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These models are only used to develop a methodology to evaluate model applicability beforehand by using sensitivity analysis and model reduction.
FIM=VarCov-1=((ST∙S)-1∙Res/(n-h))-1 (10) With S being the sensitivity matrix shown below in the case of model A: S=∂dxdt∂k1⋯∂dxdt∂kh∂dsdt∂k1⋱∂dsdt∂kh∂dpdt∂k1⋯∂dpdt∂kh (11) Res is the residual sum of squared errors between measurement and estimated data set, n is the number of measurements and h the number of parameters in the model.
Therefore it has to be determined if all of them are estimable or if complexity reduction is necessary.
Therefore for detailed analysis of the model structure further complexity reduction is necessary.
As depicted in the introduction a major challenge is the validation of a suitable model candidate with real measurement data.

In contrast, the {411}<148> recrystallization texture, which is obtained from higher cold-rolling reduction (over 90% in thickness), deteriorates formability.
Ingot Soaking (1200 °C) →Hot rolling (2.0 mm thickness) Pre-cold rolling (1.5 mm thickness, reduction: 25%) Decarburizing (750 °C, 180 min in Air → F.
Cold rolling (0.075 mm thickness, reduction: 95%) Normalizing (1100 °C, 60 min → W.
The cold-rolling textures after 95% reduction are shown in Fig. 3.
Each KAM value, which represents the average misorientation angle between the data point and all of the neighbors, corresponds to the localized plastic strain.

Through fuzz mathematics method, the optimum treating condition of cashmere for potassium permanganate was got: temperature 50℃, time 20min, potassium permanganate 3g/L, pH 3; Through the fiber whiteness based on test result, optimum reduction conditions after potassium permanganate treatment was as following: Temperature 45℃, time 10min, sodium sulfite 30g/L, HAc 2%.
The inspection target value is in the scope [0, 1] for Fuzzy statistical table request, therefore needs to transform measured data in experiment's index to the corresponding index membership through the fuzz mathematics method.
Transform measured data in experiment's index to the corresponding index membership through the fuzz mathematics method is according to the equation (2): (2) Where is the number of evaluating indicator, is the number of experiment, is the actual value of ’s testing plan and ’s evaluating indicator; is the ’s moderate value of evaluating indicator; is efficiency indicators, namely the value is the bigger the better, is appropriate indicators, namely the value is the closer the better moderate value.
Table3 The fuzzy statistical of experimental result Experim-ental number Felting ball volume Single fiber strength 1 0.678 1.897 0.815 2 0.777 0.812 0.763 3 0.948 0.749 0.755 4 0.811 0.745 0.805 5 0.958 0.793 0.930 6 0.678 1 0.838 7 0.882 0.730 0.934 8 0.609 0.968 0.904 9 1 0.880 0.758 Horizontal average value matrix of each factor according to the equation (3): （3） Where ; is the transformation value of observed value and the th different factor's level’s corresponds data.
Through the fiber whiteness based on test result, optimum reduction conditions after potassium permanganate treatment was as following: Temperature 45℃, time 10min, sodium sulfite 30g/L, HAc 2%.

Manufacturers of data servers, telecom systems, solar power inverters and drives for motor control are focused on reducing power conversion losses while simultaneously shrinking the size of power systems.
Comparing to similarly-rated state-of-the-art Si super-junction MOSFETs on the market, the GaN devices offer significant reduction in gate charge, on-resistance and output capacitance.
GaN solutions enable high frequency (high density) operation of Boost/PFC with 70% los reduction over Silicon Super-junction.
GaN/Si and GaN/SiC HEMT leakage comparison data shows minimal impact of disclocations/GaN-Si material on achieving comparable and lower leakage than GaN on SiC devices.
To get further confidence for longer term operation, we completed 2000 hours high temperature operating life (HTOL) data on GaN HEMTs running in actual boost power converters at 300 KHz providing 1:2 (200V to 400V DC) boost operation.

For single pass compression data, measurements were made in regions where the equivalent strain exceeded 3, and with the multi-pass specimens, in the center region of the specimens rolled to ٱ18mm, where the estimated cumulative strain corresponds to 3.4.
It can be clearly seen that the data for single pass compression lie on a straight line with the grain size decreasing with increasing Z parameter.
Similar behavior is exhibited by the test data of multi-pass warm caliber rolled specimens rolled at different temperatures.
For the purpose of comparison, the test data for 10m ferrite-pearlite steel (SM490) as well as HT780 QT steels were also presented.
This figure includes test data of several specimens deformed at various temperatures and strain rates resulting in a range of Z parameters obtained by single pass compression [7].

The reduction of fatigue limit under 5% NaCl environments was much larger than that under high humidity environment.
One of the effective ways to reduce gas emissions is weight reduction of the vehicles.
Figure 4 shows corrosion pit nucleation life and crack nucleation life of AZ61 in the form of an S-N plot along with the fatigue life data under high humidity [11].
As shown in Table 2, the reduction rates of fatigue strength due to 5%NaCl environment were 0.77 for AZ80-T5, 0.85 for AZ61 and 0.53 for AZ31.
The significant reduction in the fatigue strength by the NaCl environment was attributed to the formation of corrosion pits.

A mechanical behavior of solid waste during foundation pit excavation is discussed on the base of the in-situ monitoring results of earth pressure and field measured data in solid waste.
Lacking of data about foundation pit excavation in old landfill site, the collection of in-situ monitoring data is very necessary.
Fig.1 Typical soil layer distribution Borehole data and geotechnical test results.
According to the borehole data, municipal solid waste is grey black, grey, dark grey, wet – saturated and plastic-flow.
Experiment Study on Reduction of Earth Pressure in Geotextile Reinforced Soil [J].

The data sheet of RD-1097-01 damper is presented in Tab. 1 [10].
Although no data for the system with MR damper model is presented here, MR damper was considered within the whole scope of the development process.
As a separate publication, simulation data obtained thanks to embedding MR damper model within the Comsol-Simulink application, will be presented.
After completing the laboratory test rig and setting it in motion, thorough tests are expected to deliver more data for identification and control analyses.
[7] Kciuk S., Martynowicz P.: Special application magnetorheological valve numerical and experimental analysis, Diffusion and Defect Data – Solid State Data.

Data location In cloud computing environment, the use of distributed storage and dynamic scheduling mechanism make the data randomly storage in the different regions.
When you use the cloud, you probably won't know exactly where your data is hosted.
Data segregation Data in the cloud is typically in a shared environment alongside data from other customers.
Investigative support Cloud services are especially difficult to investigate, because logging and data for multiple customers may be co-located and may also be spread across an ever-changing set of hosts and data centers.
But you must be sure your data will remain available even after such an event.

The results showed that acicular ferrite forms at nucleation sites such as dislocations within austenite grains under the heavy reduction in the austensite non-recrystallization region and accelerated cooling conditions, and the inclusions such as Al2O3·MnS may have promotion effect for the formation of acicular ferrite.
The first stage rolling was in the recrystallized austenite region at 1050~1150°C the amount of total reduction was 48%, and the second stage was in the nonrecrystallized austenite region at 800~850°C with the heavy rolling reduction of 65%.
Under the conditions of controlled rolling and accelerated cooling, A heavy reduction in the austenite non-recrystalliztion region can produce a high density of dislocations and other defects such as substructures within the austenite grain stimulating acicular ferrite to nucleate, while the accelerated cooling rate provides the thermodynamic driving force for the formation of acicular ferrite.
Fig. 3 TEM micrograghs showing the nucleation sites (a) and morphological characterisitics (b)of acicular ferrite in the as-rolled specimen Table 2 The mechanical properties of as-rolled and tempered specimens samples Yield Strength (Rp0.2/MPa) Tensile Strength (Rm/MPa) Elongation (A/%) Reduction of area (Z/%) Transverse Toughness (Akv/J) 0°C -20°C -40°C As-rolled 600 745 19 70 170 140 130 550°C tempered 670 770 16 66 195 208 180 650°C tempered 710 790 15.8 69 158 132 145 Crystallographic Characteristic of Acicular Ferrite Analyzed by EBSD In Fig. 4, low-angle grain boundaries (misorientation angle 2~15 degrees) were colored in red lines, while high-angle boundaries (15 degree or more) colored in green lines.
In terms of EBSD analyses data the average grain sizes of these specimens were 3.9, 4.4 and 2.9μm respectively, and the fractions of their low-angle grain boundaries were 14.1%, 21.4% and 21.4% respectively.