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In this paper, coal-based reduction on flotation middling from iron ore containing carbonate at donganshan was studied, during which the effect of reduction temperature, reduction time, C/O mole ratio and feed layer thickness on reduction process were carried out.
It is a good reductant for coal-based reduction.
From physical chemistry of metallurgy view point, the increase of reduction temperature can promote iron reduction reaction.
Effect of Reduction Time.
The data reveals that metallization degree grew slowly from 1.0 to 1.5, but begins to take off at 1.5, while further increase in C/O results in slightly change.

The metal and slag in sponge iron obtained from reduction was melted and separated.
The sponge iron obtained from reduction was melted and separated.
At 950℃,1000℃, the reduction experiment was carried out respectively, so as to determine the time of complete direct reduction.
Results and discussions Results and discussions of direct reduction Data of direct reduction is shown in Table3 and Fig2, in which, weight loss is the weight difference between re-concentration minerals and sponge iron.
Data of direct reduction experiment Sample # Temperature/℃ Holding time /h Weight of sponge iron /g Weight loss w/g 1 950 1.0 284.14 56.86 2 950 2.0 269.23 71.77 3 950 3.0 264.05 76.95 4 950 4.0 263.99 77.01 5 1000 0.5 290.55 50.45 6 1000 1.0 278.96 62.04 7 1000 1.5 270.82 70.18 8 1000 2.0 270.91 70.09 Fig2.

Reduction of GO by NaHTe.
These XPS data indicate that the oxygen-containing functional groups have been partially removed after reduction.
XPS of Te-GO2 Summary A mild and efficient reduction system by using NaHTe as reducing agent for reduction of graphene oxide is described.
This reduction was carried out at room temperature.
Cheng, The reduction of graphene oxide, Carbon 50(2012)3210-3228

It was found that the presence of aquatic worms in the reactor significantly improved the sludge reduction by increasing the total suspended solids (TSS) reduction (Table 1).
Accordingly, sludge reduction was 36–77% [6].
Table 1 Literature data of sludge reduction with worms in aerobic wastewater treatment processes Operation conditions Main results Controlsb References Pilot activated sludge system; 20 oC; domestic wastewater Ya=0.14 SVI ↓ Control Y=0.22 Wei et al. (2003) [3] TSS↓ 39-65% VSS↓ 0.5-6.3mg/mg worm/d Y=0.10-0.27 SVI↓ Control deducted in calculations Control Y=0.25-0.49 Liang et al. (2006)[7] TSS ↓ 48(±45)% No control Wei & Liu (2006)[8] VSS↓ 0.2-0.8mg/mg worm/d Effluent TP↑ Control deducted in calculations Huang et al. (2007)[9] Pilot activated sludge system; 18-23 oC; domestic wastewater COD↓ 18-67% Y=0.15 SVI↓ phosphate↑ Control COD↓ 20% Control Y=0.4 Rensink & Rulkens (1997)[10] Sludge disposal or TSS↓ 25-50% SVI↓ Control Ratsak (1996)[4] TSS↓ 36-77% The hydraulic load↑5-15% Hendrickx et al.(2009)[6] aY refers to the sludge yield (kgSS/kgCODremoved) bControls refer to the conventional biofilter (without worms) Although the presence of worms in the aerobic wastewater
It was found that the presence of earthworms in vermifilter significantly improved the sludge reduction, the BOD5 reduction, the COD reduction and the excess sludge stabilization was also enhanced by reducing the ratio of volatile suspended solids to suspended solids (VSS/SS).
Sludge reduction with a novel combined worm-reactor.

By comparing the diffraction lines in Fig. 1 with the tabulated diffractometric data [17], three compounds may be identified in the observed system, namely NiO, WO3 and NiWO4.
The standard enthalpies and free energies of reduction of oxides, shown in Table I, one may calculate on the basis of the handbooks of thermodynamic data.
In accordance to literature data [8, 9], NiO is reduced at the lowest temperature, while pure WO3 requires the highest temperature to be reduced.
By the way, from the X-ray diffractometric data, the electrochemically obtained alloys are different in structural sense from alloys of this study.
[17] Powder Diffraction File, Joint Committee on Powder diffraction, International Center for Diffraction Data, Swarthmore, PA, 1987

Experimental Result This experiment use 41 reservoir sandstone sample data for testing, as described above, each sample data has 12 feature parameters and 2 geophysical parameters of the porosity and permeability, the 12 feature parameters as input to Elman neural network for training to calculate the corresponding geophysical parameters.
There are 9 feature parameters as input data whose correlation degree is more than 0.8.
From the contrast curve (Fig. 1 and Fig.2), the calculation results are in good accordance with the real data, which reflects that the Elman neural networks with the hybrid dimensionality reduction have strong ability of learning and calculating.
In order to detect the effect of Elman neural networks with the hybrid dimensionality reduction, sample data sets were selected respectively using only the GRA and PCA dimensionality reduction as well as no dimension reduction to calculate the sandstone reservoir geophysical parameters, and then comparing the results of the above test methods with the results of Table 1.
Future work is to collect a large number of rock samples of slices identification and geophysical property analysis data for subsequent tests, to improve the calculation precision of hybrid dimensionality reduction with Elman neural network.

In the conclusion part, quantitative data is given, offering theoretical support for the governments’ strategic policy-making in developing EV.
Therefore, the reduction and control of automotive emissions are important to the goal of achieving carbon reduction commitment in China.
The essential of this method is raw data such as average speed (km/h, by the investigation and statistics or passenger and freight volume calculating), travel distance(km) and emission factors (g/km) are plugged into a basic formula.
According to the requirement of carbon emission reduction, two pollutants including CO and CO2 were picked out to establish the emissions inventory.
Consequently, China vehicular emissions inventory of carbon material from 2004 to 2010 can be worked out by comprehensively utilized Eq.1 and data of Table 1, Table 2 and Fig.1.

FNMF-ITWC Algorithm Applied to the Cancer Gene Expression Data Duo Wang1, a 1Party School of Shijiazhuang Municipal Committee of C.P.C, Shijiazhuang, China aduow2000@163.com Keywords: cancer gene expression data, biclustering algorithm, non-negative matrix factorization, iterative clustering, data dimensionality reduction Abstract.
In the biclustering algorithm, the sum of all genes and samples of the submatrix can be unequal to those of the orignal data matrix. 2.2 FNMF-ITWC Algorithm FNMF-ITWC algorithm first makes gene choice of original gene expression data, and carries out factorization of nonnegative matrix on rows ( gene dimension), so as to realize the reduction of data dimension and diminution of data redundancy, and find out nonredundant genes which are relevant to research question.
The advantages of the FNMF-ITWC algorithm: 1)Firstly, conducting gene selection and fast non-negative matrix factorization algorithm to original data expression data can achieve data dimensionality reduction and removal of data redundancy and relativity, and finding out non-redundant genes related to research problem.
The three groups of data sets are: gastric cancer gene expression data set, colon cancer gene expression data set, and leukemia gene expression data set.
The application of the NMF-ITWC algorithm on this kind of gene expression data owning features of high dimension and small sample achieves the purpose of extracting dimensionality reduction and consistent gene characteristic.

“Energy saving and emission reduction” problem has drawn worldwide attention.
Most of these studies are based on the technology of energy saving and emission reduction in a certain machinery or industry, while studies concerning energy saving and emission reduction conditions of the whole society are few.
Modeling Optimization Model of Carbondioxide Emission Reduction.
The model to evaluate the optimal energy consumption structures is: (2) Results and Discussions Based on the relevant data of China [7], two optimization models can be solved.
Based on the data in Table 1, the carbondioxide emission reductions of each major sector are evaluated in Table 2.

Subjective evaluation by the observer on the assessment of the effect of image noise reduction, on the other hand, objective evaluation is used to contrast with the original picture parameter data, including Mean Square Error (MSE) [2], Signal Noise Ratio (SNR), Peak Signal Noise Ratio, PSNR [2][3], Entropy and so on.
The Table 1 also shows that the data is non-linear growth when we choose large windows.
With the increasing of the window size, the data tends to be a constant.
Analyzing from the data recorded in Table 2, the effect of reduction is not simply proportional or inversely proportional to the size of filter window, and the 5×5 window is the best.
MSE Entropy PSNR Noisy image 1.4722e+3 7.1873 16.4510 Method (1) 674.8619 6.4043 19.8387 Method (2) 748.3711 6.3485 19.3896 Table 7 Results of data for Simulation (III) MSE Entropy PSNR Noisy image 1.4722e+3 7.1873 16.4510 Method (1) 674.8619 6.4043 19.8387 Method (2) 748.3711 6.3485 19.3896 Table 7 Results of data for Simulation (III) Fig. 20 Noisy image Fig. 21 Step 1 and 2 by Method (1) Fig. 22 Step 1 and 2 by Method (2) As the data shows in Table 7, if a noisy image contains a large Salt & Pepper noise, the Method (1) is the best choice for noise reduction.