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The rapid growth of the global population and technological progress has significantly increased the world's total power generation.Electric energy consumption forecasts play an essential role in power system dispatch and power generation management.Aim at the complex characteristics of time series of energy consumption data,and to improve the prediction accuracy of power consumption,a novel sandwich structure is proposed,in which an Attention mechanism is placed in the double layer long short-term memory artificial neural network,namely A-DLSTM.This network structure uses the attention mechanism in the mezzanine to adaptively focus on different features in each single time unit and uses the two-layer LSTM network to capture the time information in the sequence to predict the sequence data.The experimental data comes from the UCI machine learning data set,and it is the electricity consumption of a family in the past five years.The parameters of the experiment are adjusting by the grid search method.The experiment compares the prediction performance of A-DLSTM and the existing model on energy consumption data.The network of this article reaches the state-of-the-art in terms of mean square error,root mean square error,average absolute error,and average absolute percentage error.By analyzing the heat map's attention layer,the factor that has the most significant impact on electricity consumption forecasting is determined.

This study aimed to assess how the bacterial–enzyme co-fermentation of corn straw affects fermentation quality, the digestion rate in Hu sheep, and rumen microorganisms. Orthogonal experiments were utilized to establish the optimal fermentation conditions, which were subsequently applied in bacterial–enzyme fermentation trials involving four groups: group A (control), group B (enzyme added), group C (bacteria added), and group D (bacteria + enzyme). The results show that the optimal fermentation conditions were the addition of 2% corn flour, 2% cottonseed meal, and 60% moisture. In comparison with group A, group D demonstrated the most favorable outcomes, notably reducing the pH and cellulose content while enhancing the lactic acid content. The relative abundances of Pantoea and Weissella reduced, whereas those of Lactiplantibacillus and Limosilactobacillus increased, in the fermented corn straw. In the animal experiments, group D exhibited significantly higher digestibility of NDF and ADF; total VFA, acetic acid, and NH3-N contents; and enzyme activity compared with groups A and B. Additionally, the relative abundances of Prevotella, NK4A214-group, Entodinium, and Polyplastron increased, while those of Dasytricha and Enoploplastron decreased, in group D compared with group A. It can be concluded that Lactobacillus plantarum and cellulase treatments enhance the nutritional value of corn straw by improving ruminal fermentation and regulating the sheep rumen microbiota. [ABSTRACT FROM AUTHOR]

Soybean cyst nematode (SCN; Heterodera glycines Ichinohe), one of the most devastating soybean (Glycine max) pathogens, causes significant yield loss in soybean production. Nematode infection triggers plant defense responses; however, the components involved in the upstream signaling cascade remain largely unknown. In this study, we established that a mitogen-activated protein kinase (MAPK) signaling module, activated by nematode infection or wounding, is crucial for soybeans to establish SCN resistance. GmMPK3 and GmMPK6 directly interact with CDG1-LIKE1 (GmCDL1), a member of the receptor-like cytoplasmic kinase (RLCK) subfamily VII. These kinases phosphorylate GmCDL1 at Thr-372 to prevent its proteasome-mediated degradation. Functional analysis demonstrated that GmCDL1 positively regulates immune responses and promotes SCN resistance in soybeans. GmMPK3-mediated and GmMPK6-mediated phosphorylation of GmCDL1 enhances GmMPK3 and GmMPK6 activation and soybean disease resistance, representing a positive feedback mechanism. Additionally, 2 L-type lectin receptor kinases, GmLecRK02g and GmLecRK08g, associate with GmCDL1 to initiate downstream immune signaling. Notably, our study also unveils the potential involvement of GmLecRKs and GmCDL1 in countering other soybean pathogens beyond nematodes. Taken together, our findings reveal the pivotal role of the GmLecRKs–GmCDL1–MAPK regulatory module in triggering soybean basal immune responses. The soybean receptor-like cytoplasmic kinase GmCDL1 directly links L-type lectin receptor kinases for MAP kinase activation and undergoes MAP kinase–mediated phosphorylation to boost immune signaling. [ABSTRACT FROM AUTHOR]

High quality and nutritional benefits are ultimately the desirable features that influence the commercial value and market share of broad bean (Vicia faba L.). Different cultivars vary greatly in taste, flavor, and nutrition. However, the molecular basis of these traits remains largely unknown. Here, the grain metabolites of the superior Chinese landrace Cixidabaican (CX) were detected by a widely targeted metabolomics approach and compared with the main cultivar Lingxiyicun (LX) from Japan. The analyses of global metabolic variations revealed a total of 149 differentially abundant metabolites (DAMs) were identified between these two genotypes. Among them, 84 and 65 were up- and down-regulated in CX compared with LX. Most of the DAMs were closely related to healthy eating substances known for their antioxidant and anti-cancer properties, and some others were involved in the taste formation. The KEGG-based classification further revealed that these DAMs were significantly enriched in 21 metabolic pathways, particularly in flavone and flavonol biosynthesis. The differences in key secondary metabolites, including flavonoids, terpenoids, amino acid derivates, and alkaloids, may lead to more nutritional value in a healthy diet and better adaptability for the seed germination of CX. The present results provide important insights into the taste/quality-forming mechanisms and contributes to the conservation and utilization of germplasm resources for breeding broad bean with superior eating quality.