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During treatment, the data were taken at 30 minute brushing time intervals for 360 [min] respectively.
Results and discussion 3.1 Spectrometer data of the tooth lightness 50 52 54 56 58 60 62 64 66 68 70 0 60 120 180 240 300 360 Times[min] Lightness [L*] HA/H2O2 H2O2 Figure 2 shows the lightness (L*) changes corresponding to the HA/H2O2 composite toothpaste and H2O2 alone treatment.
These results suggested that HA/H2O2 composite perform whitening ability on the tooth enamel layer without damaging to the surface of the tooth. 3.4 Identification peak of garlic aroma Figure 4 display the chromatogram data recorded by monitoring the gas samples evaporated from a peeled clove garlic and slice garlic in the bottles.
This results allow quantification of garlic aroma be evaluated to compare peak area of deodorization ability for each deodorizer material. 05 10 15 20 25 30 35 0123456 Retention Time[min] Peak Intensity Slice garlic Clove garlic 4.695 4.695 Fig.5 The deodorization ability of HA, HA/H2O2 and charcoal 0 10 20 30 40 50 60 70 80 90 100 MaterialsDeodorization Ability[%] HA HA/H2O2 Charcoal 93.6% 82.3% 89.5% Fig.4 Retention time of garlic aroma peak 3.5 Deodorization ability data Figure 5 shows the absorption rate corresponding to the garlic aroma reduction.

Introduction Sn2P2S6 family ferroelectric crystals, which undergo a phase transition with symmetry reduction from 2/m to m are particularly interesting both from applied and fundamental physics in relation to their semiconducting, ferroelectric and piezoelectric properties.
There are no data on ultrasonic properties in (Sn,Pb)2P2S6 system.
It was confirmed using our measurements for ferroelectric phase using data from Fig. 2 after calculations of = f(T).
The polarisation relaxation time estimated from our attenuation data was τ ~10-10/(TC-T) s.
The polarisation relaxation time estimated from our attenuation data was τ ~ 6·10-9/(TC-T) s.

Take the relevant data of similar days as the historical data sample, part of the data as training samples to determine predictive models and the rest to check prediction model.
Benefit is reduction of Expected Energy Not Served (EENS) after the configuration of spinning reserve; Cost is measured by the opportunity cost of giving up generating or reserve quotation.
[2] Qing Xia, Shaojun Wang and Niande Xiang: Proceedings of the CSEE, 18(6): 429-433(2000) [3] Chongqing Kang, Lichao Bai and Qing Xia: Proceedings of the CSEE, 22(9): 6-11(2004) [4] Shaohua Zhang, Zhiwei Yu and Yuzeng Li: Proceedings of the CSEE, 25(22): 12-20(2007) [5] Jincui Wang:“Study on forecasting the wind speed and wind power based on the measured data of a wind farm,” Thesis, Northeast Dianli University, 2010

Inside the initially thinned region, an extra width reduction is made in the ROI, to concentrate the stresses in this previously pre-strained zone (Fig. 1b).
Temperature (MPa) k (MPa) n Room temperature 200 1179 0.65 340°C 120 1028 0.60 Experimental data (symbols) and identified hardening curves (full lines) for different experimental situations (20°C, and 340 °C, ) are compared in Fig. 2.
Fig. 2: Experimental data (symbols) and identified hardening curves (full lines) at room temperature, and 340 °C, .
Experimental data for ROI during step 2 (in green).
Strain is measured by an extensometer CD (Fig. 1b) in the ROI and displacement data is collected at one extremity of the specimen.

X-ray diffraction pattern of (1-x) LCMO: x TiO2 composites (x = 0.00, 0.05, 0.10, 0.15 and 0.20) The XRD data were further analysed using Rietveld refinement and the crystal structural parameters of the LCMO composites are presented in Table 1.
All data have a good fitting with the calculated data and with RWP that less than 6 % and GoF of less than 1.5.
Rietveld refinement data of pure LCMO and LCMO: TiO2 composites Sample Lanthanum Calcium Manganate Crystal Structure Orthorhombic Space group Pnma (62) TiO2 Composition, x 0.00 0.05 0.10 0.15 0.20 Lattice Parameter a (Å) 5.450 5.455 5.453 5.454 5.455 b (Å) 7.704 7.707 7.704 7.706 7.724 c (Å) 5.471 5.477 5.477 5.474 5.458 Bond Angle ∠Mn-O1-Mn (°) 162.574 162.580 162.581 162.578 162.543 ∠Mn-O2-Mn (°) 161.150 161.136 161.129 161.141 161.236 Bond Length Mn-O1 (Å) 1.931 1.933 1.932 1.932 1.930 Mn-O1 (Å) 1.976 1.978 1.977 1.977 1.974 Mn-O2 (Å) 1.952 1.953 1.953 1.953 1.957 Mn-O2 (Å) 1.952 1.953 1.953 1.953 1.957 REXP (%) 4.623 4.415 4.643 4.695 4.622 RP (%) 3.532 3.508 3.930 4.144 4.128 RWP (%) 4.636 4.603 5.040 5.537 5.423 Goodness of Fit 1.006 1.087 1.179 1.413 1.377 Sample Titanium Oxide (Rutile) Crystal Structure Tetragonal Space group P42/mnm (136) Lattice Parameter a (Å) - - 4.585 4.596 4.592 b (Å) - - 4.585 4.596 4.592 c (Å) - - 2.953 2.951 2.955 The magnetic properties of samples
So, this will further lead to the reduction of resistivity therefore enhancing the low field MR (LFMR).

Data analysis of experimental diagram shows a descending tendency as grinding is longer with some exceptions.
Data analysis of grinding results shows a descending trend as grinding is longer (from 2 to 4 min).
Data analysis represents increasing tendency of sand/cement ratio as compressive strength is lower.
Some conclusions can be done by analyzing the data obtained from the mixtures I and IV.
Korjakins, Reduction of the Capillary Water Absorption of Foamed Concrete by Using the Porous Aggregate, Mat.

They also pointed out that N2O post oxidation annealing (POA) leads to a 40% reduction of charges with fast de-trapping time constant (<10s) with respect to Ar POA, leaving unchanged the density of slower traps located deeper inside the SiO2 layer [4].
In particular, the first VG sweep shows a “non-steady” (temperature dependent) behavior corresponding to a low hole barrier height (<2eV) if data are fitted with the standard FN model.
However, as shown in Fig. 2, the experimental data exhibit a transient of the gate current.
The experimental data were fitted using Eq. (3) (solid lines in Fig. 2) and the NITs density was determined as fit parameter (Ntrap=2×1011cm-2).
The fit of the experimental current transient data allowed to determine a density of near interface traps (NITs) in the order of 2×1011cm-2.

During the modal test, total 8 accelerometer sensors (PCB 3801G3FB3G) are applied to measure modal parameters of this bridge, and the SCADASIII data acquisition system (LMS company) is used to acquire the acceleration signal.
The sensor and data acquisition system are shown in Fig. 4.
Figure 4 Photo of accelerometer sensor and data acquisition system Figure 5 Measured acceleration signal and auto-power spectrum of acceleration response The acceleration data collected at each hour are identified by the ERA (eigen-system realization algorithm) [9] combining NExT (natural excitation technique) [10], and five identified frequencies during the period of 20/10/2009-30/10/2009 are obtained (Table 2) Table 2 Comparison between analytical frequencies and measured frequencies (Temperature: 19°C) Mode Analytical frequency (Hz) Identified results by ERA method Frequency (Hz) Error (%) MAC 1 1.31 1.20 8.40 0.88 2 1.93 1.71 11.39 0.89 3 2.14 2.22 -3.74 0.99 4 2.83 2.59 9.26 0.91 5 2.97 2.82 5.05 0.92 According to the comparison between measured and analytical modal parameters, the following conclusion are drawn: (1) ERA combining NExT is an effective method to identify the modal parameters of practical bridges; (2) The difference between measured and analytical frequencies
An Eigensystem Realization Algorithm for Modal Parameter Identification and Model Reduction.

To realize the goal of energy-saving and emission-reduction and the goal of economic structure reform, series of policies have been released to encourage and promote the development of electric vehicle industry, not only on public transportation, but also on private electric vehicles.
They meter the actual discharged amount, and transmit the data to the on-board terminals.
And this imposes more demands to electric vehicle manufacturers to reserve enough spaces to accommodate ammeters and equip vehicles with terminals that can collect data from ammeters.
As to the battery exchanging mode, which takes minutes to replace battery, it cannot afford enough long time to acquire the metering data.
Therefore, it cannot spread before unified standards on installation, wiring, data collection and communications are issued and supported by all sorts of vehicle suppliers.

Introduction The application of renewable energy in buildings is one of six fields of energy-saving construction with maximum potential, and is the strategic core and advancing front of energy-saving and emission-reduction in construction.
Therefore the government attaches importance to actual operation effect of renewable energy application in buildings, and successively promulgates the Testing Guidance on Demonstration Projects with Renewable Energy Application in Buildings and the Technical Guidance on Data-monitoring System to Demonstration Projects with Renewable Energy Application in Buildings to guide the operation and acceptance of projects[1-3].
From the data of property management department, we can only get the power consumption per square meter during the heating supply.
For being short of data on total cooling and heating volume, the government authorities can not calculate the normal energy substitution correctly, and can not make corresponding policies according to the data.