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Application of Improved Back-propagation Neural Network and Genetic Algorithm to the Preparation Processing of the Mg,Al-hydrotalcite/polymer Nanocomposite Qiang Luo 1 and Qingli Ren2,b 1 The Second Artillery Engineering College, Xi'an 710025, P.R.China 2 School of Technical Physics, Xidian University, Xi'an 710071, P.R.China b qlren@mail.xidian.edu.cn Keywords: Neural network; Genetic algorithm; Dimensionality reduction and pre-whitening Abstract.
And in order to accelerate the converging rate and avoid the local minimum, dimensionality reduction and pre-whitening methods were used.
Training 5000 times and the results are shown in Table 1. 1x 2x M nx 1y 2y my M V Fig.1 dimensionality reduction and pre-whitening of data Table 1 Test data and predicted results Sample No. 1 2 3 4 5 6 7 N (%) X1 5 5 5 5 5 1 2 pH value X2 10 10 10 10 10 10 10 A (%) X3 80 70 60 50 30 80 80 s (%) Y 193 408 421 633 390 190 225 s * (%) Y 198 412 424 629 287 189 226 Sample No. 8 9 10 11 12 13 14 15 N (%) X1 3 4 6 5 5 5 5 5 pH value X2 10 10 10 11 10 9 9.5 12 A (%) X3 70 70 70 70 70 70 70 90 s (%) Y 273 310 325 293 422 420 450 165 s * (%) Y 269 314 327 295 425 423 452 167 Notes: N: the amount of oleic acid sodium; A: the amount of Mg,Al-hydrotalcite; s : the break percentage elongation of Mg,Al-hydrotalcite/PE composites; * represents the predicted results.

The aim of this work was to optimize the growth and sulfate reduction capacity of mixed bacterial cultures.
This phenomenon was observed for sample growing in lactate medium, pH 5.5 after a 48h incubation period thus confirming previously reported data [6].
Maximum sulfate reduction was observed during the first 120h.
However, it is possible to affirm that LG1 consortia have some metal tolerant SRB which are being identified (data not shown).
However, when growing with ethanol as organic carbon source a sulfate reduction of about 50% was achieved.

Then, a linear two-step reduction technique is used to reduce the high-order process to a second-order plus time delay model based on the frequency response data.
In 1995, Wang and Cluett[7] provided an identification method to deal with noisy step response data by using the Laguerre series expansions.
Then an SOPDT model is obtained based on a simple model reduction technique in frequency domain.
Here, a simple model reduction technique based on the frequency response is used.
Building transfer function models from noisy step response data using the Laguerre network[J].

The scaled El Centro 1940 earthquake data is used as input excitation.
In order to evaluate the performance of the newly developed TMD, dynamic tests are performed by employing scaled El Centro 1940 earthquake data [8].
Input excitations: (a) original El Centro 1940 earthquake data, (b) scaled data for 20 sec and (c) scaled data for 25 sec Herein the scaled El Centro 1940 is employed due to the existing laboratory setup.
The input excitation such as El Centro data remains same as experimental tests input (see Fig. 6) for the simulations.
Along with the full-time history, a zoomed view of 0-5 sec are also provided for better visualization of the data.

Fig. 5(b) shows the process to generate the function from the test data.
BOP shearing test data.
Illustrations of using shearing test data to develop the damage parameter function, f.
Illustrations of using shearing test data to develop the damage parameter function, f.
The predicted shearing pressure agrees with the test data, and max error is 11%.

The data were recorded every 30sec from the start to the end of coke combustion.
So the reduction of CO emission is obvious as La2O3 particles size decreasing.
It was reasonably to believe that La2O3 served as a catalyst for NO reduction by coke and CO.
The NOx reduction ratio is over 10% as La2O3 (38-47µm) loading is 2.0wt%
A method of NOx reduction by coke modified with additives in sintering process.

Two national monitoring section data was analyzed according to two contributing factors, which were time and location.
The monitored data was collected from Chongqing environmental monitoring center.
The data were processed by SPSS 13.
Acknowledgment The authors gratefully acknowledge the data support from the Chongqing environmental monitoring center.
We also thank Xia ting-ting for his assistance with the data collection.

The methodology involves analyzing technical and operational data from Canada, Finland, Zambia, and South Africa, adapting it to Ukrainian conditions, and modeling the integral economic effect.
The dependence is described by a saturating Hill function, with parameters determined via the least squares’ method using empirical and calculated data.
For the first time, global operational data have been integrated with modeling for Ukrainian open pits, producing a tool adaptable to various mining and geological conditions.
Practical data indicate that the effect increases nonlinearly: the largest gains are achieved when electrifying the most energy-intensive segments, after which the rate of growth declines.
The first year of operation should be conducted as a "pilot phase" with detailed data collection on fuel savings, emission reductions, changes in trip times, and maintenance costs.

Those kinematic data was carried out through three major steps: wavelet transformation, Principle Component Analysis (PCA) -based dimensionality reduction and k-fold cross-validation, followed by implementing a best classifier to distinguish 6 difference actions.
Capturing acceleration data.
The data coming from volunteers 1# to 5# and 12#, 13# are arranged used for train, the data coming from volunteers 6# to 11# are arranged used for test.
So that, a total of 492 kinematic data were recorded from 13 volunteers, 420 data of them will be used in train from 7 volunteers, and 72 data which any data from subjects did not been used by train will be used in test from 6 volunteers.
First, raw data are pre-processing by segmenting interest single action from original kinematic data, i.e., the action signal data, through shifting; cropping and size normalizations to save processing time and increase processing speed.

Data are shown as mean ± SD of 4 replications (3 batches of plants for 1 replicates per treatment).
Data are shown as mean ± SD of three replicates.
After 12 days of Cd treatment, significant reductions were observed in Pn (Fig. 4A), Gs (Fig. 4B) and E (Fig. 4D).
By day 12, reductions in Pn were 57% and 35% in the mutant and wild type rice under Cd treatments, respectively, reductions in Gs were 49% and 29%, respectively, and reductions in E were 31% and 19%, respectively (Fig. 4).
Conclusions Cd reduced plant height, dry mass and chlorophyll content, and the reduction became larger with increased Cd exposure time.