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This paper proposes a new method for short-term electric load forecasting using a Ridgelet Neural Network (RNN) combined with a wavelet transform and optimized by a Self-Adapted (SA) Kho-Kho algorithm (SAKhoKho). The aim of this method is to improve the accuracy and reliability of electric load forecasting, which is essential for the planning and operation of competitive electrical networks. The proposed method uses the Wavelet Transform (WT) to decompose the load data into different frequency components and applies the RNN to each component separately. The RNN is, then, optimized by the SAKhoKho algorithm, which is an improved version of the KhoKho algorithm that can adapt the search parameters dynamically. The proposed method is trained and tested on the Zone Preliminary Billing Data from the PJM regulatory area, which is updated every two weeks based on the Intercontinental Exchange (ICE) figures. The proposed method is compared with six other cutting-edge methods from the literature, including SVM/SA, hybrid, ARIMA, MLP/PSO, CNN, and RNN/KhoKho/WT. The results show that the proposed method achieves the lowest Mean Absolute Error (MAE) of 7.7704 and Root Mean Square Error (RMSE) of 17.4132 among all the methods, indicating its superior performance. The proposed method can capture the temporal dependencies in the load data and optimize the RNN's weights to minimize the error function. The proposed method is a promising technique for electric load forecasting, as it can provide accurate and reliable predictions for the next hour based on the previous 24 h of data., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (© 2024 The Author(s).)

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.

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 768bp 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.