Coupling of Immobilized Photosynthetic Bacteria with a Graphene Oxides/PSF Composite Membrane for Textile Wastewater Treatment: Biodegradation Performance and Membrane Anti-Fouling Behavior.

The membrane bioreactor (MBR), as one of the promising technologies, has been widely applied for treatments of wastewater. However, serious membrane fouling and low microbial activity have been reported as major problems hindering the development of the MBR. To overcome these drawbacks, we intend to improve the MBR process in the view of membrane surface modification and efficient granular bacteria cultivation. In the present study, immobilized photosynthetic bacteria integration with graphene oxide (GO)/polysulfone (PSF) composite membrane separation (IPMBR) was first applied for textile wastewater treatment. Due to the high activity of immobilized cells, the IPMBR system exhibited higher efficiency on the removal of color, ammonia-nitrogen, and chemical oxygen demand than the conventional MBR system. In comparison with a pure PSF membrane, GO/PSF composite membrane presented the higher hydrophilicity (water contact angles of 62.9°) and more attractive permeability (178.5 L/m2h) by reducing the adhesion of hydrophobic foulants. During the whole operation, the immobilized photobioreactor exhibited approximately seven times higher membrane permeability that that of the conventional MBR. Meanwhile, the effect of the structure and character of immobilized photosynthetic bacteria on the membrane fouling reduction was investigated in detail. The change of extracellular polymeric substance concentration, settleability and particle size of flocs was very beneficial to alleviate membrane fouling. As a result, this research will open a new avenue for developing efficient and anti-fouling MBR technology in the future.

Diagnosing Coronavirus Disease 2019 (COVID-19): Efficient Harris Hawks-Inspired Fuzzy K-Nearest Neighbor Prediction Methods.

This study is devoted to proposing a useful intelligent prediction model to distinguish the severity of COVID-19, to provide a more fair and reasonable reference for assisting clinical diagnostic decision-making. Based on patients' necessary information, pre-existing diseases, symptoms, immune indexes, and complications, this article proposes a prediction model using the Harris hawks optimization (HHO) to optimize the Fuzzy K-nearest neighbor (FKNN), which is called HHO-FKNN. This model is utilized to distinguish the severity of COVID-19. In HHO-FKNN, the purpose of introducing HHO is to optimize the FKNN's optimal parameters and feature subsets simultaneously. Also, based on actual COVID-19 data, we conducted a comparative experiment between HHO-FKNN and several well-known machine learning algorithms, which result shows that not only the proposed HHO-FKNN can obtain better classification performance and higher stability on the four indexes but also screen out the key features that distinguish severe COVID-19 from mild COVID-19. Therefore, we can conclude that the proposed HHO-FKNN model is expected to become a useful tool for COVID-19 prediction.