https://www.selleckchem.com/products/cabotegravir-gsk744-gsk1265744.html The regrowth of Methanosarcina coupled well with the recovery of TCE dechlorination capacity, which implied the important role of methanogens in TCE dechlorination. Via MMI method, a simpler but more active microbial consortium could be established to enhance PCE remediation efficiency. Methanogens may act as the indicators or biomarkers for TCE dechlorination, suggesting that methanogenic activity should also be monitored when enriching dechlorination cultures and remediating PCE contaminated sites. CAPSULE A rapid perchloroethene dechlorinator was gotten via magnetic nanoparticles and dechlorination of trichloroethene coupled well with growth of Methanosarcina.This paper presents a road vehicle emission model that integrates an artificial neural network (ANN) model with a vehicle dynamics model to predict the instantaneous carbon dioxide (CO2), nitrogen oxides (NOx) and total hydrocarbon (THC) emissions of diesel light-duty vehicles. Real-world measurement data were used to train a multi-layer feed-forward ANN model. The optimal combination of the various experimental variables was selected as the ANN input through a parametric study considering both practicality and accuracy. For CO2 prediction, two variables (engine speed and engine torque) are enough to develop an accurate ANN model. In order to achieve satisfactory accuracy for CO and NOx prediction, more variables were used for ANN training. The trained ANN model was used to predict road vehicle emissions by integrating the vehicle dynamics model, which was used as a supplementary tool to produce ANN input data. The integrated model is practical because it requires relatively simple data for input such as vehicle specifications, velocity, and road gradient. In the accuracy validation, the proposed model showed satisfactory prediction accuracy for road vehicle emissions.Evaluation of carbon sequestration in various land cover types is a v