Performance and mechanisms of zero valent iron enhancing short-chain fatty acids production during thermophilic anaerobic fermentation of waste activated sludge.

This work first explored the feasibility and possible mechanisms of zero valent iron (ZVI) pretreatment on the generation of short-chain fatty acids (SCFAs) during thermophilic anaerobic fermentation of waste activated sludge (WAS). Results showed that ZVI enhanced the quantity of SCFAs. On Day 6, the SCFAs production reached 455.84 ± 47.88 mg COD/g VSS at 5 g/L of ZVI addition, which increased by 63.80 % relative to control. The presence of ZVI can effectively promote butyric-based fermentation. ZVI accelerated the destruction of extracellular polymeric substances (EPS) and interior sludge cells, as well as improved biodegradation of soluble organics. Also, ZVI enhanced key enzyme activities (i.e., BK and CoA-), thus promoting degradation rates of acidogenesis (6.30 ± 0.84 mg/(gVSS·h) in glucose) and acetogenesis (74.63 ± 0.29 mg/(gVSS·h) in butyrate). Compared to Fe(III), the contribution of Fe(II) was higher among the decomposition products of ZVI. Besides, ZVI favored Proteobacteria and Actinobacteria, which enhanced acetate formation and organic compounds disassimilation of the process, respectively. The abundance of Tepidiphilus, Thermobrachium and Tepidimicrobium was increased, indicating promoting the system stability of SCFAs production in thermophilic anaerobic fermentation.

Proteomic analysis reveals mechanisms of mixed wastewater with different N/P ratios affecting the growth and biochemical characteristics of Chlorella pyrenoidosa.

Effects of different nutrient ratios on the biochemical compositions of microalgae and the changes were rarely studied at the molecular level. In this study, the impacts of various nitrogen to phosphorus (N/P) ratios on growing of C. pyrenoidosa, as well as biochemical compositions and the metabolic regulation mechanism in mixed sewage, were investigated. The results suggested that 18 was optimal N/P ratio, while the dry weight (1.0 g/L), chlorophyll-a (Chla) (3.63 mg/L), and lipid production (0.28 g/L) were all the highest comparing with other groups. In contrast, the protein production (0.37 g/L) was the least. The nature of the regulatory mechanisms inthe metabolic pathways of these biochemical compositions was revealed by proteomic results, and there were 62 different expression proteins (DEPs) taken part in fatty acid and lipid biosynthesis metabolism (FA), amino acid biosynthesis metabolism (AA), photosynthesis (PHO), carbon fixation in photosynthetic organisms (CFP), and central carbon metabolism (CCM).

Evaluation of driver fatigue on two channels of EEG data.

Electroencephalogram (EEG) data is an effective indicator to evaluate driver fatigue. The 16 channels of EEG data are collected and transformed into three bands (θ, α, and ß) in the current paper. First, 12 types of energy parameters are computed based on the EEG data. Then, Grey Relational Analysis (GRA) is introduced to identify the optimal indicator of driver fatigue, after which, the number of significant electrodes is reduced using Kernel Principle Component Analysis (KPCA). Finally, the evaluation model for driver fatigue is established with the regression equation based on the EEG data from two significant electrodes (Fp1 and O1). The experimental results verify that the model is effective in evaluating driver fatigue.