35 research outputs found

    Diagnosis of a malayan filariasis case using a shotgun diagnostic metagenomics assay

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    Typing of blast hits after analyzing subcutaneous tissue sample and details of the phylogenetic MEGAN output. (DOC 198 kb

    Drought Assessment by a Short-/Long-Term Composited Drought Index in the Upper Huaihe River Basin, China

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    Accurate and reliable drought monitoring is of primary importance for drought mitigation and reduction of social-ecological vulnerability. The aim of the paper was to propose a multiscale composited drought index (CDI) which could be widely used for drought monitoring and early warning in China. In the study, the upper Huaihe River basin above the Xixian gauge station, which has been hit by severe droughts frequently in recent decades, was selected as the case study site. The newly built short-term/long-term CDI comprehensively considered three natural forms of drought (meteorological, hydrological, and agricultural) by selection of different variables that are related to each drought type. The short-term/long-term CDI was developed using the Principle Component Analysis of related drought components. The thresholds of the short-term/long-term CDI were determined according to frequency statistics of different drought indices. Finally, the feasibility of the two CDI was investigated against the self-calibrating Palmer drought severity index, the standardized precipitation evapotranspiration index, and the historical drought records. The results revealed that the short-term/long-term CDI could capture the onset, severity, and persistence of drought events very well with the former being better at identifying the dynamic evolution of drought condition and the latter better at judging the changing trend of drought over a long time period

    Three-Phase Fault Arc Phase Selection Based on Global Attention Temporal Convolutional Neural Network

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    For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method based on a global temporal convolutional network. First, this method builds a low-voltage three-phase arc fault data acquisition platform and establishes a dataset. Second, the experimental data were decomposed by variational mode decomposition and analyzed in the time-frequency domain. The decomposed data are reconstructed and used as input to the model. Finally, in order to reduce the fault features lost during the causal convolution operation, the global attention mechanism is used to extract deep fault characterization to identify faults and their differences. The experimental results show that the accuracy of the three-phase arc fault arc phase selection of the model can reach 98.62%, and the accuracy of single-phase fault detection can reach 99.39%. This model can effectively extract three-phase arc fault and phase characteristics. This paper provides a new idea for series fault arc detection and three-phase fault arc phase selection research

    Three-Phase Fault Arc Phase Selection Based on Global Attention Temporal Convolutional Neural Network

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    For low-voltage three-phase systems, the deep fault arc features are difficult to extract, and the phase information has strong timing. This phenomenon leads to the problem of low accuracy of fault phase selection. This paper proposes a three-phase fault arc phase selection method based on a global temporal convolutional network. First, this method builds a low-voltage three-phase arc fault data acquisition platform and establishes a dataset. Second, the experimental data were decomposed by variational mode decomposition and analyzed in the time-frequency domain. The decomposed data are reconstructed and used as input to the model. Finally, in order to reduce the fault features lost during the causal convolution operation, the global attention mechanism is used to extract deep fault characterization to identify faults and their differences. The experimental results show that the accuracy of the three-phase arc fault arc phase selection of the model can reach 98.62%, and the accuracy of single-phase fault detection can reach 99.39%. This model can effectively extract three-phase arc fault and phase characteristics. This paper provides a new idea for series fault arc detection and three-phase fault arc phase selection research

    Are droughts becoming more frequent or severe in China based on the Standardized Precipitation Evapotranspiration Index: 1951–2010?

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    The Standardized Precipitation Evapotranspiration Index (SPEI) was computed based on the monthly precipitation and air temperature values at 609 locations over China during the period 1951–2010.Various characteristics of drought across China were examined including: long-term trends, percentage of area affected, intensity, duration, and drought frequency. The results revealed that severe and extreme droughts have become more serious since late 1990s for all of China (with dry area increasing by ∼3.72% per decade); and persistent multi-year severe droughts were more frequent in North China, Northeast China, and western Northwest China; significant drying trends occurred over North China, the southwest region of Northeast China, central and eastern regions of Northwest China, the central and southwestern parts of Southwest China and southwestern and northeastern parts of western Northwest mainly due to a decrease in precipitation coupled with a general increase in temperature. In addition, North China, the western Northwest China, and the Southwest China had their longest drought durations during the 1990s and 2000s. Droughts also affected western Northwest, eastern Northwest, North, and Northeast regions of China more frequently during the recent three decades. The results of this article could provide certain references and triggers for establishing a drought early warning system in China

    Evaluation of Fe<sup>2+</sup>/Peracetic Acid to Degrade Three Typical Refractory Pollutants of Textile Wastewater

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    In this work, the degradation performance of Fe2+/PAA/H2O2 on three typical pollutants (reactive black 5, ANL, and PVA) in textile wastewater was investigated in comparison with Fe2+/H2O2. Therein, Fe2+/PAA/H2O2 had a high removal on RB5 (99%) mainly owing to the contribution of peroxyl radicals and/or Fe(IV). Fe2+/H2O2 showed a relatively high removal on PVA (28%) mainly resulting from ·OH. Fe2+/PAA/H2O2 and Fe2+/H2O2 showed comparative removals on ANL. Additionally, Fe2+/PAA/H2O2 was more sensitive to pH than Fe2+/H2O2. The coexisting anions (20–2000 mg/L) showed inhibition on their removals and followed an order of HCO3− > SO42− > Cl−. Humic acid (5 and 10 mg C/L) posed notable inhibition on their removals following an order of reactive black 5 (RB5) > ANL > PVA. In practical wastewater effluent, PVA removal was dramatically inhibited by 88%. Bioluminescent bacteria test results suggested that the toxicity of Fe2+/PAA/H2O2 treated systems was lower than that of Fe2+/H2O2. RB5 degradation had three possible pathways with the proposed mechanisms of hydroxylation, dehydrogenation, and demethylation. The results may favor the performance evaluation of Fe2+/PAA/H2O2 in the advanced treatment of textile wastewater
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