Wastewater treatment with algal based membrane bioreactor for the future: Removing emerging containments.

The difficulty of developing pollutants in aquatic ecosystems and their potential effects on animals and plants have been raised. Sewage effluent can seriously harm a river's plant and animal life by reducing the water's oxygen content. Due to their increasing use and poor elimination in traditional municipal wastewater treatment plants (WWTPs), pharmaceuticals are one of the developing pollutants that have the potential to penetrate aquatic ecosystems.

SARS-CoV-2 removal by mix matrix membrane: A novel application of artificial neural network based simulation in MATLAB for evaluating wastewater reuse risks.

The COVID-19 outbreak led to the discovery of SARS-CoV-2 in sewage; thus, wastewater treatment plants (WWTPs) could have the virus in their effluent. However, whether SARS-CoV-2 is eradicated by sewage treatment is virtually unknown. Specifically, the objectives of this study include (i) determining whether a mixed matrixed membrane (MMM) is able to remove SARS-CoV-2 (polycarbonate (PC)-hydrous manganese oxide (HMO) and PC-silver nanoparticles (Ag-NP)), (ii) comparing filtration performance among different secondary treatment processes, and (iii) evaluating whether artificial neural networks (ANNs) can be employed as performance indicators to reduce SARS-CoV-2 in the treatment of sewage.

An approach to removing COD and BOD based on polycarbonate mixed matrix membranes that contain hydrous manganese oxide and silver nanoparticles: A novel application of artificial neural network based simulation in MATLAB.

This study aimed to determine the efficacy of novel ultrafiltration and mixed matrix membrane (MMM) composed of hydrous manganese oxide (HMO) and silver nanoparticles (Ag-NPs) for the removal of biological oxygen demand (BOD) and chemical oxygen demand (COD). In the polycarbonate (PC) MMM, the weight percent of HMO and Ag-NP has been increased from 5% to 10%. A neural network (ANN) was used in this study to compare PC-HMO and Ag-NP.

MRI plaque imaging detects carotid plaques with a high risk for future cerebrovascular events in asymptomatic patients.

Purpose: The aim of this study was to investigate prospectively whether MRI plaque imaging can identify patients with asymptomatic carotid artery stenosis who have an increased risk for future cerebral events. MRI plaque imaging allows categorization of carotid stenosis into different lesion types (I-VIII). Within these lesion types, lesion types IV-V and VI are regarded as rupture-prone plaques, whereas the other lesion types represent stable ones.