The emission standard for adsorbable organic halogen (AOX) has been adjusted as a mandatory assessment indicator in the papermaking industrial pollutants emission standards of China. To provide a theoretical basis to reduce AOX formation, a kinetic model of the first chlorine dioxide bleaching stage (D0) is presented for elemental chlorinefree (ECF) bleaching of eucalyptus kraft pulp. . R 2 was greater than 0.9, which means that the model was shown to have high prognostic ability and feasibility. In the D0 stage, mostly lignin was removed and the reaction was fast. Much AOX was formed at the beginning of bleaching, and the reaction rate was primarily determined by the lignin content and chlorine dioxide dosage. H + existed primarily as a catalyst and had little influence on AOX formation. The AOX formation occurs easily, as the reaction activation energy is less than 30 kJ.mol -1 .
The field of visual question answering (VQA) has seen a growing trend of integrating external knowledge sources to improve performance. However, owing to the potential incompleteness of external knowledge sources and the inherent mismatch between different forms of data, current knowledge-based visual question answering (KBVQA) techniques are still confronted with the challenge of effectively integrating and utilizing multiple heterogeneous data. To address this issue, a novel approach centered on a multi-modal semantic graph (MSG) is proposed. The MSG serves as a mechanism for effectively unifying the representation of heterogeneous data and diverse types of knowledge. Additionally, a multi-modal semantic graph knowledge reasoning model (MSG-KRM) is introduced to perform reasoning and deep fusion of image–text information and external knowledge sources. The development of the semantic graph involves extracting keywords from the image object detection information, question text, and external knowledge texts, which are then represented as symbol nodes. Three types of semantic graphs are then constructed based on the knowledge graph, including vision, question, and the external knowledge text, with non-symbol nodes added to connect these three independent graphs and marked with respective node and edge types. During the inference stage, the multi-modal semantic graph and image–text information are embedded into the feature semantic graph through three embedding methods, and a type-aware graph attention module is employed for deep reasoning. The final answer prediction is a blend of the output from the pre-trained model, graph pooling results, and the characteristics of non-symbolic nodes. The experimental results on the OK-VQA dataset show that the MSG-KRM model is superior to existing methods in terms of overall accuracy score, achieving a score of 43.58, and with improved accuracy for most subclass questions, proving the effectiveness of the proposed method.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.