The present study evaluated the potential of white-rot fungal strain Coriolus versicolor to decolorize five structurally different dyes in sequential batch reactors under optimized conditions. The experiments were run...The present study evaluated the potential of white-rot fungal strain Coriolus versicolor to decolorize five structurally different dyes in sequential batch reactors under optimized conditions. The experiments were run continuously for seven cycles of 8 d each. High decolorizing activity was observed even during the repeated reuse of the fungus, especially when the old medium was replaced with fresh medium after every cycle. Biodegradation was the dominating factor as the fungus was able to produce the enzyme laccase mainly, to mineralize synthetic dyes. The nutrients and composition of the medium played important roles in sustaining the decolorisation potential of the fungus. Corncob was found be an easy and cheap substitute for carbon source for the fungus. Glucose consumption by the fungus was in accordance to its decolorisation activity and chemical oxygen demand （COD） reduction.展开更多
Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task.A deep learning-based model for live predictions of stock values is aimed to be developed he...Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task.A deep learning-based model for live predictions of stock values is aimed to be developed here.The authors'have proposed two models for different applications.The first one is based on Fast Recurrent Neural Networks(Fast RNNs).This model is used for stock price predictions for the first time in this work.The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs,Convolutional Neural Networks,and Bi-Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company.The 1-min time interval stock data of four companies for a period of one and three days is considered.Along with the lower Root Mean Squared Error(RMSE),the proposed models have low computational complexity as well,so that they can also be used for live predictions.The models'performance is measured by the RMSE along with computation time.The model outperforms Auto Regressive Integrated Moving Average,FBProphet,LSTM,and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.展开更多
基金the funding agencies, Department of Science and Technology, India and International Foundation for Science Sweden, for providing the financial support to conduct the studies reported in this article
文摘The present study evaluated the potential of white-rot fungal strain Coriolus versicolor to decolorize five structurally different dyes in sequential batch reactors under optimized conditions. The experiments were run continuously for seven cycles of 8 d each. High decolorizing activity was observed even during the repeated reuse of the fungus, especially when the old medium was replaced with fresh medium after every cycle. Biodegradation was the dominating factor as the fungus was able to produce the enzyme laccase mainly, to mineralize synthetic dyes. The nutrients and composition of the medium played important roles in sustaining the decolorisation potential of the fungus. Corncob was found be an easy and cheap substitute for carbon source for the fungus. Glucose consumption by the fungus was in accordance to its decolorisation activity and chemical oxygen demand （COD） reduction.
文摘Predicting the correct values of stock prices in fast fluctuating high-frequency financial data is always a challenging task.A deep learning-based model for live predictions of stock values is aimed to be developed here.The authors'have proposed two models for different applications.The first one is based on Fast Recurrent Neural Networks(Fast RNNs).This model is used for stock price predictions for the first time in this work.The second model is a hybrid deep learning model developed by utilising the best features of FastRNNs,Convolutional Neural Networks,and Bi-Directional Long Short Term Memory models to predict abrupt changes in the stock prices of a company.The 1-min time interval stock data of four companies for a period of one and three days is considered.Along with the lower Root Mean Squared Error(RMSE),the proposed models have low computational complexity as well,so that they can also be used for live predictions.The models'performance is measured by the RMSE along with computation time.The model outperforms Auto Regressive Integrated Moving Average,FBProphet,LSTM,and other proposed hybrid models on both RMSE and computation time for live predictions of stock values.
