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where A/Apore – effective surface area available for reaction, Ф – potential of the medium оn outer to coating, Фin – potential under coating on the steel surface, ablk – suppression of oxygen transport through the barrier, V – stationary potential of steel, ЕFe – equilibrium potential of iron corrosion, ЕО2 – equilibrium potential of oxygen reduction, ЕН2 – equilibrium potential of hydrogen reduction, bFe, bO2, bH2 – Tafel coefficients, ilim,O2 – oxygen reduction current density, rfilm – coating resistivity, dfilm – coating thickness.
Summary From the presented data it can be concluded that the polyelectrolyte-epoxy composites can find their place in the practice of corrosion protection and on their basis can be created intellectual coatings, which together with cathodic protection could solve one of the actual problems of protection of metals, namely the prevention of underfilm corrosion.

This should be achieved by the optimum use of non-renewable natural raw materials, by the application of energy saving technologies, the utilization of industrial wastes as well as the reduction of CO2 emissions.
Partial replacement of Portland cements by one or more additives to produce blended cements leads to the reduction in the CO2 emission and energy saving in cement production process and offers green cementitious systems to the construction industry [1, 2].
The compressive strength data of the multi-component cements was determined on 40 x 40 x 160 mm mortar prisms with water/cement ratios of 0.50 according to EN 196-1.
Conclusion The rheological properties of the strength development of Portland-composite cement CEM II/B-M (S-P-L) improve and its density increase due to the following factors: 1) the optimization of the particle size distribution of the constituents using the values of incremental coefficient of surface activity, 2) the effect of the hydraulic properties of the GBFS combined with the pozzolanic action of the zeolitic tuff, 3) the filling effect of the finely dispersed limestone powder, 4) the reduction in the clinker factor.

Further more, cost reduction and improved device performance (see Ref. [1]) paved the way for SiC high-voltage power devices to gain an increasing market share.
The data of pure Si-based module was taken from datasheet of a 25A, 1200V IGBT4 module.
For further reduction of switching losses, we showed that the internal distributed gate resistor plays a significant role.
As next steps, also drain source capacitance and RON x A must be reduced in order to achieve further reduction of switching losses and thus improve the efficiency.

These targets include emissions of carbon dioxide (the UK target over 1990 levels 34% by 2020 and 80% reduction by 2050 [1]) and other regulated pollutants, as well as the utilization of renewable energy.
This includes: using energy at higher levels of efficiency (this alone will alter the patterns of energy demand, most notably of space heating); the electrification of user services such as heating and transport; a reduction in fossil fuels used for heating will be replaced where technically appropriate with electricity to heat pumps, district heating, biogas, biomass, solar.
Doing this in addition to detailed dynamic modeling is a significant modeling and data challenge.
If these are emphasized, large reductions could be achieved in a timely and cost-efficient manner even as renewable energy sources are still undergoing commercial development and as a problematic low-carbon resources such as nuclear power lose market share.

Introduction Recent studies of the SiO2/4H-SiC interface show that a reduction of the electron trap density and an increase of the electron MOSFET channel mobility are achieved when a high N concentration is present at the SiO2/SiC interface [1-4].
Recently, we have shown that the N-implantation strongly suppresses the formation of NIToxfast while the reduction in NIToxslow is only a factor of ~1.5 [7].
C-V characteristics were computed based on the density versus distributions of NIToxfast and Dit extracted from the TDRC data and they do reproduce the main features of the measured C-V curves.
The beneficial effect of the Nimplantation (strongest on Dit and NIToxfast) can be thus explained by the substitution of carbon dimers with N atoms, a process that leads to a reduction in both Dit and NIToxfast.

Thirdly, since the ionic radius of La3+ is smaller than Ba2+, substitution of La for Ba will lead to the reduction of the ionic spaces between Co and O ions [14].
The theoretical calculation predicts the shrinkage of the charge-transfer energy gaps between O 2p and Co eg levels by the reduction in Co-O spaces [15].
The result in Fig. 3 indicates that the enhancement effect of the Co-O spacing and grain size may be stronger than the reduction effect of the electron doping.
Fig. 5 shows the temperature dependence of the power factors of BBCO, BPBCO and BPBLCO samples, S2σ, calculated by using the data in Fig. 3 and Fig. 4.

At one-motor mode, only the Motor1 works and the ring gear is locked by the brake, so the simple planetary gear train is equivalent to a pair of parallel axis gears with a high reduction ratio.
(3) (4) Where, are the torques of the Motor1 and the Motor2; , are the rotate speeds of the Motor1 and the Motor2; is the torque of the output shaft; is the rotate of the output shaft; is the secondary reduction ratio in coupling box. 4 Control strategy of the DMCP 4.1 Control method of the motor The motors of DMCP adopt Permanent Magnet Synchronous Motor (PMSM) which has high efficiency and high power density. [5] The motor controllers use the torque method.
The components of the model are described by analytical models representing the hydraulic, pneumatic, electric or mechanical behavior of the system. [6] We can conveniently build the vehicle dynamic model including the vehicle body, the reduction ratio, the planetary gear train, the motors, the motors’ controllers, the propulsion control unit, the driver, the brake and the hydraulic system.
In AMESim the submodel of mission profile and ambient data can define the mission profile based on the driving cycle file.

Ren, 1998) and let Yi represent the utilization efficiency of production factors, so the efficiency reduction caused by entropy is expressed as follows.
Fig. 1 The reduction of production factors’ utilization efficiency The low utilization efficiency of production factors and all kinds of waste lead to poor performance.
Through the above steps, the number variable H in equation (1) will be reduced, in other words, work simplification and standardization will enhance the degree of order with entropy reduction and finally an explicit, stable and predictable network structure is developed.
At this time, Total quality management (TQM) is introduced to analyze the data to find the causal relationship, so that the abnormal trend can be found and prevented timely.

Case based reasoning prediction model of the dimensionality reduction Based on RBF neural network prediction model of the total coal The basic idea is as follows,using RBF as a hidden unit base , establish the hidden layer space,so that it can be input directlyis mapped to the hidden layer space.In the setting of RBF center point, the nonlinear mapping relation can be defined and the hidden layer to output space mapping is linear.Model input variables 6.The output variable 1.Number of hidden layer nodes according to the training samples is determined by OLS( orthogonal least squares ) algorithm.In this paper using the sample data, and set the number of hidden layer nodes 19.
(1) On the formula (1) is the i hidden layer nodes output,V is an input vector, is the hidden layer data center of i node function, is a width of the function around the center point, m is the number of hidden layer nodes, is Euclidean Distance of between input vector V and data center .Based on RBF neural network prediction model for total coal (2) On the formula (2) is the output of the network, is a weights from i hidden layer nodes to k output node. is i hidden layer nodes output.In the RBF neural network, , and three parameters which need to be processed.By literature [6] proposed design method can solve, because of the implicit layer and the output layer is a linear relationship, therefore least square method can be used to solve the . to meet this equation: (3) On the formula (3) is the maximum distance of between i data center and the other data centers.
The oxygen content of the flue gas of the GM ( 1, 1) In this algorithm we first do is the raw sequence data smooth processing,then by using GM ( 1, 1) model to prediction of oxygen content in flue gas.The oxygen content of the flue gas of the original data sequence is as follows, ，Introducing the smooth generating operator .For the original data processing,,To obtain the smoothed data sequence,Then the sequence order accumulation operation, generating a sequence as follows： (7) (8) On the formula (8) a is the model development coefficient and b as a model of coordination coefficient.Mark by using the least squares method according to (9 ) determine the parameter type
The two model prediction error respectively:, and.and is the weight coefficient. and , The output of the combination forecasting model (11) The error and variance respectively (12) (13) about to finding the minimum value of (14) Due to and get the value through the two independent of oxygen content in flue gas of prediction model,the correlation between the two is very small, therefore ，weight coefficient (15) Based on the actual operating data analysis and industrial trial operation In order to verify the reliability of integrated prediction model and adaptive, We used 558 groups operation part of oxygen content in flue gas data from the power plant.These data through exception handling and normalization processing.The oxygen content of the flue gas intelligent hybrid prediction model to predict the effect of Figure 1, shown in figure 2.
Soft-sensing model of oxygen control based on data fusion[A].

The data obtained had suggested that the doping distribution in the silicon nanowires were lower and this may have been affected by the surface depletion effect.
Based on the sheet resistance data, the range of the resistivity values after the self-doping process is between 0.012 to 0.015 ohm-cm.
Fig. 2 - The four-point probe data distribution for each silicon nanowire length.
According to the data distribution shown in Fig. 4, the carrier densities are lower than the doping concentration of the sample.
Summary The experimental data had suggested that doping distribution in the silicon nanowires were lower and it has been suggested that one of the reason is due to the surface depletion effect.