A potential explanation for the effect of carbon source on the characteristics of acetate-fed and glucose-fed aerobic granules

This paper proposes a new theory to account for the effect of carbon source on the characteristics of acetate-fed and glucose-fed aerobic granules. It is well known that reactor pH can vary in response to the oxidation of glucose or sodium acetate. As such, the effects associated with the carbon sources may be explained by the changed pH. The proposal was explored by experiments. Aerobic granules were cultivated in three identical sequencing batch reactors (SBRs, R1, R2 and R3), fed with sodium acetate, glucose, glucose and maintained pH at 4.5 - 5.5 (the variation of reactor pH in the oxidation of glucose), 4.5 - 5.5 and 7.5 - 8.5 (the variation of reactor pH in the oxidation of sodium acetate), respectively, and the effects of carbon source and reactor pH on the characteristics of aerobic granules were assessed. The results showed that the characteristics of aerobic granules, including microbial structure, mixed liquor suspended solids (MLSS), sludge volume index (SVI) and nitrification-denitrification, were strongly affected by reactor pH, but were independent with the carbon source supplied. These results fully supported the validity of the new theory. The theory suggests that the cultivation of aerobic granules with glucose or sodium acetate should take more attention to reactor pH rather than carbon source itself. The implications of this theory are discussed with regards to the other common carbon sources as well as better understanding of the mechanisms of aerobic granulation.Keywords: Acetate-fed granules, glucose-fed granules, reactor pH, carbon source, characteristicsAfrican Journal of Biotechnology Vol. 9(33), pp. 5357-5365, 16 August, 201

Semisupervised hypergraph discriminant learning for dimensionality reduction of hyperspectral image.

Semisupervised learning is an effective technique to represent the intrinsic features of a hyperspectral image (HSI), which can reduce the cost to obtain the labeled information of samples. However, traditional semisupervised learning methods fail to consider multiple properties of an HSI, which has restricted the discriminant performance of feature representation. In this article, we introduce the hypergraph into semisupervised learning to reveal the complex multistructures of an HSI, and construct a semisupervised discriminant hypergraph learning (SSDHL) method by designing an intraclass hypergraph and an interclass graph with the labeled samples. SSDHL constructs an unsupervised hypergraph with the unlabeled samples. In addition, a total scatter matrix is used to measure the distribution of the labeled and unlabeled samples. Then, a low-dimensional projection function is constructed to compact the properties of the intraclass hypergraph and the unsupervised hypergraph, and simultaneously separate the characteristics of the interclass graph and the total scatter matrix. Finally, according to the objective function, we can obtain the projection matrix and the low-dimensional features. Experiments on three HSI data sets (Botswana, KSC, and PaviaU) show that the proposed method can achieve better classification results compared with a few state-of-the-art methods. The result indicates that SSDHL can simultaneously utilize the labeled and unlabeled samples to represent the homogeneous properties and restrain the heterogeneous characteristics of an HSI