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This article studied the sludge reduction and supernatant changes during peracetic acid (PAA) oxidation and investigated subsequent anaerobic biodegradation. PAA oxidation treatment effectively leads to a disintegration of sewage sludge. The results suggested that the sludge solid concentration and volatile solid concentration decreased by 24.5 and 39.0% after 120 min, respectively, when the PAA dose was 30 mg PAA/g SS. The VSS to SS ratio dropped from 0.85 to 0.71. The concentration of supernatant soluble chemical oxygen demand, protein, total phosphorus, polysaccharide, and ammonia nitrogen increased by 530, 177, 166, 165, and 222%, respectively. Biogas production was enhanced when PAA was employed as a pretreatment. Compared with the control system, the cumulative biogas production increased by 20.0% after 28 days of anaerobic digestion (AD). The biogas production rate in digester with pretreated sludge reached the maximum on day 8 at 87mL/d, but the peak value was obtained on day 11 at 74mL/d in digester with the raw sludge, which indicated that PAA pretreatment could decrease the retention time of AD and increase biogas yield. © 2018 American Institute of Chemical Engineers Environ Prog, 37: 1901–1907, 2018

Accurate and efficient detection of sleep spindles is a methodological challenge. The present study describes a method of using non-experts for manual detection of sleep spindles. We recruited five experts and 168 non-experts to manually identify spindles in stage N2 and stage N3 sleep data using a MATLAB interface. Scorers classified each spindle into definite and indefinite spindle (with weights of 1 and 0.5, respectively). First, a method of optimizing the thresholds of the expert/non-expert group consensus according to the results of experts and non-experts themselves is described. Using this method, we established expert and non-expert group standards from expert and non-expert scorers, respectively, and evaluated the performance of the non-expert group standards by compared with the expert group standard (termed EGS). The results indicated that the highest performance was the non-expert group standard when definite spindles were only considered (termed nEGS-1; F1 score = 0.78 for N2; 0.68 for N3). Second, four automatic spindle detection methods were compared with the EGS. We found that the performance of nEGS-1 versus EGS was higher than that of the four automated methods. Our results also showed positive correlation between the mean F1 score of individual expert in EGS and the F1 score of nEGS-1 versus EGS across 30 segments of stage N2 data (r = 0.61, P < 0.001). Further, we found that six and nine non-experts were needed to manually identify spindles in stages N2 and N3, respectively, while maintaining acceptable performance of nEGS-1 versus EGS (F1 score = 0.79 for N2; 0.64 for N3). In conclusion, this study establishes a detailed process for detection of sleep spindles by non-experts in a crowdsourcing scheme.

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Abstract. According to prediction of landslide based on grey vector machine model, the landslide is affected by multiple factors such as soil layer structure and these factors show complicated non-linear relation with landslide, while it is hard for the traditional model to reflect the non-linear complicated relation. The prediction model for law of landslide established by grey model and neural network model can better predict the complicated surface subsidence and can effectively improve the predicted precision compared with the single model. Not only the reliability of prediction system is improved, but also the cost of the whole system is reduced.