The effect of cowpea (Vigna unguiculata) with root mucilage on phenanthrene (PHE) dissipation and microbial community composition using phospholipid fatty acid (PLFA) analysis and artificial neural network (ANN) modeling

The study examined artificial neural network (ANN) modeling for the prediction of chlorpyrifos, cypermethrin and chlorothalonil pesticides degradation by the FeGAC/H2O2 process. The operating condition was the optimum condition from a series of experiments. Under these conditions; FeGAC 5 g/L, H2O2 concentration 100 mg/L, pH 3 and 60 min reaction time, the COD removal obtained was 96.19%. The ANN model was developed using a three-layer multilayer perceptron (MLP) neural network to predict pesticide degradation in terms of COD removal. The configuration of the model with the smallest mean square error (MSE) of 0.000046 contained 5 inputs, 9 hidden and, 1 output neuron. The Levenberg–Marquardt backpropagation training algorithm was used for training the network, while tangent sigmoid and linear transfer functions were used at the hidden and output neurons, respectively. The predicted results were in close agreement with the experimental results with correlation coefficient (R2) of 0.9994 i.e. 99.94% showing a close agreement to the actual experimental results. The sensitivity analysis showed that FeGAC dose had the highest influence with relative importance of 25.33%. The results show how robust the ANN model could be in the prediction of the behavior of the FeGAC/H2O2 process.

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Artificial Neural Network (ANN) modeling of the pulsed heat load during ITER CS magnet operation

Cryogenics ◽

10.1016/j.cryogenics.2014.03.003 ◽

2014 ◽

Vol 63 ◽

pp. 231-240 ◽

Cited By ~ 8

Author(s):

L. Savoldi Richard ◽

R. Bonifetto ◽

S. Carli ◽

A. Froio ◽

A. Foussat ◽

...

Keyword(s):

Neural Network ◽

Artificial Neural Network ◽

Heat Load ◽

Ann Modeling ◽

Artificial Neural ◽

Artificial Neural Network Ann

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Forest floor microbial community response to tree species and fertilization of regenerating coniferous forests

Canadian Journal of Forest Research ◽

10.1139/x04-028 ◽

2004 ◽

Vol 34 (7) ◽

pp. 1426-1435 ◽

Cited By ~ 28

Author(s):

S E Leckie ◽

C E Prescott ◽

S J Grayston

Keyword(s):

Fatty Acid ◽

Microbial Community ◽

Tree Species ◽

Forest Floor ◽

Phospholipid Fatty Acid ◽

Microbial Community Composition ◽

Community Response ◽

Community Level ◽

Thuja Plicata ◽

Culturable Bacteria

We studied the effect of tree species and fertilization on the forest floor microbial community of 15-year-old regenerating forests. We sampled F and H forest floor layers of plots planted to Thuja plicata (Donn ex D. Don.) or Tsuga heterophylla (Raf.) Sarg. on N-poor and N-rich sites, with and without fertilizer treatments. Microbial community composition was assessed using phospholipid fatty acid analysis and by enumerating populations of culturable bacteria and fungi. Potential microbial functioning was assessed using community-level physiological profiling. There was little differentiation of community-level physiological profiles of F and H layers and few differences among the treatments. Total microbial biomass was greater in the F than H layer, and the two layers had distinct phospholipid fatty acid profiles. Site effects were detected mainly in the residual H layer, and tree species effects were seen mainly in the F layer, which has developed since harvesting. The effect of fertilization depended on site and tree species, with very little response in cedar plots, and the greatest effects in hemlock plots, coinciding with the greater growth response of hemlock. These results indicate that differences in plant growth rates, rather than direct effects of fertilization, influenced the microbial communities.

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