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It can cause a significant reduction in the compressive load-carrying capacity of a structure.
This model utilised experimental data obtained from [16-18], which is presented in Figure 2 below.
The slope of displacement indicates there is a significant reduction in the transverse velocity at the back face, which is further discussed below.
Significant reduction in interfacial delamination was observed in the Polyurea model.
Noticeable reductions in the transverse back face velocity of the composite panel were also observed for the elastomer-enhanced panel.

Currently, this effect will be compensated with help of: · Mechanical energy storages (electrically coupled flywheel) · Electrical energy storages (array of capacitors) [8] Both types of energy storage systems are cost-intensive, but allow buffering of electric energy and thus a reduction of power peaks in the electricity grid.
The angle of the eccentric shaft, the rotational speed of the primary and secondary motor and the required ram kinematic are the input data of the control.
The integrated PID controller generates a torque-signal for the secondary motor that corresponds to the required reduction of the control deviation.
The required flywheel, the planetary gear, the reduction gearings and the clutch-break combination are integrated in one housing to reach a compact design.
A reduction gearing at the output of the superimposed gearbox reduce the rotational speed of the eccentric shaft and the torque at the gearbox components.

Table1 Calculation result of Plus Calcium Oxide Grade, Minus Calcium Oxide Grade of iron ore[%] Name TFe SiO2 CaO MgO Al2O3 TFe-CaO Relative increase TFe+CaO Relative increase A ore 50.0 11.0 14.3 — — 58.34 16.68 51.12 2.24 B ore 53.0 19.0 2.5 — — 54.36 2.57 44.76 -15.55 Meishan (rich) 59.35 2.50 1.99 0.93 0.71 60.56 2.04 58.90 -0.76 Hainan Island 55.90 16.20 0.26 0.08 0.95 56.05 0.27 47.55 -14.94 Handan 42.59 19.03 9.58 5.55 0.47 47.10 10.59 38.25 -10.19 Ma’anshan Aoshan Ore 43.19 14.12 9.30 47.62 10.26 40.66 -5.86 Baotou (hematite) 52.30 4.81 8.78 0.99 0.22 57.33 9.62 54.19 3.61 Average 50.90 — — — — 54.48 7.03 47.92 -5.85 The calculation result above shows that all the minus grades increased at an average increase of 3.58 grades and average relative increase of 7.03 percentage points while some of the Plus Calcium Oxide Grades increased and some reduced at an average reduction of 2.98 grades and average relative increase of -5.85 percentage points.
Table2 Calculation of Plus Calcium Oxide Grade of Sinter [%] Sinter TFe SiO2 CaO MgO Al2O3 R2 TFe-CaO Relative increase TFe+CaO Relative increase Benchmark 58.3 4.60 8.06 2.02 1.73 1.75 63.41 8.8 60.10 3.1 S-1 60.24 4.57 4.89 2.07 1.24 1.07 63.34 5.1 60.16 -0.1 S-2 60.11 4.65 6.03 2.12 1.26 1.30 63.97 6.4 60.67 0.9 S-3 59.56 4.48 6.31 2.10 1.22 1.41 63.57 6.7 60.39 1.4 S-4 59.31 4.48 6.88 2.08 1.20 1.54 63.69 7.4 60.49 2.0 S-5 58.88 4.50 7.33 2.05 1.18 1.63 63.21 7.4 60.32 2.4 S-6 58.62 4.52 7.91 2.13 1.22 1.75 63.66 8.6 60.39 3.0 S-7 58.56 4.53 8.10 2.13 1.22 1.79 63.72 8.8 60.44 3.2 S-8 58.32 4.46 8.42 2.12 1.22 1.89 63.68 9.2 60.44 3.6 S-9 57.74 4.52 9.19 2.14 1.24 2.03 63.58 10.1 60.28 4.4 Table 2 shows the experiment sinter data of 4.5% at 4.5% when sintered from Meishan concentrate alone.
Table3 Calculation of Plus Calcium Oxide Grade of pellet [%] Name TFe SiO2 CaO MgO Al2O3 TFe-CaO Relative increase TFe+CaO Relative increase Australian pellet 63.86 5.28 0.85 0.12 2.75 64.21 0.55 60.65 -4.73 Ukrainian pellet 64.81 5.22 0.26 — 0.81 64.98 0.26 61.44 -520 Swede pellet 67.16 2.32 0.56 — 0.18 67.54 0.57 65.85 -1.95 08-3 pellet 64.37 8.06 0.34 — — 64.59 0.34 59.31 -7.86 07 uniform pellet 63.29 8.94 0.42 — — 63.56 0.43 57.84 -8.61 In Table 3 above, the benchmark grades of the 5 pellets range from 63.29 to 67.16 and averaged 64.70, their Minus Calcium Oxide Grades averaged 64.98 and the relative increase averaged 0.43, showing there is little variation in theMinus Calcium Oxide Grade; while their Plus Calcium Oxide Grades averaged 61.02, relative increase averaged -5.67, showing there is large reduction of the Plus Calcium Oxide Grade, especially the 2007 uniform pellet, whose Plus Calcium Oxide Grade is only 57.84, a reduction of 8.61%.
As a lot of SiO2 is added to pellet, its smelting value largely reduced, which is consistent with the large reduction of its Plus Calcium Oxide Grade.

Magnetic track brake system is one brake independent of the adhesion between wheel and rail and independent of a traction power failure. 2.2 The control principle Magnetic track brake system has a few main interface, such as driving/braking lever, the traction control unit (TCU), the central control unit (CCU), drivers display unit (DDU), automatic train protection (ATP) and so on, exchanges information through the hard line and Controller Area Network (data Bus).
When fault state, the failure of the regenerative braking train bogie on exerting the corresponding magnetic track brake force size, make train implementation level required of train braking speed reduction. 3 Verify the feasibility 3.1 Meet the requirements of braking force calculation Different brake systems and brake control method will produce different braking distance, in all sorts of different ways of braking, emergency braking distance is as short as possible, is the basic for testing train braking ability and operation safety technical conditions, is also a communication signal system design and the important basis of transport organization.
Checks the scheme for emergency braking, see table 1, in considering the brake response time under the premise of meet the emergency braking speed reduction of 1.8 m/s2.
Table 1: Emergency braking capacity checking The initial speed（km/h） 20 30 40 50 60 70 The average coefficient of friction [2] 0.153 0.132 0.116 0.104 0.094 0.086 AW0 The average braking force of the vehicle （N） 208386 179784 157992 141648 128028 117132 AW2 208386 179784 157992 141648 128028 117132 AW3 208386 179784 157992 141648 128028 117132 AW0 The average speed reduction （m/s2） 4.07 3.70 3.34 3.03 2.77 2.55 AW2 3.14 2.82 2.53 2.29 2.08 1.91 AW3 2.97 2.66 2.38 2.16 1.96 1.80 3.2 Braking force is adjustable Because of low floor light rail train secondary suspension is steel spring, so the load information needed to provide 1 set of load sensors on each of the bogie, the microcomputer brake control unit (BECU) decodes the signal collected, gets vehicle load information, makes the braking force distribution according to it.
Table 2: Service braking capacity checking The initial speed（km/h） 20 30 40 50 60 70 AW0 PWM duty ratio control 25% 28% 32% 35% 39% 43% AW2 33% 38% 43% 47% 52% 57% AW3 35% 40% 45% 50% 56% 61% The average speed reduction under AW0/AW2/AW3 （m/s2） 1.10 3.3 Holding Brake The slide force of train in 35 ‰ slope in AW0, AW2 and AW3 load conditions, is 14583 N, 19792 N and 21111 N respectively.The static friction coefficient is 0.2 between the rail and the friction material of sliding wedge on magnetic track brake, parking brake force of magnetic track brake device is 272400 n, in AW0, AW2 and AW3 train’s load under the condition of parking brake safety coefficient is 18.68, 13.76 and 12.90, respectively.

Fig.1 Flat triangle-arc triangle-round pass system Fig.2 Finished pass Advantages of flat triangle-arc triangle-round pass system is that there is a large reduction and extension coefficient in flat triangular pass, thus the shape of rolling can be effectively changed at roughing stage.
Table 1 Parameter of the pass Form factor Theoretical width Reduction rate% Extension coefficient Fill factor Flat triangle 0.75 130 10.5 1.13 0.86 Arc triangle 0.6675 86.6 3.75 1.06 0.9 Round 1.732 73.61 1.76 1.03 0.995 Hot rolling numerical simulation Finite element model Magnesium alloy exhibit special structural features during hot rolling compared with other hot-rolled materials vary greatly, it is necessary to simulation its hot rolling process using finite element method.
As shown, the largest position of rolling reduction P1 in rolling, but also the position of maximum stress, etc, is 80.2MPa, less than the ultimate strength of 150MPa [10], it can remain stable deformation rolling.
The reduction of flat triangle pass is largest so that flat triangle pass has maximum rolling force, and the maximum rolling force is 94.8kN.
(3) According to process requirements, air treatment performed after rolling mill Analysis (1) Rolling force To combine actual data and simulation results, the comparative analysis between rolling force simulation and measured values of flat triangle-arc triangle-round pass at stable rolling stage is obtained, see Figure 6.

Introduction A very high increase in the use of composites in aircraft, road vehicles, trains and ships due to the weight reductions that can be achieved and as a resultant fuel reduction and low emission.
The weight reduction achievable with composites makes the composites as enabling applications which would not be performed with metals.
Table 2 The E glass fibre mechanical properties used in FEM calculation Property Value[MPa] Ultimate tensile strength, σut 67 Ultimate compressive strength, σuc 109.5 Ultimate flexural strength, σuf 138.4 Ultimate in-plane shear strength, τu 58 In-plane modulus, E 4500 In-plane shear modulus, G 2665 Ship deck hull FEM analysis Further a static structural FEM analysis was done using Ansys [12] software package, to investigate if some weight reduction can be gained or if the rule based dimensioning is fulfilling the criteria regarding deck deflection.
[12] Ansys, Workbench v15 Engineering Data Sources.

Vehicle networking technology promote the development of intelligent transportation Yi Hu1, a *, Yonghui Xiao1,2,b 1Central University of Finance and Economics, Beijing, 100081, China 2Yunnan Minzu University, Kunming, 650031, China aalex_hy@163.com, bxiaoyonghui-2002@163.com Keywords: Vehicle networking; RFID ; Sensing technology ; Big data; Mobile computing Abstract.
With RFID, cameras, sensors, GPS and electronic equipment such as image processing, realize the vehicle, road and traffic environment information collection; Cloud center adopts computer technology analysis and processing vehicle data information, and the best way of different vehicles is calculated, timely report and arrange the light cycle, road to smart people, vehicles, road monitoring, scheduling and management.
Vehicle networking is a typical application of the Internet of things technology in the field of traffic system, is the result of the information society and social integration. 1.2 System functional requirements (1) Radio communication ability，such as radio signal propagation difficult compensation levels, for example, using the roadside Unit (RSU, Road Side Unit) to satisfy the exchange of information between vehicle and infrastructure; (2) Network communication function, such as the mode of transmission, uncast broadcast and multicast, specific area of broadcasting; Data aggregation.
The priority of the message; Channel and connectivity management method; Support IPv6 and IPv4 addressing; Associated with the Internet access to the mobile node mobility management; (3) Vehicle positioning function, such as global navigation satellite system (GNSS), global positioning system (GPS), Bei Dou navigation positioning system (BDS); Combination the positioning of the function, such as by a global navigation satellite system and the information provided by the combination of the combination of local map location; (4) The vehicle's safety communications functions, such as respect the anonymity and privacy, integrity and confidentiality, external attack resistance, received the authenticity of the data, data and system integrity.
According to the received digital information, the car will know the status of the networked vehicle around, including distance of the relative velocity and acceleration, etc., and in the case of emergency braking, can make the following networked automobile synchronous reduction, effectively prevent the happening of the accident.

Results and Discussion Chemical reduction method for Ag-NPs production is widely used, although this resulted in harmful chemicals which have harmful effect on the environment.
The absorbance in the range of 400 nm to 450 nm has been operated as an indicator to identify the reduction of Ag+ to metallic Ag0.
An broad absorbance peak at around 350-550 nm confirmed the reduction of AgNO3 into nanoparticles.
sample conc µl/100µl Viability % (3 Replicates) Mean Inhibitory % S.D.(±) 1st 2nd 3rd 100 3.67 4.03 4.75 4.15 95.85 0.55 50 10.98 8.76 10.98 10.24 89.76 1.28 25 21.83 26.75 20.64 23.07 76.93 3.24 12.5 34.76 40.93 31.85 35.85 64.15 4.64 6.25 60.54 78.81 72.39 70.58 29.42 9.27 3.125 81.73 88.62 85.47 85.27 14.73 3.45 1.56 93.48 97.06 94.69 95.08 4.92 1.82 0.78 99.26 99.26 98.71 99.08 0.92 0.32 0 100 100 100 100 0 0 Figure 7: Relation between silver nitrates concentrations and cell viability From data obtained in table (2) and figure (7), it was clear that the cytotoxicity against vero cells was detected with 50% cytotoxic concentration CC50 =9.87±0.90 µl/100 µl.
Reduction of the Ag+ to Ag0 during exposure to the Citrus sinensis peel extract was followed by color change of the solution from colorless to dark brown.

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].

This is because both fillers are non-deformable solid and thus give rise to reduction of deformable rubber portion with increasing filler loadings.
This can be related to the processing difficulty with more filler in the blends as proven in previous data, ML.
Apart from the dilution effect, the reduction of tensile strength also can be related to high filler-filler interaction.
The addition of CB caused a drastic EB reduction even at 10 phr.
On the other hand, 10 phr of silanized-silica showed an optimum EB before further loadings and caused reduction trend in EB.