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We illustrate the methodology with published data from micromechanical tests on small-scale flexure specimens from the literature.
A comprehensive set of data on built-in single crystal silicon beam specimens is available for analysis [7].
The fitted lines through the data afford evaluations of the Weibull parameters m and σ* for each data set.
Data reproduced from Namazu et al [7].
The data for the two groups of specimens show distinctly different spreads, suggesting fundamentally different flaw populations.

Connecting these data with microstructures examined in optical and electron microscopes and resultant mechanical properties have improved the understanding on complex phenomena occurring in the processing of these steels and the role of numerous process variables in the optimization of enhanced mechanical properties.
Based on the data generated on a Gleeble simulator, new equations have been proposed for the predictions of SRX kinetics and the SRX grain size in TWIP steels [17].
An example of the ultra-fine grain structure obtained in 301LN steel, cold rolled to 60% reduction and annealed at 800°C for 1 s is shown in Fig. 5.
Simulation experiments based on the industrial processing conditions, both with or without controlled deformation below Tnr, followed by cooling at different rates, enabled generation of reliable phase transformation data for plotting of the CCT diagrams.
Summary Physical simulation has been effectively employed to generate relevant material data for improved modeling of microstructural phenomena and thermomechanically controlled processes during the development of advanced high strength steel types.

Bioprocess automation is developing fast for the reasons of quality control, production cost reduction et. al..
Second, it is needed to conform to the government regulation requirement for production process variable documentation of some bioproducts so as to obtain the online data of the production line.
The aimed value of nutrient feeding rate (F) can be calculated by using the above mathematical model, the real value of F can be calculated from the online data of balance 2, and then F can be feedback controlled by using servo control.
It also has the functions of set-point setting, data logging, online data and history data displaying, process variable trend graphic displaying, and alarming.

The EIS data were fitted using ZSimpWin (ver. 3.2).
Clearly from the EDX data large percentage of Co coatings consists of Co (83.1%), O (13.2%), S (2.4%), and small amounts of gold (Au) and lead (Pb).
Reduction in Rct means increased corrosion rate.
To characterize the EIS spectral data visually, two circuit models were used to fit the impedance data are shown in Fig. 6d.
Also, the EIS data are listed in Table 2.

The cascade correlation algorithm that is cascade-correlation(CC) algorithms, CC network structure and CC network weights learning algorithm are introduced, based on the real data in hydropower station, considering the cavitation characteristics, the network model is established based on CC algorithm, and the applications of CC and BP algorithm of turbine are compared.
In first place, the growth of the turbine output power is based on the reduction of dimensions to decrease the cost of its components.
To date, we have explored the all-sigmoid and all-Gaussian cases, but we do not yet have extensive simulation data on networks with mixed unit-types.
Model and Application of Vibration Characteristics based on CC Algorithm In this paper, the operating data were analyzed, part of the measured data were shown in Table 1, the model of cavitation characteristics f = (H, �) was established based on the CC, where H is unit rotation ( 1 11Q l s−⋅ ), � is the unit discharge( 1 11 minn r − ⋅ ), and f is the turbine runner cavitation index of output.
Conclusion In this paper, the definition of CC algorithm and structure of the network are described, and based on the operating data, the model of CC vitation characteristics was established based on the CC, the application of BP neural network and CC algorithms were compared.

After the application of Taguchi Method, the rejection rate dropped down to 5.84% from 11.33%, which is a 48.45% reduction. 1.
Further, four important factors were identified and multiple regression analysis was used to generate data sets.
Hence, reduction in % rejection = (11.33-5.584)/11.33 = 48.45% Table 6 Confirmation run results Set No.

Those data drive from various fields, such as the economic, social, financial, technological fields and so on.
Financial feasibility evaluate mainly according to the comparative and analysis of quantitative indicators based on specific data.
Those data report that the ability of debt repayment and the profitability of projects.
Quantitative indicators are mainly through data computation and comparison analysis.
Choose comparative evaluate method to collect data.

Data are presented as the are the same as in A.
Data are presented as the means means of triplicate experiments.
Data are presented as the means are the same as in A.
Data are presented as the means are the same as in A.
Data are presented as the means of triplicate experiments.

., Pokrovsk, 85300, Ukraine a*saik.nmu@gmail.com, bpetlyovany@ukr.net, clvg.nmu@gmail.com, dkateryna.sai@gmail.com, eartem.merzlikin@donntu.edu.ua Keywords: Coal Reserves, Underground Coal Gasification, Solid Fuel Conversion, Data Search, Mining Techniques, Generator Gas.
According to data of the majority of experts, too unprofitable mines should be closed down.
The authors have applied modern, from the viewpoint of evaluation adequacy, approach being qualitative comparative analysis of publications of such advanced scientometric data bases as Scopus by Elsevier and Web of Science by Clarivate Analytics [36, 37].
The timeframe (i.e. the past fifty years) has been selected in view of scientometric data bases fullness.
The proposed approach is simple to be used in terms of any research; however, objective evaluation of the obtained data remains to be important.

The data that passes the actual diagraph builds up the transportation to three parameters of flow speed- density model, taking this model as the foundation, deducing a speed- density model.
The method in this paper is to regard the bridge as observation point, filming the flow of the three lanes which are from east to north, filming period is 7:30am-8:30am in October 2010 and January 2011 ,then guide the data into the computer through video capture card ,generating video files so that to process the further data processing.
In research section, snow speed-density curves of the actual data estimates as the table 1.
Through the above calculation, graphs and analysis, we can get the folding traffic capacity reduction factor with the road covered with ice and snow, the method is as follows: asnow==0.96 According to the actual data of the section which the paper studies, we can get the snow correction coefficient of the capacity is 0.96.
Conclusion First, building the speed-density model of the traffic three parameters thorough the actually measured data, infer the speed-flow model based on that model.