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We also expected them to cause a reduction in body weight caused by their CR mimetic effect.
Data analysis All calculations were performed by using SPSS 13.0 statistical software.
Data were analyzed with one-way ANOVA.
When we planned this pilot study, we anticipated that resveratrol would cause body weight reduction because resveratrol is a CR mimetic chemical, but that was not the case.
The observed reduction in MDA level in the SAM following the administration of polygonum cuspidatum extract suggests that resveratrol may have an antioxidant effect.

The technique generated random data used Bootstrap.
Each of the rule sets produced are then evaluated on the original training data and on the test data.
There is no missing data present in the training dataset.
The technique generated random data called Bootstrap [22].
Due to ensemble techniques, there is a reduction of the bias problem that arises from the collection of data used in learning including the fixed set of parameters.

Furthermore, the UV-Vis analysis data shows the typical absorption of GO at 232 nm and 301 nm, while at 266 nm and 278 nm, it belongs to graphite and rGO.
However, the GO reduction process requires chemicals such as hydrazine hydrate which are harmful to humans and the environment, thus an approach with the green synthesis method is urgently needed [7].
The AV extract contains various bioactive compounds, including phenolic and flavonoids, which can participate in the reduction reaction [14].
Moreover, the oxygen functional groups contained in GO have a significant reduction as indicated by a decrease in the interlayer spacing of rGO from 9.76 Å to 3.72 Å [19].
Summary In this study, rGO sheets were successfully synthesized by the green-reduction method.

Each simulation subsystem can be independently performed and the simulated results of one simulation system can be used as input data for the other system.
In the surface temperature simulation, air temperature and velocity are assumed to be uniformly distributed in the analyzed space at the un-selected time, and given from input weather data.
Simulations were performed using hourly weather data for a typical sunny summer day (August 5, 2000) in Tokyo.
The input weather data was prepared based on Expanded AMeDAS Weather Data provided by Architectural Institute of Japan.
Each simulation subsystem can be independently performed and the simulated results of one simulation system can be used as input data for the other system.

The experimental data were collected through the video recording of the domain during a two-phase flow on a transparent porous medium.
Image processing To calibrate the parameters of our model using actual data acquired from our experiments, it is important to establish a means to compare the outcomes of the numerical simulations with the empirical data.
Given that our numerical model replicates the material dispersion across the porous medium, it becomes essential to devise a methodology for approximating the material dispersion within the experimental domain by leveraging the accumulated data.
This operation facilitates improved differentiation of image features and patterns, contributing to enhanced interpretability and analysis of the experimental data.
Hence, the results were consistent with the experimental data, although they missed certain aspects of the fluid displacement.

According to the results, the non-linear characteristic of the concrete material exhibited in the loading process is reflected by the phenomenon that the slope of the later stage shows some reduction in the simulated load - loading point displacement curve.
(a) The beam specimen model (b) The column specimen model Fig.1 The numerical simulation models Fig.2 Typical load vs. loading point displacement The non-linear characteristic of the concrete material exhibited in the loading process is reflected by the phenomenon that the slope of the later stage shows some reduction in the simulated load - loading point displacement curve.
Critical curve and fracture criterion of concrete fracture derived from numerical simulation Through the analysis of results of numerical simulation, it is in appropriate to fit the Ⅰ-Ⅱmixed mode, Ⅰ-Ⅲ mixed mode or Ⅰ-Ⅱ-Ⅲ mixed mode data with a curve or surface.
In the absence of actual test data or lack of laboratory equipment, numerical simulation is still an effective estimation method, and the results can be adopted for engineering projects.

Reduction of forces cause the tools to be more strong, but this reduction of expense in comparing with expenses for buying coolant, making ready, washing pieces and cleaning machinery cannot be exploited and dry machining is better.
The reduction temperature which is made on tool is 2.5 percent.
This reduction of force and also temperature, in machining 5083 has less effect on strength of the tool that if we compare with the expenses for making ready the coolant, cleaning the machinery and machined pieces would be less than the other.
Table 2: Cutting test data Simulation Result Experimental Result T(℉) Error T(℉) Wet T(℉) Dry Fc(N) Error Fc(N) Wet Fc(N) Dry Fc(N) Error Fc(N) Wet Fc (N) Dry v (m/min) t1 (mm) t0 (mm) -1 % 297 300 +8 % 54 50 -13% 52 60 25 0.40 0.044 1 -1.6 % 299 304 -8.4 % 55 60 -12% 58 66 50 0.25 0.044 2 +2 % 417 409 +3.6 % 85 82 -8% 83 90 26 0.33 0.094 3 -6 % 457 484 -5.6 % 85 90 -14% 79 92 52 0.38 0.094 4 -2.7 % 390 401 -20 % 80 100 -11% 104 117 24 0.43 0.141 5 -2 % 429 438 +3.1 % 98 95 -17% 108 131 48 0.60 0.141 6 -5.5 % 448 474 -8 % 140 152 -17% 121 147 24 0.64 0.188 7 +0.6% 513 511 -7.5 % 148 160 -5% 144 153 47 0.68 0.188 8 -3 % 474 489 +4.7 % 220 210 2% 193 189 25 0.52 0.282 9 -3.5 % 533 552 -4.3 % 220 230 11% 205 185 49 0.75 0.282 10 -2.3 % 504 516 0 % 300 300 56% 248 158 22 0.27 0.380 11 -5.2 % 543 573 +1.6 % 305 300 54% 290 188 45 0.90 0.380 12 There are many applications where cutting is carried out dry, in air, with no advantage being found in the use of a cutting fluid.
Comparison of experimental data in dry and coolant machining References [1] E.O.

Thus, the failure of packaging materials is one of the most important reasons of lifetime reduction of the whole LED models.
In order to monitor the luminance varying during aging test, online test was designed to acquire the luminance data.
Then, the computer collected the measuring data by acquisition software.
Thus only the luminance reduction was regarded as failure criterion.
Moreover, the pure chip sample had a relatively high reliability for the luminance reduction was only 8%.

Examples of experiments show this method is useful and some merits are achieved, such as reduction of hardware complexity, saved memory largely and achieved real-time data transmission.
All these methods aim at decrease the amount of data.
Original signals would be reconstructed via the data of multi-channel after down sampling.
Generally, , it can reduce 10 times the amount of data.
Memory largely saved due to data compressed and relatively low transmission requirement especially real-time data.

Calculated with each corresponding two-point, the virtual impulse response signals are taken as input data of WP decomposition method.
After establishment of bridge health monitoring system, the analysis of system sampled data to detect structural damage and assess structural state is required.
(5) The absolute values of -th WPE increments calculated using the data of damaged and undamaged are illustrated below: (5) (6) (6) Damage localization.
Nevertheless, the data in 2(1) are larger than those in 3(4) consistently from Fig.3(a), so damage could be localized between measuring point 1 and 2; the data in 3(4) are larger than those in 2(1) consistently from Fig.3(b) and Fig.3(c), so damage could be localized between measuring point 3 and 4.
Carrying spatial information of structure behavior, the virtual impulse response signals obtained by NExT are set as input data of damage detection.