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In order to locate the delamination damage, the fiber Bragg grating (FBG) strain sensors network was embedded in SRM shell, and a multi-step approach of delamination damage location based on strain energy was performed: the strain field was measured in a scatter grid by the FBG strain sensors network in hydraulic testing; then the continuous displacement and strain field was reconstructed with relative sensors data using a moving least square (MLS) mesh-free fitting method; the strain energy of each subregions was calculated from the reconstructed data; finally the damaged subregions was identified successfully by singularity value of strain energy.
Delamination was one of the most common damages in composite structure result from impact or manufacturing imperfections, which cause a considerable reduction in compressive strength and performance of composite structures.
According to the former analysis, the delamination induced the structure performance reduction without the mass loss.
Scale SRM models embedded FBG sensors network To simulate delamination taken place in the composite laminates, a thin super-resin area was inserted between the 8th and 9th layers during the fabrication process. the scale SRM models were applied internal hydraulic pressure increasing from 0 MPa to 8 MPa at an interval of 1 MPa, simulating the internal pressure creasing at SRM working situation, in order to record the data, the pressure was hold on several minutes properly.
Then the internal water pressure was unloaded from 8 MPa to 0 MPa at an interval of 1 MPa, in order to record the data, the pressure was hold on several minutes properly, too.

As such, effort is geared at reducing head injuries from concussion by optimizing helmet design through shock absorption increase and stress amplification reduction during impact.
The degree of reduction of concussion by a helmet is a function of the impact shock absorption capacity of the helmet.
Vilar-Carrasquillo and Anderson [18] studied the effects of externally applied foam used in football helmets for impact reduction.
The tetrahedron mesh consists of 2916 material, node and element data, 2997 material properties, and 160112 element data for the composite structure.
The reduction of the maximum stress and acceleration were due to the absorption of more impact load by the composite liner.

Based on the data of thermodynamics and kinetics, the Gibbs free energies of the main interface reactions in the real brazing system condition were calculated.
At 1155K, Ti would change from β phase to α phase; Al would change from liquid to solid at 932 K, so Ti and Al could be described by Eq.9 and Eq.10: (9) (10) The standard enthalpy and entropy changes of Al2O3 reduction reaction could be described by Eq. 11 and 12: (11) (12) Table 1 listed the reference data of thermodynamic [12-14].
If we introduced these data to Eq.3 and 4, we could get the reaction equilibrium constant of TiO (Kα) [15].
Based on the thermo-chemical data of reactant and resultant, the resultant enthalpy was calculated, and the entropy change after the brazing condition correction items was introduced.
Ye, Practical handbook of thermodynamic data of inorganic, Beijing, Metallurgical Industry Press (1981)

Additionally, a higher percentage of RGP in the concrete allows for cost savings of up to 12.2%, demonstrating a progressive reduction.
According to data from the National Institute of Statistics and Informatics, in 2019, it produced 3,614,000 tons of waste, representing a 4.6% increase compared to the previous year [2].
Table 3 summarizes the data obtained according to the percentage of recycled glass powder in fresh state and presents the values of slump tests, temperature, and air content.
Below, Table 4 presents the synthesis of the data collected in relation to the percentage of recycled glass powder and the elapsed time.
Likewise, PV-05 shows a reduction of 4.47%.

The survey and reserch takes the data during the morning rush-hour crush.
Phase diagram Analysis of vehicle flow and flow direction during the morning rush-hour crush Our survey and reseach took the data of the morning rush-hour crush.
As the setting of left-turn waiting area, we got following data based on actual observation.
Observation data showed that each left-turn waiting lane could have a capacity of 5 vehicles.
This paper simulate intersection various imported traffic capacity using following steps: Extend lanes (prevent vehicle queue length more than vehicle produce position)if VISSIM error, and termination; The vehicles generator produce vehicles by 3600 peu/h, provideing saturated flow; Set the simulation 5 h, get a statistical data every 30 min; Extract the biggest set of data from 10 sets of data, through calculation get all imports traffic conpacity ; Simulation get each import traffic capacity contrast histogram of no stay line areas, setting left-turn waiting area ,settings left-turn and straight-on waiting areas ,as shown in left picture of Fig.6.From Fig.6, we can see that all import traffic capacity is higher than calculated value because the simulation is under the most ideal situation .The simulation show that setting left-turn waiting area , the intersection capacity increase 7% than no stay line areas set ; Setting left-turn waiting and traight-on waiting area

The authors in [8] deploy least squares error correction based on overall median of individual single lead data to reduce baseline drift.
The ECG data comes from the database of MIT-BIH.
Fig. 4 and Fig. 5 describe No. 109 and No. 203 ECG data in the MIT-BIH arrhythmia database including a lot of baseline drift.
We choose eight group data from the database, and the data of No. 100 and No. 102 keep the same accuracy rate for the two methods.
Our method can improve the accuracy rate from 0.1% to 5% in the other six group data, especially for No. 203 and No. 222 data including a deal of baseline drift noise.

Maximum Aggregate Size 12 mm Figure 6: Effect of water cement ratio on the compressive strength of concrete Deductions from the presented data in Figures 6a showed that the compressive strength of concrete at 0% RISS aggregate replacement decreases by 2.95% between WCR 0.65 and 0.60; decreases by 0.80% between WCR 0.60 and 0.55 while at 20% RISS aggregate replacement the values of compressive strength decreases by 3.12% between WCR 0.65 and 0.60 and decreases by 0.66% between WCR 0.60 and 0.55; at 60% RISS aggregate replacement the values of compressive strength decreases by 4.71% between water cement ratios of 0.65 and 0.60, and decreases by 0.60% between WCR 0.60 and 0.55; deductions from Figure 6b showed that the compressive strength of concrete at 0% RISS aggregate replacement decreases by 0.98% between WCR of 0.65 and 0.60 while 0.27% reduction in compressive strength was observed between WCR 0.60 and 0.55.
At 20% RISS aggregate replacement reduction in compressive strength observed was 1.06% between WCR 0.65 and 0.60 and 0.79% between WCR 0.60 and 0.55 while at 60% RISS aggregate replacement reduction in compressive strength observed was 0.82% between WCR 0.65 and 0.60 and 0.98% between WCR 0.60 and 0.55.
Similar trend was observed in Figure 6c a reduction of 1.70% was observed between WCR of 0.65 and 0.60, while a reduction of 0.38% in compressive strength was observed between WCR 0.60 and 0.55 at 0% RISS aggregate replacement; while at 20% RISS aggregate replacement a decreases of 1.91% reduction in compressive strength was observed between WCR 0.65 and 0.60 and a reduction of 1.90% was observed between WCR of 0.60 and 0.55 and at 60% RISS aggregate replacement the compressive strength reduces by 1.30% between WCR of 0.65 and 0.60 and a reduction of 1.48% in compressive strength between WCR 0.60 and 0.55.
Shamsai [32] showed that reduction of water cement ratio by 0.33 and 0.50 improves the strength by 34.4% and 35.2%.
The coefficient of determination, R2 obtained for the three mix ratios are 0.777, 0.681 and 0.791 indicating that the deductions from the analysis are reliable and can be used to interpret the data generated to arrive at a valid conclusion and inferences.

Danison, Maddison and Chenery et al. proved the positive effect of industry structure transition by comparing data of many countries' economic growth[2,4,5].
Data, Analysis and Results The econometric model and variable explanation of contribution of the 3 industries of Shenzhen to economic growth.
We made a normal OLS regression with the data of 1979-2009 of Shenzhen.
Data origins from Shenzhen statistics year book(2010), and software uses IBM SPSS Statistcs 19, the data and model regression are as below: Table 1 Coefficients table Unstandardized coefficients standardized coefficients t Sig.
The specific data and comparison would illustrated in graph 1 to graph 5, data was selected from Shenzhen statistics year book(2010), software used IBM SPSS Statistics 19, and graph was made by OFFICE Excel 2007.

The patterns of XRD analysis of the films with thickness (235, 250, and 270 nm) show that the synthesis CdS films have polycrystalline and hexagonal nanostructure with three notable peaks along (100), (002), and (101) planes which are matched with standard data card of CdS (JCPDS card no. 41-1049) and preferentially orientated along (101) plane.
This reduction of the CdO structure is due to the higher ionic radius of sulfur comparing to that of oxygen.
All the XRD data of the films are tabulated in Table 1.
The lattice constants (a) and (c) are close to the lattice constant values (a= 4.1 Ǻ, c= 6.7 Ǻ) of standard data card of CdS (JCPDS card no. 41-1049).
The reduction of the band gap can be due to the increment of the crystallite size [19] with the increase of the laser pulses.

However, the high-dimensional data feature can lead to too long learning time and poor classification effect.
First of all, using the traditional way to conduct pre-dimensionality reduction of global feature selection, then, using the improved way to conduct secondary dimensionality reduction in the dimensionality space.
During the experiment, we conduct feature selection by using document frequency (DF), statistical method, information gain (IG), and the proposed method, then, select 400 features and calculate TF-IDF values, normalize the text which express as VSM, input data of LIBSVM.
Afterwards, expresses training text in the unified way according to selected feature subset, and normalizes them to unified format as the input data for classifier learning of machine learning.
Data Mining: Concepts and Techniques[M].