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The metaphor is a pervasive linguistic device that has become an active research topic in the computer field because of its essential role in language's cognitive and communicative processes. Currently, the rapid expansion of social media encourages the development of multimodal. As the most popular communication method in social media, memes have attracted the attention of many linguists, who believe that metaphors contain rich metaphorical information. However, multimodal metaphor detection suffers from insufficient information due to the short text of memes and lacks effective multimodal fusion methods. To address these problems, we utilize a single-pass non-autoregressive text generation method to convert images into text to provide additional textual information for the model. In addition, the information of different modes is fused by a multi-layer fusion module consisting of a prefix guide module and a similarity-aware aggregator. The module can reduce the heterogeneity between modes, learn fine-grained information, and better integrate the characteristic information of different modes. We conducted many experiments on the Met-Meme dataset. Compared with the strong baseline model in the experiment, the weighted F1 of our model on three data types of the MET-Meme dataset improved by 1.95%, 1.55%, and 1.72%, respectively. To further demonstrate the effectiveness of the proposed method, we also conducted experiments on a multimodal sarcasm dataset and obtained competitive results. [ABSTRACT FROM AUTHOR]

Various catalysts are developed to improve the performance of metal oxide semiconductor gas sensors, but achieving high selectivity and response intensity in chemiresistive gas sensors (CGSs) remains a significant challenge. In this study, an in situ‐annealing approach to synthesize Cu catalytic sites on ultrathin WO2.72 nanowires for detecting toluene at ultralow concentrations (Ra/Rg = 1.9 at 10 ppb) with high selectivity is developed. Experimental and molecular dynamic studies reveal that the Cu single atoms (SAs) act as active sites, promoting the oxidation of toluene and increasing the affinity of Cu single‐atom catalysts (SACs)‐containing sensing materials for toluene while weakening the association with carbon dioxide or water vapor. Density functional theory studies show that the selective binding of toluene to Cu SAs is due to the favorable binding sites provided by Cu SAs for toluene molecules over other gaseous species, which aids the adsorption of toluene on WO2.72 nanowires. This study demonstrates the successful atomic‐level interface regulation engineering of WO2.72 nanowire‐supported Cu SAs, providing a potential strategy for the development of highly active and durable CGSs. [ABSTRACT FROM AUTHOR]

In this paper, α-Fe2O3 was prepared by hydrothermal and chemical precipitation methods, respectively, and its gas sensing properties toward CH4 were systematically studied. The larger specific surface area of the materials provided them potential and considerable gas adsorption abilities. The results of gas-sensing measurements indicated the synthesized α-Fe2O3 materials presented a p-type conductive nature. The sensitivities of the devices based on the different material fabrication methods were measured to be 195% and 214%, respectively, at 370°C for the concentration of 2000 ppm CH4. In addition, the sensing mechanism was discussed and simulated by first-principles calculation. The results revealed the higher surface adsorption energy of α-Fe2O3, which verified the experimental conclusions of our work. Our research suggests that α-Fe2O3 is a promising candidate for CH4 gas sensing in many fields such as mining safety and environmental monitoring. [ABSTRACT FROM AUTHOR]