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Electricity load forecasting is of great significance for the overall operation of the power system and the orderly use of electricity at a later stage. However, traditional load forecasting does not consider the change in load quantity at each time point, while the information on the time difference of the load data can reflect the dynamic evolution information of the load data, which is a very important factor for load forecasting. In addition, the research topics in recent years mainly focus on the learning of the complex relationships of load sequences in time latitude by graph neural networks. The relationships between different variables of load sequences are not explicitly captured. In this paper, we propose a model that combines a differential learning network and a multidimensional feature graph attention layer, it can model the time dependence and dynamic evolution of load sequences by learning the amount of load variation at different time points, while representing the correlation of different variable features of load sequences through the graph attention layer. Comparative experiments show that the prediction errors of the proposed model have decreased by 5–26% compared to other advanced methods in the UC Irvine Machine Learning Repository Electricity Load Chart public dataset. [ABSTRACT FROM AUTHOR]

Carbohydrate is the cheapest source of energy among the three major nutrient groups, an appropriate amount of carbohydrates can reduce feed cost and improve growth performance, but carnivorous aquatic animals cannot effectively utilize carbohydrates. The objectives of the present study are aimed at exploring the effects of dietary corn starch levels on glucose loading capacity, insulin-mediated glycemic responses, and glucose homeostasis for Portunus trituberculatus. After two weeks of feeding trial, swimming crabs were starved and sampled at 0, 1, 2, 3, 4, 5, 6, 12, and 24 hours, respectively. The results indicated that crabs fed diet with 0% corn starch exhibited lower glucose concentration in hemolymph than those fed with the other diets, and glucose concentration in hemolymph remained low with the extension of sampling time. The glucose concentration in hemolymph of crabs fed with 6% and 12% corn starch diets reached the peak after 2 hours of feeding; however, the glucose concentration in hemolymph of crabs fed with 24% corn starch attained the highest value after 3 hours of feeding, and the hyperglycemia lasted for 3 hours and decreased rapidly after 6 hours of feeding. Enzyme activities in hemolymph related to glucose metabolism such as pyruvate kinase (PK), glucokinase (GK), and phosphoenolpyruvate carboxykinase (PEPCK) were significantly influenced by dietary corn starch levels and sampling time. Glycogen content in hepatopancreas of crabs fed with 6% and 12% corn starch first increased and then decreased; however, the glycogen content in hepatopancreas of crabs fed with 24% corn starch significantly increased with the prolongation of feeding time. In the 24% corn starch diet, insulin-like peptide (ILP) in hemolymph reached a peak after 1 hour of feeding and then significantly decreased, whereas crustacean hyperglycemia hormone (CHH) was not significantly influenced by dietary corn starch levels and sampling time. ATP content in hepatopancreas peaked at 1 h after feeding and then decreased significantly in different corn starch feeding groups, while the opposite trend was observed in NADH. The activities of mitochondrial respiratory chain complexes I, II, III, and V of crabs fed with different corn starch diets significantly increased first and then decreased. In addition, relative expressions of genes related to glycolysis, gluconeogenesis, glucose transport, glycogen synthesis, insulin signaling pathway, and energy metabolism were significantly affected by dietary corn starch levels and sampling time. In conclusion, the results of the present study reveal glucose metabolic responses were regulated by different corn starch levels at different time points and play an important role in clearing glucose through increased activity of insulin, glycolysis, and glycogenesis, along with gluconeogenesis suppression. [ABSTRACT FROM AUTHOR]