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A new model of dislocation-strain-related Poole-Frenkel effect that accounts for the own electric field of internal charge of dislocation line is suggested and compared with the experimental data.
The theoretical calculations of PFE due to presence of 60° dislocation was found to be in a good agreement with experimental data on p-type samples [4].
The symbols correspond to the experimental data obtained from the position of ITS peaks, the lines represent the best linear fits.
The results of the calculations together with the experimental data on ER-DLTS are presented in Fig. 3b.
A good quantitative agreement between the theoretically calculated and experimental data derived from the energy-resolved DLTS measurements was established.

Other reported preparation methods are mostly the conventional impregnation procedure associate with borohydride reduction.
Sun et al reported a formic acid reduction method to prepare carbon black suppored Pt nanowires at room temperature [14].
In this study, a modified formic acid reduction method was developed and applied to load Pt nanoparticles on carbon aerogel.
Electrochemical measurements were performed using an Autolabpotentiostat PGSTAT 30 (Eco Chemie, Utretch, The Netherlands) driven by the General Purpose Electrochemical Systems data processing software (GPES and FRA, version 4.9, Eco Chemie).
Conclusions Pt/CA catalyst with improved Pt dispersion was prepared by a mild formic acid reduction method.

Top gear reduction of the system is iH=nT/n0=20.8 and low gear reduction is iL=TT/ηn0=50.6. η stands for low gear efficiency, take η=75%.
According to single stage spur gear reduction ratio, the stage of the top gear reduction and low gear reduction is 2-4 and 3-5 respectively.
System structure The system is designed to be with minimum radial and axial dimension on the basis of each reduction meets the involute cylindrical gears reduction gear ratio.
To simplify the reduction between top gear and low gear take i34=i56, because i34*i56=iL/iH, so i34=i56=1.6.Reduction i78=iH/i12=5.2.
So the system works as below: The controller calculates the output torque and speed by sampling data of motor currency and encoder output data.

But data are always sparse.
The one hand, the customer and commodity score data and customer is not an accurate score.
On the other hand, with the continuous growth of customers and merchandise, the cause of the extreme sparseness of the user ratings data, the results for some customers can not be recommended in order to affect the accuracy of the recommended Literature using dimension reduction techniques, the use of a single value decomposition technique to Reduction in customer referral system - the dimension of the Trade Matrix data.
The literature offered by the association of sparse data can improve the recommendation quality.
But data are always sparse.

Wu Han, Hubei 430061, China; a 110190112@qq.com, b 196291812@qq.com, c 360059903@qq.com Keywords: phyllite, support structure, stability analysis, stress changes Abstract: Taking Liang Zhuang phyllite tunnel as the research object, the paper tries to analyze the variation rules of formation stress and initial support stress in the construction process based on numerical simulation of surrounding rocks grade III, IV and V, and also by using the measured data to have a better analysis of the stress state of surrounding rocks and support in the construction process, thus to grasp the rules for better construction.
Taking Liang Zhuang phyllite tunnel as the research object, the paper tries to analyze the variation rules of formation stress and initial support stress in the construction process based on numerical simulation of surrounding rocks graded III, IV and V, and also by using the measured data to have a better analysis of the stress state of surrounding rocks and support in the construction process, thus to grasp the rules for better construction. 2.
Fig.1 Tunnel lining unit model Table 1 Parameters of surrounding rocks and initial support material E /GPa C /MPa j ( o ) μ γ /kg/m3 note Surrounding rocks III 11 0.14 30 0.3 2350 soft rock reduction Surrounding rocks IV 1.3 0.12 28 0.36 2100 soft rock reduction Surrounding rocks V 0.75 0.07 16 0.42 1800 soft rock reduction Initial support 25.7 0. 2 2450 Including reinforced net and steel arch 3.
We will analyze the data of the section of 10 to 20meters in the middle of the model.

The results indicate that the reduction in wear rate on adding SiC or reducing the grain size is caused by a reduction in the area fraction of surface pullout by brittle fracture.
For low SiC contents (~2vol%), this reduction in surface pullout is a consequence of the reduction in size of the individual pullouts.
It is evident that increasing the SiC content reduces the area fraction of pullout, fpo, and that this reduction accrues both through a reduction in the number of pullouts per unit area, and through a reduction in the diameter of the individual pullouts.
Comparison of the contributions to pullout reduction from the reduction in pullout formation rate, pullout dimensions and wear rate relative to coarse grained alumina.
Discussion of Micromechanisms for the Inhibition of Pullout Formation The 10% SiC data point in Fig. 12 gives convincing evidence that the addition of sufficient SiC can directly inhibit the nucleation of pullouts during grinding.

Noise Reduction.
Lagrange function is used to determine the most optimal hyper-plane as shown as in Eq.8-9, where xi is i-th training data, and yi is the output of the SVM for i-th training data. xr is all support vector of positive data and xs is all support vector of negative sample data.
The best SVM model was used to classify testing data (the non-training data).
There are 60 testing data that contain 20 data for each weld defect type.
Table 3 is the result of testing using simulation data.

The expression of, and are the mean of each frequency band and n is number of data.
I-kaz analysis provides a 3D data distribution along with the I-kaz coefficient, Z∞.
The Z∞measures the degree of data scattering with respect to the data centroid.
Z∞was adequately corresponds to the degree of data scattering.
The higher value ofZ∞ for sound signal of carbon steel S50C was illustrated by the bigger space of data scattering of I-kaz display compared to the lower Z∞ for cast iron which was illustrated by the smaller space of data scattering.

In the spare parts demand forecasting of equipment maintenance based on virtual warehousing, a large number of historical consumption data are needed.
But among these data, there are probably redundant , inaccurate or incomplete information, which can be dealt with well by rough set theory to take attribute reduction and to extract useful information.
The first step is to take a discretization on continuous data.
The number of the clustering in this algorithmis no need to specify in advance, but to realize the automatic clustering based on the compatibility constraints and clustering distance threshold of the consistent degree of decision table after clustering. 3.2.2 Attribute reduction algorithm After the discretization of data processing, attribute reduction follows.
For security reasons, data of influencing factors cannot be listed here one by one, which are handled dimensionlessly.

With the increasing development of clean energy technologies, the application of new energy and carbon capture technology would have a positive effect for CO2 emissions reduction.
Literature [9-11] studied the importance of the carbon capture technology when it used to achieve the carbon reduction goal of a electric power system.
On the basis of traditional power constraints, adding the constraints of power capacity, wind power integrating capacity, carbon emission reduction targets, etc.
The constraints of annual carbon emissions reduction during the planning period: The emissions reduction targets as a constraint is the most direct and effective way to control the CO2 emission.
Put in the initial data, including information such as the original power supply, power supply plan to build.