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Based on Arduino data acquisition results, thinner IPMC (t1) has more than 5 times respond speed than thicker IPMC (t2).
Simultaneous data logging analysis and real time control experiment can be done without great hassle with Arduino microcontroller.
Real time control data logging can be provided by MATLAB/Simulink through ArduinoIO programming which already uploaded previously into Arduino memory.
Besides, instead of MATLAB/Simulink, open source and light softwares such as Processing and PLX-DAQ can be used for data logging which ultimately allow Arduino to achieve near to its maximum 10 KHz sampling rate.
Based on Arduino data acquisition, thinner IPMC (t1) have more than 5 times respond speed than thicker IPMC (t2).

EPS or SMP are filling with the interspaces of the cake layer and resulting in the gradual reduction of the porosity ratio in the cake layer.
The effect of 0.1µm PVDF and 0.1 µm PP MF membranes on membrane fouling using the cake model can be observed in Fig. 4, which represents the evolution of TMP with the filtration progress for experiments conducted with MLSS of 10g/L at a constant membrane flux of 25 L×m−2×h−1.The model calculation shows good agreement with experimental data for both 0.1 µm PVDF membrane and 0.1µm PP membrane.
Deviation of the experimental data from cake filtration mechanisms are less than 5 % while TOC removal for both membranes is found almost no difference between both membranes.
The obtained experimental data have been interpreted by the cake filtration theory and the effect of fouling behavior was quantified by calculating using the cake governing law.
The four-step cake formation mechanism has been put forward as the increment of TMP and the reduction of the cake porosity ratio.

Chou and Hang [3] simulated and optimized the value of die gap and punch travel on springback reduction in ‘U’-channel bending using ABAQUS.
Geometry of tools and specimen of CAD data were imported to the simufact for further simulation by using either as Binary or ASCII STL (.stl) file, as Bulk Data (.bdf) file or as an Initial Graphics Exchange Specification (.igs) fail format.
Table 2 contains simulation experiment data, where 2 variable parameters for the parametric investigation on the springback amount.
The experiments data was obtained by the automatic signal acquisition system of UTM.
Dimensions and material parameters of simulation analysis Material data Workpiece DIN 1.0980 Geometry data: Punch corner radius, Rp (mm) 3 variable (2, 4, 6) Die opening, W2 (mm) 3 variable (30, 36, 48) Workpiece thickness, t (mm) 2.6 Workpiece width and length (mm) 50 x 150 Simulation data: Punch velocity, vp (mm/s) 45 Punch travel, Tp (mm) 52.6 Initial temperature, To (oC) 20 (workpiece, tools) Friction coefficient, μ 0.05 Press type Hydraulic Material Thickness [mm] YS 0.2 [MPa] UTS [MPa] E [Gpa] Elongation (%) SPFH 590 3 546 654 574 29 Table 3.

The specific steps of the trading regulatory risk evaluation are as follows: Step1: Collect the prime data of the secondary grade index Ik,j (the jth secondary grade index which is included by the kth first grade index), calculate the risk value Fk,j and determine the risk level based on the risk value.
Matrix Explanation Order 1 HG Comprehensive evaluation matrix 6×6 5 HD trading behavior 3×3 2 HA market coordination 3×3 6 HE transmission channels 2×2 3 HB transmission and distribution electricity quotation 3×3 7 HF energy conservation and emission reduction 3×3 4 HC trading scheme 3×3 A Numerical Example Simulated data are served for demonstrating the developed approach.
Secondly, based on the given data of all the secondary indices, the membership values of the secondary indices can be obtained depending on the fuzzy evaluation models.
The simulated data and the corresponding risk value are shown in Table 3.
Table 3 The simulated data and the corresponding risk value No. weights Risk value No. weights Simulated data Risk value No. weights Risk value No. weights Simulated data Risk value A 0.21 2.14 A1 0.40 0.68 2.18 B 0.18 1.31 B1 0.33 0.21 1.32 A2 0.30 0.94 1.69 B2 0.33 0.23 1.41 A3 0.30 0.25 2.45 B3 0.34 0.08 1.19 C 0.17 1.51 C1 0.40 0.83 1.66 D 0.16 1.65 D1 0.33 1.00 5.00 C2 0.40 0.05 1.74 D2 0.33 0 0 C3 0.20 0.1 0.74 D3 0.34 0 0 E 0.14 1.85 E1 0.50 0.69 1.93 F 0.14 2.34 F1 0.35 0.50 2.73 E2 0.50 0.34 1.76 F2 0.35 0.2 0.71 G 1.79 / / / F3 0.30 0.8 3.77 According to the maximum membership degree principle, the evaluation results for the 6 first grade regulatory risk indices of A~F and the comprehensive evaluation index G are “medium”, where the risk value of index F is maximum.

In the end, the method has been verified to be successful by calculating and analysing simulated data and practical measured data.
Analysis of Simulated Data.
Table 1 Concrete situation of simulated data No.
Analysis of Practical Measured Data.
Finally, it has been verified that this method is successful by calculating and analysing simulated data and practical measured data

Experimental I-V data from Schottky Barrier Diodes (SBDs), combined with TCAD simulation, demonstrated that approximately 40 % of boron atoms were activated in the SiC lattice (at a depth of 30-40 nm) without the need for high temperature ion implant activation.
TCAD simulation of Schottky barrier diodes (SBDs) with different percentage of boron activation with correlation to experimentally obtained I-V data.
The SCR of treated samples extracted, exhibited a one order of magnitude reduction from 10-4 Ω∙cm2 (untreated) to 5.6×10-5 Ω∙cm2 (treated) as shown in Fig. 6 (a-b).
This novel single step plasma treatment allows for the reduction of SCR at the source region in p-SiC.
Hannan et al., “The Effect of Nitrogen Plasma Treatment Process on Ohmic Contact Formation to N-Type 4H-SiC,” Diffusion and defect data, solid state data.

Data collection .Data is collected through multi-methods in which interviews, questionnaires and observations are used.
Partially Grounded Theory technique is used to clarify the data collected from different sources and some respondents were again interviewed to obtain missing data.
Data analysis.
So the gathered data is valid.
Thus it leads to a conclusion that the data gathered to be reliable.

Based on a defined product model, a workflow with all operations data will be generated according to the rules from the knowledge base as follows.
First is the order of piping products with different due dates, described in a fixed data format and delivered by the customer.
A complete order is made by six data tables which can be divided into three groups as being common information, assembling information and parts information.
Various information of each product is easy to be located during data processing.
The plan collects a total data of 3,342 operations within 25 working days.

Factors of construction waste’s management Management of Construction waste mainly refers to the construction waste’s reduction, harmless and recycling.
Each parameter to be estimated with the observed data can be obtained only one estimate.
Using AMOS software to analyze the data obtained from the questionnaire, the model’s path coefficients can be obtained, as shown: Fig. 2 Path coefficient of structure equation modeling Table 1 Regression Weights Behavior Behavior Esti- mate S.E.
Tan, Research of construction waste’s reduction deeds, Xi'an University of Architecture and Technology, 2011
Tan, Construction waste’s reduction measure, Construction Technology, 10,35(2004):732-734

The initial products (mixture of non-stoichiometric magnetite and maghemite) were formed during iron reduction of synthesized ferrihydrite by bacterium G. ferrihydriticus.
Moreover, we suggest a new procedure for particle size and magnetic moment estimation with Mossbauer spectroscopy data.
During bacterial iron reduction non-stoicheometric magnetite (Fe3O4) and siderite (FeCO3) are formed.