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The developed finite element model has been verified with the experimental data.
Fig. 2(b) shows the effect of the reduction on strip shape, with an increase of reduction, the strip shape varies from middle waves to edge waves.
A Pentium III computer was used for data collection by using Lab Window Software in the experiment.
(a) (a) (b) (b) 0.150 0.200 0.250 0.300 0.350 0.400 0 10 20 30 40 50 60 70 80 90 100 Distribution across width (mm) Thickness (mm) Unlubricated 30% reduction trendline Lubricated 30% reduction trendline Unlubricated 35% reduction trendline Lubricated 35% reduction trendline Unlubricated 45% reduction trendline Lubricated 45% reduction trendline Unlubricated 60% reduction trendline Lubricated 60% reduction trendline Fig. 5 Effect of reduction on strip thickness distribution 0.1 0.12 0.14 0.16 0.18 0.2 0.22 0.24 -0.06 -0.04 -0.02 0 0.02 0.04 0.06 Strip width (m) Exit thickness (mm) Calculated Measured Fig. 6 Comparison of calculated strip thickness difference with measured value Conclusion An elasto-plastic FEM model has been developed to simulate asymmetrical rolling of thin strip.
The calculated thickness along the strip width is consistent with the experimental data.

Cu-supported on Acid-treated Sepiolite: Characterization and Selective Catalytic Reduction (SCR) of NO by Propene Yunfang Qi, Yang Li, Zhihao Zhang, Lisha Liu, Qing Ye*, Dao Wang Key Laboratory of Beijing on Regional Air Pollution Control, College of Environmental and Energy Engineering, Beijing University of Technology, Beijing 100124, China a E-mail address: yeqing@bjut.edu.cn Keywords: Selective Catalytic Reduction of NO by Propylene; sepiolite; Cu Abstract.
Selective catalytic reduction with hydrocarbons is one of the most extended and efficient commercial DeNOx processes for nitric oxide removal from stationary sources.
Reports on a large number of catalysts for the selective catalytic reduction (SCR) have appeared since 1990, being the majority of these catalysts ion exchanged zeolites [1].
The pattern of the H2-TPR spectra of Cu/H-Sep was decomposed in three peaks with temperature at ca. 220, 260 and 440 oC, corresponding to the multiple reduction of Cu2+ and Cu+ species.
Catalyst Surf.area (m2/g) CuO cry. size (nm) a Cu2O cry. size (nm) b The peaks of H2-TPR Tmax (oC)/ NO conv. (%) Sep 57.6 - - - - H-Sep 110.7 - - - - Cu /H-Sep 97.9 25.5 15.8 200/260/440 352/52 a The data were estimated according to the Scherrer equation using the FWHM of the (111) line for CuO b The data were estimated according to the Scherrer equation using the FWHM of the (111) line for Cu2O Acknowledgments This work was supported by the National Natural Science Foundation of China (Grant Nos. 21277008 and 20777005), Beijing City Board of Education Science and Technology Development Program (KM2013100050010) and the Project of Construction of Innovative Teams and Teacher Career Development for Universities and Colleges Under Beijing Municipality (IDHT20140504).

From the measured data for aluminum experiments on Gleeble-1500, Yuhua Kang established a material model to simulate rolling process on aluminum alloys, and analyzed the reduction, rolling speed and contact heat transfer coefficientps influence on temperature differ ence acr oss the thickness of slabs and flow str ess curvature.
a b e d c Fig.2 Metal strain field of difference reduction.
Reduction: a-5%, b-10%, c-15%, d-20%, e-25% Research on strain field.
Such as 20% for pass reduction to make the plate core metal obtain enough plastic strain.
Such as 20% for pass reduction to make the plate core metal obtain enough plastic strain

Accuracy of data reduction stage was lowly, the reason was mainly software and human aspects.
It had resulted error or distortion in reduction of data.
As a result, high quality data and high precision data reduction need a set of feasible solutions.
(3) Data reduction was completed by grid processing command and processing command.
The method can effectively solve key problems such as complicated product structure design, data loss, data reduction inaccurately.

A comparison on grounding resistance value for copper rod, steel rod and galvanized iron rod was examined and the selection of the best grounding rod was determined from the experimental data.
Based on experiment data, the performance for each grounding rod are compared and analyzed.
The formula for percentage of reduction is given as in Eq.1
The percentage of reduction for the parallel installation system between three types of rod clearly shows that the peak of reduction is at day 2, which the reduction for copper is 29%, galvanized iron 14% and steel 18%.
Discussion Based on the data that collected for both parallel and single grounding system, it shows that copper rod have the highest resistance value compared to steel and galvanized iron.

X-ray diffraction patterns of reaction products obtained by titanium thermal reduction method and carbothermic reduction.
Fig.4 shows the Ti4O7 sample obtained by carbothermic reduction.
The thermodynamic calculation data of titanium thermal and carbothermic reductions are compared, such as formula (1), (2).
As can be seen, the Ti4O7 powder from titanium thermal reduction is more homogenous, while that from carbothermic reduction already shows caking and micro-sintering.
Fig. 8 compares the representative cyclic voltammograms for carbothermic reduction Ti4O7 and titanium thermal reduction Ti4O7 film electrodes.

We are presenting a feature based mapping procedure applied on data reduction to the relevant information used for autonomous navigation.
This is why data reduction becomes an important factor excluding any possible shape dissimilarities of the mapped area.
The reduction of data becomes consistent when clustering points into linear segments.
(2) Until reaching the final map structure data reduction is done in two steps.
A scan matching method assumes that generated lines can be compared and matched together within the map manager similar to the way of the data reduction.

Namely, the related data of the materials characters, cylinder compression bulging, and how they were associated with friction coefficient was obtained by the finite element method.
A number of testing methods have already been employed in an attempt to obtain quantitative data on the friction coefficient of workpiece/ die interface in metal processing.
Namely, the related data of the materials characters, cylinder compression bulging, and their relations with the friction coefficient was obtained by the finite element method.
PSE= FSE + pK , where FSE is the average squared error of the network for fitting the training data and pK is the complex penalty of the network, shown as the equation: N Q CPMK p p 2 2σ = , where CPM is the complex penalty multiplier, Q is a coefficients in the network, N is the number of training data to be used, and 2 pσ is a prior estimate of the model error variance.
Employing these analyzed data, the predictive model of billet properties and bulging deformation to the friction coefficient was constructed by using the abductive network. 2、Construction of the friction coefficient predicted model of cylindrical compression In this study, one hundred data sets were used as training data for abductive network to construct the predictive model of friction coefficient, as shown in Fig 4.

Anytime systems are able to provide short response time and are able to maintain the information processing even in cases of missing input data, temporary shortage of time, or computational power [1].
SVD Based Anytime Modeling In recourse, data, and time insufficient conditions, the so-called anytime algorithms, models, and systems [1] can be used advantageously.
They are able to provide guaranteed response time and are flexible with respect to the available input data, time, and computational power.
Further improvement can be obtained by utilizing training data and some learning algorithm.
The (HO)SVD based anytime models can advantageously be used in many types of applications during resource and data insufficient conditions.

Mechanochemical Reduction of MoO3 Powder by Silicone to Synthesize Nanocrystalline MoSi2 H.
Further milling resulted in a gradual decrease in MoO2 peak intensities because of its continuous reduction.
Milling time [h] d(MoO2) [nm] d(Si) [nm] d(βMoSi2) [nm] d(αMoSi2) [nm] Strain η [%] 6 16.6 49.2 --- ---- 0.275 12 --- 204 --- ---- 0.687 17 --- 15.2 32 48.2 ------ 22 --- --- 17.6 26 0.36 28 --- --- 17.6 25.7 0.52 33 --- --- 12.5 19.2 0.64 50 --- --- 11 9 0.93 Table 2. shows the thermodynamic data for the starting materials and the reactions occur during the mechanical alloying [10].
Right after, due to release of the reactions heat, temperature rises up to which the reaction between molybdenum and silicone starts concurrent with MoO2 reduction reaction.
Thermodynamic data for starting materials and reactions occur during the mechanical alloying at room temperature [10].