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New energy power plants generate vast amounts of operational data and experience highly variable operating conditions， posing a significant challenge when it comes to diagnosing faults in generator sets. For this reason， a fault diagnosis model based on an improved SVM （support vector machine） algorithm is introduced. Firstly， the concept and principle of SVM for new energy power plants are analyzed. A multivariate SVM classifier is used to optimize the SVM. Secondly， the fault signal extraction method and fault characterization method of photovoltaic power stations and wind farms are studied. Moreover， a fault diagnosis model is proposed. Finally， sample data are obtained from new energy power plants， and an improved SVM fault diagnosis model based on decision level fusion is built. Additionally， the model is trained using fault feature vectors. The results show that the fault diagnosis accuracy for photovoltaic power plants reaches 97.5%， while the fault diagnosis accuracy for wind farms reaches 98.09%， which verify the accuracy of the method.

A carbon emission monitoring method based on carbon emission flow is proposed for the integrated electricity and gas systems （IEGS）. This method distributes carbon emissions on the generation side to various node loads and branch power， thereby achieving accurate tracking and traceability of carbon emissions. Firstly， the principle of carbon emission flow is introduced. Subsequently， the application of carbon emission flow in the power grid and gas network is investigated. A carbon emission monitoring model for the power grid and gas network is established， and a carbon emission monitoring method is proposed based on the theory of carbon emission flow. Finally， simulation results demonstrate that this carbon emission monitoring method for the integrated energy system can simultaneously elucidate the temporal and spatial carbon footprint of the system， overcoming the limitations of conventional carbon measurement methods that tend to be overly macroscopic.

Disruptions in circadian rhythms are associated with an increased risk of developing metabolic diseases. General control nonderepressible 2 (GCN2), a primary sensor of amino acid insufficiency and activator of the integrated stress response (ISR), has emerged as a conserved regulator of the circadian clock in multiple organisms. The objective of this study was to examine diurnal patterns in hepatic ISR activation in the liver and whole body rhythms in metabolism. We hypothesized that GCN2 activation cues hepatic ISR signaling over a natural 24-h feeding-fasting cycle. To address our objective, wild-type (WT) and whole body Gcn2 knockout (GCN2 KO) mice were housed in metabolic cages and provided free access to either a control or leucine-devoid diet (LeuD) for 8 days in total darkness. On the last day, blood and livers were collected at CT3 (CT = circadian time) and CT15. In livers of WT mice, GCN2 phosphorylation followed a diurnal pattern that was guided by intracellular branched-chain amino acid concentrations (r2 = 0.93). Feeding LeuD to WT mice increased hepatic ISR activation at CT15 only. Diurnal oscillations in hepatic ISR signaling, the hepatic transcriptome including lipid metabolic genes, and triglyceride concentrations were substantially reduced or absent in GCN2 KO mice. Furthermore, mice lacking GCN2 were unable to maintain circadian rhythms in whole body energy expenditure, respiratory exchange ratio, and physical activity when fed LeuD. In conclusion, GCN2 activation functions to maintain diurnal ISR activation in the liver and has a vital role in the mechanisms by which nutrient stress affects whole body metabolism. NEW & NOTEWORTHY: This work reveals that the eIF2 kinase GCN2 functions to support diurnal patterns in the hepatic integrated stress response during natural feeding and is necessary to maintain circadian rhythms in energy expenditure, respiratory exchange ratio, and physical activity during amino acid stress. [ABSTRACT FROM AUTHOR]

Author(s): Ilse Gantois [1, 2]; Arkady Khoutorsky [1, 2, 3, 4]; Jelena Popic [1, 2]; Argel Aguilar-Valles [1, 2, 5, 6]; Erika Freemantle [5, 6]; Ruifeng Cao [1, 2, 7]; [...]