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The application of artificial intelligence in smart grid operations, particularly for defect detection, has seen remarkable progress. However, effectively leveraging multi‐modal data sources to enhance defect detection performance remains challenging. This paper presents a novel Knowledge Graph‐based Multimodal Neural Network (KGMNN) approach for robust and efficient defect detection in smart grids. Our proposed method first leverages multiple heterogeneous data sources, such as structural data, temporal sensor readings, and maintenance records, creating a comprehensive representation of the grid system. The system is then modeled as a knowledge graph, with nodes representing various entities and edges signifying their interconnections and interactions. This paper introduces a novel neural network architecture that effectively leverages the knowledge graph structure. This architecture incorporates multi‐modal fusion techniques, enabling the model to process and integrate diverse data sources. It can recognize intricate patterns indicative of potential defects that are often undetectable in individual data modes. Experimental results on several real‐world smart grid datasets demonstrate that the KGMNN significantly outperforms state‐of‐the‐art techniques in terms of precision, recall, and computational efficiency. Furthermore, the KGMNN's ability to provide explainable predictions, aided by the inherent interpretability of the knowledge graph structure, is of significant practical value in the field. This research paves the way for a new generation of AI‐powered defect detection in smart grids, facilitating enhanced grid reliability and operational efficiency. [ABSTRACT FROM AUTHOR]

In recent years, with the development of societies and economies, the demand for social electricity has further increased. The efficiency and accuracy of electric-load forecasting is an important guarantee for the safety and reliability of power system operation. With the sparrow search algorithm (SSA), long short-term memory (LSTM), and random forest (RF), this research proposes an SSA-LSTM-RF daily peak-valley forecasting model. First, this research uses the Pearson correlation coefficient and the random forest model to select features. Second, the forecasting model takes the target value, climate characteristics, time series characteristics, and historical trend characteristics as input to the LSTM network to obtain the daily-load peak and valley values. Third, the super parameters of the LSTM network are optimized by the SSA algorithm and the global optimal solution is obtained. Finally, the forecasted peak and valley values are input into the random forest as features to obtain the output of the peak-valley time. The forest value of the SSA-LSTM-RF model is good, and the fitting ability is also good. Through experimental comparison, it can be seen that the electric-load forecasting algorithm based on the SSA-LSTM-RF model has higher forecasting accuracy and provides ideal performance for electric-load forecasting with different time steps. [ABSTRACT FROM AUTHOR]

The apetala/ethylene responsive factor (AP2/ERF) family is one of the largest plant-specific transcription factors and plays a vital role in plant development and response to stress. The apetala 2.4 (RAP2.4) gene is a member of the AP2/ERF family. In this study, ClRAP2.4 cDNA fragment with 768 bp open reading frame was cloned and the resistance of ClRAP2.4 overexpression to low temperature was investigated to understand whether RAP2.4 is involved in low-temperature stress in chrysanthemum (Chrysamthemum lavandulifolium). Phylogenetic analysis showed that ClRAP2.4 belonged to the DREB subfamily and was most closely related to AT1G22190. ClRAP2.4 was localised in cell nucleus and promotes transcriptional activation in yeast. In addition, ClRAP2.4 was transformed by using the Agrobacterium -mediated leaf disc method, and four overexpression lines (OX-1, OX-2, OX-7, and OX-8) were obtained. The activities of superoxide dismutase and peroxidase, and proline content in leaves in the four overexpression line were higher than those in the wild type (WT), whereas the electrical conductivity and malondialdehyde content were decreased, indicating that the tolerance of plants with ClRAP2.4 overexpression to cold stress was increased. RNA-Seq showed 390 differentially expressed genes (DEGs) between the transgenic and WT plants(229 upregulated, 161 downregulated). The number of ABRE , LTR , and DRE cis -elements in the promoters of DEGs were 175, 106, and 46, respectively. The relative expression levels of ClCOR , ClFe/MnSOD , ClPOD , ClNCL , ClPLK , ClFAD , and ClPRP in the transgenic plants were higher than those in WT plants at low temperatures. These data suggest that ClRAP2.4 may increase chrysanthemum tolerance to cold stress. The apetala/ethylene responsive factor (AP2/ERF) family plays important in plant development and response to stress. This article mainly tells about how RAP2.4 is involved in low-temperature stress of chrysanthemum (Chrysamthemum lavandulifolium). We found that the overexpression of ClRAP2.4 in chrysanthemum increased tolerance to cold stress. The relative expression levels of ClCOR and other genes were higher in transgenic plants at low temperature. This experiment provides a theoretical basis for the research on the tolerance of chrysanthemum to cold stress. [ABSTRACT FROM AUTHOR]

Knowledge graph embedding aims to learn representation vectors for the entities and relations. Most of the existing approaches learn the representation from the structural information in the triples, which neglects the content related to the entity and relation. Though there are some approaches proposed to exploit the related multimodal content to improve knowledge graph embedding, such as the text description and images associated with the entities, they are not effective to address the heterogeneity and cross-modal correlation constraint of different types of content and network structure. In this paper, we propose a multi-modal content fusion model (MMCF) for knowledge graph embedding. To effectively fuse the heterogenous data for knowledge graph embedding, such as text description, related images and structural information, a cross-modal correlation learning component is proposed. It first learns the intra-modal and inter-modal correlation to fuse the multimodal content of each entity, and then they are fused with the structure features by a gating network. Meanwhile, to enhance the features of relation, the features of the associated head entity and tail entity are fused to learn relation embedding. To effectively evaluate the proposed model, we compare it with other baselines in three datasets, i.e., FB-IMG, WN18RR and FB15k-237. Experiment result of link prediction demonstrates that our model outperforms the state-of-the-art in most of the metrics significantly, implying the superiority of the proposed method.