45 research outputs found
How to introduce demand side resources in the design of low-carbon power systems in China
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Research on service strategy of electricity selling company under the reform of electricity market
No full textFilamentation and inhibition of prokaryotic CTP synthase with ligands
No full textCytidine triphosphate synthase (CTPS) plays a pivotal role in the de novo synthesis of cytidine triphosphate (CTP), a fundamental building block for RNA and DNA that is essential for life. CTPS is capable of directly binding to all four nucleotide triphosphates: adenine triphosphate, uridine triphosphate, CTP, and guanidine triphosphate. Furthermore, CTPS can form cytoophidia in vivo and metabolic filaments in vitro, undergoing regulation at multiple levels. CTPS is considered a potential therapeutic target for combating invasions or infections by viral or prokaryotic pathogens. Utilizing cryo‐electron microscopy, we determined the structure of Escherichia coli CTPS (ecCTPS) filament in complex with CTP, nicotinamide adenine dinucleotide (NADH), and the covalent inhibitor 6‐diazo‐5‐oxo‐ l‐norleucine (DON), achieving a resolution of 2.9 Å. We constructed a phylogenetic tree based on differences in filament‐forming interfaces and designed a variant to validate our hypothesis, providing an evolutionary perspective on CTPS filament formation. Our computational analysis revealed a solvent‐accessible ammonia tunnel upon DON binding. Through comparative structural analysis, we discern a distinct mode of CTP binding of ecCTPS that differs from eukaryotic counterparts. Combining biochemical assays and structural analysis, we determined and validated the synergistic inhibitory effects of CTP with NADH or adenine on CTPS. Our results expand our comprehension of the diverse regulatory aspects of CTPS and lay a foundation for the design of specific inhibitors targeting prokaryotic CTPS
Nondestructive Evaluation of Composite Bonding Structure Used in Electrical Insulation Based on Active Infrared Thermography
No full textNondestructive testing and evaluation of composite insulating components of electrical equipment is extremely necessary for assuring the safety of a power system. However, most existing nondestructive testing methods are not applicable for fast and effective live detection due to their time-consuming operation, high cost, and contact or near-field measurement. In this work, the effectiveness of active infrared thermography was investigated for detecting defects in silicone rubber (SIR)–fiber-reinforced plastic (FRP) bonding structures, which have been commonly used in insulating components of power equipment. The effectiveness of differential thermal image for enhancing the contrast of defective and sound areas and eliminating additive noise was demonstrated. Particularly, frame difference thermal image obtained by subtracting two differential thermal images extracting from respectively before and after the contrast inversion was proposed to enhance defect identification. The results revealed that defects of various sizes and depths such as voids, cracks, and interface disbonding of the SIR–FRP bonding structure were accurately detected by thermographic data. With the advantages of a quick and simple process, safety, universal applicability, visual results, far-field measurement, and quantitative defect estimation capabilities, active infrared thermography would be quite promising for live detection of electrical equipment
Association between serum S100A1 level and Global Registry of Acute Coronary Events score in patients with non-ST-segment elevation acute coronary syndrome
No full textObjective Acute coronary syndrome (ACS) is associated with several clinical syndromes, one of which is acute non-ST-segment ACS (NSTE-ACS). S100A1 is a calcium-dependent regulator of heart contraction and relaxation. We investigated the association between the serum S100A1 level and the Global Registry of Acute Coronary Events (GRACE) risk score in patients with NSTE-ACS and the potential of using the serum S100A1 level to predict the 30-day prognosis of NSTE-ACS. Methods The clinical characteristics of 162 patients with NSTE-ACS were analyzed to determine the GRACE score. The serum S100A1 concentration was determined using fasting antecubital venous blood. The patients were divided into different groups according to the serum S100A1 level, and the 30-day NSTE-ACS prognosis was evaluated using Kaplan–Meier analysis. Results The serum S100A1 levels differed significantly among the groups. Correlation analysis showed that the serum S100A1 level was positively correlated with the GRACE score. Kaplan–Meier analysis revealed that the number of 30-day cardiac events was significantly higher in patients with an S100A1 level of >3.41 ng/mL. Conclusions S100A1 is a potential biomarker that can predict the progression of NSTE-ACS and aid in its early risk stratification and prognosis. </jats:sec
Facile amidation of esters with aromatic amines promoted by lanthanide tris (amide) complexes
No full textA multi-level classification based ensemble and feature extractor for credit risk assessment
No full textWith the growth of people’s demand for loans, banks and other financial institutions put forward higher requirements for customer credit risk level classification, the purpose is to make better loan decisions and loan amount allocation and reduce the pre-loan risk. This article proposes a Multi-Level Classification based Ensemble and Feature Extractor (MLCEFE) that incorporates the strengths of sampling, feature extraction, and ensemble classification. MLCEFE uses SMOTE + Tomek links to solve the problem of data imbalance and then uses a deep neural network (DNN), auto-encoder (AE), and principal component analysis (PCA) to transform the original variables into higher-level abstract features for feature extraction. Finally, it combined multiple ensemble learners to improve the effect of personal credit risk multi-classification. During performance evaluation, MLCEFE has shown remarkable results in the multi-classification of personal credit risk compared with other classification methods
