach showcases enhanced learning capabilities through modified ViT architecture and ResNet architecture. The dual-pathway training enriches feature extraction, culminating in a comprehensive feature vector. Preliminary results demonstrate significant potential for accurate detection of MI. Our study introduces a hybrid ResNet-ViT model for advanced MI detection, highlighting the synergy between global and local feature extraction. This approach holds promise for elevating MI classification accuracy, with implications for improved patient care. Further validation and clinical applicability exploration are warranted.

With the large-scale application of computational propaganda, disinformation campaigns have emerged globally in response to the logic of “post-truth” politics. Organized disinformation campaigns operate frequently on overseas social media platforms, with China often being the target. In order to understand the dissemination mechanisms of such disinformation campaigns, the study found that the dissemination of disinformation became a key strategy for the campaign’s emotional mobilization. The main subjects of disinformation have formed an international communication matrix, creating and spreading all kinds of disinformation on a large scale. The “coalition of protesters” is based on the shared emotional experience evoked by disinformation and characterized by the act of spreading disinformation. Then, the widespread dissemination of the corresponding emotions leads to different perceptions of the “target,” thus prompting protesters to adopt different types of collective action. This mechanism of emotional mobilization shows that facts are “too big to know,” which exacerbates confirmation bias and provides more space for the spread of disinformation. The strong emotions embedded in disinformation contributed to the completion of the protesters’ imagination of community, and emotions became the dominant factor in coalescing the group and providing motivational support for collective action.

Maize (Zea mays L.) Fusarium ear rot (FER), which is caused by the fungal pathogen Fusarium verticillioides, is a major ear disease affecting maize production in China. Developing resistant maize hybrids is a cost‐effective and environmentally friendly method of reducing yield losses due to FER. Using the nail punch inoculation method, 17 highly resistant and 16 highly susceptible inbred lines were identified in this study, and a large‐scale maize hybrid consisting of 528 F1's was produced using a half‐diallel cross model. The hybrids were inoculated with F. verticillioides in 6 year–location environment combinations during 2020–2022. Our results showed that estimates of genotypic, environmental, and genotype × environment interaction variance components were very significant (p < 0.001). All the crosses generated from highly resistant inbred lines exhibited high kernel resistance, whereas those generated from highly susceptible inbred lines mostly exhibited high susceptibility to FER. Crosses between resistant and susceptible inbred lines showed intermediate resistance to FER. The general combining ability (GCA) effect was greater than the specific combining ability effect across multiple environments, indicating that the inheritance of FER resistance was mainly influenced by additive genetic effects. The correlation coefficient between the expected GCA value and hybrid resistance was 0.789, whereas that between the mid‐parent value and hybrid resistance was only 0.644. Inbred performance per se and their corresponding hybrids across multiple environments were significantly correlated (r = 0.823). However, no relationship was observed between the FER resistance of F1's and that of their parental heterotic groups. Core Ideas: In breeding, selecting parents with Fusarium ear rot (FER) resistance is more crucial than selecting the hybrids themselves.The additive effect is the main genetic component of ear rot in maize, whereas the non‐additive effect is minor.Additional inoculation tests for FER resistance are required for specific hybrid combinations.Hybrid resistance can be inferred based on the general combining ability of parental inbred lines.The additive effects of resistance genes in highly inbred lines are minor. [ABSTRACT FROM AUTHOR]