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In addition to aforementioned incentives that polymers bring, i.e. low specific weight and intrinsic lubricity, the engineering community benefits from other merits of polymers including noise reduction, corrosion-free and shock resistance.
Moreover, parts integration as additional gain could reduce assembling time and number of parts, leading to eventual cost reduction.
Table 1 shows the gear data used in the example.
Gear data The model is a plane strain model and simulated as a quasi-static motion of the gear mesh.
Material data Results & Discussion The results are evaluated at 100 evenly distributed points in time and that means 0.05 rad of the input gear wheel.

The ways to evaluate braking performance of electric bicycle are usually divided into three categories: (l)Obtaining data of Electric bicycle in the practical road test to evaluate the electric bicycle brake performance.
To simulate a variety of road conditions of various parameter data and the electric bicycle brake performance assessment
Simulation of dynamic process of electric bicycle braking simulation method by use of computer, then evaluating electric bicycle braking performance through the simulation data.
The brake is loaded in pure flywheel inertia test platform, the relationship between its braking torque and inertia fly wheel and the rotational speed is as follows: (3) Where αj is the test platform frame spindle angular speed reduction.
If replace fly wheel stored kinetic energy with motor output energy, and keep the relationship between braking torque and speed reduction, can be used for simulating the mechanical inertia, called electrical inertia simulation.

Reference [2] had done some researches on collection and management about crane reliability data.
Loading coefficients like hoisting impact factor, acceleration of gravity, wind-force coefficient, reduction coefficient of wind, etc are certain values and can be chosen from Reference [5] by designers.
Reliability Analysis for Crane Boom Relevant Data of Crane Boom.
Table 2 Loading Coefficients Items Values Items Values hoisting impact factor 1.1 structural substantial ratio 0.3 acceleration of gravity 9.8 [N/Kg] min value of hoisting dynamic loading factor 1.05 reduction coefficient of wind 0.63 hoisting state level factor 0.17 wind-force coefficient 1.2 multiplying factor of pulley group 9 Mean values [5] and variation coefficients of calculation loadings are shown in Table 3.
Data inside frame is reliability of crane boom, and the value is 98.0121% when confidence level is 95.00%.

Secondly, the non-referential approaches verify the board based on the design specification data.
In addition, each printed board is analyzed, according to the availability of the artwork data.
The diagram for product inspection processes Methodologies Images Preprocessing Image Preprocessing consists of two steps – to wit: original image’s gray scale transformation and noise reduction: Step I.
Noise reduction Average Filter is employed to reduce noises from images, piling up noises η(x,y) on original image f(x,y) in order to produce a noise image g(x,y), shown as following equation: g(x,y)= f(x,y)+ η(x,y) For every coordinate (x,y), all noises are unrelated to one another and their means are zero.
A lens and an image measurement system are sufficient to read all data and save millions of dollars for equipment purchase and maintenance.

It was observed that increase in particle size brought about a reduction in the physical properties (TS and WA) of the board.
Results from Table 1 and Fig 2 show that the MOR data ranged from 11.11 to 14.12N/mm2.
The range of data in the MOE from Table 1and Fig. 1 was from 1590 to 1958 N/mm2.
The IB data from Table 1 and Fig 3 ranged from 0.28 to 0.44N/mm2.
Results showed that an increase in particle size brought about a reduction in the mechanical properties (Modulus of elasticity, modulus of rupture and Internal bond strength) and dimensional stability (Thickness swell and water absorption) of the produced particleboard.

It is of great practical interest to generalize the data obtained by researchers in this field in order to develop a theory of the formation of defects in the structure of electrolytic iron, allowing to explain the origin of its so-called physical and mechanical properties [6-8].
Experimental data were processed using mathematical statistics methods.
In course of processing the experimental data, regression coefficients were determined that adequately describe the dependencies of the mosaic block size (D), dislocation density (ρ), micro-distortions (ε) and micro-hardness of the coatings upon the parameters of electrolysis (electrolyte flow rate - Х1; concentration of electrocorundum particles - Х2 and cathodic current density - Х3).
More severe electrolysis regime (further increase of cathodic current density) causes increase of dislocation density and reduction of mosaic block size to limit values (at dilatation δ = 0.08 … 0.09), followed by formation of stable submicrocracks in them (Fig. 2).
The investigation of fine structure of coatings obtained during high-speed deposition showed that the increase of coating microhardness is caused by reduction of block size and increase of the density of dislocations.

In accordance with this, and throughout this work, a warm-up period of 56 hours (3360 minutes) was considered, during which the data collected were not considered for statistical purposes [5, 11-13].
This fact has proved to be useful since it allows for a reduction of the costs incurring from a smaller number of circulating pallets [5].
Table 2 (where PL1 represents the number of pallets circulating on loop 1), and considering the data presented in table 1, presents a comparative analysis of the results obtained by Resano [6] through analytical models, with those reached by resorting to the simulation model.
This fact has proved to be useful since it may allow for the reduction of the costs resulting from a smaller number of circulating pallets.
In table 4 (where PL2 represents the number of pallets circulating on loop 2), and considering the data presented in table 3, a comparative analysis is undertaken of the results reached by Resano [6], through analytical methods, and those obtained by resorting to the simulation model.

The scheme of boric acid mass transfer in WWER-TOI reactor in LB LOCA. 1 – perforated reactor barrel; 2 – separating collar; 3 – emergency core cooling system nozzle; 4 – core;  – steam flow;  – condensate flow;  – flow of boric acid from hydroaccumulators According to literature data, the density of the boric acid solution depends on its concentration and the density of water [6].
The maximum concentration of boric acid solution corresponding to the beginning of crystallization depends on the temperature [7]: increase of temperature leads to a sharp increase in the maximum concentration (solubility) of boric acid solution, the reduction of temperature leads to the decrease.
Considering boiling process in the reactor core, one may talk of water proportion reduction in the reactor by the process of vaporization and increasing of boric acid concentration in the core bottom where it is cooled in interaction with the colder metal structures.
Calculation of Boric Acid Accumulation in the Core Depending on its Concentration in HA-3 System In the calculation, the number of assumptions have been made, necessitated by the complexity of the processes occurring in the loop, or lack of data on the properties of water solutions of boric acid: 1.
Initial data for the calculation were the characteristics of passive core reflooding systems, shown in Table 2.

The wet-oxidized sample reveals a higher DIT close to the conduction band edge than the dry-oxidized one, which leads to a reduction of the free electron density in the channel of the wet-oxidized MOSFET.
In case of sample B, the calculated curve (solid) and experimental points (triangles) proceed largely parallel, whereas for sample A the experimental data (squares) strongly deviate from the theoretical curve (dotted).
According to [5] the trap concentration can be calculated by forming the difference between the experimental data and the ideal theoretical curve at fixed free electron density.
It is almost identical in both samples and increases with increasing UG indicating that screening of charged traps by free electrons leads to a reduction of Coulomb scattering [6].
The decrease of µH,e below 150K is dominated by Coulomb scattering at charged traps; in this temperature range, no gate voltage VG(V) 10 100 50 electron Hall mobility µ H,e (cm2/Vs) 2 3 4 5 6 7 8 9 10 10 100 electron Hall mobility µ H,e (cm2/Vs) 200 100 bulk 6H 300 500 temperature T (K) a) b) sample A (wet) sample B (dry) 50 sample A (wet) 296K 143K sample B (dry) 296K 143K Fig. 2a) Electron Hall mobility µH,e as a function of gate voltage UG of sample A (squares) and sample B (triangles) taken at 296K (open symbols) and 143K (full symbols). b) Temperature dependence of the electron Hall mobility; the symbols correspond to experimental data, the dotted curve is an eye guide connecting the experimental points.

Data acquisition was achieved via the Matlab connected to a real-time workshop of d-Space, throughout the experiment.
In this figure, experiment and model data match before reaching the peak but show different paths of relaxation.
One shows an initial fast response (A) and follows relaxation (B) and further reduction in force generation capability (C).
Further reduction in force generation, the reversal to the initial direction, occurs in Region-C.
The obtained modulus data and electro-responsive blocking force curves, as given in ref. 6, indicate that M-CNTs appear to be uniformly dispersed in a continuous polymer matrix and significantly affect the mechanical properties and electromechanical responses.