Chinese text dual attention network for aspect-level sentiment classification.

English text has a clear and compact subject structure, which makes it easy to find dependency relationships between words. However, Chinese text often conveys information using situational settings, which results in loose sentence structures, and even most Chinese comments and experimental summary texts lack subjects. This makes it challenging to determine the dependency relationship between words in Chinese text, especially in aspect-level sentiment recognition. To solve this problem faced by Chinese text in the field of sentiment recognition, a Chinese text dual attention network for aspect-level sentiment recognition is proposed. First, Chinese syntactic dependency is proposed, and sentiment dictionary is introduced to quickly and accurately extract aspect-level sentiment words, opinion extraction and classification of sentimental trends in text. Additionally, in order to extract context-level features, the CNN-BILSTM model and position coding are also introduced. Finally, to better extract fine-grained aspect-level sentiment, a two-level attention mechanism is used. Compared with ten advanced baseline models, the model's capabilities are being further optimized for better performance, with Accuracy of 0.9180, 0.9080 and 0.8380 respectively. This method is being demonstrated by a vast array of experiments to achieve higher performance in aspect-level sentiment recognition in less time, and ablation experiments demonstrate the importance of each module of the model.

Intelligent fault detection strategy for knowledge entities in fault semantic networks of distribution network based on siamese networks.

The advent of smart grid technologies has brought about a paradigm shift in the management and operation of distribution networks, allowing for intricate system information to be encapsulated within semantic network models. These models, while robust, are not immune to faults within their knowledge entities, which can arise from a myriad of issues, potentially leading to verification failures and operational disruptions. Addressing this critical vulnerability, our research delves into the development of a novel fault detection methodology specifically tailored for the knowledge entity variables of semantic networks in distribution networks. In our approach, we first construct a state space equation that models the behavior of knowledge entity variables in the presence of faults. This foundational framework enables us to apply an unknown input observer strategy to effectively detect anomalies within the system. To bolster the fault identification process, we introduce the innovative use of a siamese network, a neural network architecture which is proficient in differentiating between similar datasets. Through simulation scenarios, we demonstrate the efficacy of our proposed fault detection method.

Whole-cell biocatalysis for ε-poly-l-lysine production by a food-grade recombinant Bacillus subtilis.

ε-Poly-l-lysine (ε-PL), a natural food preservative with various advantages, is primarily produced by Streptomyces. It has attracted considerable attentions for the outstanding antibacterial activity, safety, heat stability, water solubility and other remarkable properties. In this study, a food-grade recombinant Bacillus subtilis was constructed for the biocatalysis of ε-PL. Firstly, the d-alanine racemase gene (alrA) was deleted from the genome of Bacillus subtilis 168 to construct an auxotrophic B. subtilis 168 (alrA-). Based on the shuttle plasmid pMA5, a food-grade plasmid pMA5a was constructed by replacing the genes of kanamycin resistance (Kanr) and ampicillin resistance (Ampr) with alrA and the gene encoding α-peptide of ß-galactosidase (lacZα), respectively. Subsequently, codon-optimized ε-PL synthase gene (pls) and P-pls were ligated into pMA5a and transformed in E. coli DH5α and expressed in B. subtilis 168 (alrA-). Finally, the whole-cell biocatalysis conditions for ε-PL production by B. subtilis 168 (alrA-)/pMA5a-pls were optimized, and the optimal conditions were 30°C, pH 4, l-lysine concentration of 0.6 g/L, bacterial concentration of 15 % (w/v) and a catalytic time of 7 h. The ε-PL production reached a maximum of 0.33 ± 0.03 g/L. The product was verified to be ε-PL by HPLC and tricine-SDS-PAGE. The information obtained in this study shows critical reference for the food-grade heterologous expression of ε-PL.