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Multimeter is widely used in power system which should be operated correctly, otherwise may lead to the occurrence of power incidents. Two power incidents caused by misoperation of multimeter are enumerated in the paper. The root cause of the incidents is the lack of effective error-proof measurement function of multimeter.For this reason, a special multimeter for power system with the function to prevent mismeasurement is proposed. By the technical means of voice report and reading, automatic identification of gear and live condition of external circuit, double fingerprint input to realize monitoring, canceling current gear to prevent error measurement, the design for the error-proof measurement function of multimeter is proved to be effective.

Carbon dioxide (CO₂) is the most important anthropogenic greenhouse gas and its concentration in atmosphere has been increasing rapidly due to the increase of anthropogenic CO₂ emissions. Quantifying anthropogenic CO₂ emissions is essential to evaluate the measures for mitigating climate change. Satellite-based measurements of greenhouse gases greatly advance the way of monitoring atmospheric CO₂ concentration. In this study, we propose an approach for estimating anthropogenic CO₂ emissions by an artificial neural network using column-average dry air mole fraction of CO₂ (XCO₂) derived from observations of Greenhouse gases Observing SATellite (GOSAT) in China. First, we use annual XCO₂ anomalies (dXCO₂) derived from XCO₂ and anthropogenic emission data during 2010–2014 as the training dataset to build a General Regression Neural Network (GRNN) model. Second, applying the built model to annual dXCO₂ in 2015, we estimate the corresponding emission and verify them using ODIAC emission. As a results, the estimated emissions significantly demonstrate positive correlation with that of ODIAC CO₂ emissions especially in the areas with high anthropogenic CO₂ emissions. Our results indicate that XCO₂ data from satellite observations can be applied in estimating anthropogenic CO₂ emissions at regional scale by the machine learning. This developed method can estimate carbon emission inventory in a data-driven way. In particular, it is expected that the estimation accuracy can be further improved when combined with other data sources, related CO₂ uptake and emissions, from satellite observations.

This paper introduces the process of checking, analyzing and dealing with the causes of maloperation of the first set protection of 500 kV reactor in a substation. By checking the secondary circuit of 500 kV reactor protection for power outage, it is analyzed that the main cause of reactor protection malfunction is that the cable core of the current circuit of the first set protection is damaged at the secondary terminal box of TA of the high voltage casing pipes, which causes the induction current to enter in series, the abnormal sampling of the first set of protection B phase, and the protection outlet action. Aiming at the existing problems, this paper gives corresponding treatment methods and preventive measures.

The regional uncertainty of the column-averaged dry air mole fraction of CO2 (XCO2) retrieved using different algorithms from the Greenhouse gases Observing SATellite (GOSAT) and its attribution are still not well understood. This paper investigates the regional performance of XCO2 within a latitude band of 37–42° N segmented into 8 cells in a grid of 5° from west to east (80–120° E) in China, where typical land surface types and geographic conditions exist. The former includes desert, grassland and built-up areas mixed with cropland; and the latter includes anthropogenic emissions that change from small to large from west to east, including those from the megacity of Beijing. For these specific cells, we evaluate the regional uncertainty of GOSAT XCO2 retrievals by quantifying and attributing the consistency of XCO2 retrievals from four algorithms (ACOS, NIES, OCFP and SRFP) by intercomparison. These retrievals are then specifically compared with simulated XCO2 from the high-resolution nested model in East Asia of the Goddard Earth Observing System 3-D chemical transport model (GEOS-Chem). We also introduce the anthropogenic CO2 emissions data generated from the investigation of surface emitting point sources that was conducted by the Ministry of Environmental Protection of China to GEOS-Chem simulations of XCO2 over the Chinese mainland. The results indicate that (1) regionally, the four algorithms demonstrate smaller absolute biases of 0.7–1.1 ppm in eastern cells, which are covered by built-up areas mixed with cropland with intensive anthropogenic emissions, than those in the western desert cells (1.0–1.6 ppm) with a high-brightness surface from the pairwise comparison results of XCO2 retrievals. (2) Compared with XCO2 simulated by GEOS-Chem (GEOS-XCO2), the XCO2 values from ACOS and SRFP have better agreement, while values from OCFP are the least consistent with GEOS-XCO2. (3) Viewing attributions of XCO2 in the spatio-temporal pattern, ACOS and SRFP demonstrate similar patterns, while OCFP is largely different from the others. In conclusion, the discrepancy in the four algorithms is the smallest in eastern cells in the study area, where the megacity of Beijing is located and where there are strong anthropogenic CO2 emissions, which implies that XCO2 from satellite observations could be reliably applied in the assessment of atmospheric CO2 enhancements induced by anthropogenic CO2 emissions. The large inconsistency among the four algorithms presented in western deserts which displays a high albedo and dust aerosols, moreover, demonstrates that further improvement is still necessary in such regions, even though many algorithms have endeavored to minimize the effects of aerosols scattering and surface albedo.

Background: Age-related macular degeneration (AMD), a most common eye disease, can lead to irreversible visual impairment. Age, genetic and environmental factors have been implicated in AMD. Chemokine (C-X3-C motif) receptor 1 (CX3CR1) gene polymorphisms could influence the susceptibility of AMD. Methods: We tested the association between AMD and single nocleotide polymorphisms (SNPs) of CX3CR1 gene (rs3732378 and rs3732379) in 102 cases and 115 controls from China. Genotypes were determined by MassArray genotyping assay method. Association between CX3CR1 gene polymorphisms and AMD were examined by χ2 test and logistic regression. Results: Genotype distribution of CX3CR1 gene polymorphisms were in accordance with HWE examination. No obvious differences were observed in the genotypes of rs3732378 polymorphism between case and control groups (P>0.05), but A allele of it could increase the risk of AMD (P=0.025, OR=2.391, 95% CI=1.092-5.237). Both TT genotype and T allele of rs3732379 were significantly associated with the susceptibility of AMD (P=8.663, OR=8.663, 95% CI=1.044-71.874; P=0.021, OR=2.076, 95% CI=1.104-3.903). Age, gender and smoking status were used as common confounders to adjust the association between CX3CR1 gene polymorphism and AMD risk. Then we found that rs3732378 had no obvious association with AMD susceptibility. TT genotype of rs3732379 related to the occurrence of AMD, but the association was not significant (P=0.050, OR=8.274, 95% CI=1.002-69.963). T allele of rs3732379 might increase the susceptibility of AMD (P=0.029, OR=2.033, 95% CI=1.077-3.838). Conclusion: T allele of rs3732379 might have a positive association with the susceptibility of AMD.